diff --git a/data/DMSO-backfill/DMSO-backfill.ipynb b/data/DMSO-backfill/DMSO-backfill.ipynb new file mode 100644 index 0000000..4d4ecd6 --- /dev/null +++ b/data/DMSO-backfill/DMSO-backfill.ipynb @@ -0,0 +1,1347 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Testing effect of DMSO backfill on Affinity" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The experiment was performed on April 20, 2016. Full details can be found in Fluorescence Assay Lab Notebook in the google doc. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Roughly, 50 uL of 0.5uM of p38 in Kinase Buffer in a 384 well plate. A 24 well titration of each ligand was performed by the D300. A row of just buffer with the ligand titration was also measured along side our binding assay. In half the plate DMSO concentrations were normalized. In the other half they were not. (Note it is likely Gefitinib was not dispensed for unknown reasons, but just an observation upon watching the EVO pipette.)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "DMSO percent in the DMSO backfill is to normalize concentration to the highest concentration which is: 0.2% DMSO" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Couldn't import dot_parser, loading of dot files will not be possible.\n", + "Populating the interactive namespace from numpy and matplotlib\n" + ] + } + ], + "source": [ + "from assaytools import platereader\n", + "import matplotlib.pyplot as plt\n", + "import string\n", + "%pylab inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Image of DMSObackfill](DMSObackfill.png)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "part1 = \"p38_singlet1_20160420_153238.xml\"\n", + "part2 = \"p38_singlet2_20160420_154750.xml\"" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "part1_data = platereader.read_icontrol_xml(part1)\n", + "part2_data = platereader.read_icontrol_xml(part2)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#part1_data" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['280_480_TOP_120',\n", + " '280_340_TOP_100',\n", + " '280_480_TOP_100',\n", + " '340_480_BOT_120',\n", + " '280_480_BOT_100',\n", + " '340_480_TOP_100',\n", + " '280_340_BOT_100',\n", + " '340_480_BOT_100',\n", + " '280_480_BOT_120',\n", + " '340_480_TOP_120']" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "part1_data.keys()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['ABS_280', '280_340_BOT_120', 'ABS_340', 'ABS_480', '280_340_TOP_120']" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "part2_data.keys()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "part1_data_A = platereader.select_data(part1_data, '280_480_TOP_120', 'A')" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "part1_data_B = platereader.select_data(part1_data, '280_480_TOP_120', 'B')" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "well = dict()\n", + "for j in string.ascii_uppercase:\n", + " for i in range(1,25):\n", + " well['%s' %j + '%s' %i] = i" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def plotting(data_protein,data_buffer,ligand):\n", + " \n", + " x,y = [],[]\n", + "\n", + " for key in data_protein:\n", + " x.append(ligand[key])\n", + " y.append(data_protein[key])\n", + " \n", + " a,b = [],[]\n", + "\n", + " for key in data_buffer:\n", + " a.append(ligand[key])\n", + " b.append(data_buffer[key])\n", + " \n", + " plt.scatter(x,y)\n", + " plt.scatter(a,b,color='k');" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/hansons/anaconda/lib/python2.7/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", + " if self._edgecolors == str('face'):\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plotting(part1_data_A,part1_data_B,well)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "part1_data_C = platereader.select_data(part1_data, '280_480_TOP_120', 'C')\n", + "part1_data_D = platereader.select_data(part1_data, '280_480_TOP_120', 'D')" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "part1_data_E = platereader.select_data(part1_data, '280_480_TOP_120', 'E')\n", + "part1_data_F = platereader.select_data(part1_data, '280_480_TOP_120', 'F')\n", + "plotting(part1_data_E,part1_data_F,well)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+IuIK4HvA9cC7gCOZeRI4GRHPA+cBFwF/UP/8IeAT7caXtKCrT1cvlOzs7HXs23f51uVv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\nSPUAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "part1_data_G = platereader.select_data(part1_data, '280_480_TOP_120', 'G')\n", + "part1_data_H = platereader.select_data(part1_data, '280_480_TOP_120', 'H')\n", + "plotting(part1_data_G,part1_data_H,well)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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MBhN2E7n7o2a2oKKpciDlMNAJzAcOjtN+aIK2cvvZUwkup8o+/LcWnaORtO6BSGPUOoD8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\nFQMREUHFQEREUDEQERHg/wN+/r9qKZeQaQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "part1_data_I = platereader.select_data(part1_data, '280_480_TOP_120', 'I')\n", + "part1_data_J = platereader.select_data(part1_data, '280_480_TOP_120', 'J')\n", + "plotting(part1_data_I,part1_data_J,well)\n", + "plotting(part1_data_A,part1_data_B,well)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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RujZR801UMVgLfG1Y87o8z4cXiHqlvO1B+cum/InQSWeTx9GCbSIiJ9CRQfNN1JFBs+6N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\nZxijGHyujSfF1z9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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "part1_data_K = platereader.select_data(part1_data, '280_480_TOP_120', 'K')\n", + "part1_data_L = platereader.select_data(part1_data, '280_480_TOP_120', 'L')\n", + "plotting(part1_data_K,part1_data_L,well)\n", + "plotting(part1_data_C,part1_data_D,well)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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B7gbeA54HVrp7amZXAEuB/cA6d3/UzGYBG4FDgL3AYnd/fYSXi31gZ9Lnz/uYzXF6HOek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\nRCRQQRAREUAFQUREAhUEEREB4P8BWpWc4GynXLAAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "part1_data_M = platereader.select_data(part1_data, '280_480_TOP_120', 'M')\n", + "part1_data_N = platereader.select_data(part1_data, '280_480_TOP_120', 'N')\n", + "plotting(part1_data_M,part1_data_N,well)\n", + "plotting(part1_data_E,part1_data_F,well)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "part1_data_O = platereader.select_data(part1_data, '280_480_TOP_120', 'O')\n", + "part1_data_P = platereader.select_data(part1_data, '280_480_TOP_120', 'P')\n", + "plotting(part1_data_O,part1_data_P,well)\n", + "plotting(part1_data_G,part1_data_H,well)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'O1': '2436',\n", + " 'O10': '2590',\n", + " 'O11': '2541',\n", + " 'O12': '2681',\n", + " 'O13': '2556',\n", + " 'O14': '2514',\n", + " 'O15': '2571',\n", + " 'O16': '2414',\n", + " 'O17': '2519',\n", + " 'O18': '2418',\n", + " 'O19': '2466',\n", + " 'O2': '2415',\n", + " 'O20': '2479',\n", + " 'O21': '2526',\n", + " 'O22': '2479',\n", + " 'O23': '2472',\n", + " 'O24': '2394',\n", + " 'O3': '2480',\n", + " 'O4': '2427',\n", + " 'O5': '2415',\n", + " 'O6': '2438',\n", + " 'O7': '2403',\n", + " 'O8': '2485',\n", + " 'O9': '2599'}" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "part1_data_O" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "#While this way of plotting works fine, I need to have this data in a list in the right order \n", + "#to analyze as I currently have it" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Lstated = np.array([20.0e-6,14.0e-6,9.82e-6,6.88e-6,4.82e-6,3.38e-6,2.37e-6,1.66e-6,1.16e-6,0.815e-6,0.571e-6,0.4e-6,0.28e-6,0.196e-6,0.138e-6,0.0964e-6,0.0676e-6,0.0474e-6,0.0320e-6,0.0240e-6,0.0160e-6,0.0120e-6,0.008e-6,0.00001e-6], np.float64) # ligand concentration, M\n" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#sorted(well.keys(), key=lambda k:well[k])\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def reorder2list(data,wells):\n", + " \n", + " sorted_keys = sorted(well.keys(), key=lambda k:well[k])\n", + " \n", + " reorder_data = []\n", + " \n", + " for key in sorted_keys:\n", + " try:\n", + " reorder_data.append(data[key])\n", + " except:\n", + " pass\n", + " \n", + " reorder_data = np.asarray(reorder_data,np.float64)\n", + " \n", + " return reorder_data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "reorder_A = reorder2list(part1_data_A,well)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'A1': '15298',\n", + " 'A10': '8146',\n", + " 'A11': '6850',\n", + " 'A12': '11834',\n", + " 'A13': '4940',\n", + " 'A14': '4305',\n", + " 'A15': '3722',\n", + " 'A16': '3483',\n", + " 'A17': '3040',\n", + " 'A18': '2956',\n", + " 'A19': '2912',\n", + " 'A2': '15558',\n", + " 'A20': '2887',\n", + " 'A21': '2649',\n", + " 'A22': '2558',\n", + " 'A23': '2537',\n", + " 'A24': '2500',\n", + " 'A3': '15186',\n", + " 'A4': '14461',\n", + " 'A5': '13961',\n", + " 'A6': '13245',\n", + " 'A7': '11926',\n", + " 'A8': '11053',\n", + " 'A9': '9478'}" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "part1_data_A" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 15298., 15558., 15186., 14461., 13961., 13245., 11926.,\n", + " 11053., 9478., 8146., 6850., 11834., 4940., 4305.,\n", + " 3722., 3483., 3040., 2956., 2912., 2887., 2649.,\n", + " 2558., 2537., 2500.])" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "reorder_A" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "reorder_B = reorder2list(part1_data_B,well)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.semilogx()\n", + "plt.plot(Lstated,reorder_A,Lstated,reorder_B);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Analysis" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Below everything needs to be changed to be relevant to 384 well plate." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Stated concentrations of protein and ligand.\n", + "#ligand_conc from before\n", + "Pstated = 0.5e-6 * np.ones([24],np.float64) # protein concentration, M" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Assay configuration details\n", + "import math\n", + "assay_volume = 50e-6 # assay volume, L\n", + "well_area = 0.1369 # well area, cm^2 for 4ti-0203 [http://4ti.co.uk/files/3113/4217/2464/4ti-0201.pdf]\n", + "path_length = assay_volume * 1000 / well_area # cm, needed for inner filter effect corrections \n", + "\n", + "# Uncertainties in protein and ligand concentrations.\n", + "dPstated = 0.35 * Pstated # protein concentration uncertainty\n", + "dLstated = 0.08 * Lstated # ligand concentraiton uncertainty (due to gravimetric preparation and HP D300 dispensing)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def quick_model(protein_data, buffer_data,name):\n", + " reorder_protein = reorder2list(protein_data,well)\n", + " reorder_buffer = reorder2list(buffer_data,well)\n", + " \n", + " from assaytools import pymcmodels\n", + " pymc_model = pymcmodels.make_model(Pstated, dPstated, Lstated, dLstated, \n", + " top_complex_fluorescence=reorder_protein,\n", + " top_ligand_fluorescence=reorder_buffer,\n", + " use_primary_inner_filter_correction=True, \n", + " use_secondary_inner_filter_correction=True, \n", + " assay_volume=assay_volume, DG_prior='uniform')\n", + " \n", + " mcmc = pymcmodels.run_mcmc(pymc_model)\n", + " \n", + " from assaytools import plots\n", + " figure = plots.plot_measurements(Lstated, Pstated, pymc_model, mcmc=mcmc)\n", + " \n", + " map = pymcmodels.map_fit(pymc_model)\n", + " \n", + " pymcmodels.show_summary(pymc_model, map, mcmc)\n", + " \n", + " DeltaG = map.DeltaG.value\n", + " \n", + " np.save('DeltaG_%s.npy'%name,DeltaG)\n", + " np.save('DeltaG_trace_%s.npy'%name,mcmc.DeltaG.trace())\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MAP fitting cycle 5/5\n", + "DeltaG = -34.5 +- 6.0 kT\n", + "Kd = 1.0 nM +- 57518692.2 fM\n" + ] + }, + { + "data": { + "image/png": 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zLU8iZ/rMx3oigq3J1LRtDaAUE4ktyK3V4nSO32HuZLZfMSYmy8kVv5yIVVtt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\neHQznvMw4M0oih5C9Y7OA86I43iSX/+436YWmBrHcTVQHUXRbGBXYB/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RozayYecGB/4C+B59LY5/jkp9IbGDp0KOgPBxGRjWljNGq9Xqe7T0+Npj4wFBERKZMtK32HZrYC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\nIlLU/wMO0mvVSOwq5AAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "quick_model(part1_data_A,part1_data_B,'BOS_w_backfill')" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MAP fitting cycle 5/5\n", + "DeltaG = -15.1 +- 1.9 kT\n", + "Kd = 290.4 nM +- 171.2 nM\n" + ] + }, + { + "data": { + "image/png": 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D81wOHApcjdn+NWtPmCvtDVUtnAxsCdSral3+qjZoPBb4JRUTeUVkPXy8dfF4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\ngH436p6gj4J+P+reoI+Dnhd1P9DxoBdEvQHo86CXRr0Z6H9Bfx71l0B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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "quick_model(part1_data_I,part1_data_J,'BOS')" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "DeltaG_trace_BOS_w_backfill = np.load('DeltaG_trace_BOS_w_backfill.npy')\n", + "DeltaG_trace_BOS = np.load('DeltaG_trace_BOS.npy')" + ] + }, + { + "cell_type": "code", + "execution_count": 146, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "329 >3000.0\n", + "Name: Affinity (nM), dtype: object" + ] + }, + "execution_count": 146, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "bos_affinity = pd.read_csv('../literature_affinity/bosutinib.csv')\n", + "\n", + "# bos_affinity[['Name in screen']] == 'p38-alpha'\n", + "# bos_affinity[['Affinity (nM)']] \n", + "bos_affinity[bos_affinity['Name in screen'] == 'p38-alpha']['Affinity (nM)']\n", + "#bos_affinity." + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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cl7Ib8FgftpW6MIaZBJVFXo9lsXjV4kXx5bRKBFNlw/a4aG1t3WHWCZdfN25i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\nibKo4+MCwr1n84HPFkzbB9i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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(DeltaG_trace_BOS, 40, alpha=0.75, label='Bos no backfill')\n", + "plt.hist(DeltaG_trace_BOS_w_backfill, 40, alpha=0.75, label='Bos with backfill')\n", + "plt.plot([DeltaG_trace_BOS_w_backfill.mean(),DeltaG_trace_BOS_w_backfill.mean()],[0, 350],'g-')\n", + "plt.plot([DeltaG_trace_BOS.mean(),DeltaG_trace_BOS.mean()],[0, 350],'b-')\n", + "#plt.plot([literature_Bos,literature_Bos],[0, 350],'r-',label='IUPHARM data')\n", + "plt.xlabel('$\\Delta G$ ($k_B T$)');\n", + "plt.ylabel('$P(\\Delta G)$');\n", + "plt.title('histogram of estimates for binding free energy');\n", + "plt.legend(loc=0);" + ] + }, + { + "cell_type": "code", + "execution_count": 149, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MAP fitting cycle 5/5\n", + "DeltaG = -13.3 +- 0.6 kT\n", + "Kd = 1.7 uM +- 1.4 uM\n" + ] + }, + { + "data": { + "image/png": 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bEAHStjnn+81sf+ASYurmqsC5wOHAVjnnv3f3ouotpfS/wFnuPjul9DtgvLtftqDPq6cp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\ntyPK5f4ZWB54IaU0rt4nJiI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Q0AQs29DQsP2QIUM4+OCDt3jssceG7rfffuvccccdq7z0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\nyMj4v3I4sKaIXBRtPRKoFJGHnXN9roOFu4FLfjzuBH4G9IW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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "quick_model(part1_data_C,part1_data_D,'BSI_w_backfill')" + ] + }, + { + "cell_type": "code", + "execution_count": 150, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MAP fitting cycle 5/5\n", + "DeltaG = -13.0 +- 0.6 kT\n", + "Kd = 2.4 uM +- 3.0 uM\n" + ] + }, + { + "data": { + "image/png": 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zwOIGEURuT0pvAl8h58e7qPXSiloyjaPdfUQXtace9K1Yealvy039W17q23JT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\nc7Oofxd1z3geLorHuVE8bxfG87BB1P8X9YY5et2c7S+IeuO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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "quick_model(part1_data_K,part1_data_L,'BSI')" + ] + }, + { + "cell_type": "code", + "execution_count": 151, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "DeltaG_trace_BSI_w_backfill = np.load('DeltaG_trace_BSI_w_backfill.npy')\n", + "DeltaG_trace_BSI = np.load('DeltaG_trace_BSI.npy')" + ] + }, + { + "cell_type": "code", + "execution_count": 153, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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L6564/XvvZSNrXosq/fZKM/sO8LC735S8/7u7b7WezUREZAyMhyaG\nB4HDAMxsNvBUebMjIiIF46E56xbgQDN7MHl/fDkzIyIia1R8c5aIiFSu8dCcJSIiFUpBREREUlMQ\nERGR1MZDx/qQmNlRwNHufmzyflvgSqAe6ASOcfdlZczisJQo31HAt1hzT/q57n5fufI3XP3LV5R+\nFrCDux9TnpyNjBLf3z6E7y8G7nX3L5Yzf8NRomzvAc4HssBi4N/cvXOQXVS8UuenmZ0JbO7uXypf\nzoavxPc3G7iE8DDlne7+1cG2r4qaiJl9D/gaUPwo9/8AZ7v73OT128qRt5EwQPl2Ab7g7vsn/8Zz\nAClVPszsUMLt3eP67o8Byncx8K/uvhewh5ntXJbMDdMAZbscODL523sB+FQ58jZS+pfRzJrM7Hrg\nM1TnuXkl4aJ7DrDn+s7NqggihGdJPk3Rl0wY1uMIM7sH2Av4Y/myN2xrlS+xK/AJM7vPzL5tZjWl\nNx0X1ilfUpM8ETiXfsFlHCr1/e3h7gvMbALQBqwsS86Gr1TZ5rr7kuR1HaElYDzrX8YG4FrgQqrs\n3DSziUCDu/8tWX4HcMBgOxhXzVlm9kngzH7JH3f3n5nZfkVpk4G3A6e6+5fN7GrgY8CPxian6WxA\n+QB+C9zi7vPN7PvAyYQrwIo11PIlP6yXA8cB249dDodnQ74/d88nzQY3AM8C/xibXKazgWV7Ldnm\nA8Bc4OwxyeQwDbWM7r4c+K2ZfWws8zccG/D99R9maiWw9WD7HldBxN1/CPxwCKu+Aax093uT978G\nDqTCg8gGlA/gGndfkbz+JfDB0cnVyNmA8h0EbA7cCEwCtjCzL7j7N0czf8O1gd8f7v4w8GYzOx/4\nIvCVUcrasG1o2czss8AHgEPcvWd961eCDS3jeLIBZWsHWoveTwSWD7ZBtTRnrSXpxHvezOYkSXOB\nZ8qYpRFlZhHwpJm9KUk6AHisjFkaUe5+s7vv7O77E66e7q70ALIhzCwys/vNbFKStIoqmtnRzM4G\n5gAHuvsb5c6PDJ27twM9ZrZ18jtzEDBof2s1BZGYtTu5PglcZGZ/ADYDripLrkZOX/ncPSaU7+dm\n9ntCG23VlG+AZeNd/+/vW8Btyfe3E2GenPGqr2xmtjlwDjCdUL57zOzkcmZuhAx0flbVuZk4Gbie\n0I/8Z3d/dLCNNeyJiIikVk01ERERGWMKIiIikpqCiIiIpKYgIiIiqSmIiIhIagoiIiKSmoKIiIik\npiAiIiKpjauxs0TGgpnVA48SRqNdZ9wgM2smjHxaQxhXqJ4wbMkf3P2JovVqgJ8Bx7p7l5ntAvwU\nOMDdXxliXrYA5iXbzQSOAr4JTAP2BV5x94+nLKrIsKkmIrKuDxEm++o/6ilmNhm4Bbjd3b/p7v9D\nGHLm1OIAkvh0sl4XgLv/GVg81ACSmAsc5O6XAi8CP3f377v7V4AfA68l866IlIWCiEgRM6sljBz8\n78CpZtbab5Vrgf9292cLCe7eTagdFO8nAk4l1CAKaW8GXtrALL3s7quS1/sADxQte5IQSP5zA/cp\nMmIURETWdhQwz92dMGfLaYUFZrYr8BZ3/1X/jdz9un5JOwEr3L14sql9gN+b2Rwzu8TMvrC+zLh7\n8WRqe1MURNz98SSYbW9mbUMom8iIU5+ISMLMMoQ5s19Lki4E7jazS9x9NaFp6e6i9TcFPgq8G/i1\nu/+gaHdvAV7t9xH7JGm/Bj5ftJ9tCXM9XAbMAGJ3v7hf3mYAXe6+tETWXwW2Af68YSUWGT7VRETW\nOJLQ3wGAuz8D3A+ckiT1AiuKli8BfgLs0i+AQOho7+2XtkOSdrm797p7YflLwJ/cfR6h9vPmEnmb\nk+SllCyQH7xoIqNDQUSEvj6MLd29/zS1FwCfM7NG4P+AOcndWwXvBf5UYpcvEe6mKux/M2CJu18I\nTDOz+qIO8XcAXWZ2MHA68LkS+5tDmA+7lK2Avw2wTGRUqTlLJDgCOHeAfoqpwAnuflkyle3FZvYM\n0A28BtxRYpungKlmNjGZLW5n4LZk2UPAh4Gbkvd7Av/P3Z8zs7cB+xNqJJjZOwjNaB8gBJp93b1v\nprlk+V+LpkoWGVOalEpklJjZaUDe3S9fz3qXAme6ez55/ZNk/vWhfMbFwJ3uftt6VxYZBWrOEhk9\nVwIHJk1hJZnZXoRaylFmdiLwwgYEkK2ATRVApJxUExERkdRUExERkdQUREREJDUFERERSU1BRERE\nUlMQERGR1BREREQkNQURERFJTUFERERSUxAREZHU/j9PcKDE+/l5FwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(DeltaG_trace_BSI, 40, alpha=0.75, label='Bsi no backfill')\n", + "plt.hist(DeltaG_trace_BSI_w_backfill, 40, alpha=0.75, label='Bsi with backfill')\n", + "plt.plot([DeltaG_trace_BSI_w_backfill.mean(),DeltaG_trace_BSI_w_backfill.mean()],[0, 350],'g-')\n", + "plt.plot([DeltaG_trace_BSI.mean(),DeltaG_trace_BSI.mean()],[0, 350],'b-')\n", + "#plt.plot([literature_Bos,literature_Bos],[0, 350],'r-',label='IUPHARM data')\n", + "plt.xlabel('$\\Delta G$ ($k_B T$)');\n", + "plt.ylabel('$P(\\Delta G)$');\n", + "plt.title('histogram of estimates for binding free energy');\n", + "plt.legend(loc=0);" + ] + }, + { + "cell_type": "code", + "execution_count": 154, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MAP fitting cycle 5/5\n", + "DeltaG = -11.5 +- 0.5 kT\n", + "Kd = 10.4 uM +- 12.0 uM\n" + ] + }, + { + "data": { + "image/png": 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iIiIdwMyq8ZGBdwH7AdcDA3j30NMmn559X4v39M0Epg0bNgwz+0JlWlx8dRuK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\nQeNtu13NOIYQ9iX/4dphrFizW0s95xV3nUNuHL8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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "quick_model(part1_data_E,part1_data_F,'ERL_w_backfill')" + ] + }, + { + "cell_type": "code", + "execution_count": 155, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MAP fitting cycle 5/5\n", + "DeltaG = -10.4 +- 0.2 kT\n", + "Kd = 29.9 uM +- 5.8 uM\n" + ] + }, + { + "data": { + "image/png": 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ldHqM8UBgl5TSMTHG54H/w6ft/TdwWkrpmRaaorH4xab8FpvyW1zKbbEpv8Wl3BZb3fMb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\nR9Ps7yshhPnkMxjPzHbtmpop9vhL5Clut5H/gL0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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "quick_model(part1_data_M,part1_data_N,'ERL')" + ] + }, + { + "cell_type": "code", + "execution_count": 156, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "DeltaG_trace_ERL_w_backfill = np.load('DeltaG_trace_ERL_w_backfill.npy')\n", + "DeltaG_trace_ERL = np.load('DeltaG_trace_ERL.npy')" + ] + }, + { + "cell_type": "code", + "execution_count": 157, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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http://matplotlib.org/examples/api/barchart_demo.html" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/data/DMSO-backfill/DMSObackfill.png b/data/DMSO-backfill/DMSObackfill.png new file mode 100644 index 0000000..6162695 Binary files /dev/null and b/data/DMSO-backfill/DMSObackfill.png differ diff --git a/data/DMSO-backfill/making_figs.py b/data/DMSO-backfill/making_figs.py new file mode 100644 index 0000000..cef5d41 --- /dev/null +++ b/data/DMSO-backfill/making_figs.py @@ -0,0 +1,103 @@ +# This is a script version of DMSO-backfill.ipynb + +from assaytools import platereader +import matplotlib.pyplot as plt +import string +import numpy as np + +part1 = "p38_singlet1_20160420_153238.xml" +part2 = "p38_singlet2_20160420_154750.xml" + +part1_data = platereader.read_icontrol_xml(part1) +part2_data = platereader.read_icontrol_xml(part2) + +part1_data_C = platereader.select_data(part1_data, '280_480_TOP_120', 'C') +part1_data_D = platereader.select_data(part1_data, '280_480_TOP_120', 'D') +part1_data_K = platereader.select_data(part1_data, '280_480_TOP_120', 'K') +part1_data_L = platereader.select_data(part1_data, '280_480_TOP_120', 'L') + +well = dict() +for j in string.ascii_uppercase: + for i in range(1,25): + well['%s' %j + '%s' %i] = i + +Lstated = np.array([20.0e-6,14.0e-6,9.82e-6,6.88e-6,4.82e-6,3.38e-6,2.37e-6,1.66e-6,1.16e-6,0.815e-6,0.571e-6,0.4e-6,0.28e-6,0.196e-6,0.138e-6,0.0964e-6,0.0676e-6,0.0474e-6,0.0320e-6,0.0240e-6,0.0160e-6,0.0120e-6,0.008e-6,0.00001e-6], np.float64) # ligand concentration, M + +# Stated concentrations of protein and ligand. +Pstated = 0.5e-6 * np.ones([24],np.float64) # protein concentration, M + +# Assay configuration details +import math +assay_volume = 50e-6 # assay volume, L +well_area = 0.1369 # well area, cm^2 for 4ti-0203 [http://4ti.co.uk/files/3113/4217/2464/4ti-0201.pdf] +path_length = assay_volume * 1000 / well_area # cm, needed for inner filter effect corrections + +# Uncertainties in protein and ligand concentrations. +dPstated = 0.35 * Pstated # protein concentration uncertainty +dLstated = 0.08 * Lstated # ligand concentraiton uncertainty (due to gravimetric preparation and HP D300 dispensing) + +def reorder2list(data,wells): + + sorted_keys = sorted(well.keys(), key=lambda k:well[k]) + + reorder_data = [] + + for key in sorted_keys: + try: + reorder_data.append(data[key]) + except: + pass + + reorder_data = np.asarray(reorder_data,np.float64) + + return reorder_data + +def quick_model(protein_data, buffer_data,name): + reorder_protein = reorder2list(protein_data,well) + reorder_buffer = reorder2list(buffer_data,well) + + print name + + from assaytools import pymcmodels + pymc_model = pymcmodels.make_model(Pstated, dPstated, Lstated, dLstated, + top_complex_fluorescence=reorder_protein, + top_ligand_fluorescence=reorder_buffer, + use_primary_inner_filter_correction=True, + use_secondary_inner_filter_correction=True, + assay_volume=assay_volume, DG_prior='uniform') + + mcmc = pymcmodels.run_mcmc(pymc_model) + + from assaytools import plots + figure = plots.plot_measurements(Lstated, Pstated, pymc_model, mcmc=mcmc) + + map = pymcmodels.map_fit(pymc_model) + + pymcmodels.show_summary(pymc_model, map, mcmc) + + DeltaG = map.DeltaG.value + + np.save('DeltaG_%s.npy'%name,DeltaG) + np.save('DeltaG_trace_%s.npy'%name,mcmc.DeltaG.trace()) + + +quick_model(part1_data_C,part1_data_D,'BSI_w_backfill') + +quick_model(part1_data_K,part1_data_L,'BSI') + +DeltaG_trace_BSI_w_backfill = np.load('DeltaG_trace_BSI_w_backfill.npy') +DeltaG_trace_BSI = np.load('DeltaG_trace_BSI.npy') + +plt.clf() + +plt.hist(DeltaG_trace_BSI, 40, alpha=0.75, label='Bsi no backfill') +plt.hist(DeltaG_trace_BSI_w_backfill, 40, alpha=0.75, label='Bsi with backfill') +plt.plot([DeltaG_trace_BSI_w_backfill.mean(),DeltaG_trace_BSI_w_backfill.mean()],[0, 350],'g-') +plt.plot([DeltaG_trace_BSI.mean(),DeltaG_trace_BSI.mean()],[0, 350],'b-') +#plt.plot([literature_Bos,literature_Bos],[0, 350],'r-',label='IUPHARM data') +plt.xlabel('$\Delta G$ ($k_B T$)'); +plt.ylabel('$P(\Delta G)$'); +plt.title('histogram of estimates for binding free energy'); +plt.legend(loc=0); + +plt.savefig('BSI_w_backfill.png') diff --git a/data/literature_affinity/bosutinib.csv b/data/literature_affinity/bosutinib.csv new file mode 100644 index 0000000..9783c04 --- /dev/null +++ b/data/literature_affinity/bosutinib.csv @@ -0,0 +1 @@ +Target,Name in screen,Sp.,Type,Action,Affinity (-log10),Units,Affinity (nM),Units "ABL proto-oncogene 1, non-receptor tyrosine kinase",ABL1(F317L)-phosphorylated,Hs,Inhibitor,Inhibition,10.5,pKd,0,Kd "ABL proto-oncogene 1, non-receptor tyrosine kinase",ABL1(Y253F)-phosphorylated,Hs,Inhibitor,Inhibition,10.4,pKd,0,Kd "ABL proto-oncogene 1, non-receptor tyrosine kinase",ABL1(M351T)-phosphorylated,Hs,Inhibitor,Inhibition,10.4,pKd,0,Kd "ABL proto-oncogene 1, non-receptor tyrosine kinase",ABL1(Q252H)-phosphorylated,Hs,Inhibitor,Inhibition,10.4,pKd,0,Kd "ABL proto-oncogene 1, non-receptor tyrosine kinase",ABL1(E255K)-phosphorylated,Hs,Inhibitor,Inhibition,10.3,pKd,0,Kd "ABL proto-oncogene 1, non-receptor tyrosine kinase",ABL1(H396P)-phosphorylated,Hs,Inhibitor,Inhibition,10.2,pKd,0.1,Kd "ABL proto-oncogene 1, non-receptor tyrosine kinase",ABL1-phosphorylated,Hs,Inhibitor,Inhibition,10.2,pKd,0.1,Kd "ABL proto-oncogene 1, non-receptor tyrosine kinase",ABL1(H396P)-nonphosphorylated,Hs,Inhibitor,Inhibition,10.2,pKd,0.1,Kd "ABL proto-oncogene 1, non-receptor tyrosine kinase",ABL1(Q252H)-nonphosphorylated,Hs,Inhibitor,Inhibition,10.1,pKd,0.1,Kd "ABL proto-oncogene 1, non-receptor tyrosine kinase",ABL1(F317L)-nonphosphorylated,Hs,Inhibitor,Inhibition,10,pKd,0.1,Kd "ABL proto-oncogene 1, non-receptor tyrosine kinase",ABL1-nonphosphorylated,Hs,Inhibitor,Inhibition,9.9,pKd,0.1,Kd "ABL proto-oncogene 1, non-receptor tyrosine kinase",ABL1(F317I)-phosphorylated,Hs,Inhibitor,Inhibition,9.7,pKd,0.2,Kd mitogen-activated protein kinase kinase kinase kinase 5,MAP4K5,Hs,Inhibitor,Inhibition,9.3,pKd,0.5,Kd "LCK proto-oncogene, Src family tyrosine kinase",LCK,Hs,Inhibitor,Inhibition,9.2,pKd,0.6,Kd "ABL proto-oncogene 1, non-receptor tyrosine kinase",ABL1(F317I)-nonphosphorylated,Hs,Inhibitor,Inhibition,9.2,pKd,0.6,Kd erb-b2 receptor tyrosine kinase 3,ERBB3,Hs,Inhibitor,Inhibition,9.1,pKd,0.8,Kd "SRC proto-oncogene, non-receptor tyrosine kinase",SRC,Hs,Inhibitor,Inhibition,9,pKd,1,Kd cyclin G associated kinase,GAK,Hs,Inhibitor,Inhibition,8.9,pKd,1.3,Kd fyn related Src family tyrosine kinase,FRK,Hs,Inhibitor,Inhibition,8.9,pKd,1.4,Kd "ABL proto-oncogene 2, non-receptor tyrosine kinase",ABL2,Hs,Inhibitor,Inhibition,8.8,pKd,1.5,Kd serine/threonine kinase 35,STK35,Hs,Inhibitor,Inhibition,8.7,pKd,2,Kd mitogen-activated protein kinase kinase kinase kinase 2,MAP4K2,Hs,Inhibitor,Inhibition,8.5,pKd,3.1,Kd misshapen-like kinase 1,MINK,Hs,Inhibitor,Inhibition,8.5,pKd,3.2,Kd "BLK proto-oncogene, Src family tyrosine kinase",BLK,Hs,Inhibitor,Inhibition,8.5,pKd,3.3,Kd "HCK proto-oncogene, Src family tyrosine kinase",HCK,Hs,Inhibitor,Inhibition,8.5,pKd,3.4,Kd "ABL proto-oncogene 1, non-receptor tyrosine kinase",ABL1(T315I)-phosphorylated,Hs,Inhibitor,Inhibition,8.4,pKd,3.6,Kd "YES proto-oncogene 1, Src family tyrosine kinase",YES,Hs,Inhibitor,Inhibition,8.4,pKd,4,Kd "LYN proto-oncogene, Src family tyrosine kinase",LYN,Hs,Inhibitor,Inhibition,8.4,pKd,4.2,Kd STE20 like kinase,SLK,Hs,Inhibitor,Inhibition,8.3,pKd,4.7,Kd Bruton tyrosine kinase,BTK,Hs,Inhibitor,Inhibition,8.3,pKd,4.8,Kd mitogen-activated protein kinase kinase kinase kinase 3,MAP4K3,Hs,Inhibitor,Inhibition,8.3,pKd,5.1,Kd EPH receptor A3,EPHA3,Hs,Inhibitor,Inhibition,8.2,pKd,5.8,Kd "FGR proto-oncogene, Src family tyrosine kinase",FGR,Hs,Inhibitor,Inhibition,8.2,pKd,6.3,Kd serine/threonine kinase 10,LOK,Hs,Inhibitor,Inhibition,8.1,pKd,7,Kd mitogen-activated protein kinase kinase kinase kinase 4,MAP4K4,Hs,Inhibitor,Inhibition,8.1,pKd,8.2,Kd mitogen-activated protein kinase kinase 5,MEK5,Hs,Inhibitor,Inhibition,8.1,pKd,8.2,Kd EPH receptor B2,EPHB2,Hs,Inhibitor,Inhibition,8.1,pKd,8.4,Kd EPH receptor A8,EPHA8,Hs,Inhibitor,Inhibition,8.1,pKd,9,Kd EPH receptor B4,EPHB4,Hs,Inhibitor,Inhibition,8,pKd,9.2,Kd mitogen-activated protein kinase kinase 2,MEK2,Hs,Inhibitor,Inhibition,8,pKd,9.9,Kd "FYN proto-oncogene, Src family tyrosine kinase",FYN,Hs,Inhibitor,Inhibition,8,pKd,11,Kd mitogen-activated protein kinase kinase kinase kinase 1,HPK1,Hs,Inhibitor,Inhibition,7.8,pKd,15,Kd mitogen-activated protein kinase kinase kinase 19,YSK4,Hs,Inhibitor,Inhibition,7.8,pKd,16,Kd epidermal growth factor receptor,EGFR(E746-A750del),Hs,Inhibitor,Inhibition,7.7,pKd,18,Kd EPH receptor A2,EPHA2,Hs,Inhibitor,Inhibition,7.7,pKd,18,Kd EPH receptor A4,EPHA4,Hs,Inhibitor,Inhibition,7.7,pKd,18,Kd mitogen-activated protein kinase kinase 1,MEK1,Hs,Inhibitor,Inhibition,7.7,pKd,19,Kd "ABL proto-oncogene 1, non-receptor tyrosine kinase",ABL1(T315I)-nonphosphorylated,Hs,Inhibitor,Inhibition,7.7,pKd,21,Kd epidermal growth factor receptor,EGFR(L858R),Hs,Inhibitor,Inhibition,7.6,pKd,23,Kd epidermal growth factor receptor,EGFR(G719S),Hs,Inhibitor,Inhibition,7.6,pKd,26,Kd erb-b2 receptor tyrosine kinase 4,ERBB4,Hs,Inhibitor,Inhibition,7.6,pKd,26,Kd EPH receptor A5,EPHA5,Hs,Inhibitor,Inhibition,7.6,pKd,27,Kd epidermal growth factor receptor,"EGFR(L747-E749del, A750P)",Hs,Inhibitor,Inhibition,7.5,pKd,29,Kd salt inducible kinase 2,SIK2,Hs,Inhibitor,Inhibition,7.5,pKd,29,Kd "tyrosine kinase, non-receptor, 2",TNK2,Hs,Inhibitor,Inhibition,7.5,pKd,29,Kd epidermal growth factor receptor,"EGFR(L747-T751del,Sins)",Hs,Inhibitor,Inhibition,7.5,pKd,30,Kd mitogen-activated protein kinase kinase kinase 2,MAP3K2,Hs,Inhibitor,Inhibition,7.5,pKd,30,Kd salt inducible kinase 1,SIK,Hs,Inhibitor,Inhibition,7.5,pKd,30,Kd TRAF2 and NCK interacting kinase,TNIK,Hs,Inhibitor,Inhibition,7.5,pKd,31,Kd c-src tyrosine kinase,CSK,Hs,Inhibitor,Inhibition,7.5,pKd,32,Kd EPH receptor B1,EPHB1,Hs,Inhibitor,Inhibition,7.5,pKd,33,Kd epidermal growth factor receptor,EGFR,Hs,Inhibitor,Inhibition,7.5,pKd,35,Kd serine/threonine-protein kinase MST4,MST4,Hs,Inhibitor,Inhibition,7.4,pKd,37,Kd serine/threonine kinase 33,STK33,Hs,Inhibitor,Inhibition,7.4,pKd,37,Kd epidermal growth factor receptor,"EGFR(L747-S752del, P753S)",Hs,Inhibitor,Inhibition,7.4,pKd,38,Kd AXL receptor tyrosine kinase,AXL,Hs,Inhibitor,Inhibition,7.3,pKd,52,Kd "inhibitor of kappa light polypeptide gene enhancer in B-cells, kinase epsilon",IKK-epsilon,Hs,Inhibitor,Inhibition,7.3,pKd,53,Kd mitogen-activated protein kinase kinase kinase 3,MAP3K3,Hs,Inhibitor,Inhibition,7.3,pKd,54,Kd TYRO3 protein tyrosine kinase,TYRO3,Hs,Inhibitor,Inhibition,7.2,pKd,61,Kd SIK family kinase 3,QSK,Hs,Inhibitor,Inhibition,7.2,pKd,64,Kd epidermal growth factor receptor,EGFR(L861Q),Hs,Inhibitor,Inhibition,7.2,pKd,71,Kd TXK tyrosine kinase,TXK,Hs,Inhibitor,Inhibition,7.1,pKd,72,Kd KIT proto-oncogene receptor tyrosine kinase,KIT(D816V),Hs,Inhibitor,Inhibition,7.1,pKd,73,Kd ZAK sterile alpha motif and leucine zipper containing kinase AZK,ZAK,Hs,Inhibitor,Inhibition,7.1,pKd,80,Kd dystrophia myotonica protein kinase,DMPK,Hs,Inhibitor,Inhibition,7,pKd,91,Kd WEE1 homolog 2 (S. pombe),WEE2,Hs,Inhibitor,Inhibition,7,pKd,94,Kd epidermal growth factor receptor,EGFR(G719C),Hs,Inhibitor,Inhibition,7,pKd,98,Kd src-related kinase lacking C-terminal regulatory tyrosine and N-terminal myristylation sites,SRMS,Hs,Inhibitor,Inhibition,7,pKd,100,Kd mitogen-activated protein kinase kinase kinase 7,TAK1,Hs,Inhibitor,Inhibition,7,pKd,100,Kd casein kinase 1 epsilon,CSNK1E,Hs,Inhibitor,Inhibition,7,pKd,110,Kd epidermal growth factor receptor,EGFR(S752-I759del),Hs,Inhibitor,Inhibition,7,pKd,110,Kd mitogen-activated protein kinase kinase kinase 4,MAP3K4,Hs,Inhibitor,Inhibition,7,pKd,110,Kd "MER proto-oncogene, tyrosine kinase",MERTK,Hs,Inhibitor,Inhibition,7,pKd,110,Kd checkpoint kinase 2,CHEK2,Hs,Inhibitor,Inhibition,6.9,pKd,120,Kd discoidin domain receptor tyrosine kinase 1,DDR1,Hs,Inhibitor,Inhibition,6.9,pKd,120,Kd homeodomain interacting protein kinase 4,HIPK4,Hs,Inhibitor,Inhibition,6.9,pKd,130,Kd KIT proto-oncogene receptor tyrosine kinase,"KIT(V559D,V654A)",Hs,Inhibitor,Inhibition,6.9,pKd,130,Kd discoidin domain receptor tyrosine kinase 2,DDR2,Hs,Inhibitor,Inhibition,6.8,pKd,140,Kd TNNI3 interacting kinase,TNNI3K,Hs,Inhibitor,Inhibition,6.8,pKd,170,Kd KIT proto-oncogene receptor tyrosine kinase,KIT(D816H),Hs,Inhibitor,Inhibition,6.7,pKd,180,Kd serine/threonine kinase 4,MST1,Hs,Inhibitor,Inhibition,6.7,pKd,190,Kd BMX non-receptor tyrosine kinase,BMX,Hs,Inhibitor,Inhibition,6.7,pKd,200,Kd platelet derived growth factor receptor beta,PDGFRB,Hs,Inhibitor,Inhibition,6.7,pKd,200,Kd EPH receptor B3,EPHB3,Hs,Inhibitor,Inhibition,6.7,pKd,210,Kd "NUAK family, SNF1-like kinase, 2",SNARK,Hs,Inhibitor,Inhibition,6.7,pKd,220,Kd casein kinase 1 delta,CSNK1D,Hs,Inhibitor,Inhibition,6.6,pKd,240,Kd TANK binding kinase 1,TBK1,Hs,Inhibitor,Inhibition,6.6,pKd,250,Kd eukaryotic translation initiation factor 2 alpha kinase 4,"GCN2(Kin.Dom.2,S808G)",Hs,Inhibitor,Inhibition,6.6,pKd,270,Kd KIT proto-oncogene receptor tyrosine kinase,KIT(A829P),Hs,Inhibitor,Inhibition,6.6,pKd,270,Kd casein kinase 1 alpha 1,CSNK1A1,Hs,Inhibitor,Inhibition,6.6,pKd,280,Kd spleen tyrosine kinase,SYK,Hs,Inhibitor,Inhibition,6.5,pKd,290,Kd CDC like kinase 3,CLK3,Hs,Inhibitor,Inhibition,6.5,pKd,300,Kd "FES proto-oncogene, tyrosine kinase",FES,Hs,Inhibitor,Inhibition,6.5,pKd,330,Kd epidermal growth factor receptor,EGFR(T790M),Hs,Inhibitor,Inhibition,6.5,pKd,340,Kd phosphorylase kinase gamma subunit 1,PHKG1,Hs,Inhibitor,Inhibition,6.5,pKd,340,Kd TAO kinase 3,TAOK3,Hs,Inhibitor,Inhibition,6.5,pKd,340,Kd BMP2 inducible kinase,BIKE,Hs,Inhibitor,Inhibition,6.5,pKd,350,Kd Microtubule associated serine/threonine kinase 1,MAST1,Hs,Inhibitor,Inhibition,6.5,pKd,350,Kd "protein kinase, membrane associated tyrosine/threonine 1",PKMYT1,Hs,Inhibitor,Inhibition,6.5,pKd,350,Kd FER tyrosine kinase,FER,Hs,Inhibitor,Inhibition,6.4,pKd,360,Kd interleukin 1 receptor associated kinase 4,IRAK4,Hs,Inhibitor,Inhibition,6.4,pKd,360,Kd Rho associated coiled-coil containing protein kinase 2,ROCK2,Hs,Inhibitor,Inhibition,6.4,pKd,360,Kd serine/threonine kinase 3,MST2,Hs,Inhibitor,Inhibition,6.4,pKd,370,Kd colony stimulating factor 1 receptor,CSF1R,Hs,Inhibitor,Inhibition,6.4,pKd,380,Kd serine/threonine kinase 24,MST3,Hs,Inhibitor,Inhibition,6.4,pKd,380,Kd dual serine/threonine and tyrosine protein kinase,RIPK5,Hs,Inhibitor,Inhibition,6.4,pKd,380,Kd epidermal growth factor receptor,"EGFR(L858R,T790M)",Hs,Inhibitor,Inhibition,6.4,pKd,390,Kd protein tyrosine kinase 2 beta,PYK2,Hs,Inhibitor,Inhibition,6.4,pKd,390,Kd KIT proto-oncogene receptor tyrosine kinase,KIT,Hs,Inhibitor,Inhibition,6.4,pKd,420,Kd KIT proto-oncogene receptor tyrosine kinase,KIT(V559D),Hs,Inhibitor,Inhibition,6.4,pKd,430,Kd unc-51 like autophagy activating kinase 2,ULK2,Hs,Inhibitor,Inhibition,6.3,pKd,450,Kd unc-51 like kinase 3,ULK3,Hs,Inhibitor,Inhibition,6.3,pKd,460,Kd mitogen-activated protein kinase kinase kinase 13,LZK,Hs,Inhibitor,Inhibition,6.3,pKd,480,Kd myosin light chain kinase,MYLK,Hs,Inhibitor,Inhibition,6.3,pKd,490,Kd leucine-rich repeat kinase 2,LRRK2,Hs,Inhibitor,Inhibition,6.3,pKd,500,Kd WEE1 G2 checkpoint kinase,WEE1,Hs,Inhibitor,Inhibition,6.3,pKd,510,Kd homeodomain interacting protein kinase 1,HIPK1,Hs,Inhibitor,Inhibition,6.3,pKd,520,Kd KIT proto-oncogene receptor tyrosine kinase,KIT(L576P),Hs,Inhibitor,Inhibition,6.3,pKd,520,Kd calcium/calmodulin dependent protein kinase ID,CAMK1D,Hs,Inhibitor,Inhibition,6.3,pKd,550,Kd protein tyrosine kinase 2,FAK,Hs,Inhibitor,Inhibition,6.2,pKd,570,Kd serine/threonine kinase 36,STK36,Hs,Inhibitor,Inhibition,6.2,pKd,570,Kd tousled like kinase 1,TLK1,Hs,Inhibitor,Inhibition,6.2,pKd,570,Kd bone morphogenetic protein receptor type II,BMPR2,Hs,Inhibitor,Inhibition,6.2,pKd,580,Kd CDC like kinase 1,CLK1,Hs,Inhibitor,Inhibition,6.2,pKd,600,Kd interleukin 1 receptor associated kinase 1,IRAK1,Hs,Inhibitor,Inhibition,6.2,pKd,600,Kd eukaryotic translation initiation factor 2 alpha kinase 1,EIF2AK1,Hs,Inhibitor,Inhibition,6.2,pKd,630,Kd NIMA related kinase 11,NEK11,Hs,Inhibitor,Inhibition,6.2,pKd,650,Kd ribosomal protein S6 kinase B1,S6K1,Hs,Inhibitor,Inhibition,6.2,pKd,660,Kd mitogen-activated protein kinase kinase kinase 12,DLK,Hs,Inhibitor,Inhibition,6.2,pKd,670,Kd anaplastic lymphoma receptor tyrosine kinase,ALK,Hs,Inhibitor,Inhibition,6.2,pKd,690,Kd vaccinia related kinase 2,VRK2,Hs,Inhibitor,Inhibition,6.2,pKd,710,Kd calcium/calmodulin-dependent protein kinase kinase 2,CAMKK2,Hs,Inhibitor,Inhibition,6.1,pKd,780,Kd calcium/calmodulin-dependent serine protein kinase (MAGUK family),CASK,Hs,Inhibitor,Inhibition,6.1,pKd,830,Kd leucine-rich repeat kinase 2,LRRK2(G2019S),Hs,Inhibitor,Inhibition,6.1,pKd,830,Kd Rho associated coiled-coil containing protein kinase 1,ROCK1,Hs,Inhibitor,Inhibition,6.1,pKd,870,Kd serine/threonine kinase 25,YSK1,Hs,Inhibitor,Inhibition,6.1,pKd,900,Kd protein kinase C theta,PRKCQ,Hs,Inhibitor,Inhibition,6,pKd,910,Kd polo like kinase 2,PLK2,Hs,Inhibitor,Inhibition,6,pKd,960,Kd colony stimulating factor 1 receptor,CSF1R-autoinhibited,Hs,Inhibitor,Inhibition,6,pKd,1000,Kd fms related tyrosine kinase 3,FLT3(D835H),Hs,Inhibitor,Inhibition,6,pKd,1000,Kd EPH receptor A6,EPHA6,Hs,Inhibitor,Inhibition,6,pKd,1100,Kd fms related tyrosine kinase 3,FLT3(D835Y),Hs,Inhibitor,Inhibition,6,pKd,1100,Kd Janus kinase 2,JAK2(JH1domain-catalytic),Hs,Inhibitor,Inhibition,6,pKd,1100,Kd cyclin-dependent kinase 7,CDK7,Hs,Inhibitor,Inhibition,5.9,pKd,1200,Kd Janus kinase 3,JAK3(JH1domain-catalytic),Hs,Inhibitor,Inhibition,5.9,pKd,1200,Kd ribosomal protein S6 kinase A3,RSK2(Kin.Dom.1-N-terminal),Hs,Inhibitor,Inhibition,5.9,pKd,1200,Kd neurotrophic tyrosine kinase receptor type 2,TRKB,Hs,Inhibitor,Inhibition,5.9,pKd,1200,Kd unc-51 like autophagy activating kinase 1,ULK1,Hs,Inhibitor,Inhibition,5.9,pKd,1200,Kd mitogen-activated protein kinase kinase 6,MEK6,Hs,Inhibitor,Inhibition,5.9,pKd,1300,Kd phosphorylase kinase gamma subunit 2,PHKG2,Hs,Inhibitor,Inhibition,5.9,pKd,1300,Kd TAO kinase 1,TAOK1,Hs,Inhibitor,Inhibition,5.9,pKd,1300,Kd cyclin-dependent kinase 14,PFTK1,Hs,Inhibitor,Inhibition,5.8,pKd,1400,Kd serine/threonine-protein kinase NIM1,NIM1,Hs,Inhibitor,Inhibition,5.8,pKd,1500,Kd NIMA related kinase 2,NEK2,Hs,Inhibitor,Inhibition,5.8,pKd,1600,Kd cyclin-dependent kinase 15,PFTAIRE2,Hs,Inhibitor,Inhibition,5.8,pKd,1600,Kd protein kinase C delta,PRKCD,Hs,Inhibitor,Inhibition,5.8,pKd,1600,Kd RIO kinase 3,RIOK3,Hs,Inhibitor,Inhibition,5.8,pKd,1600,Kd zeta chain of T cell receptor associated protein kinase 70kDa,ZAP70,Hs,Inhibitor,Inhibition,5.8,pKd,1600,Kd CDC like kinase 2,CLK2,Hs,Inhibitor,Inhibition,5.8,pKd,1700,Kd G protein-coupled receptor kinase 7,GRK7,Hs,Inhibitor,Inhibition,5.8,pKd,1700,Kd IL2 inducible T-cell kinase,ITK,Hs,Inhibitor,Inhibition,5.8,pKd,1700,Kd polo like kinase 1,PLK1,Hs,Inhibitor,Inhibition,5.7,pKd,1800,Kd serine/threonine kinase 32A,YANK1,Hs,Inhibitor,Inhibition,5.7,pKd,1800,Kd homeodomain interacting protein kinase 2,HIPK2,Hs,Inhibitor,Inhibition,5.7,pKd,1900,Kd p21 protein (Cdc42/Rac)-activated kinase 2,PAK2,Hs,Inhibitor,Inhibition,5.7,pKd,1900,Kd TAO kinase 2,TAOK2,Hs,Inhibitor,Inhibition,5.7,pKd,1900,Kd ankyrin repeat and kinase domain containing 1,ANKK1,Hs,Inhibitor,Inhibition,5.7,pKd,2100,Kd tousled like kinase 2,TLK2,Hs,Inhibitor,Inhibition,5.7,pKd,2100,Kd fms related tyrosine kinase 3,FLT3,Hs,Inhibitor,Inhibition,5.7,pKd,2200,Kd fms related tyrosine kinase 3,FLT3(K663Q),Hs,Inhibitor,Inhibition,5.7,pKd,2200,Kd "MET proto-oncogene, receptor tyrosine kinase",MET,Hs,Inhibitor,Inhibition,5.7,pKd,2200,Kd nemo-like kinase,NLK,Hs,Inhibitor,Inhibition,5.7,pKd,2200,Kd RIO kinase 1,RIOK1,Hs,Inhibitor,Inhibition,5.7,pKd,2200,Kd AP2 associated kinase 1,AAK1,Hs,Inhibitor,Inhibition,5.6,pKd,2300,Kd EPH receptor A1,EPHA1,Hs,Inhibitor,Inhibition,5.6,pKd,2300,Kd mitogen-activated protein kinase 7,ERK5,Hs,Inhibitor,Inhibition,5.6,pKd,2300,Kd myosin IIIB,MYO3B,Hs,Inhibitor,Inhibition,5.6,pKd,2300,Kd p21 protein (Cdc42/Rac)-activated kinase 1,PAK1,Hs,Inhibitor,Inhibition,5.6,pKd,2300,Kd citron rho-interacting serine/threonine kinase,CIT,Hs,Inhibitor,Inhibition,5.6,pKd,2400,Kd cyclin-dependent kinase 16,PCTK1,Hs,Inhibitor,Inhibition,5.6,pKd,2400,Kd protein kinase C epsilon,PRKCE,Hs,Inhibitor,Inhibition,5.6,pKd,2400,Kd erb-b2 receptor tyrosine kinase 2,ERBB2,Hs,Inhibitor,Inhibition,5.6,pKd,2500,Kd casein kinase 1 alpha 1 like,CSNK1A1L,Hs,Inhibitor,Inhibition,5.6,pKd,2600,Kd mitogen-activated protein kinase kinase 3,MEK3,Hs,Inhibitor,Inhibition,5.6,pKd,2600,Kd mitogen-activated protein kinase kinase 4,MEK4,Hs,Inhibitor,Inhibition,5.6,pKd,2600,Kd mitogen-activated protein kinase kinase kinase 9,MLK1,Hs,Inhibitor,Inhibition,5.6,pKd,2700,Kd mitogen-activated protein kinase kinase kinase 11,MLK3,Hs,Inhibitor,Inhibition,5.6,pKd,2700,Kd ribosomal protein S6 kinase A6,RSK4(Kin.Dom.1-N-terminal),Hs,Inhibitor,Inhibition,5.6,pKd,2700,Kd fms related tyrosine kinase 3,FLT3(ITD),Hs,Inhibitor,Inhibition,5.5,pKd,2900,Kd protein kinase N1,PKN1,Hs,Inhibitor,Inhibition,5.5,pKd,2900,Kd tyrosine kinase with immunoglobulin like and EGF like domains 1,TIE1,Hs,Inhibitor,Inhibition,5.5,pKd,2900,Kd activin A receptor type 1,ACVR1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd activin A receptor type 1B,ACVR1B,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd activin A receptor type 2A,ACVR2A,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd activin A receptor type 2B,ACVR2B,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd activin A receptor type IL,ACVRL1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd aarF domain containing kinase 3,ADCK3,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd aarF domain containing kinase 4,ADCK4,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd v-akt murine thymoma viral oncogene homolog 1,AKT1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd v-akt murine thymoma viral oncogene homolog 2,AKT2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd v-akt murine thymoma viral oncogene homolog 3,AKT3,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd protein kinase AMP-activated catalytic subunit alpha 2,AMPK-alpha2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase kinase kinase 5,ASK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase kinase kinase 6,ASK2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd aurora kinase A,AURKA,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd aurora kinase B,AURKB,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd aurora kinase C,AURKC,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd bone morphogenetic protein receptor type IA,BMPR1A,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd bone morphogenetic protein receptor type IB,BMPR1B,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "B-Raf proto-oncogene, serine/threonine kinase",BRAF,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "B-Raf proto-oncogene, serine/threonine kinase",BRAF(V600E),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd protein tyrosine kinase 6,BRK,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd BR serine/threonine kinase 1,BRSK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd BR serine/threonine kinase 2,BRSK2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd calcium/calmodulin-dependent protein kinase I,CAMK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd calcium/calmodulin dependent protein kinase IG,CAMK1G,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd calcium/calmodulin-dependent protein kinase II beta subunit,CAMK2B,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd calcium/calmodulin-dependent protein kinase II delta subunit,CAMK2D,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd calcium/calmodulin-dependent protein kinase IV,CAMK4,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd calcium/calmodulin-dependent protein kinase kinase 1,CAMKK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd cyclin-dependent kinase 11B,CDC2L1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd cyclin-dependent kinase 11A,CDC2L2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd cyclin-dependent kinase 13,CDC2L5,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd cyclin-dependent kinase 19,CDK11,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd cyclin-dependent kinase 2,CDK2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd cyclin-dependent kinase 3,CDK3,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd cyclin-dependent kinase 4,CDK4-cyclinD1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd cyclin-dependent kinase 4,CDK4-cyclinD3,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd cyclin-dependent kinase 5,CDK5,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd cyclin-dependent kinase 8,CDK8,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd cyclin-dependent kinase 9,CDK9,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd cyclin dependent kinase like 1,CDKL1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd cyclin dependent kinase like 2,CDKL2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd cyclin-dependent kinase like 3,CDKL3,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd cyclin-dependent kinase like 4,CDKL5,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd checkpoint kinase 1,CHEK1,Hs,Inhibitor,Inhibition,5.5,pKd,3000,Kd casein kinase 1 gamma 1,CSNK1G1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd casein kinase 1 gamma 2,CSNK1G2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd casein kinase 1 gamma 3,CSNK1G3,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "casein kinase 2, alpha 1 polypeptide subunit",CSNK2A1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "casein kinase 2, alpha prime polypeptide subunit",CSNK2A2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd death associated protein kinase 1,DAPK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd death-associated protein kinase 2,DAPK2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd death-associated protein kinase 3,DAPK3,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd doublecortin like kinase 1,DCAMKL1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd doublecortin like kinase 2,DCAMKL2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd CDC42 binding protein kinase gamma,DMPK2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd serine/threonine kinase 17a,DRAK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd serine/threonine kinase 17b,DRAK2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd dual specificity tyrosine phosphorylation regulated kinase 1A,DYRK1A,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd dual specificity tyrosine phosphorylation regulated kinase 1B,DYRK1B,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd dual specificity tyrosine phosphorylation regulated kinase 2,DYRK2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd EPH receptor A7,EPHA7,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd EPH receptor B6,EPHB6,Hs,Inhibitor,Inhibition,5.5,pKd,3000,Kd mitogen-activated protein kinase 3,ERK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase 1,ERK2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase 6,ERK3,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase 4,ERK4,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase 15,ERK8,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd endoplasmic reticulum to nucleus signaling 1,ERN1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd fibroblast growth factor receptor 1,FGFR1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd fibroblast growth factor receptor 2,FGFR2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd fibroblast growth factor receptor 3,FGFR3,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd fibroblast growth factor receptor 3,FGFR3(G697C),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd fibroblast growth factor receptor 4,FGFR4,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd fms related tyrosine kinase 1,FLT1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd fms related tyrosine kinase 3,FLT3(R834Q),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd fms related tyrosine kinase 4,FLT4,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd G protein-coupled receptor kinase 1,GRK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd glycogen synthase kinase 3 alpha,GSK3A,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd glycogen synthase kinase 3 beta,GSK3B,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd intestinal cell (MAK-like) kinase,ICK,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd Insulin-like growth factor I receptor,IGF1R,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd conserved helix-loop-helix ubiquitous kinase,IKK-alpha,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "inhibitor of kappa light polypeptide gene enhancer in B-cells, kinase beta",IKK-beta,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd Insulin receptor,INSR,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd Insulin receptor-related receptor,INSRR,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd interleukin 1 receptor associated kinase 3,IRAK3,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd Janus kinase 1,JAK1(JH1domain-catalytic),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd Janus kinase 1,JAK1(JH2domain-pseudokinase),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase 8,JNK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase 9,JNK2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase 10,JNK3,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd KIT proto-oncogene receptor tyrosine kinase,"KIT(V559D,T670I)",Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd large tumor suppressor kinase 1,LATS1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd large tumor suppressor kinase 2,LATS2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd LIM domain kinase 1,LIMK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd LIM domain kinase 2,LIMK2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd serine/threonine kinase 11,LKB1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd leukocyte receptor tyrosine kinase,LTK,Hs,Inhibitor,Inhibition,5.5,pKd,3000,Kd male germ cell associated kinase,MAK,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase kinase kinase 15,MAP3K15,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase-activated protein kinase 2,MAPKAPK2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase-activated protein kinase 5,MAPKAPK5,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd microtubule affinity regulating kinase 1,MARK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd microtubule affinity regulating kinase 2,MARK2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd microtubule affinity regulating kinase 3,MARK3,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd microtubule affinity regulating kinase 4,MARK4,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd maternal embryonic leucine zipper kinase,MELK,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd MAP kinase interacting serine/threonine kinase 1,MKNK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd MAP kinase interacting serine/threonine kinase 2,MKNK2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd myosin light chain kinase 3,MLCK,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase kinase kinase 10,MLK2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd CDC42 binding protein kinase alpha,MRCKA,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd CDC42 binding protein kinase beta,MRCKB,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mechanistic target of rapamycin,MTOR,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "muscle, skeletal, receptor tyrosine kinase",MUSK,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd myosin light chain kinase 2,MYLK2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd myosin light chain kinase family member 4,MYLK4,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd Serine/threonine kinase 38,NDR1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd Serine/threonine kinase 38 like,NDR2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd NIMA related kinase 1,NEK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd NIMA related kinase 3,NEK3,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd NIMA related kinase 4,NEK4,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd NIMA related kinase 5,NEK5,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd NIMA related kinase 6,NEK6,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd NIMA related kinase 7,NEK7,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd NIMA related kinase 8,NEK9,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd oxidative stress responsive 1,OSR1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase 14,p38-alpha,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase 11,p38-beta,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase 13,p38-delta,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase 12,p38-gamma,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd p21 protein (Cdc42/Rac)-activated kinase 4,PAK4,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd p21 protein (Cdc42/Rac)-activated kinase 6,PAK6,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd p21 protein (Cdc42/Rac)-activated kinase 7,PAK7,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd cyclin-dependent kinase 17,PCTK2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd cyclin-dependent kinase 18,PCTK3,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd 3-phosphoinositide dependent protein kinase 1,PDPK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd phosphatidylinositol-4-phosphate 3-kinase catalytic subunit type 2 beta,PIK3C2B,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd phosphatidylinositol-4-phosphate 3-kinase catalytic subunit type 2 gamma,PIK3C2G,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha",PIK3CA,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha",PIK3CA(C420R),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha",PIK3CA(E542K),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha",PIK3CA(E545A),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha",PIK3CA(E545K),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha",PIK3CA(H1047L),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha",PIK3CA(H1047Y),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha",PIK3CA(I800L),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha",PIK3CA(M1043I),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha",PIK3CA(Q546K),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit beta",PIK3CB,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit delta",PIK3CD,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit gamma",PIK3CG,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd phosphatidylinositol 4-kinase alpha,PIK4CB,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "Pim-1 proto-oncogene, serine/threonine kinase",PIM1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "Pim-2 proto-oncogene, serine/threonine kinase",PIM2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "Pim-3 proto-oncogene, serine/threonine kinase",PIM3,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "phosphatidylinositol-4-phosphate 5-kinase, type I, gamma",PIP5K1C,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "phosphatidylinositol-5-phosphate 4-kinase, type II, beta",PIP5K2B,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "phosphatidylinositol-5-phosphate 4-kinase, type II, gamma",PIP5K2C,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "protein kinase, cAMP-dependent, catalytic, alpha subunit",PKAC-alpha,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "protein kinase, cAMP-dependent, catalytic, beta subunit",PKAC-beta,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd polo like kinase 3,PLK3,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd polo like kinase 4,PLK4,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd protein kinase C eta,PRKCH,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd protein kinase C iota,PRKCI,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd protein kinase D1,PRKD1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd protein kinase D2,PRKD2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd Protein kinase G (PKG) 1,PRKG1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd Protein kinase G (PKG) 2,PRKG2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd eukaryotic translation initiation factor 2 alpha kinase 2,PRKR,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "protein kinase, X-linked",PRKX,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd pre-mRNA processing factor 4B,PRP4,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "Raf-1 proto-oncogene, serine/threonine kinase",RAF1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd ret proto-oncogene,RET(V804L),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd RIO kinase 2,RIOK2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd receptor interacting serine/threonine kinase 1,RIPK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd receptor interacting serine/threonine kinase 4,RIPK4,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "c-ros oncogene 1, receptor tyrosine kinase",ROS1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd ribosomal protein S6 kinase A4,RPS6KA4(Kin.Dom.1-N-terminal),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd ribosomal protein S6 kinase A4,RPS6KA4(Kin.Dom.2-C-terminal),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd ribosomal protein S6 kinase A5,RPS6KA5(Kin.Dom.1-N-terminal),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd ribosomal protein S6 kinase A5,RPS6KA5(Kin.Dom.2-C-terminal),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd ribosomal protein S6 kinase A1,RSK1(Kin.Dom.1-N-terminal),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd ribosomal protein S6 kinase A1,RSK1(Kin.Dom.2-C-terminal),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd ribosomal protein S6 kinase A2,RSK3(Kin.Dom.2-C-terminal),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd ribosomal protein S6 kinase A6,RSK4(Kin.Dom.2-C-terminal),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd SH3 domain binding kinase 1,SBK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd sugen kinase 110,SgK110,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd serum/glucocorticoid regulated kinase family member 3,SGK3,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd SNF related kinase,SNRK,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd SRSF protein kinase 1,SRPK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd SRSF protein kinase 2,SRPK2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd serine/threonine kinase 16,STK16,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd testis-specific kinase 1,TESK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd transforming growth factor beta receptor 1,TGFBR1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd transforming growth factor beta receptor II,TGFBR2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "tyrosine kinase, non-receptor, 1",TNK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd TRPM6,TRPM6,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd TTK protein kinase,TTK,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd kinase insert domain receptor,VEGFR2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd serine/threonine kinase 32B,YANK2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd serine/threonine kinase 32C,YANK3,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "MET proto-oncogene, receptor tyrosine kinase",MET(Y1235D),Hs,Inhibitor,Inhibition,5.5,pKd,3100,Kd "neurotrophic tyrosine kinase, receptor, type 1",TRKA,Hs,Inhibitor,Inhibition,5.5,pKd,3100,Kd CDC like kinase 4,CLK4,Hs,Inhibitor,Inhibition,5.5,pKd,3200,Kd homeodomain interacting protein kinase 3,HIPK3,Hs,Inhibitor,Inhibition,5.5,pKd,3200,Kd protein kinase N2,PKN2,Hs,Inhibitor,Inhibition,5.5,pKd,3200,Kd ribosomal protein S6 kinase A2,RSK3(Kin.Dom.1-N-terminal),Hs,Inhibitor,Inhibition,5.5,pKd,3200,Kd tec protein tyrosine kinase,TEC,Hs,Inhibitor,Inhibition,5.5,pKd,3200,Kd "phosphatidylinositol-4-phosphate 5-kinase, type I, alpha",PIP5K1A,Hs,Inhibitor,Inhibition,5.5,pKd,3300,Kd fms related tyrosine kinase 3,FLT3(N841I),Hs,Inhibitor,Inhibition,5.5,pKd,3400,Kd "neurotrophic tyrosine kinase, receptor, type 3",TRKC,Hs,Inhibitor,Inhibition,5.5,pKd,3400,Kd calcium/calmodulin-dependent protein kinase II gamma subunit,CAMK2G,Hs,Inhibitor,Inhibition,5.4,pKd,3600,Kd receptor interacting serine/threonine kinase 2,RIPK2,Hs,Inhibitor,Inhibition,5.4,pKd,3700,Kd serine/threonine kinase 39,STK39,Hs,Inhibitor,Inhibition,5.4,pKd,3700,Kd ret proto-oncogene,RET(M918T),Hs,Inhibitor,Inhibition,5.4,pKd,3800,Kd protein kinase AMP-activated catalytic subunit alpha 1,AMPK-alpha1,Hs,Inhibitor,Inhibition,5.4,pKd,3900,Kd SRSF protein kinase 3,SRPK3,Hs,Inhibitor,Inhibition,5.4,pKd,3900,Kd TEK receptor tyrosine kinase,TIE2,Hs,Inhibitor,Inhibition,5.4,pKd,3900,Kd ret proto-oncogene,RET(V804M),Hs,Inhibitor,Inhibition,5.4,pKd,4200,Kd mitogen-activated protein kinase kinase kinase 1,MAP3K1,Hs,Inhibitor,Inhibition,5.4,pKd,4400,Kd calcium/calmodulin-dependent protein kinase II alpha subunit,CAMK2A,Hs,Inhibitor,Inhibition,5.3,pKd,4500,Kd tyrosine kinase 2,TYK2(JH2domain-pseudokinase),Hs,Inhibitor,Inhibition,5.3,pKd,4500,Kd protein kinase D3,PRKD3,Hs,Inhibitor,Inhibition,5.3,pKd,4600,Kd testis specific serine kinase 1B,TSSK1B,Hs,Inhibitor,Inhibition,5.3,pKd,4600,Kd ret proto-oncogene,RET,Hs,Inhibitor,Inhibition,5.3,pKd,4800,Kd G protein-coupled receptor kinase 4,GRK4,Hs,Inhibitor,Inhibition,5.3,pKd,4900,Kd hormonally up-regulated Neu-associated kinase,HUNK,Hs,Inhibitor,Inhibition,5.3,pKd,5000,Kd platelet derived growth factor receptor alpha,PDGFRA,Hs,Inhibitor,Inhibition,5.3,pKd,5100,Kd mitogen-activated protein kinase kinase 7,MKK7,Hs,Inhibitor,Inhibition,5.3,pKd,5300,Kd doublecortin like kinase 3,DCAMKL3,Hs,Inhibitor,Inhibition,5.3,pKd,5400,Kd p21 protein (Cdc42/Rac)-activated kinase 3,PAK3,Hs,Inhibitor,Inhibition,5.2,pKd,5700,Kd megakaryocyte-associated tyrosine kinase,CTK,Hs,Inhibitor,Inhibition,5.2,pKd,5800,Kd NUAK family kinase 1,ARK5,Hs,Inhibitor,Inhibition,5.2,pKd,5900,Kd myosin IIIA,MYO3A,Hs,Inhibitor,Inhibition,5.2,pKd,6200,Kd macrophage stimulating 1 receptor,MST1R,Hs,Inhibitor,Inhibition,5.2,pKd,6300,Kd tyrosine kinase 2,TYK2(JH1domain-catalytic),Hs,Inhibitor,Inhibition,5.2,pKd,6500,Kd "MET proto-oncogene, receptor tyrosine kinase",MET(M1250T),Hs,Inhibitor,Inhibition,5.2,pKd,6800,Kd KIT proto-oncogene receptor tyrosine kinase,KIT-autoinhibited,Hs,Inhibitor,Inhibition,5.1,pKd,9000,Kd \ No newline at end of file diff --git a/data/literature_affinity/erlotinib.csv b/data/literature_affinity/erlotinib.csv new file mode 100644 index 0000000..e75a7eb --- /dev/null +++ b/data/literature_affinity/erlotinib.csv @@ -0,0 +1 @@ +Target,Name in screen,Sp.,Type,Action,Affinity (-log10),Units,Affinity (nM),Units epidermal growth factor receptor,"EGFR(L747-T751del,Sins)",Hs,Inhibitor,Inhibition,9.5,pKd,0.3,Kd epidermal growth factor receptor,"EGFR(L747-S752del, P753S)",Hs,Inhibitor,Inhibition,9.3,pKd,0.5,Kd epidermal growth factor receptor,EGFR(E746-A750del),Hs,Inhibitor,Inhibition,9.3,pKd,0.5,Kd epidermal growth factor receptor,EGFR(G719S),Hs,Inhibitor,Inhibition,9.3,pKd,0.5,Kd epidermal growth factor receptor,"EGFR(L747-E749del, A750P)",Hs,Inhibitor,Inhibition,9.3,pKd,0.5,Kd epidermal growth factor receptor,EGFR,Hs,Inhibitor,Inhibition,9.2,pKd,0.7,Kd epidermal growth factor receptor,EGFR(G719C),Hs,Inhibitor,Inhibition,9.1,pKd,0.9,Kd epidermal growth factor receptor,EGFR(L858R),Hs,Inhibitor,Inhibition,9,pKd,1,Kd epidermal growth factor receptor,EGFR(L861Q),Hs,Inhibitor,Inhibition,8.9,pKd,1.2,Kd epidermal growth factor receptor,EGFR(S752-I759del),Hs,Inhibitor,Inhibition,8.8,pKd,1.6,Kd cyclin G associated kinase,GAK,Hs,Inhibitor,Inhibition,8.5,pKd,3.1,Kd serine/threonine kinase 10,LOK,Hs,Inhibitor,Inhibition,7.7,pKd,19,Kd mitogen-activated protein kinase kinase kinase 19,YSK4,Hs,Inhibitor,Inhibition,7.6,pKd,25,Kd STE20 like kinase,SLK,Hs,Inhibitor,Inhibition,7.6,pKd,26,Kd "ABL proto-oncogene 1, non-receptor tyrosine kinase",ABL1(H396P)-phosphorylated,Hs,Inhibitor,Inhibition,7.2,pKd,58,Kd "ABL proto-oncogene 1, non-receptor tyrosine kinase",ABL1(T315I)-phosphorylated,Hs,Inhibitor,Inhibition,7.2,pKd,59,Kd "ABL proto-oncogene 1, non-receptor tyrosine kinase",ABL1(Q252H)-phosphorylated,Hs,Inhibitor,Inhibition,7.2,pKd,61,Kd "ABL proto-oncogene 1, non-receptor tyrosine kinase",ABL1(E255K)-phosphorylated,Hs,Inhibitor,Inhibition,7.2,pKd,63,Kd "ABL proto-oncogene 1, non-receptor tyrosine kinase",ABL1(M351T)-phosphorylated,Hs,Inhibitor,Inhibition,7.2,pKd,66,Kd "ABL proto-oncogene 1, non-receptor tyrosine kinase",ABL1-phosphorylated,Hs,Inhibitor,Inhibition,7.1,pKd,76,Kd "ABL proto-oncogene 1, non-receptor tyrosine kinase",ABL1(Y253F)-phosphorylated,Hs,Inhibitor,Inhibition,7.1,pKd,76,Kd "ABL proto-oncogene 1, non-receptor tyrosine kinase",ABL1(H396P)-nonphosphorylated,Hs,Inhibitor,Inhibition,7.1,pKd,89,Kd mitogen-activated protein kinase kinase 5,MEK5,Hs,Inhibitor,Inhibition,7,pKd,96,Kd fms related tyrosine kinase 3,FLT3(D835Y),Hs,Inhibitor,Inhibition,6.9,pKd,130,Kd epidermal growth factor receptor,EGFR(T790M),Hs,Inhibitor,Inhibition,6.8,pKd,140,Kd "ABL proto-oncogene 1, non-receptor tyrosine kinase",ABL1(F317L)-phosphorylated,Hs,Inhibitor,Inhibition,6.8,pKd,150,Kd "BLK proto-oncogene, Src family tyrosine kinase",BLK,Hs,Inhibitor,Inhibition,6.7,pKd,190,Kd epidermal growth factor receptor,"EGFR(L858R,T790M)",Hs,Inhibitor,Inhibition,6.7,pKd,190,Kd "ABL proto-oncogene 2, non-receptor tyrosine kinase",ABL2,Hs,Inhibitor,Inhibition,6.7,pKd,200,Kd "ABL proto-oncogene 1, non-receptor tyrosine kinase",ABL1(Q252H)-nonphosphorylated,Hs,Inhibitor,Inhibition,6.6,pKd,230,Kd erb-b2 receptor tyrosine kinase 4,ERBB4,Hs,Inhibitor,Inhibition,6.6,pKd,230,Kd "LCK proto-oncogene, Src family tyrosine kinase",LCK,Hs,Inhibitor,Inhibition,6.6,pKd,250,Kd "ABL proto-oncogene 1, non-receptor tyrosine kinase",ABL1-nonphosphorylated,Hs,Inhibitor,Inhibition,6.5,pKd,330,Kd ret proto-oncogene,RET(M918T),Hs,Inhibitor,Inhibition,6.5,pKd,330,Kd fms related tyrosine kinase 3,FLT3(D835H),Hs,Inhibitor,Inhibition,6.5,pKd,350,Kd EPH receptor A6,EPHA6,Hs,Inhibitor,Inhibition,6.4,pKd,440,Kd casein kinase 1 epsilon,CSNK1E,Hs,Inhibitor,Inhibition,6.3,pKd,500,Kd fms related tyrosine kinase 3,FLT3(N841I),Hs,Inhibitor,Inhibition,6.3,pKd,500,Kd "LYN proto-oncogene, Src family tyrosine kinase",LYN,Hs,Inhibitor,Inhibition,6.3,pKd,530,Kd TNNI3 interacting kinase,TNNI3K,Hs,Inhibitor,Inhibition,6.2,pKd,570,Kd aurora kinase C,AURKC,Hs,Inhibitor,Inhibition,6.2,pKd,600,Kd "ABL proto-oncogene 1, non-receptor tyrosine kinase",ABL1(T315I)-nonphosphorylated,Hs,Inhibitor,Inhibition,6.2,pKd,620,Kd "tyrosine kinase, non-receptor, 1",TNK1,Hs,Inhibitor,Inhibition,6.2,pKd,630,Kd "ABL proto-oncogene 1, non-receptor tyrosine kinase",ABL1(F317L)-nonphosphorylated,Hs,Inhibitor,Inhibition,6.2,pKd,640,Kd citron rho-interacting serine/threonine kinase,CIT,Hs,Inhibitor,Inhibition,6.2,pKd,680,Kd receptor interacting serine/threonine kinase 2,RIPK2,Hs,Inhibitor,Inhibition,6.2,pKd,680,Kd MAP kinase interacting serine/threonine kinase 1,MKNK1,Hs,Inhibitor,Inhibition,6.2,pKd,690,Kd Janus kinase 3,JAK3(JH1domain-catalytic),Hs,Inhibitor,Inhibition,6.2,pKd,700,Kd "SRC proto-oncogene, non-receptor tyrosine kinase",SRC,Hs,Inhibitor,Inhibition,6.2,pKd,700,Kd EPH receptor A5,EPHA5,Hs,Inhibitor,Inhibition,6.2,pKd,710,Kd discoidin domain receptor tyrosine kinase 1,DDR1,Hs,Inhibitor,Inhibition,6.1,pKd,790,Kd fms related tyrosine kinase 3,FLT3(ITD),Hs,Inhibitor,Inhibition,6.1,pKd,820,Kd tyrosine kinase with immunoglobulin like and EGF like domains 1,TIE1,Hs,Inhibitor,Inhibition,6.1,pKd,850,Kd leukocyte receptor tyrosine kinase,LTK,Hs,Inhibitor,Inhibition,6.1,pKd,890,Kd unc-51 like kinase 3,ULK3,Hs,Inhibitor,Inhibition,6,pKd,920,Kd EPH receptor A8,EPHA8,Hs,Inhibitor,Inhibition,6,pKd,940,Kd homeodomain interacting protein kinase 4,HIPK4,Hs,Inhibitor,Inhibition,6,pKd,960,Kd myosin light chain kinase 2,MYLK2,Hs,Inhibitor,Inhibition,6,pKd,970,Kd "MER proto-oncogene, tyrosine kinase",MERTK,Hs,Inhibitor,Inhibition,6,pKd,980,Kd MAP kinase interacting serine/threonine kinase 2,MKNK2,Hs,Inhibitor,Inhibition,6,pKd,1000,Kd "phosphatidylinositol-5-phosphate 4-kinase, type II, gamma",PIP5K2C,Hs,Inhibitor,Inhibition,6,pKd,1000,Kd "ABL proto-oncogene 1, non-receptor tyrosine kinase",ABL1(F317I)-phosphorylated,Hs,Inhibitor,Inhibition,6,pKd,1100,Kd EPH receptor B1,EPHB1,Hs,Inhibitor,Inhibition,6,pKd,1100,Kd erb-b2 receptor tyrosine kinase 3,ERBB3,Hs,Inhibitor,Inhibition,6,pKd,1100,Kd "FGR proto-oncogene, Src family tyrosine kinase",FGR,Hs,Inhibitor,Inhibition,6,pKd,1100,Kd "MET proto-oncogene, receptor tyrosine kinase",MET(Y1235D),Hs,Inhibitor,Inhibition,6,pKd,1100,Kd AP2 associated kinase 1,AAK1,Hs,Inhibitor,Inhibition,5.9,pKd,1200,Kd anaplastic lymphoma receptor tyrosine kinase,ALK,Hs,Inhibitor,Inhibition,5.9,pKd,1200,Kd BMP2 inducible kinase,BIKE,Hs,Inhibitor,Inhibition,5.9,pKd,1200,Kd fms related tyrosine kinase 3,FLT3,Hs,Inhibitor,Inhibition,5.9,pKd,1200,Kd SH3 domain binding kinase 1,SBK1,Hs,Inhibitor,Inhibition,5.9,pKd,1200,Kd dual specificity tyrosine phosphorylation regulated kinase 2,DYRK2,Hs,Inhibitor,Inhibition,5.9,pKd,1300,Kd fms related tyrosine kinase 3,FLT3(K663Q),Hs,Inhibitor,Inhibition,5.9,pKd,1300,Kd KIT proto-oncogene receptor tyrosine kinase,"KIT(V559D,T670I)",Hs,Inhibitor,Inhibition,5.9,pKd,1300,Kd misshapen-like kinase 1,MINK,Hs,Inhibitor,Inhibition,5.9,pKd,1300,Kd eukaryotic translation initiation factor 2 alpha kinase 2,PRKR,Hs,Inhibitor,Inhibition,5.9,pKd,1300,Kd ret proto-oncogene,RET,Hs,Inhibitor,Inhibition,5.9,pKd,1300,Kd aurora kinase B,AURKB,Hs,Inhibitor,Inhibition,5.8,pKd,1400,Kd EPH receptor A7,EPHA7,Hs,Inhibitor,Inhibition,5.8,pKd,1400,Kd platelet derived growth factor receptor beta,PDGFRB,Hs,Inhibitor,Inhibition,5.8,pKd,1400,Kd KIT proto-oncogene receptor tyrosine kinase,KIT(D816V),Hs,Inhibitor,Inhibition,5.8,pKd,1600,Kd EPH receptor B6,EPHB6,Hs,Inhibitor,Inhibition,5.8,pKd,1700,Kd KIT proto-oncogene receptor tyrosine kinase,KIT,Hs,Inhibitor,Inhibition,5.8,pKd,1700,Kd "HCK proto-oncogene, Src family tyrosine kinase",HCK,Hs,Inhibitor,Inhibition,5.7,pKd,1800,Kd platelet derived growth factor receptor alpha,PDGFRA,Hs,Inhibitor,Inhibition,5.7,pKd,1800,Kd aarF domain containing kinase 3,ADCK3,Hs,Inhibitor,Inhibition,5.7,pKd,1900,Kd polo like kinase 4,PLK4,Hs,Inhibitor,Inhibition,5.7,pKd,1900,Kd fyn related Src family tyrosine kinase,FRK,Hs,Inhibitor,Inhibition,5.7,pKd,2000,Kd mitogen-activated protein kinase 9,JNK2,Hs,Inhibitor,Inhibition,5.7,pKd,2000,Kd fms related tyrosine kinase 4,FLT4,Hs,Inhibitor,Inhibition,5.7,pKd,2100,Kd aurora kinase A,AURKA,Hs,Inhibitor,Inhibition,5.7,pKd,2200,Kd EPH receptor B4,EPHB4,Hs,Inhibitor,Inhibition,5.7,pKd,2200,Kd "YES proto-oncogene 1, Src family tyrosine kinase",YES,Hs,Inhibitor,Inhibition,5.7,pKd,2200,Kd mitogen-activated protein kinase kinase kinase 3,MAP3K3,Hs,Inhibitor,Inhibition,5.6,pKd,2300,Kd EPH receptor A3,EPHA3,Hs,Inhibitor,Inhibition,5.6,pKd,2400,Kd tyrosine kinase 2,TYK2(JH2domain-pseudokinase),Hs,Inhibitor,Inhibition,5.6,pKd,2400,Kd aarF domain containing kinase 4,ADCK4,Hs,Inhibitor,Inhibition,5.6,pKd,2500,Kd mitogen-activated protein kinase 4,ERK4,Hs,Inhibitor,Inhibition,5.6,pKd,2500,Kd mitogen-activated protein kinase kinase kinase 2,MAP3K2,Hs,Inhibitor,Inhibition,5.6,pKd,2500,Kd salt inducible kinase 2,SIK2,Hs,Inhibitor,Inhibition,5.6,pKd,2600,Kd "MET proto-oncogene, receptor tyrosine kinase",MET(M1250T),Hs,Inhibitor,Inhibition,5.6,pKd,2800,Kd dystrophia myotonica protein kinase,DMPK,Hs,Inhibitor,Inhibition,5.5,pKd,2900,Kd erb-b2 receptor tyrosine kinase 2,ERBB2,Hs,Inhibitor,Inhibition,5.5,pKd,2900,Kd leucine-rich repeat kinase 2,LRRK2,Hs,Inhibitor,Inhibition,5.5,pKd,2900,Kd activin A receptor type 1,ACVR1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd activin A receptor type 1B,ACVR1B,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd activin A receptor type 2A,ACVR2A,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd activin A receptor type 2B,ACVR2B,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd activin A receptor type IL,ACVRL1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd v-akt murine thymoma viral oncogene homolog 1,AKT1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd v-akt murine thymoma viral oncogene homolog 2,AKT2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd v-akt murine thymoma viral oncogene homolog 3,AKT3,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd protein kinase AMP-activated catalytic subunit alpha 1,AMPK-alpha1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd protein kinase AMP-activated catalytic subunit alpha 2,AMPK-alpha2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd ankyrin repeat and kinase domain containing 1,ANKK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd NUAK family kinase 1,ARK5,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase kinase kinase 5,ASK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase kinase kinase 6,ASK2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd bone morphogenetic protein receptor type IA,BMPR1A,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd bone morphogenetic protein receptor type IB,BMPR1B,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd bone morphogenetic protein receptor type II,BMPR2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd BMX non-receptor tyrosine kinase,BMX,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "B-Raf proto-oncogene, serine/threonine kinase",BRAF,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "B-Raf proto-oncogene, serine/threonine kinase",BRAF(V600E),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd protein tyrosine kinase 6,BRK,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd BR serine/threonine kinase 1,BRSK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd BR serine/threonine kinase 2,BRSK2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd Bruton tyrosine kinase,BTK,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd calcium/calmodulin-dependent protein kinase I,CAMK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd calcium/calmodulin dependent protein kinase ID,CAMK1D,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd calcium/calmodulin dependent protein kinase IG,CAMK1G,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd calcium/calmodulin-dependent protein kinase II alpha subunit,CAMK2A,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd calcium/calmodulin-dependent protein kinase II beta subunit,CAMK2B,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd calcium/calmodulin-dependent protein kinase II gamma subunit,CAMK2G,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd calcium/calmodulin-dependent protein kinase IV,CAMK4,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd calcium/calmodulin-dependent protein kinase kinase 1,CAMKK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd calcium/calmodulin-dependent protein kinase kinase 2,CAMKK2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd calcium/calmodulin-dependent serine protein kinase (MAGUK family),CASK,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd cyclin-dependent kinase 11B,CDC2L1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd cyclin-dependent kinase 11A,CDC2L2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd cyclin-dependent kinase 13,CDC2L5,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd cyclin-dependent kinase 19,CDK11,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd cyclin-dependent kinase 2,CDK2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd cyclin-dependent kinase 3,CDK3,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd cyclin-dependent kinase 4,CDK4-cyclinD1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd cyclin-dependent kinase 4,CDK4-cyclinD3,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd cyclin-dependent kinase 5,CDK5,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd cyclin-dependent kinase 7,CDK7,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd cyclin-dependent kinase 8,CDK8,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd cyclin-dependent kinase 9,CDK9,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd cyclin dependent kinase like 1,CDKL1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd cyclin dependent kinase like 2,CDKL2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd cyclin-dependent kinase like 3,CDKL3,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd cyclin-dependent kinase like 4,CDKL5,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd checkpoint kinase 1,CHEK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd checkpoint kinase 2,CHEK2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd CDC like kinase 1,CLK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd CDC like kinase 2,CLK2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd CDC like kinase 3,CLK3,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd CDC like kinase 4,CLK4,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd colony stimulating factor 1 receptor,CSF1R,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd casein kinase 1 alpha 1,CSNK1A1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd casein kinase 1 alpha 1 like,CSNK1A1L,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd casein kinase 1 gamma 1,CSNK1G1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd casein kinase 1 gamma 2,CSNK1G2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd casein kinase 1 gamma 3,CSNK1G3,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "casein kinase 2, alpha 1 polypeptide subunit",CSNK2A1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "casein kinase 2, alpha prime polypeptide subunit",CSNK2A2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd megakaryocyte-associated tyrosine kinase,CTK,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd death associated protein kinase 1,DAPK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd death-associated protein kinase 2,DAPK2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd doublecortin like kinase 1,DCAMKL1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd doublecortin like kinase 2,DCAMKL2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd doublecortin like kinase 3,DCAMKL3,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd discoidin domain receptor tyrosine kinase 2,DDR2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase kinase kinase 12,DLK,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd dual specificity tyrosine phosphorylation regulated kinase 1A,DYRK1A,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd dual specificity tyrosine phosphorylation regulated kinase 1B,DYRK1B,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd eukaryotic translation initiation factor 2 alpha kinase 1,EIF2AK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd EPH receptor A1,EPHA1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd EPH receptor A2,EPHA2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd EPH receptor A4,EPHA4,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd EPH receptor B2,EPHB2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd EPH receptor B3,EPHB3,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase 3,ERK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase 1,ERK2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase 6,ERK3,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase 7,ERK5,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase 15,ERK8,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd endoplasmic reticulum to nucleus signaling 1,ERN1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd protein tyrosine kinase 2,FAK,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd FER tyrosine kinase,FER,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "FES proto-oncogene, tyrosine kinase",FES,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd fibroblast growth factor receptor 1,FGFR1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd fibroblast growth factor receptor 2,FGFR2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd fibroblast growth factor receptor 3,FGFR3,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd fibroblast growth factor receptor 3,FGFR3(G697C),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd fibroblast growth factor receptor 4,FGFR4,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd fms related tyrosine kinase 3,FLT3(R834Q),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "FYN proto-oncogene, Src family tyrosine kinase",FYN,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd G protein-coupled receptor kinase 1,GRK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd G protein-coupled receptor kinase 7,GRK7,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd glycogen synthase kinase 3 alpha,GSK3A,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd glycogen synthase kinase 3 beta,GSK3B,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd homeodomain interacting protein kinase 1,HIPK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd homeodomain interacting protein kinase 2,HIPK2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd homeodomain interacting protein kinase 3,HIPK3,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase kinase kinase kinase 1,HPK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd hormonally up-regulated Neu-associated kinase,HUNK,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd intestinal cell (MAK-like) kinase,ICK,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd Insulin-like growth factor I receptor,IGF1R,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd conserved helix-loop-helix ubiquitous kinase,IKK-alpha,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "inhibitor of kappa light polypeptide gene enhancer in B-cells, kinase beta",IKK-beta,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "inhibitor of kappa light polypeptide gene enhancer in B-cells, kinase epsilon",IKK-epsilon,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd Insulin receptor,INSR,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd Insulin receptor-related receptor,INSRR,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd interleukin 1 receptor associated kinase 1,IRAK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd interleukin 1 receptor associated kinase 3,IRAK3,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd interleukin 1 receptor associated kinase 4,IRAK4,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd IL2 inducible T-cell kinase,ITK,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd Janus kinase 1,JAK1(JH1domain-catalytic),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd Janus kinase 1,JAK1(JH2domain-pseudokinase),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase 8,JNK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd KIT proto-oncogene receptor tyrosine kinase,KIT(A829P),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd KIT proto-oncogene receptor tyrosine kinase,KIT(D816H),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd KIT proto-oncogene receptor tyrosine kinase,"KIT(V559D,V654A)",Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd large tumor suppressor kinase 1,LATS1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd large tumor suppressor kinase 2,LATS2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd LIM domain kinase 1,LIMK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd LIM domain kinase 2,LIMK2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd serine/threonine kinase 11,LKB1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase kinase kinase 13,LZK,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd male germ cell associated kinase,MAK,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase kinase kinase 1,MAP3K1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase kinase kinase 15,MAP3K15,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase kinase kinase 4,MAP3K4,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase kinase kinase kinase 2,MAP4K2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase kinase kinase kinase 3,MAP4K3,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase kinase kinase kinase 4,MAP4K4,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase kinase kinase kinase 5,MAP4K5,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase-activated protein kinase 2,MAPKAPK2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase-activated protein kinase 5,MAPKAPK5,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd microtubule affinity regulating kinase 1,MARK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd microtubule affinity regulating kinase 2,MARK2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd microtubule affinity regulating kinase 3,MARK3,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd microtubule affinity regulating kinase 4,MARK4,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd Microtubule associated serine/threonine kinase 1,MAST1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase kinase 1,MEK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase kinase 2,MEK2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase kinase 3,MEK3,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase kinase 4,MEK4,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase kinase 6,MEK6,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd maternal embryonic leucine zipper kinase,MELK,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase kinase 7,MKK7,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd myosin light chain kinase 3,MLCK,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase kinase kinase 9,MLK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase kinase kinase 10,MLK2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase kinase kinase 11,MLK3,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd CDC42 binding protein kinase alpha,MRCKA,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd CDC42 binding protein kinase beta,MRCKB,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd serine/threonine kinase 4,MST1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd macrophage stimulating 1 receptor,MST1R,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd serine/threonine kinase 3,MST2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd serine/threonine kinase 24,MST3,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd serine/threonine-protein kinase MST4,MST4,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mechanistic target of rapamycin,MTOR,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "muscle, skeletal, receptor tyrosine kinase",MUSK,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd myosin light chain kinase,MYLK,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd myosin light chain kinase family member 4,MYLK4,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd myosin IIIA,MYO3A,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd myosin IIIB,MYO3B,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd Serine/threonine kinase 38,NDR1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd Serine/threonine kinase 38 like,NDR2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd NIMA related kinase 1,NEK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd NIMA related kinase 11,NEK11,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd NIMA related kinase 2,NEK2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd NIMA related kinase 3,NEK3,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd NIMA related kinase 4,NEK4,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd NIMA related kinase 5,NEK5,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd NIMA related kinase 6,NEK6,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd NIMA related kinase 7,NEK7,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd NIMA related kinase 8,NEK9,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd serine/threonine-protein kinase NIM1,NIM1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd nemo-like kinase,NLK,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd oxidative stress responsive 1,OSR1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase 14,p38-alpha,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase 11,p38-beta,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase 13,p38-delta,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase 12,p38-gamma,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd p21 protein (Cdc42/Rac)-activated kinase 1,PAK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd p21 protein (Cdc42/Rac)-activated kinase 2,PAK2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd p21 protein (Cdc42/Rac)-activated kinase 3,PAK3,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd p21 protein (Cdc42/Rac)-activated kinase 4,PAK4,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd p21 protein (Cdc42/Rac)-activated kinase 6,PAK6,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd p21 protein (Cdc42/Rac)-activated kinase 7,PAK7,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd cyclin-dependent kinase 16,PCTK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd cyclin-dependent kinase 17,PCTK2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd cyclin-dependent kinase 18,PCTK3,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd 3-phosphoinositide dependent protein kinase 1,PDPK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd cyclin-dependent kinase 15,PFTAIRE2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd cyclin-dependent kinase 14,PFTK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd phosphorylase kinase gamma subunit 1,PHKG1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd phosphorylase kinase gamma subunit 2,PHKG2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd phosphatidylinositol-4-phosphate 3-kinase catalytic subunit type 2 beta,PIK3C2B,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd phosphatidylinositol-4-phosphate 3-kinase catalytic subunit type 2 gamma,PIK3C2G,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha",PIK3CA,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha",PIK3CA(C420R),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha",PIK3CA(E542K),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha",PIK3CA(E545A),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha",PIK3CA(E545K),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha",PIK3CA(H1047L),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha",PIK3CA(H1047Y),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha",PIK3CA(I800L),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha",PIK3CA(M1043I),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha",PIK3CA(Q546K),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit beta",PIK3CB,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit delta",PIK3CD,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit gamma",PIK3CG,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd phosphatidylinositol 4-kinase alpha,PIK4CB,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "Pim-1 proto-oncogene, serine/threonine kinase",PIM1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "Pim-2 proto-oncogene, serine/threonine kinase",PIM2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "Pim-3 proto-oncogene, serine/threonine kinase",PIM3,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "phosphatidylinositol-4-phosphate 5-kinase, type I, alpha",PIP5K1A,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "phosphatidylinositol-4-phosphate 5-kinase, type I, gamma",PIP5K1C,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "phosphatidylinositol-5-phosphate 4-kinase, type II, beta",PIP5K2B,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "protein kinase, cAMP-dependent, catalytic, alpha subunit",PKAC-alpha,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "protein kinase, cAMP-dependent, catalytic, beta subunit",PKAC-beta,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "protein kinase, membrane associated tyrosine/threonine 1",PKMYT1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd protein kinase N1,PKN1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd protein kinase N2,PKN2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd polo like kinase 1,PLK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd polo like kinase 2,PLK2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd polo like kinase 3,PLK3,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd protein kinase C delta,PRKCD,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd protein kinase C epsilon,PRKCE,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd protein kinase C eta,PRKCH,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd protein kinase C iota,PRKCI,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd protein kinase C theta,PRKCQ,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd protein kinase D1,PRKD1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd protein kinase D2,PRKD2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd protein kinase D3,PRKD3,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd Protein kinase G (PKG) 1,PRKG1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd Protein kinase G (PKG) 2,PRKG2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "protein kinase, X-linked",PRKX,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd pre-mRNA processing factor 4B,PRP4,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd protein tyrosine kinase 2 beta,PYK2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd SIK family kinase 3,QSK,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "Raf-1 proto-oncogene, serine/threonine kinase",RAF1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd ret proto-oncogene,RET(V804L),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd ret proto-oncogene,RET(V804M),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd RIO kinase 1,RIOK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd RIO kinase 2,RIOK2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd RIO kinase 3,RIOK3,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd receptor interacting serine/threonine kinase 1,RIPK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd dual serine/threonine and tyrosine protein kinase,RIPK5,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd Rho associated coiled-coil containing protein kinase 1,ROCK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd Rho associated coiled-coil containing protein kinase 2,ROCK2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "c-ros oncogene 1, receptor tyrosine kinase",ROS1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd ribosomal protein S6 kinase A4,RPS6KA4(Kin.Dom.1-N-terminal),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd ribosomal protein S6 kinase A5,RPS6KA5(Kin.Dom.1-N-terminal),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd ribosomal protein S6 kinase A5,RPS6KA5(Kin.Dom.2-C-terminal),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd ribosomal protein S6 kinase A1,RSK1(Kin.Dom.1-N-terminal),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd ribosomal protein S6 kinase A1,RSK1(Kin.Dom.2-C-terminal),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd ribosomal protein S6 kinase A3,RSK2(Kin.Dom.1-N-terminal),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd ribosomal protein S6 kinase A2,RSK3(Kin.Dom.1-N-terminal),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd ribosomal protein S6 kinase A2,RSK3(Kin.Dom.2-C-terminal),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd ribosomal protein S6 kinase A6,RSK4(Kin.Dom.1-N-terminal),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd ribosomal protein S6 kinase A6,RSK4(Kin.Dom.2-C-terminal),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd ribosomal protein S6 kinase B1,S6K1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd sugen kinase 110,SgK110,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd serum/glucocorticoid regulated kinase family member 3,SGK3,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd salt inducible kinase 1,SIK,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "NUAK family, SNF1-like kinase, 2",SNARK,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd SNF related kinase,SNRK,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd src-related kinase lacking C-terminal regulatory tyrosine and N-terminal myristylation sites,SRMS,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd SRSF protein kinase 1,SRPK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd SRSF protein kinase 2,SRPK2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd serine/threonine kinase 16,STK16,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd serine/threonine kinase 33,STK33,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd serine/threonine kinase 35,STK35,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd serine/threonine kinase 39,STK39,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd spleen tyrosine kinase,SYK,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase kinase kinase 7,TAK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd TAO kinase 1,TAOK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd TAO kinase 2,TAOK2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd TAO kinase 3,TAOK3,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd tec protein tyrosine kinase,TEC,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd testis-specific kinase 1,TESK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd transforming growth factor beta receptor 1,TGFBR1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd transforming growth factor beta receptor II,TGFBR2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd TEK receptor tyrosine kinase,TIE2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd tousled like kinase 1,TLK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd tousled like kinase 2,TLK2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "tyrosine kinase, non-receptor, 2",TNK2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "neurotrophic tyrosine kinase, receptor, type 1",TRKA,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd neurotrophic tyrosine kinase receptor type 2,TRKB,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "neurotrophic tyrosine kinase, receptor, type 3",TRKC,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd TRPM6,TRPM6,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd testis specific serine kinase 1B,TSSK1B,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd tyrosine kinase 2,TYK2(JH1domain-catalytic),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd unc-51 like autophagy activating kinase 1,ULK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd unc-51 like autophagy activating kinase 2,ULK2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd vaccinia related kinase 2,VRK2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd WEE1 G2 checkpoint kinase,WEE1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd WEE1 homolog 2 (S. pombe),WEE2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd serine/threonine kinase 32A,YANK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd serine/threonine kinase 32B,YANK2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd serine/threonine kinase 32C,YANK3,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd serine/threonine kinase 25,YSK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd ZAK sterile alpha motif and leucine zipper containing kinase AZK,ZAK,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd zeta chain of T cell receptor associated protein kinase 70kDa,ZAP70,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd KIT proto-oncogene receptor tyrosine kinase,KIT(V559D),Hs,Inhibitor,Inhibition,5.5,pKd,3100,Kd TANK binding kinase 1,TBK1,Hs,Inhibitor,Inhibition,5.5,pKd,3100,Kd CDC42 binding protein kinase gamma,DMPK2,Hs,Inhibitor,Inhibition,5.5,pKd,3400,Kd casein kinase 1 delta,CSNK1D,Hs,Inhibitor,Inhibition,5.5,pKd,3500,Kd KIT proto-oncogene receptor tyrosine kinase,KIT(L576P),Hs,Inhibitor,Inhibition,5.5,pKd,3500,Kd Janus kinase 2,JAK2(JH1domain-catalytic),Hs,Inhibitor,Inhibition,5.4,pKd,3700,Kd TTK protein kinase,TTK,Hs,Inhibitor,Inhibition,5.4,pKd,3700,Kd "MET proto-oncogene, receptor tyrosine kinase",MET,Hs,Inhibitor,Inhibition,5.4,pKd,3800,Kd TXK tyrosine kinase,TXK,Hs,Inhibitor,Inhibition,5.4,pKd,3800,Kd TYRO3 protein tyrosine kinase,TYRO3,Hs,Inhibitor,Inhibition,5.4,pKd,3900,Kd AXL receptor tyrosine kinase,AXL,Hs,Inhibitor,Inhibition,5.4,pKd,4000,Kd mitogen-activated protein kinase 10,JNK3,Hs,Inhibitor,Inhibition,5.4,pKd,4300,Kd fms related tyrosine kinase 1,FLT1,Hs,Inhibitor,Inhibition,5.4,pKd,4400,Kd eukaryotic translation initiation factor 2 alpha kinase 4,"GCN2(Kin.Dom.2,S808G)",Hs,Inhibitor,Inhibition,5.4,pKd,4400,Kd serine/threonine kinase 36,STK36,Hs,Inhibitor,Inhibition,5.4,pKd,4400,Kd TRAF2 and NCK interacting kinase,TNIK,Hs,Inhibitor,Inhibition,5.3,pKd,4500,Kd leucine-rich repeat kinase 2,LRRK2(G2019S),Hs,Inhibitor,Inhibition,5.3,pKd,4800,Kd receptor interacting serine/threonine kinase 4,RIPK4,Hs,Inhibitor,Inhibition,5.3,pKd,4900,Kd kinase insert domain receptor,VEGFR2,Hs,Inhibitor,Inhibition,5.2,pKd,5700,Kd death-associated protein kinase 3,DAPK3,Hs,Inhibitor,Inhibition,5.2,pKd,6000,Kd serine/threonine kinase 17a,DRAK1,Hs,Inhibitor,Inhibition,5.2,pKd,6300,Kd calcium/calmodulin-dependent protein kinase II delta subunit,CAMK2D,Hs,Inhibitor,Inhibition,5.2,pKd,6400,Kd ribosomal protein S6 kinase A4,RPS6KA4(Kin.Dom.2-C-terminal),Hs,Inhibitor,Inhibition,5.2,pKd,7000,Kd G protein-coupled receptor kinase 4,GRK4,Hs,Inhibitor,Inhibition,5.1,pKd,7300,Kd "ABL proto-oncogene 1, non-receptor tyrosine kinase",ABL1(F317I)-nonphosphorylated,Hs,Inhibitor,Inhibition,5.1,pKd,8200,Kd SRSF protein kinase 3,SRPK3,Hs,Inhibitor,Inhibition,5.1,pKd,8300,Kd serine/threonine kinase 17b,DRAK2,Hs,Inhibitor,Inhibition,5,pKd,9400,Kd c-src tyrosine kinase,CSK,Hs,Inhibitor,Inhibition,5,pKd,9600,Kd \ No newline at end of file diff --git a/data/literature_affinity/gefitinib.csv b/data/literature_affinity/gefitinib.csv new file mode 100644 index 0000000..fed1bcf --- /dev/null +++ b/data/literature_affinity/gefitinib.csv @@ -0,0 +1 @@ +Target,Name in screen,Sp.,Type,Action,Affinity (-log10),Units,Affinity (nM),Units epidermal growth factor receptor,"EGFR(L747-T751del,Sins)",Hs,Inhibitor,Inhibition,9.3,pKd,0.5,Kd epidermal growth factor receptor,EGFR(E746-A750del),Hs,Inhibitor,Inhibition,9.3,pKd,0.5,Kd epidermal growth factor receptor,"EGFR(L747-E749del, A750P)",Hs,Inhibitor,Inhibition,9.2,pKd,0.6,Kd epidermal growth factor receptor,"EGFR(L747-S752del, P753S)",Hs,Inhibitor,Inhibition,9.2,pKd,0.6,Kd epidermal growth factor receptor,EGFR(L858R),Hs,Inhibitor,Inhibition,9,pKd,0.9,Kd epidermal growth factor receptor,EGFR(S752-I759del),Hs,Inhibitor,Inhibition,9,pKd,1,Kd epidermal growth factor receptor,EGFR,Hs,Inhibitor,Inhibition,9,pKd,1,Kd epidermal growth factor receptor,EGFR(G719S),Hs,Inhibitor,Inhibition,9,pKd,1.1,Kd epidermal growth factor receptor,EGFR(L861Q),Hs,Inhibitor,Inhibition,8.9,pKd,1.4,Kd epidermal growth factor receptor,EGFR(G719C),Hs,Inhibitor,Inhibition,8.7,pKd,2,Kd cyclin G associated kinase,GAK,Hs,Inhibitor,Inhibition,7.9,pKd,13,Kd epidermal growth factor receptor,EGFR(T790M),Hs,Inhibitor,Inhibition,7.4,pKd,40,Kd interleukin 1 receptor associated kinase 1,IRAK1,Hs,Inhibitor,Inhibition,7.2,pKd,69,Kd epidermal growth factor receptor,"EGFR(L858R,T790M)",Hs,Inhibitor,Inhibition,6.8,pKd,140,Kd "ABL proto-oncogene 1, non-receptor tyrosine kinase",ABL1(Q252H)-phosphorylated,Hs,Inhibitor,Inhibition,6.6,pKd,230,Kd mitogen-activated protein kinase kinase kinase 19,YSK4,Hs,Inhibitor,Inhibition,6.6,pKd,240,Kd MAP kinase interacting serine/threonine kinase 1,MKNK1,Hs,Inhibitor,Inhibition,6.5,pKd,290,Kd homeodomain interacting protein kinase 4,HIPK4,Hs,Inhibitor,Inhibition,6.5,pKd,310,Kd "ABL proto-oncogene 1, non-receptor tyrosine kinase",ABL1(Y253F)-phosphorylated,Hs,Inhibitor,Inhibition,6.4,pKd,360,Kd "ABL proto-oncogene 1, non-receptor tyrosine kinase",ABL1(E255K)-phosphorylated,Hs,Inhibitor,Inhibition,6.4,pKd,400,Kd erb-b2 receptor tyrosine kinase 4,ERBB4,Hs,Inhibitor,Inhibition,6.4,pKd,410,Kd casein kinase 1 epsilon,CSNK1E,Hs,Inhibitor,Inhibition,6.4,pKd,430,Kd "ABL proto-oncogene 1, non-receptor tyrosine kinase",ABL1(H396P)-phosphorylated,Hs,Inhibitor,Inhibition,6.3,pKd,460,Kd serine/threonine kinase 10,LOK,Hs,Inhibitor,Inhibition,6.3,pKd,470,Kd "ABL proto-oncogene 1, non-receptor tyrosine kinase",ABL1-phosphorylated,Hs,Inhibitor,Inhibition,6.3,pKd,480,Kd "ABL proto-oncogene 1, non-receptor tyrosine kinase",ABL1(M351T)-phosphorylated,Hs,Inhibitor,Inhibition,6.3,pKd,520,Kd receptor interacting serine/threonine kinase 2,RIPK2,Hs,Inhibitor,Inhibition,6.3,pKd,530,Kd interleukin 1 receptor associated kinase 4,IRAK4,Hs,Inhibitor,Inhibition,6.3,pKd,540,Kd SH3 domain binding kinase 1,SBK1,Hs,Inhibitor,Inhibition,6.3,pKd,560,Kd EPH receptor A6,EPHA6,Hs,Inhibitor,Inhibition,6.2,pKd,590,Kd mitogen-activated protein kinase kinase 5,MEK5,Hs,Inhibitor,Inhibition,6.2,pKd,600,Kd cyclin-dependent kinase 7,CDK7,Hs,Inhibitor,Inhibition,6.2,pKd,610,Kd "LCK proto-oncogene, Src family tyrosine kinase",LCK,Hs,Inhibitor,Inhibition,6.2,pKd,630,Kd "ABL proto-oncogene 1, non-receptor tyrosine kinase",ABL1(H396P)-nonphosphorylated,Hs,Inhibitor,Inhibition,6.2,pKd,680,Kd "ABL proto-oncogene 1, non-receptor tyrosine kinase",ABL1(F317L)-phosphorylated,Hs,Inhibitor,Inhibition,6.1,pKd,780,Kd erb-b2 receptor tyrosine kinase 3,ERBB3,Hs,Inhibitor,Inhibition,6.1,pKd,790,Kd checkpoint kinase 2,CHEK2,Hs,Inhibitor,Inhibition,6.1,pKd,800,Kd STE20 like kinase,SLK,Hs,Inhibitor,Inhibition,6,pKd,920,Kd "LYN proto-oncogene, Src family tyrosine kinase",LYN,Hs,Inhibitor,Inhibition,6,pKd,990,Kd fms related tyrosine kinase 3,FLT3(D835Y),Hs,Inhibitor,Inhibition,6,pKd,1000,Kd "ABL proto-oncogene 1, non-receptor tyrosine kinase",ABL1(Q252H)-nonphosphorylated,Hs,Inhibitor,Inhibition,6,pKd,1100,Kd fms related tyrosine kinase 3,FLT3(D835H),Hs,Inhibitor,Inhibition,6,pKd,1100,Kd "BLK proto-oncogene, Src family tyrosine kinase",BLK,Hs,Inhibitor,Inhibition,5.9,pKd,1200,Kd MAP kinase interacting serine/threonine kinase 2,MKNK2,Hs,Inhibitor,Inhibition,5.9,pKd,1200,Kd ribosomal protein S6 kinase A4,RPS6KA4(Kin.Dom.2-C-terminal),Hs,Inhibitor,Inhibition,5.9,pKd,1200,Kd citron rho-interacting serine/threonine kinase,CIT,Hs,Inhibitor,Inhibition,5.9,pKd,1300,Kd EPH receptor B1,EPHB1,Hs,Inhibitor,Inhibition,5.9,pKd,1300,Kd EPH receptor A5,EPHA5,Hs,Inhibitor,Inhibition,5.8,pKd,1500,Kd interleukin 1 receptor associated kinase 3,IRAK3,Hs,Inhibitor,Inhibition,5.8,pKd,1500,Kd "ABL proto-oncogene 2, non-receptor tyrosine kinase",ABL2,Hs,Inhibitor,Inhibition,5.8,pKd,1600,Kd mitogen-activated protein kinase 6,ERK3,Hs,Inhibitor,Inhibition,5.8,pKd,1600,Kd mitogen-activated protein kinase 9,JNK2,Hs,Inhibitor,Inhibition,5.8,pKd,1700,Kd AXL receptor tyrosine kinase,AXL,Hs,Inhibitor,Inhibition,5.7,pKd,1800,Kd EPH receptor A8,EPHA8,Hs,Inhibitor,Inhibition,5.7,pKd,1800,Kd KIT proto-oncogene receptor tyrosine kinase,KIT(A829P),Hs,Inhibitor,Inhibition,5.7,pKd,1800,Kd misshapen-like kinase 1,MINK,Hs,Inhibitor,Inhibition,5.7,pKd,1800,Kd myosin light chain kinase 2,MYLK2,Hs,Inhibitor,Inhibition,5.7,pKd,1900,Kd serine/threonine kinase 17a,DRAK1,Hs,Inhibitor,Inhibition,5.7,pKd,2000,Kd fyn related Src family tyrosine kinase,FRK,Hs,Inhibitor,Inhibition,5.7,pKd,2000,Kd mitogen-activated protein kinase kinase kinase 3,MAP3K3,Hs,Inhibitor,Inhibition,5.7,pKd,2100,Kd salt inducible kinase 2,SIK2,Hs,Inhibitor,Inhibition,5.7,pKd,2100,Kd "ABL proto-oncogene 1, non-receptor tyrosine kinase",ABL1-nonphosphorylated,Hs,Inhibitor,Inhibition,5.7,pKd,2200,Kd EPH receptor B4,EPHB4,Hs,Inhibitor,Inhibition,5.6,pKd,2500,Kd "FGR proto-oncogene, Src family tyrosine kinase",FGR,Hs,Inhibitor,Inhibition,5.6,pKd,2600,Kd "ABL proto-oncogene 1, non-receptor tyrosine kinase",ABL1(F317L)-nonphosphorylated,Hs,Inhibitor,Inhibition,5.6,pKd,2700,Kd phosphorylase kinase gamma subunit 2,PHKG2,Hs,Inhibitor,Inhibition,5.6,pKd,2700,Kd doublecortin like kinase 3,DCAMKL3,Hs,Inhibitor,Inhibition,5.5,pKd,2900,Kd fms related tyrosine kinase 3,FLT3(ITD),Hs,Inhibitor,Inhibition,5.5,pKd,2900,Kd AP2 associated kinase 1,AAK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "ABL proto-oncogene 1, non-receptor tyrosine kinase",ABL1(F317I)-nonphosphorylated,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "ABL proto-oncogene 1, non-receptor tyrosine kinase",ABL1(T315I)-nonphosphorylated,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "ABL proto-oncogene 1, non-receptor tyrosine kinase",ABL1(T315I)-phosphorylated,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd activin A receptor type 1,ACVR1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd activin A receptor type 1B,ACVR1B,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd activin A receptor type 2A,ACVR2A,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd activin A receptor type 2B,ACVR2B,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd activin A receptor type IL,ACVRL1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd aarF domain containing kinase 3,ADCK3,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd aarF domain containing kinase 4,ADCK4,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd v-akt murine thymoma viral oncogene homolog 1,AKT1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd v-akt murine thymoma viral oncogene homolog 2,AKT2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd v-akt murine thymoma viral oncogene homolog 3,AKT3,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd anaplastic lymphoma receptor tyrosine kinase,ALK,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd protein kinase AMP-activated catalytic subunit alpha 1,AMPK-alpha1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd protein kinase AMP-activated catalytic subunit alpha 2,AMPK-alpha2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd ankyrin repeat and kinase domain containing 1,ANKK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd NUAK family kinase 1,ARK5,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase kinase kinase 5,ASK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase kinase kinase 6,ASK2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd aurora kinase A,AURKA,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd aurora kinase B,AURKB,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd aurora kinase C,AURKC,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd BMP2 inducible kinase,BIKE,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd bone morphogenetic protein receptor type IA,BMPR1A,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd bone morphogenetic protein receptor type IB,BMPR1B,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd bone morphogenetic protein receptor type II,BMPR2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd BMX non-receptor tyrosine kinase,BMX,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "B-Raf proto-oncogene, serine/threonine kinase",BRAF,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "B-Raf proto-oncogene, serine/threonine kinase",BRAF(V600E),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd protein tyrosine kinase 6,BRK,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd BR serine/threonine kinase 1,BRSK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd BR serine/threonine kinase 2,BRSK2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd Bruton tyrosine kinase,BTK,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd calcium/calmodulin-dependent protein kinase I,CAMK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd calcium/calmodulin dependent protein kinase ID,CAMK1D,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd calcium/calmodulin dependent protein kinase IG,CAMK1G,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd calcium/calmodulin-dependent protein kinase II alpha subunit,CAMK2A,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd calcium/calmodulin-dependent protein kinase II beta subunit,CAMK2B,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd calcium/calmodulin-dependent protein kinase II delta subunit,CAMK2D,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd calcium/calmodulin-dependent protein kinase II gamma subunit,CAMK2G,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd calcium/calmodulin-dependent protein kinase IV,CAMK4,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd calcium/calmodulin-dependent protein kinase kinase 1,CAMKK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd calcium/calmodulin-dependent protein kinase kinase 2,CAMKK2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd calcium/calmodulin-dependent serine protein kinase (MAGUK family),CASK,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd cyclin-dependent kinase 11B,CDC2L1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd cyclin-dependent kinase 11A,CDC2L2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd cyclin-dependent kinase 13,CDC2L5,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd cyclin-dependent kinase 19,CDK11,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd cyclin-dependent kinase 2,CDK2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd cyclin-dependent kinase 3,CDK3,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd cyclin-dependent kinase 4,CDK4-cyclinD1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd cyclin-dependent kinase 4,CDK4-cyclinD3,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd cyclin-dependent kinase 5,CDK5,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd cyclin-dependent kinase 8,CDK8,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd cyclin-dependent kinase 9,CDK9,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd cyclin dependent kinase like 1,CDKL1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd cyclin dependent kinase like 2,CDKL2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd cyclin-dependent kinase like 3,CDKL3,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd cyclin-dependent kinase like 4,CDKL5,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd checkpoint kinase 1,CHEK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd CDC like kinase 1,CLK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd CDC like kinase 2,CLK2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd CDC like kinase 3,CLK3,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd CDC like kinase 4,CLK4,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd colony stimulating factor 1 receptor,CSF1R,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd c-src tyrosine kinase,CSK,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd casein kinase 1 alpha 1,CSNK1A1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd casein kinase 1 alpha 1 like,CSNK1A1L,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd casein kinase 1 gamma 1,CSNK1G1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd casein kinase 1 gamma 2,CSNK1G2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd casein kinase 1 gamma 3,CSNK1G3,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "casein kinase 2, alpha 1 polypeptide subunit",CSNK2A1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "casein kinase 2, alpha prime polypeptide subunit",CSNK2A2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd megakaryocyte-associated tyrosine kinase,CTK,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd death associated protein kinase 1,DAPK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd death-associated protein kinase 2,DAPK2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd doublecortin like kinase 1,DCAMKL1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd doublecortin like kinase 2,DCAMKL2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd discoidin domain receptor tyrosine kinase 1,DDR1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd discoidin domain receptor tyrosine kinase 2,DDR2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase kinase kinase 12,DLK,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd CDC42 binding protein kinase gamma,DMPK2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd dual specificity tyrosine phosphorylation regulated kinase 1A,DYRK1A,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd dual specificity tyrosine phosphorylation regulated kinase 1B,DYRK1B,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd dual specificity tyrosine phosphorylation regulated kinase 2,DYRK2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd eukaryotic translation initiation factor 2 alpha kinase 1,EIF2AK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd EPH receptor A2,EPHA2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd EPH receptor A4,EPHA4,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd EPH receptor A7,EPHA7,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd EPH receptor B2,EPHB2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd EPH receptor B3,EPHB3,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase 3,ERK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase 1,ERK2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase 7,ERK5,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase 15,ERK8,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd endoplasmic reticulum to nucleus signaling 1,ERN1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd protein tyrosine kinase 2,FAK,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd FER tyrosine kinase,FER,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "FES proto-oncogene, tyrosine kinase",FES,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd fibroblast growth factor receptor 1,FGFR1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd fibroblast growth factor receptor 2,FGFR2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd fibroblast growth factor receptor 3,FGFR3,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd fibroblast growth factor receptor 3,FGFR3(G697C),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd fibroblast growth factor receptor 4,FGFR4,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd fms related tyrosine kinase 1,FLT1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd fms related tyrosine kinase 3,FLT3(N841I),Hs,Inhibitor,Inhibition,5.5,pKd,3000,Kd fms related tyrosine kinase 4,FLT4,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "FYN proto-oncogene, Src family tyrosine kinase",FYN,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd eukaryotic translation initiation factor 2 alpha kinase 4,"GCN2(Kin.Dom.2,S808G)",Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd G protein-coupled receptor kinase 1,GRK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd G protein-coupled receptor kinase 4,GRK4,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd G protein-coupled receptor kinase 7,GRK7,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd glycogen synthase kinase 3 alpha,GSK3A,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd glycogen synthase kinase 3 beta,GSK3B,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd homeodomain interacting protein kinase 1,HIPK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd homeodomain interacting protein kinase 2,HIPK2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd homeodomain interacting protein kinase 3,HIPK3,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase kinase kinase kinase 1,HPK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd hormonally up-regulated Neu-associated kinase,HUNK,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd intestinal cell (MAK-like) kinase,ICK,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd Insulin-like growth factor I receptor,IGF1R,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd conserved helix-loop-helix ubiquitous kinase,IKK-alpha,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "inhibitor of kappa light polypeptide gene enhancer in B-cells, kinase beta",IKK-beta,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "inhibitor of kappa light polypeptide gene enhancer in B-cells, kinase epsilon",IKK-epsilon,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd Insulin receptor,INSR,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd Insulin receptor-related receptor,INSRR,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd IL2 inducible T-cell kinase,ITK,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd Janus kinase 1,JAK1(JH1domain-catalytic),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd Janus kinase 1,JAK1(JH2domain-pseudokinase),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd Janus kinase 2,JAK2(JH1domain-catalytic),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd Janus kinase 3,JAK3(JH1domain-catalytic),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase 8,JNK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd KIT proto-oncogene receptor tyrosine kinase,KIT,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd KIT proto-oncogene receptor tyrosine kinase,KIT(L576P),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd KIT proto-oncogene receptor tyrosine kinase,KIT(V559D),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd KIT proto-oncogene receptor tyrosine kinase,"KIT(V559D,T670I)",Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd KIT proto-oncogene receptor tyrosine kinase,"KIT(V559D,V654A)",Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd large tumor suppressor kinase 1,LATS1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd large tumor suppressor kinase 2,LATS2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd LIM domain kinase 1,LIMK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd LIM domain kinase 2,LIMK2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd serine/threonine kinase 11,LKB1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd leucine-rich repeat kinase 2,LRRK2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd leucine-rich repeat kinase 2,LRRK2(G2019S),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase kinase kinase 13,LZK,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd male germ cell associated kinase,MAK,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase kinase kinase 1,MAP3K1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase kinase kinase 15,MAP3K15,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase kinase kinase 4,MAP3K4,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase kinase kinase kinase 2,MAP4K2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase kinase kinase kinase 3,MAP4K3,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase kinase kinase kinase 4,MAP4K4,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase kinase kinase kinase 5,MAP4K5,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase-activated protein kinase 2,MAPKAPK2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase-activated protein kinase 5,MAPKAPK5,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd microtubule affinity regulating kinase 1,MARK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd microtubule affinity regulating kinase 2,MARK2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd microtubule affinity regulating kinase 3,MARK3,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd microtubule affinity regulating kinase 4,MARK4,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd Microtubule associated serine/threonine kinase 1,MAST1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase kinase 1,MEK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase kinase 2,MEK2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase kinase 3,MEK3,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase kinase 4,MEK4,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase kinase 6,MEK6,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd maternal embryonic leucine zipper kinase,MELK,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "MER proto-oncogene, tyrosine kinase",MERTK,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "MET proto-oncogene, receptor tyrosine kinase",MET,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "MET proto-oncogene, receptor tyrosine kinase",MET(M1250T),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase kinase 7,MKK7,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd myosin light chain kinase 3,MLCK,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase kinase kinase 9,MLK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase kinase kinase 10,MLK2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase kinase kinase 11,MLK3,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd CDC42 binding protein kinase alpha,MRCKA,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd CDC42 binding protein kinase beta,MRCKB,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd serine/threonine kinase 4,MST1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd macrophage stimulating 1 receptor,MST1R,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd serine/threonine kinase 3,MST2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd serine/threonine kinase 24,MST3,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd serine/threonine-protein kinase MST4,MST4,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mechanistic target of rapamycin,MTOR,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "muscle, skeletal, receptor tyrosine kinase",MUSK,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd myosin light chain kinase,MYLK,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd myosin light chain kinase family member 4,MYLK4,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd myosin IIIA,MYO3A,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd myosin IIIB,MYO3B,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd Serine/threonine kinase 38,NDR1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd Serine/threonine kinase 38 like,NDR2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd NIMA related kinase 1,NEK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd NIMA related kinase 11,NEK11,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd NIMA related kinase 2,NEK2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd NIMA related kinase 3,NEK3,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd NIMA related kinase 4,NEK4,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd NIMA related kinase 5,NEK5,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd NIMA related kinase 6,NEK6,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd NIMA related kinase 7,NEK7,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd NIMA related kinase 8,NEK9,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd serine/threonine-protein kinase NIM1,NIM1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd oxidative stress responsive 1,OSR1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase 14,p38-alpha,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase 11,p38-beta,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase 13,p38-delta,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase 12,p38-gamma,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd p21 protein (Cdc42/Rac)-activated kinase 1,PAK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd p21 protein (Cdc42/Rac)-activated kinase 2,PAK2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd p21 protein (Cdc42/Rac)-activated kinase 3,PAK3,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd p21 protein (Cdc42/Rac)-activated kinase 4,PAK4,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd p21 protein (Cdc42/Rac)-activated kinase 6,PAK6,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd p21 protein (Cdc42/Rac)-activated kinase 7,PAK7,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd cyclin-dependent kinase 16,PCTK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd cyclin-dependent kinase 17,PCTK2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd cyclin-dependent kinase 18,PCTK3,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd platelet derived growth factor receptor alpha,PDGFRA,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd platelet derived growth factor receptor beta,PDGFRB,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd 3-phosphoinositide dependent protein kinase 1,PDPK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd cyclin-dependent kinase 15,PFTAIRE2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd cyclin-dependent kinase 14,PFTK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd phosphatidylinositol-4-phosphate 3-kinase catalytic subunit type 2 beta,PIK3C2B,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd phosphatidylinositol-4-phosphate 3-kinase catalytic subunit type 2 gamma,PIK3C2G,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha",PIK3CA,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha",PIK3CA(C420R),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha",PIK3CA(E542K),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha",PIK3CA(E545A),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha",PIK3CA(E545K),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha",PIK3CA(H1047L),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha",PIK3CA(H1047Y),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha",PIK3CA(I800L),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha",PIK3CA(M1043I),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha",PIK3CA(Q546K),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit beta",PIK3CB,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit delta",PIK3CD,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit gamma",PIK3CG,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd phosphatidylinositol 4-kinase alpha,PIK4CB,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "Pim-1 proto-oncogene, serine/threonine kinase",PIM1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "Pim-2 proto-oncogene, serine/threonine kinase",PIM2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "phosphatidylinositol-4-phosphate 5-kinase, type I, alpha",PIP5K1A,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "phosphatidylinositol-4-phosphate 5-kinase, type I, gamma",PIP5K1C,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "phosphatidylinositol-5-phosphate 4-kinase, type II, beta",PIP5K2B,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "protein kinase, cAMP-dependent, catalytic, alpha subunit",PKAC-alpha,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "protein kinase, cAMP-dependent, catalytic, beta subunit",PKAC-beta,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "protein kinase, membrane associated tyrosine/threonine 1",PKMYT1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd protein kinase N1,PKN1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd protein kinase N2,PKN2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd polo like kinase 1,PLK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd polo like kinase 2,PLK2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd polo like kinase 3,PLK3,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd polo like kinase 4,PLK4,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd protein kinase C delta,PRKCD,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd protein kinase C epsilon,PRKCE,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd protein kinase C eta,PRKCH,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd protein kinase C iota,PRKCI,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd protein kinase C theta,PRKCQ,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd protein kinase D2,PRKD2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd protein kinase D3,PRKD3,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd Protein kinase G (PKG) 1,PRKG1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd Protein kinase G (PKG) 2,PRKG2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd eukaryotic translation initiation factor 2 alpha kinase 2,PRKR,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "protein kinase, X-linked",PRKX,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd pre-mRNA processing factor 4B,PRP4,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd protein tyrosine kinase 2 beta,PYK2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd SIK family kinase 3,QSK,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "Raf-1 proto-oncogene, serine/threonine kinase",RAF1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd ret proto-oncogene,RET,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd ret proto-oncogene,RET(M918T),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd ret proto-oncogene,RET(V804L),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd ret proto-oncogene,RET(V804M),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd RIO kinase 1,RIOK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd RIO kinase 2,RIOK2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd RIO kinase 3,RIOK3,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd receptor interacting serine/threonine kinase 1,RIPK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd receptor interacting serine/threonine kinase 4,RIPK4,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd dual serine/threonine and tyrosine protein kinase,RIPK5,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd Rho associated coiled-coil containing protein kinase 1,ROCK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd Rho associated coiled-coil containing protein kinase 2,ROCK2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "c-ros oncogene 1, receptor tyrosine kinase",ROS1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd ribosomal protein S6 kinase A4,RPS6KA4(Kin.Dom.1-N-terminal),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd ribosomal protein S6 kinase A5,RPS6KA5(Kin.Dom.1-N-terminal),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd ribosomal protein S6 kinase A5,RPS6KA5(Kin.Dom.2-C-terminal),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd ribosomal protein S6 kinase A1,RSK1(Kin.Dom.1-N-terminal),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd ribosomal protein S6 kinase A1,RSK1(Kin.Dom.2-C-terminal),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd ribosomal protein S6 kinase A3,RSK2(Kin.Dom.1-N-terminal),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd ribosomal protein S6 kinase A2,RSK3(Kin.Dom.1-N-terminal),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd ribosomal protein S6 kinase A2,RSK3(Kin.Dom.2-C-terminal),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd ribosomal protein S6 kinase A6,RSK4(Kin.Dom.1-N-terminal),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd ribosomal protein S6 kinase A6,RSK4(Kin.Dom.2-C-terminal),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd ribosomal protein S6 kinase B1,S6K1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd sugen kinase 110,SgK110,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd serum/glucocorticoid regulated kinase family member 3,SGK3,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd salt inducible kinase 1,SIK,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd SNF related kinase,SNRK,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd src-related kinase lacking C-terminal regulatory tyrosine and N-terminal myristylation sites,SRMS,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd SRSF protein kinase 1,SRPK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd SRSF protein kinase 2,SRPK2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd SRSF protein kinase 3,SRPK3,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd serine/threonine kinase 16,STK16,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd serine/threonine kinase 33,STK33,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd serine/threonine kinase 35,STK35,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd serine/threonine kinase 39,STK39,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd spleen tyrosine kinase,SYK,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase kinase kinase 7,TAK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd TAO kinase 1,TAOK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd TAO kinase 2,TAOK2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd TAO kinase 3,TAOK3,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd TANK binding kinase 1,TBK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd tec protein tyrosine kinase,TEC,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd testis-specific kinase 1,TESK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd transforming growth factor beta receptor 1,TGFBR1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd transforming growth factor beta receptor II,TGFBR2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd tyrosine kinase with immunoglobulin like and EGF like domains 1,TIE1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd TEK receptor tyrosine kinase,TIE2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd tousled like kinase 1,TLK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd tousled like kinase 2,TLK2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "tyrosine kinase, non-receptor, 1",TNK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "tyrosine kinase, non-receptor, 2",TNK2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd TNNI3 interacting kinase,TNNI3K,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "neurotrophic tyrosine kinase, receptor, type 1",TRKA,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd neurotrophic tyrosine kinase receptor type 2,TRKB,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "neurotrophic tyrosine kinase, receptor, type 3",TRKC,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd TRPM6,TRPM6,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd testis specific serine kinase 1B,TSSK1B,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd TTK protein kinase,TTK,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd tyrosine kinase 2,TYK2(JH1domain-catalytic),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd tyrosine kinase 2,TYK2(JH2domain-pseudokinase),Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd TYRO3 protein tyrosine kinase,TYRO3,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd unc-51 like autophagy activating kinase 1,ULK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd unc-51 like autophagy activating kinase 2,ULK2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd unc-51 like kinase 3,ULK3,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd kinase insert domain receptor,VEGFR2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd vaccinia related kinase 2,VRK2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd WEE1 G2 checkpoint kinase,WEE1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd WEE1 homolog 2 (S. pombe),WEE2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd serine/threonine kinase 32A,YANK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd serine/threonine kinase 32B,YANK2,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd serine/threonine kinase 32C,YANK3,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd "YES proto-oncogene 1, Src family tyrosine kinase",YES,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd serine/threonine kinase 25,YSK1,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd ZAK sterile alpha motif and leucine zipper containing kinase AZK,ZAK,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd zeta chain of T cell receptor associated protein kinase 70kDa,ZAP70,Hs,Inhibitor,Inhibition,<5.5,pKd,>3000.0,Kd mitogen-activated protein kinase 4,ERK4,Hs,Inhibitor,Inhibition,5.5,pKd,3100,Kd fms related tyrosine kinase 3,FLT3,Hs,Inhibitor,Inhibition,5.5,pKd,3100,Kd casein kinase 1 delta,CSNK1D,Hs,Inhibitor,Inhibition,5.5,pKd,3200,Kd EPH receptor B6,EPHB6,Hs,Inhibitor,Inhibition,5.5,pKd,3200,Kd mitogen-activated protein kinase 10,JNK3,Hs,Inhibitor,Inhibition,5.5,pKd,3200,Kd mitogen-activated protein kinase kinase kinase 2,MAP3K2,Hs,Inhibitor,Inhibition,5.5,pKd,3300,Kd erb-b2 receptor tyrosine kinase 2,ERBB2,Hs,Inhibitor,Inhibition,5.5,pKd,3500,Kd fms related tyrosine kinase 3,FLT3(R834Q),Hs,Inhibitor,Inhibition,5.5,pKd,3500,Kd "MET proto-oncogene, receptor tyrosine kinase",MET(Y1235D),Hs,Inhibitor,Inhibition,5.5,pKd,3500,Kd protein kinase D1,PRKD1,Hs,Inhibitor,Inhibition,5.5,pKd,3500,Kd phosphorylase kinase gamma subunit 1,PHKG1,Hs,Inhibitor,Inhibition,5.4,pKd,3700,Kd serine/threonine kinase 17b,DRAK2,Hs,Inhibitor,Inhibition,5.4,pKd,3800,Kd "SRC proto-oncogene, non-receptor tyrosine kinase",SRC,Hs,Inhibitor,Inhibition,5.4,pKd,3800,Kd EPH receptor A1,EPHA1,Hs,Inhibitor,Inhibition,5.4,pKd,4000,Kd nemo-like kinase,NLK,Hs,Inhibitor,Inhibition,5.4,pKd,4200,Kd KIT proto-oncogene receptor tyrosine kinase,KIT(D816V),Hs,Inhibitor,Inhibition,5.4,pKd,4300,Kd "HCK proto-oncogene, Src family tyrosine kinase",HCK,Hs,Inhibitor,Inhibition,5.4,pKd,4400,Kd "NUAK family, SNF1-like kinase, 2",SNARK,Hs,Inhibitor,Inhibition,5.3,pKd,4600,Kd "ABL proto-oncogene 1, non-receptor tyrosine kinase",ABL1(F317I)-phosphorylated,Hs,Inhibitor,Inhibition,5.3,pKd,4700,Kd fms related tyrosine kinase 3,FLT3(K663Q),Hs,Inhibitor,Inhibition,5.3,pKd,5100,Kd EPH receptor A3,EPHA3,Hs,Inhibitor,Inhibition,5.3,pKd,5500,Kd KIT proto-oncogene receptor tyrosine kinase,KIT(D816H),Hs,Inhibitor,Inhibition,5.3,pKd,5500,Kd leukocyte receptor tyrosine kinase,LTK,Hs,Inhibitor,Inhibition,5.3,pKd,5500,Kd death-associated protein kinase 3,DAPK3,Hs,Inhibitor,Inhibition,5.2,pKd,5700,Kd serine/threonine kinase 36,STK36,Hs,Inhibitor,Inhibition,5.2,pKd,5700,Kd "Pim-3 proto-oncogene, serine/threonine kinase",PIM3,Hs,Inhibitor,Inhibition,5.2,pKd,5800,Kd TXK tyrosine kinase,TXK,Hs,Inhibitor,Inhibition,5.2,pKd,6000,Kd dystrophia myotonica protein kinase,DMPK,Hs,Inhibitor,Inhibition,5.2,pKd,6900,Kd TRAF2 and NCK interacting kinase,TNIK,Hs,Inhibitor,Inhibition,5.2,pKd,6900,Kd "phosphatidylinositol-5-phosphate 4-kinase, type II, gamma",PIP5K2C,Hs,Inhibitor,Inhibition,5.1,pKd,7500,Kd \ No newline at end of file diff --git a/data/spectra/Abl/AblD382N_BosI_20151218_bw2020_gain12014-55-55_plate_1.xml b/data/spectra/Abl/AblD382N_BosI_20151218_bw2020_gain12014-55-55_plate_1.xml new file mode 100755 index 0000000..184b4b7 --- /dev/null +++ b/data/spectra/Abl/AblD382N_BosI_20151218_bw2020_gain12014-55-55_plate_1.xml @@ -0,0 +1,5842 @@ + + + + 1 + +
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+ + + + + + infinite M1000 PRO + 1311007572 + + + MEX, V 1.20 Safire2 MCR (V 1.20 Safire2 MCR ) + MEM, V 1.20 Safire2 MCR (V 1.20 Safire2 MCR ) + FPO, V_1.03_Safire2 FP (V_1.03_Safire2 FP ) + ABS, V 1.00 MCR Abs 4 Channel (V 1.00 MCR Abs 4 Channel) + LUM, V_1.05_02/2015_LUMINESCENCE (Feb 24 2015/17.31.16) + BCR, V_1.00_02/2008_S3FTCAN + INA, V_1.05_11/2011_S3LCE_ALPHA (Nov 3 2011/09.27.24) + INB, V_1.05_11/2011_S3LCE_ALPHA (Nov 3 2011/09.27.24) + TCAN, V_1.00_02/2008_S3FTCAN (Feb 21 2008/17.19.16) + + + + + + +
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+ + + + + + infinite M1000 PRO + 1311007572 + + + MEX, V 1.20 Safire2 MCR (V 1.20 Safire2 MCR ) + MEM, V 1.20 Safire2 MCR (V 1.20 Safire2 MCR ) + FPO, V_1.03_Safire2 FP (V_1.03_Safire2 FP ) + ABS, V 1.00 MCR Abs 4 Channel (V 1.00 MCR Abs 4 Channel) + LUM, V_1.05_02/2015_LUMINESCENCE (Feb 24 2015/17.31.16) + BCR, V_1.00_02/2008_S3FTCAN + INA, V_1.05_11/2011_S3LCE_ALPHA (Nov 3 2011/09.27.24) + INB, V_1.05_11/2011_S3LCE_ALPHA (Nov 3 2011/09.27.24) + TCAN, V_1.00_02/2008_S3FTCAN (Feb 21 2008/17.19.16) + + + + + + +
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+ \ No newline at end of file diff --git a/data/spectra/README.md b/data/spectra/README.md new file mode 100644 index 0000000..ef7cbf0 --- /dev/null +++ b/data/spectra/README.md @@ -0,0 +1,8 @@ +# Spectra Data + +Right now this is a duplicate of some of the data in `assaytools/examples/ipynbs/data-analysis/spectra/`. + + + + + diff --git a/data/spectra/Spectra-Analysis.ipynb b/data/spectra/Spectra-Analysis.ipynb new file mode 100644 index 0000000..cbcd25e --- /dev/null +++ b/data/spectra/Spectra-Analysis.ipynb @@ -0,0 +1,1340 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Couldn't import dot_parser, loading of dot files will not be possible.\n" + ] + } + ], + "source": [ + "from assaytools import platereader\n", + "import matplotlib.pyplot as plt\n", + "import string\n", + "import numpy as np\n", + "from glob import glob" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Populating the interactive namespace from numpy and matplotlib\n" + ] + } + ], + "source": [ + "%pylab inline" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "file_set = {'Src': glob(\"Src/*.xml\"),\n", + " 'SrcGK': glob(\"SrcT338I/*.xml\"),\n", + " 'AblGK': glob(\"AblT334I/*.xml\"),\n", + " 'Abl': glob(\"Abl/*.xml\")}\n", + "ligands = ['Bosutinib','Bosutinib Isomer','Erlotinib','Gefitinib']" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['Src/Src_Bos_20151215_bw2020_gain120_161211.xml',\n", + " 'Src/Src_BosI_20151215_bw2020_gain120_163633.xml',\n", + " 'Src/Src_Erl_20151215_bw2020_gain120_170056.xml',\n", + " 'Src/Src_Gef_20151215_bw2020_gain120_172518.xml']" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "file_set['Src']" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Src/Src_Bos_20151215_bw2020_gain120_161211.xml\n", + "Src/Src_BosI_20151215_bw2020_gain120_163633.xml\n", + "Src/Src_Erl_20151215_bw2020_gain120_170056.xml\n", + "Src/Src_Gef_20151215_bw2020_gain120_172518.xml\n" + ] + } + ], + "source": [ + "Src_Spectra = platereader.read_icontrol_xml(file_set['Src'][0])\n", + "for file in file_set['Src']:\n", + " print file\n", + " new_dict = platereader.read_icontrol_xml(file)\n", + " for key in Src_Spectra:\n", + " Src_Spectra[key] = dict(Src_Spectra[key].items()+new_dict[key].items())\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['em280', 'abs', 'em340']" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Src_Spectra.keys()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "SrcErl = platereader.select_data(Src_Spectra, 'em280', 'E', wavelength='480')\n", + "SrcErlLig = platereader.select_data(Src_Spectra, 'em280', 'F', wavelength='480')" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "well = dict()\n", + "for j in string.ascii_uppercase:\n", + " for i in range(1,25):\n", + " well['%s' %j + '%s' %i] = i" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Stated concentrations of protein and ligand.\n", + "#ligand_conc from before\n", + "Lstated = np.array([20.0e-6,9.15e-6,4.18e-6,1.91e-6,0.875e-6,0.4e-6,0.183e-6,0.0837e-6,0.0383e-6,0.0175e-6,0.008e-6,0.0001e-6], np.float64) # ligand concentration, M\n", + "Pstated = 1.0e-6 * np.ones([12],np.float64) # protein concentration, M\n", + "# Protein concentration (M) (modified form from Sonya, eliminating last measurement because we can't deal with zero ligand concentrations yet)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "\n", + "# Assay configuration details\n", + "import math\n", + "assay_volume = 100e-6 # assay volume, L\n", + "well_area = 0.3969 # well area, cm^2 for 4ti-0223 [http://4ti.co.uk/files/1614/0542/7662/4ti-0223_Marketing_Drawing.pdf]\n", + "path_length = assay_volume * 1000 / well_area # cm, needed for inner filter effect corrections \n", + "\n", + "# Uncertainties in protein and ligand concentrations.\n", + "dPstated = 0.35 * Pstated # protein concentration uncertainty\n", + "dLstated = 0.08 * Lstated # ligand concentraiton uncertainty (due to gravimetric preparation and HP D300 dispensing)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def reorder2list(data,wells):\n", + " \n", + " sorted_keys = sorted(well.keys(), key=lambda k:well[k])\n", + " \n", + " reorder_data = []\n", + " \n", + " for key in sorted_keys:\n", + " try:\n", + " reorder_data.append(data[key])\n", + " except:\n", + " pass\n", + " \n", + " reorder_data = np.asarray(reorder_data,np.float64)\n", + " \n", + " return reorder_data" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def quick_model(protein_data, buffer_data,name):\n", + " reorder_protein = reorder2list(protein_data,well)\n", + " reorder_buffer = reorder2list(buffer_data,well)\n", + " \n", + " print name \n", + " \n", + " from assaytools import pymcmodels\n", + " pymc_model = pymcmodels.make_model(Pstated, dPstated, Lstated, dLstated, \n", + " top_complex_fluorescence=reorder_protein,\n", + " top_ligand_fluorescence=reorder_buffer,\n", + " use_primary_inner_filter_correction=True, \n", + " use_secondary_inner_filter_correction=True, \n", + " assay_volume=assay_volume, DG_prior='uniform')\n", + " \n", + " mcmc = pymcmodels.run_mcmc(pymc_model)\n", + " \n", + " from assaytools import plots\n", + " figure = plots.plot_measurements(Lstated, Pstated, pymc_model, mcmc=mcmc)\n", + " \n", + " map = pymcmodels.map_fit(pymc_model)\n", + " \n", + " pymcmodels.show_summary(pymc_model, map, mcmc)\n", + " \n", + " DeltaG = map.DeltaG.value\n", + " \n", + " np.save('DeltaG_%s.npy'%name,DeltaG)\n", + " np.save('DeltaG_trace_%s.npy'%name,mcmc.DeltaG.trace())\n", + " \n", + " import pymc\n", + " \n", + " M = pymc.MCMC(pymc_model, db='pickle', dbname='pymc_model_%s.pickle'%name)\n", + " M.db\n", + " M.sample(10)\n", + " M.db.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Src_Erl_spectra\n", + "MAP fitting cycle 5/5\n", + "DeltaG = -16.3 +- 3.0 kT\n", + "Kd = 79.7 nM +- 295.5 nM\n", + " [-----------------100%-----------------] 10 of 10 complete in 0.0 sec" + ] + }, + { + "data": { + "image/png": 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sGEpeVuMucnJ0HEBEXAZ8UEmjSM+rkzgu6e7jANy9zd0vJpc5ruN7wAXAS2V7\nQ3e/ozy/kbxA68bAXe4+3d0nAU+R7zhtAdxUjr0JGJNSGg4MqvR2jivXEBEREZFewMwWq2weAlxj\nZksBS5LXYjTgSOCW6mnk73tTyvYMYOWI2BI4mbyMBgAR8VzrWi8is1MncZySUlqhsZFS2hJ4p6uT\nUkoHAq+6e6MqltGxK/0tcgI6glxSeVb7J81hX3W/iIiIiMxjZtYPeMrM1jWz3YFvk6ucPg9cXz0U\n+ATta4UDfAEYRU4SXwfuBYiI/4uIB3ug+SIyB3USx68CNwBrpJQeAq4CvlzjvIOA7VNKtwIbkIe3\nLlV5fQTwJjkRHF7ZP3wW+2e1r3qNOkIPPWo86AVt0KPvPOgFbdCjbzzoBW3Qo+886AVtqP046qij\n4q677oo33ngjjjzyyJnA4ttvv/0jf/vb364paykCDNp8880bo84AGDBgwHY33HDDe50KhxxyyLoR\n8fTMmTM3feGFF5aJiLZ5/dn6wINe0AY9+s5jrnRZHAfem6uYyInms2VIaW0leTyEPHT1dHe/PaV0\nIXmIwh3khV03BhYi32XaABgLDHf3E1NKewFbufvYlNIDwB7AROB3wAnufm8XTQg0cVjqUaxIMxQv\nUpdiRZrRq+PFzLYCIiLuNLPFyd/dXiZPH3oDGFIO/SX5+92qldNPB46ubK9A/ry/AU6MiBtb3Pz5\nTa+OFZm/dNnjmFLaE7jf3R8ljzt/LKW0+/t4ryD/oTgxpfRH8rCFq939ZeBs8vo8t5CL50wjz41c\nJ6U0gTx04cRynUPIBXruKe3qKmkUERERke6zBblYIcBt5KU0NiKPSPtc5bg9yMlj1evA+eTvhc8A\nMyLiRWArJY0ivVud5TgeAcaUBK+xvMZ4d1+/B9rXXXQ3RupSrEgzFC9Sl2JFmtGr4sXMlgf+h5wY\nHgh8irxMxjrkJPIn5PbOJBe9Oa9y+r3AurT3Qt4REduY2cbAVyJin574DPOxXhUrMn+rM8dxYCNp\nBHD3V1rYHhERERHpXf4F7Fce04FtydVR7wD+SHvi0p88MmxG5dz1yT2S1wDTgOsAIuJeJY0ifcuA\nGsfclVK6ijw81IA9yQuyioiIiMh8qBS1WZG8pNqR5DoUhwFbAyeQ5y2OJE8lmkp7j+K6wG60V1Ad\nCLxNHrZ6Ark+hYj0QXV6HMcC9wNfIldKvY/8B0RERERE5k+jyfUkliH3NA4GNiEvyTayctxe5F7G\nqj3IieRgKJ6MAAAgAElEQVSLwEPAIpEdHxF/a3XDRaQ1uuxxdPd3Uko/BH5B+1CEkcA/WtkwERER\nEZlnhpKXPduF/1z6bEin7S+QCxx+hPxdcUhEvGNmWwMbRcQLrW6siLRel4ljSukY4Jvk8e3VSjqr\nzvoMEREREelrzGwHcsGbq4EryL2MpwILd3HqpsDa5LmP55JHqhERTwNPt6i5ItLD6sxx/AKwuru/\n2urGiIiIiMg8Mwz4FnmU2Z+ATwDD53D8DPJ3yQHAKxHxYzN7Frirxe0UkXmgTuL4d/JiriIiIiIy\nHzGzJcrTgcDe5edJwLtdnPousBnwI/KItEWBtyLilhY1VUTmsTrrOP4IGAX8P3IZZYBw95Na3Lbu\npDVupC7FijRD8SJ1KVakGT0WL2b2f8AOwP7A4+ThqZCLI1bXY3wXGFTZvi4idjezxYG2iOg8D1J6\nhv62SI+p0+P4Qnk0MkwFp4iIiMj8YWtgI2Bx4BXyEhwAJwOPkpfXgLxG4xvATHJxnNsBIuJfPdlY\nEZl3uuxxBEgpLQysDjwCDHX3t1vdsG6muzFSl2JFmqF4kboUK9KMlsZL6WW8HVgSuJRc/OafwBPk\nAjcNnwDOB5Yt22tExNNmNjQiprSqfdIU/W2RHtPlOo4ppY8ADwLXkf9wPJtS2rHVDRMRERGR7mVm\nA4GtgJ+Qv9s1ltZYhtxBUHUuuVrqH8prMwCUNIosmLpMHIFTyH9g3nD3F4BtgO+1tFUiIiIi0m3M\n7ANm1g/YAFgLWAn4Ou3fBY08r/GYymnDgQ8AHwM2iYi/91yLRaS3qZM49nP3lxob7v5XOq7nKCIi\nIiK9VEkYLyF3BixNThoHkKun3lA9FPgbsAkwFbgpIu6JiHcj4p2ebbWI9DZ1iuM8l1LaFSCltCj5\nbtQ/WtoqEREREekuq5Ir5K8K/Layvz95+GljPUYDfkxOHDcCnu7ZZopIb1anx/EQYF9yla1ngP8C\nvtjKRomIiIjI+2dmA83s52Y2EjgBGEaex3gaHUeObVleuxtoA74EPBkRj0dEV2s5isgCpMvE0d1f\nBk5z9yWB1YALqkNXRURERKTX2Qn4ELkuxVuV/ZuSh6g2LAFcQU4gPw9cHXVK7ovIAqfLoaoppVPJ\nwxW2J1feOi6ltLW7H1/j3P7Aj8iTsIPcezmNXPq5jbw+0Fh3j5TSweSezBnAye5+Q0ppCPBTYCny\nH70D3P21lNKmwJnl2Jvd/SREREREFnBmtl5EPEwuZrgasApQ/c5m5HmOxwInktdlnAIMj4hLe7Sx\nItKn1Bmquiv5rhWlp3EMsEfN6+8CtLn7luQ/UN8BTgeOcfetyX+8dkspjQSOADYHdgROSSkNAg4F\nHirHXl6uAXAhsHe57uiU0gY12yMiIiIyXzKzPYE/mNlO5CGnRp7H+FXKUhrFtuS5jjsBx0fEQRHx\nZg83V0T6mDqJY39gaGV7MLm3sEvufh35DxfkO15vABu5+x1l343kRHRj4C53n+7uk4CngPWALYCb\nyrE3AWNSSsOBQe4+sewfV64hIiIisiDbDBgBHAdU5ycuDnyc3LMI+fvfL4HbIuLUHm2hiPRZdaqq\nXgTcl1K6nnznamfygrC1uPvMlNKlwO7Ap8lDXhveAhYh/5H792z2T5rDvsb+1eq2R0RERGR+YWab\nkUdofR44quzenJwY7lk59DVgYeBB4GLg9xFR7YUUEZmjOsVxfgDsB/wT+Duwr7uf38ybuPuBQCL/\noVqo8tII4E1yIji8sn/4LPbPal/1Gl0JPfSo8aAXtEGPvvOgF7RBj77xoBe0QY++86Duseutt94f\nl1lmmf3PO++8DhVQBw8evOfmm2/+3vZ222335wsuuKAtItaLiLMj4qle8Dn16MFY0UMP5pJ1VTgr\npbQEsKG7j08pHUNejuN4d3+sq4unlPYHVnD3U1JKI8h3uZ4EvuPut6eULgRuAe4AxpOHrC5ELgm9\nAXnNyOHufmJKaS9gK3cfm1J6gDzPciLwO+AEd793Dk0Jcm+pSFcUK9IMxYvUpViRZswxXsxsIfJo\nq0WAP5bdM8jDUxvTi4I8suwLwAXkAoUfiIijW9RmmTf0t0V6TJ2hqlcBv00pBfApcjXTC4Gta5x7\nNXBpSul2YCDwZeAJ4Eel+M1jwNWlqurZwARyL+gx7j4tpXQBcFlKaQK5Gus+5bqHAFeS51+O6yJp\nFBEREZkvmNlwct2H1YCfVV4aQF5W46DGoeQb8JcBV0VEdUkOEZGm1elxvNfdN04pnQM85e5npZT+\n4u4f6pkmdgvdjZG6FCvSDMWL1KVYkWbMNl7M7BLgQOBt4NfAAZWXnwdGk0d3DQUuAWZGxMGtbKzM\nU/rbIj2mTo+jpZQ2Ihe32bYsfVHnPBERERGZS6WX8Wvk0Vc7l90Lk5PGJ4APlH0jgNfJlex3II/8\nWrwn2yoi8686y3F8A/gecLq7Pw2cT14PSERERERayMyMXCPiGOCLwEOdDvkR8JvyfHA5dqWIuDIi\npkXESz3WWBGZr3U5VBUgpbQwsDrwCDDU3d9udcO6mbrxpS7FijRD8SJ1KVakGWFm/YC1gJnkoacN\nbwCLVbYvjIhDzewEYDvgLGBQRFzVU42VeUp/W6TH1Jnj+BHyWo4DgC3Id7r2dfdxrW9et9EvldSl\nWJFmKF6kLsWK1Pbss8/Gqquu+grwDnkptC3pGD/7AJeTv5s9Qv6edn7U6Q2Q+Y3+tkiPqTNU9RRg\nK+ANd38B2IY8dFVEREREutnPfvYzgKWBlYBNgHs6HfI4MAqYAgwBPg/s1YNNFJEFUJ3EsZ+7vzc+\n3t3/SjcsICkiIiIimZkdYma3mdmvXn755epLg4GNyEVvGlYm90QuDWxILpjz655qq4gsmOpUR30+\npbQrQEppUfKaQP9oaatEREREFizfApYEZt52223QcQjiQGB58nrXGwPfLY9NI+JNQGs0ikjL1Ukc\nv0SeaL0i8Azw/8hVvURERETkfTCzwcBVwKnkEWALlZf6P/zww5DnLq5X9gUwg7xG49HkYjmjgOWA\nN3uu1SKyIKtTHOdkdz+2h9rTKpo4LHUpVqQZihepS7EiHZjZYsC/yLHxD/I8xj0rhzxDXhLtl2X7\nG8DfI+KXiLTT3xbpMXXmOH48pVTnOBERERGZDTP7jJkdVdZmbPQwGnnO4ieBaZXDl42Iq8kFCicB\nmwPnmtnKPdlmEZGGOj2Ot5LH1d8PTC27w90/1+K2dSfdjZG6FCvSDMWL1KVYEczsIvJ0nzfJ36tG\nA8Mqh9xMHoK6LPAueb3GAPpHxNtmtmxEvIRIO/1tkR5TZ47jZeVnI8M0VFVVREREZI7MbCg5GdwT\neAnYo7y0KLkn8T5g08op7wArrrrqqjMmTpw4ALgFeAH4NICSRhGZl7rscQRIKY0CtiUnmre6+4Mt\nbld3090YqUuxIs1QvEhdipUFiJn1i4g2M+tHHn46k1z5dMlZHH4vuVIqwBMRsTYQZvZV4I/kpPP/\nImJyDzRd+h79bZEe0+XcxZTS/sC1wKrkMfjXpJQ+3+qGiYiIiPQ1ZvZ14CwzWxhYhXzTfTCzThoB\nVgMOIScAI83syw888AAR8YOIuCcijlbSKCK9QZ2iN/8NbOLuX3X3o8h3xb7a2maJiIiI9A1mNqSy\nuShwOLmgzc3koaZzEhFxEbAzuWdyu89+9rOU3koRkV6jzhzHfu7+emPD3V9LKc1sYZtERERE+gQz\nWwT4m5mdTB4y+NHGS+TigtcBn5nDJR42s+HkJHP5iJj2yiuvxNJLL93WynaLiDSrTlXVK4FXgR+T\n/wh+Hljc3ffv4ryBwE/Iw1sHAycDjwOXAm3Ao8BYd4+U0sHkKmMzgJPd/YaU0hDgp8BS5HkBB5Sk\ndVPgzHLsze5+Uo3PqfHfUpdiRZqheJG6FCvzkdLDOJBcbX554CmgP/n70p3AJzqd8idgs077XiIX\nw1kaeBL4SUScU15TvEhdihXpMXWGQRxMLgn9E+CS8vywGuftC7zq7lsDOwHnAacDx5R9BuyWUhoJ\nHEFen2hH4JSU0iDgUOChcuzlwLHluhcCe7v7lsDolNIGtT6piIiISPf4FvAIcC7wG3LSCPlm9xbA\n3zsd/xzwlcr2v4FdgA8CJwAn0nFZDhGRXqfLoaruPgX4+vu49q+Aq8vzfsB0YEN3v6PsuxHYgTye\n/y53nw5MTyk9BaxH/sP73XLsTcBxKaXhwCB3n1j2jwPGAH2tyquIiIj0EWZmwAbk9RdPJn93WYk8\nWupSctK3Vjl8afJ3nreA4WXflIg408zWIN98vx3YFXgmIr7fQx9DZN4wW5OIJ+d1M2TudZk4ppRm\nNcb+RXdfYU7nufvkcv5wchJ5LFD94/gWsAgwgnznbVb7J81hX2P/al19BhEREZH3w8xWIyeKfwIm\nk3sP360cciAdv8dAHj64KPAEsCZ5STMi4nAzexD4JrAMudLqEa1rvcg8ZrYW4JjtRsT187o5Mnfq\n9Di+N5y1zFvcnTystEsppRXJQzjOc/erUkqnVV4eQb5zN4n2O3KU5533z2pf9Rp1dL1gpUimWJFm\nKF6kLsVKH3LnnXfy61//ml133ZWdd96Zww47DHLNhsUff/xx1l577erhi4wdO5bzzjuvsT2ora1t\n5ksvvcT+++/PM888s8qRRx4Za665JhHBO++8Q1tb2+ozZswYTa7AOiuKF6mr98XK9OkwcCCMGQN/\n+APAdWy/PYwfP69btqCbq/mwXRbHmZWU0kPuvn4XxywD3AYc5u63ln3XA6e7++0ppQuBW4A7gPHk\nZT4WAu4mDwcZCwx39xNTSnsBW7n72JTSA8AewETgd8AJ7n5vF03WxGGpS7EizVC8SF2KlT7AzAaT\nl8VYjvxd5AvA0+RewRs7Hf4UsEZlewXyDe2HyVN09gIWI9dpOBHYE1gY2Cgi/tFFUxQvUlfvixWz\nK8h1Sx4iTymregH4OBH393i7ZK7Vqap6QPV4YB1gG3ffpIvzzgI+DXhl95eBs4FBwGPAwaWq6hfI\n8wT6Ad9292tKVdXLgGWBacA+7v5KSmk0uapqf2Ccux9X43P2vl8q6a0UK9IMxYvUpVjppcxsILlI\nzTPkL7q/ArYnj8oaVQ77M7A4HRPF58nV379Ztn8M/JZSIRVI5GGth0fEHWY2APgQ8EhETO6iWYoX\nqav3xYrZxuSOIGPWbXuBiDlOeZPeqU7ieCntXeABvAZcUClQ0xf0vl8q6a0UK9IMxYvUpVjphcxs\nPfKyYZcCW9HeW/htco/jLpXDG9+FGv8dHycvUfbZ8vgHeYmNy4ArgY+Ucx6MiFeabJriRerqHbFi\n9hRwKhEXY/YsuXd9cZQ4zlfe11DVPqh3/FJJX6BYkWYoXqQuxUovUSqk/oo8JeZkYG9yovhrOo6S\n+js5qazalDxNZklywZxflnN+Sx5l9S5wRUQ8NZfNVLxIXb0jVsweBVYiYgRmbeQ23UYpDFWhoap9\n2GwTx5TSnHoUw937UjXT3vFLJX2BYkWaoXiRuhQr81CpjDqNPPz0V+TewlvIays21oNeDnix06kP\nVl6HPCx1FLmK6rrk/65fj4g/d3OTFS9S17yJFbOVgSvIieGW5CRxbyJ+gVkjuRhOrkmyZNlWT2Mf\nN6eqqhcBPycvZvtqzzRHREREpPuY2X8BnyHXVzidnAiuBRwHnFU59Dlyb8jylX0PAG3lnH7Al8hz\nFn9vZkOBlSLiiZZ/CJHeZxB5ePf55GVnjDzP9xeVY94B/ps8FLwN+HjPNlG625x6HJ1cCOfP7r5h\nj7aq++nOndSlWJFmKF6kLsVKDzKzzYE9I+IoM3PgdeBq8hfZxpoZj5ML4nyscurM8rN/+fkK8L/A\nreQKqV8EjoqIKa39BIoXqa3nYsVsE2BtIi7D7G3yzZSVyUO2twXaiOhf6XHsT66u+nvgWSJW7ZF2\nSsvMqcfxLvKwDksptXV6Ldy9/yzOEREREelRpWLplhFxm5l9AtgI2MPMvgG8DWxG/oJ9aOW0tYEP\ndLpUAMcCHyYvExbAMxHxdHn9L637FCK93pnApphdDwwlJ6wbkqsSz0oAfy3PB7W+edJqs00c3f1z\nwOdSSte7u7qWRUREpLdaAviFmW1J/kJ7DPD/yD2IjTmKm5O/+FYZcDOwHfk70QByorlzRISZLQa8\n2frmi/RSuZfxfuBr5Bsw9wGTae/lbANmlOcdhzFGBGavl/2/6YnmSmv16+oAJY0iIiLS25jZhWa2\nhZmtATxN/jI7ldz7YeRkcJFOp32Y/6zbcDcwsvw8DXg2yjyeiHgjFpDy8yL/wWwL4I/koaaNNU1H\n0T6UG/Lv3rvleaO41EwaSWRes/QPwPUtbq30gDkNVRURERHpFcxsXWBYRNxjZl8DtiEXs/k++Yvq\n8sAbwP6NU4B/kr/MVis5DiR/if04ubfk8xFxPLk3RUTMBhMxDZhA7k002ucCDwQWqhz9Eu298peU\nnw/T8abNjugGzHyhyx5HERERkXnBzBYpPw04GjjZzPoBhwCrkNdU/BF5sfFBwD3AHZVLDCAnjTMq\n+0YAewFfBvYA/qulH0KkrzBbHrNfAFPI1YiNnCheQ15aA3JPYrXj6R1gXHn++/Lzs+SlbsoZShrn\nF0ocRUREpNcxs48Cd5ek8QLgk+T5VQcDb5F7PQ4CLqR9vtU65KU3Zna63L7k3scgD2kdGBFnR8S1\nEaElx0SyM4A9yUO+D6/s/wi5d57ys/H7Nr0khReTf6+eASDiUSI0NHU+pMRRRERE5jkzG2xm15jZ\nQDNbh7xsxhRgC3KBmxHk7y030F7F8QjgRDoWsBkAPNjp8tOAlcjLaawQEZNa9kFE+gozw+wazP6C\n2QRg+/LKYODvlSOvoD1nuIKcJAZ5KCtEPAWsjG7CzPeUOIqIiMg8YWa7mtniZfNU8jzD7YDvkIei\nPkdeAmNl8hfVrwH/oONQue3I6zR2uDRwJHmI6t+AoRExPSIuVtIo8p5FgN2B9clVhxcr+wcAYyrH\n3Um++QJwVCl48z3ghPeOiOj8OyjzISWOIiIi0iPMrL+ZDSvPRwOnA582s+PJcw6nkpfHWB+YDuwG\nrA78lPbhccZ/Lni+Bh17HadGxDnkAjq7RsRVrflEIn2M2VjMbsPsaeC4sncAcFHlqDZybz/kYd+v\nk3v6p9BIICO+QcSEHmmz9BpKHEVERKTlzGxR8pfPb5rZVuTKpqsC9wJbk4e/rULu+fhfclEOgEeB\nbbu4fBt5OOvPycU6JpqZRcQfI+Jv3ftJRPoos0HA18k3VJYFjqq8ul/l+bu0F7z5fpnHeDiwYam2\nKgsoJY4iIiLS7Urv4j5m9i0zGwH8kDysdHHgc+RejgHkHsXjgaHl1DvJ8xarlRg/1MXbzST3Qu5D\nnsf4ea2/KEJOFs3+jdkXgauAFcsrA2gffgq5V7/hUOBy8lI2twIQ8U8ivPUNlt5M6ziKiIhItzCz\nZYDNIuJa8pIZ3yV/OW0j9yQOBF4GrgN2Lqf9klzMpprorU6e37hiZd+/yEln1SvlvPuAhUqyeEU3\nfiSRvslsABEzyL+DI4Bzae/Fpzyvbl9Erlj8DyIuLddYQUtpSFXLE8eU0mjgVHf/cEppDeBS8v9A\nHgXGunuklA4m3yGcAZzs7jeklIaQ734sRS67fYC7v5ZS2hQ4sxx7s7uf1OrPICIiIv/JzBq9hFOB\njcjD2xYys7HAYcCwsu835OGmy5N7Ez8GLFq51PrkhcSXq+zrPCrqTPIQ1sFle0pELGNmGwBDIuKJ\nbvpYIn1XXud0PLAuZoeTKw9D+02bZcp2G/AGsATwJPAweXmbld+7lpJG6aSlQ1VTSl8nL8zb+CN/\nBnCMu29NHlKyW0ppJDmoNwd2BE5JKQ0id5M/VI69HDi2XONCYG933xIYnVLaoJWfQURERDIzG1h5\nvgQ52XsC2AX4PrlH8NWy741y6CeBXwBrVy61MTnZfO9y5dzqF9W/k28gzyiPMeTvCq+W444BiIgH\nI+JP3fIBRfoqsyXLswPIc4aXJn9n7l85aqnK8+nAj8nzGT9FRBDxUyK+3RPNlb6p1XMcnyL/D6NR\n/WxDd7+jPL+R/D+BjYG73H26u08q56xHXrfppnLsTcCYlNJwYJC7Tyz7x9GxXLCIiIh0EzPrZ2bW\neA68aGaXmNnO5HL8w8k9GKsCq5XTVgauoeOX1FSOfe/S5OFzMyr7JpOX0JhC7g1ZISL2L9cJ4KGI\nuL+83/HAT7rxo4r0XWZfBV7F7DPkgjeNEYUjOh35PPl3aQqwJXk5jS0BzV2UWlqaOLr7b+j4P4Vq\n+ey3yOvHjAD+PZv9k+awr7pfREREukEjUSz+Any2LJ2xD7lX8ADgG+Q1FwMYRF53sVpoYyR5nmLV\nQrSX+IecHH6M/B0ggIWBiyNiGLm3coKZDY6IN4Gvkoe4Etm3IuKtbvi4In2T2XKYrc6f/gTty2pc\nQu58aeg8JW1/chXjnxLxFyKmEnGvKqVKXT1dHKet8nwEec2lSXS8Czl8Fvtnta96jTo0TlvqUqxI\nMxQvUlevj5Xdd9+dyy+/nClTpvDyyy+z8MILM3ny5EuXWGIJVlllFR566CGmT58OsM3QoUOZMuW9\nPHAYeS3Fqv4LL7wwb7/99ns7brnllqG77rorU6ZMYeDAgQOefPLJcSuvvDLrrbceH/zgBwefddZZ\nUwGuvfZaNtlkk32XXXbZfeG9qVbntPjj9za9Pl5kHjn7bBgxAt55B55/HtrnCw/5j2P794cIOPRQ\nOPfc25k2Dfr124RcW0QWPJ3XwG3u5FbPe00prQJc5e6bpZSuB05399tTShcCtwB3kCfxbky+G3k3\neS2mscBwdz8xpbQXsJW7j00pPQDsAUwEfgec4O73dtGMYC7/oWSBoViRZihepK5eEytm1i8i2srz\ns8i9ez8kz1d8nLyWIuX5quSRPUGeCwXtdQsaXyCqn+sZ2oesQi62cRqwJHnJjO0j4jYzu5Y8x+rY\niPDSo/l8RLzQXZ+zj+s18SK9hNmywF7k6qj/It+sMRZbDN54o3pkNXamAaPJhaV2J6I6wk+kaT21\njmPjfy5HAyemlP5I7u282t1fBs4GJpATyWPcfRpwAbBOSmkC8AXKEBVyee8rgXuA+2skjSIiIgsk\nM1vUzNaqbH8buMbMPmJmA4C1gC+TE76/kissDi6PFWhPFq3sq44cMv5zbtQvaC+KE+QbwiPJX3b/\nQvtw1qOBI6KsCxcR9yhpFKkw2xKzvTFbFLMjyQWnziAPCx9KIznsmDRCHg7+LnAtsBoRDxHxYSWN\n0h1a3uPYS+jOndSlWJFmKF6krh6JFTNbmbyO4s/L9inAx4GbgauBA4HPkuclQk7kGj2I/yzbK1cu\n+QTwgcr2I8CoyvY4cgXHxhC5qyPi02Z2BrAvMDYirrZc8XFGma8oXdPflgWN2Z7k3sHfkW/ofAdY\njLxG6Tv8//bOPMyuqsrb764MhCQkIYQhQIJAwmEyiGGQQZmMqCii2NJGBRkcsG2HdkBF7c+BVmki\njYiKQ2xRaIZGbaUVB2hERpFZkMOYkEoCmSrzXLW+P9Y6uacuNdybVOpWpX7v89yn6nfPtM85+5y7\n115rr+1JbMANw+FVW5fryyn4870cs1u3cqnFAEOGoxDtUV0R9aD6Imqlx+pKVajpVCqJY57Gp6j4\nBD5lxY+Bw4DX4w3NR4CDaJ+ev5rfAG8s6WpD8VncE1kYnnPw0NbrgDcA3zKzz6WUtgfaTEk3Nhe9\nW7Z1UvogcBrwEO6lvxBPELUcn+v8mFizDa8LXdWHNfiz+M+Y/X5rFVkIGY5CtEd1RdSD6ouolc2q\nKxFOepCZPRz6KHyqi8/hYaKH4fMnrgVuxTOZZvhwkHn41BWFodhG90NUZtPe47gET0o3OMq/Po5x\nLfBK4NdmdkZM1bGDKRyup9C7ZVvAMxQPwWw9KV2AZyRehXsVL8Sfqzb8mX57acs1dJTopkJlfPHg\nwbBx4wjgJOAmBkjDXjQGGY5CtEd1RdSD6ouolZrqSkppEO5B/GasfxAeqvYTfAzi3sD78Abnnbjx\nNiLWXYdPgTWitMsHgUPrKOca3LjcrvTdVDzvwFTgPjM7Kso6FW9H/LWO/Yva0Lulv+FG4oHAZDwP\nx0W4934EMAP4YrEmnoyqnNW0Ovy0lvtvwHksXPgjxo1TXRG9ggxHIdqjuiLqQfVF1MqmulLMk2hm\nllLaDvhfPNS0FR/T9Cvc83cX3gg9DDfmlsffkaV9vgCMLx1nfpVeixuBXdXTak/kp3DPyMF4yNzh\nsfxg4L1m9qHaT1tsJnq39HVSGo4/JycCXwFmAgfgCaHOAf6ztPb1wDtKeiNbPiXeAmA3PGxddUX0\nCjIchWiP6oqoB9UX0S0ppUkrVqx4auTIkSmlNAwfJ/hf+Jik3YFP4nWpGW9M7oYbam3ACnw6jIKV\nVAxHcGOzesxitfdiDjAh/m/Dp8EoexQvx72Yw3BD9Gh8juS34J7P8Rqr2Ovo3dIX8Od1V/y5mIIn\nfHon8GY83HQs/vydCVxV2nIusEdJV9/Pju7vWvwZLO9jR16aCKdYdipmD3SyLyG2CjIchWiP6oqo\nB9UX8RJSSmfhDcA7cCPwF4MGDdqhtbX1CdxoOxBvbBoeXlpuLHbkiaiuZ10ZhuAhrQeV9N3AUSV9\nO54JFTw09VzcI3ILcAhwoJnNj3PZ08yauz9r0cPo3dIIUtodOB+4FPhnPHnNFOAE4I/AkFjzVtzT\nWNBRB0411fe0Be8UKjqJnot97AU8gCe4ehzv5PkHYJfYbi5me3axXyG2GlvqJhdCCCG2eVJKQ81s\nffw/BjjXzGZEqOkPgdOBvwP34eFrw/AG3VpgaGtrK/i0FuXe2lRar2j4Dealnoc5wMSSbsbT9RfM\np73huAA3ThPeoH08/h4LvIiH1f0ZD4ddDjxhZq3A8SmlMym1DWQ0im0KTza1D2ZPktIOwD7AjbgR\neBwhjI8AABxJSURBVDOeIXh7YBzwASrP5S20bzOXjUbo2GjcGN8X+2jGPYgjcaPxbtxoHIOHpz+M\ne/ofBVbjz2RR7pmxDvj0OkI0BHkchWiP6oqoB9WXbYAYc7i7mc2NLKaTgQuAT+Pj+04EPoaHh/0R\n7/0vwkVbca9dOXy0esxgdT1ZT2U6C3jpuMQnaW8YPoJ7PQruAY6gYhjejDdqjwOWARcD/4En1WkB\nrjazmSmlD+PzxD1sZpfEuX8emGtmP+78CokGoHfL5uDJpU7C7PekNAp/1q4DpuNJp/YAzsPHFP8r\nFYOvlrHA3bEcT4QzCH8H3IV3+AzBvYnfwI3Ep4FVRKbkHkB1RfQaMhyFaI/qiqgH1Zc+SpGABh9D\nuAZ4E3ANnrRiFW5czcQNsjY8JG19bLMAn6sQ/B6vpn2m0u6oDjddijcYC2bhno2C52nvUXwGD1cr\nDMy/x2cX3Ki9xczOSim9FXg38JCZfSWM3i8CrWb2pbgOnw19cegzQ19dx/mI3kfvlgL38C+jYpB9\nGn9+T8efnevwqSwuwT2IewD/jXvmCs/9cjy8u3guK9NZbD6PA5Pi/w1459K+wFPATpj9+xbsux5U\nV0SvIcNRiPaoroh6UH3pAcLIGwmsMrO2CP88Afizma1KHlL2ceAKvGH4LD49xGfxpBQ3A78Evga8\nBx/D91FgEZ684u94RtDiB28V7T2E1YZetcewlvkPyyzCQ90KFlAZn1ToUXiimx3jfP6IJ8I5Lcr7\n1ijz24AXzOxKgJTS64BBZvbb0Afjv+WPht4JaDOzljrKK/oe2/67pdK5cyzuRX8t/mx8GH/eL8U7\nUI4BLsM7fXbEn8WF+DNczHXYhl+zcsho9bjDep/j6lDTmXiHz9oox5vjGIPxuRrn17HvnmTbryui\nzyDDUYj2bLW6EhNkY546m5TSUJe2IfQIvMG3JvQYYKOZrQy9M7DBzJaG3h1Yb2aLQk8E1pnZi6H3\nBdaY2bzQ+wOrzez50C/HG+rPhj4UWGlmT4U+AlhhZn8PfQywzMz+Fvp4YKmZPRT6tUCLmd0f+g3A\nEjO7N/SpwCIzuyv06cBCM7s99Bmhbw39buBFM/tD6LOB+WZ2c+j3A/PM7KbQ/wQ0m9n/hP4YMMfM\nbgz9KeB5M7su9GeB2WZ2TWn7BWZ2Q+i3xPkU5Zsa1ycPvWtLS8sLY8aMGZDvljDu2sxsQ0ppCHAK\ncIeZLUqepv7reGNvLm683YAnmyjmGbyNSqKJh/E09mfhGQvvxsfh/Qg3nG7HvQsteGNxNu55I/a3\nnkqyGMMbiOUGY3cZDat1dYOzesxhdyyK9Qfj4W9Pff7zn9/vq1/96sfxlPyP4Ak4xuEeijXWc2Fr\nYtug/7ZbUtoefyaPwj3mk/DkS5/Bn/NDge8B78XnJR2EZy3dDn9XgD/DbXTdodPT12hx7HNMHOtd\n+PjiEcABmF3Rg8fqSfpvXRH9joFhODY1GWZ/xGxao4tSpjyXV+hqQ2I43jBbG3o0HmJUGBLjcMOi\nMCR2ww2LxaEn4IbEgtD7AGtLhkSGN1gKQ+Ig3LB4LvQrcMOiMCQOxw2JJ0IfDSwvGRLH4YZFYUic\nhBsWhSHxerwhXhgSb8INi8KQeCuwuNRQfwduaBSGxHTcsCgMibPwhn7R834e3jNfGBLn44ZFYUh8\nBB/LUxgSn8ANi+tDf+bqq6/+2vTp04u51r6AGxZXhf5S6JmhLwJmmdkPQl8MPGdm3w19KfCMmX07\n9LfwsQ3fxl/0l4X+IZ5Z8dLQP8M9EZeEvgFvMF8c+td4gouvh/4D/uP2b6Fvxz0YXw39F+AJ4Muh\nH4nP/wud4xncvhB6Nt77e2HoF/BEGp8L3YInCvgcPm5jOfDb2H4W7jX5NR4y9zzu3bkxjteMh/1d\nG+WZhzfKr8INhQVxLX6IT568EA8B+i7uTVqE/6B/K85/Cc4lcX2W4o2Qi0KvwhsuX8DHl6zDDYwL\nYn+HADvF/ViGGywv4NMTXBLneg/uzbocb1hchydA+e4555xz2syZM/eP87oyyvsvMT/fV/D6PwM2\n1c/VJUP1ZPx5Ler3/vg7uTDUdwCGmdnC0MOBIWa2LPRQ3PNUdDQMAZqKaRNCDy4t3w43ZlbE+R4d\n5/xk6AvjXO+Oa3h13Kf78Abdjfik1Qvins2MT1N891m8Lo6K+/4OPJRrLF7Hjo/jDYt7OoqOWR/L\ni9DQIkHE1jQEqz2EzVRCVcGfiQl07q3YgE9bsQw/7xw3gLfHPY27xHtPjTtRK403Brzzchze+bMB\nN/j2Cf0Eno13GrAz8Ht8PPAx+HOyGH/udou9teGdPOWxvfV6AHuax/A5VF+OT7nxD5g9Gwl0mohk\nWP2AxtcVMWAYGFlVzVgNrz0vpRXz4Ou3mV2UvEf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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "quick_model(SrcErl,SrcErlLig,'Src_Erl_spectra')" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import pymc\n", + "\n", + "db = pymc.database.pickle.load('pymc_model_Src_Erl_spectra.pickle')" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([-16.34470026, -16.34470026, -16.34470026, -16.34470026,\n", + " -16.34470026, -16.34470026, -16.34470026, -16.34470026,\n", + " -16.34470026, -16.34470026])" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "db.DeltaG()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from assaytools import plots" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/hansons/anaconda/lib/python2.7/site-packages/matplotlib/axis.py:1024: UserWarning: Unable to find pixel distance along axis for interval padding of ticks; assuming no interval padding needed.\n", + " needed.\")\n", + "/Users/hansons/anaconda/lib/python2.7/site-packages/matplotlib/axis.py:1034: UserWarning: Unable to find pixel distance along axis for interval padding of ticks; assuming no interval padding needed.\n", + " needed.\")\n" + ] + }, + { + "ename": "RuntimeError", + "evalue": "Error building image", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/Users/hansons/anaconda/lib/python2.7/site-packages/IPython/core/formatters.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m 328\u001b[0m \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 329\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 330\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 331\u001b[0m \u001b[0;31m# Finally look for special method names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 332\u001b[0m \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_safe_get_formatter_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/Users/hansons/anaconda/lib/python2.7/site-packages/IPython/core/pylabtools.pyc\u001b[0m in \u001b[0;36m\u001b[0;34m(fig)\u001b[0m\n\u001b[1;32m 205\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 206\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;34m'png'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 207\u001b[0;31m \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'png'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 208\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;34m'retina'\u001b[0m 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facecolor, edgecolor, orientation, format, **kwargs)\u001b[0m\n\u001b[1;32m 2156\u001b[0m \u001b[0morientation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morientation\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2157\u001b[0m \u001b[0mdryrun\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2158\u001b[0;31m **kwargs)\n\u001b[0m\u001b[1;32m 2159\u001b[0m \u001b[0mrenderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cachedRenderer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2160\u001b[0m \u001b[0mbbox_inches\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_tightbbox\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/Users/hansons/anaconda/lib/python2.7/site-packages/matplotlib/backends/backend_agg.pyc\u001b[0m in \u001b[0;36mprint_png\u001b[0;34m(self, filename_or_obj, *args, **kwargs)\u001b[0m\n\u001b[1;32m 531\u001b[0m _png.write_png(renderer._renderer.buffer_rgba(),\n\u001b[1;32m 532\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mheight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 533\u001b[0;31m filename_or_obj, self.figure.dpi)\n\u001b[0m\u001b[1;32m 534\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 535\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mRuntimeError\u001b[0m: Error building image" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "ename": "RuntimeError", + "evalue": "Error building image", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/Users/hansons/anaconda/lib/python2.7/site-packages/IPython/core/formatters.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m 328\u001b[0m \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 329\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 330\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 331\u001b[0m \u001b[0;31m# Finally look for special method names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 332\u001b[0m \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_safe_get_formatter_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/Users/hansons/anaconda/lib/python2.7/site-packages/IPython/core/pylabtools.pyc\u001b[0m in \u001b[0;36m\u001b[0;34m(fig)\u001b[0m\n\u001b[1;32m 205\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 206\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;34m'png'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 207\u001b[0;31m \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m 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\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cachedRenderer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2160\u001b[0m \u001b[0mbbox_inches\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_tightbbox\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/Users/hansons/anaconda/lib/python2.7/site-packages/matplotlib/backends/backend_agg.pyc\u001b[0m in \u001b[0;36mprint_png\u001b[0;34m(self, filename_or_obj, *args, **kwargs)\u001b[0m\n\u001b[1;32m 531\u001b[0m _png.write_png(renderer._renderer.buffer_rgba(),\n\u001b[1;32m 532\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mheight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 533\u001b[0;31m filename_or_obj, self.figure.dpi)\n\u001b[0m\u001b[1;32m 534\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 535\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mRuntimeError\u001b[0m: Error building image" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plots.plot_measurements(Lstated,Pstated,path_length,db)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def plots_model(protein_data, buffer_data,name):\n", + " reorder_protein = reorder2list(protein_data,well)\n", + " reorder_buffer = reorder2list(buffer_data,well)\n", + " \n", + " print name \n", + " \n", + " from assaytools import pymcmodels\n", + " pymc_model = pymcmodels.make_model(Pstated, dPstated, Lstated, dLstated, \n", + " top_complex_fluorescence=reorder_protein,\n", + " top_ligand_fluorescence=reorder_buffer,\n", + " use_primary_inner_filter_correction=True, \n", + " use_secondary_inner_filter_correction=True, \n", + " assay_volume=assay_volume, DG_prior='uniform')\n", + " \n", + " mcmc = pymcmodels.run_mcmc(pymc_model)\n", + " \n", + " from assaytools import plots\n", + " figure = plots.plot_measurements(Lstated, Pstated, pymc_model, mcmc=mcmc)\n", + " \n", + " map = pymcmodels.map_fit(pymc_model)\n", + " \n", + " pymcmodels.show_summary(pymc_model, map, mcmc)\n", + " \n", + " DeltaG = map.DeltaG.value\n", + " \n", + " np.save('DeltaG_%s.npy'%name,DeltaG)\n", + " np.save('DeltaG_trace_%s.npy'%name,mcmc.DeltaG.trace())\n", + " \n", + " from assaytools import plots\n", + " plots.plot_mcmc_results(Lstated, Pstated, path_length, mcmc)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Src_Erl_spectra\n", + "MAP fitting cycle 5/5\n", + "DeltaG = -14.4 +- 0.5 kT\n", + "Kd = 545.1 nM +- 597.3 nM\n", + "(1000,)\n" + ] + }, + { + "data": { + "image/png": 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ilbLvz4GvNOeKRJZcVbKqvpP8BlyT/CaeAHzB3adWeYGU0srAneRJzW3kDFlt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\nl55nK6LP0lsdx4uAB2KMNyT9Qoxx1yoXSwghRHvp26FjYms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2ubd3zqk1gTNGikItHuLELyvpDKsdLU3rV1VVjmrYqsoSFCV4nfH8uNuTciij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\nNcsXblu3eDXkZz/+CvkxSc9AQGHO0sm4R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d+vxIAFF72soUT67O3W12gor1mv2sOnMm8XoIxBske4E1H7hj4JPmGtMTsc0c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\nHY0wH7M6WFXHGiwP68erMy7yZF7ESEqJ0Wng0EvWXHfGiac8r5Syblp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Z5s2bt1cz69CR+WpqagYmSW7zS/2mLZs3xHHs6urqtmwrV0cfq+amuXPnzmlj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\nxWnvPsNHHXnAcTz/0tMv3P/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L2T31NVL75je1rwBZHOppZseb2TnR3a1AA/Cgmc2L5i0GlhEWhPuYWbGZlQMTCE/8IiIi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\njzWzL7by2IwO7rukI8uLiIi0RoWfiIj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jusz+3QX37BNWCdaXx2ruGt+7qQJe+FAV5Mmyk+tZqWTC7JqKIp7ZvytLe5T6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OdFLAdHGeNMWZMAUfUWDwdgVR04HlRrG7NA7KzIrS3ZF3HIq2qG8ocaiEqptcDNQCtwN3Cj1vpw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\nwdoz8YvAf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11tLn7g+aWfe0198CmNlQ4DhgGOEiWpC22iJCRl2RZMWLSD2m+s1dOVe3G/QZ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\nGXg427HUB0r6RCTrNt+06wGHjB1yUHr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t+eO3EM52CWVItxqwMujbLkKxJ1CXFplq3/LUv41dqhMd+L431KQj1X3r21Ul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\nu6G5Mit1PcMf52HzOybd/nM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9xvRt6sVCP1OeVxWzHvXNuNIiHtsZa7qOqR07MWpHAJ1zLwdeMf94c+DXnXP/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\nHd3fu9lzh2pjxN6B2OLw2Qf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VwhLoaCQ6MPxo0Cx46TMnIvl743ve4kVh+k4fRG58OqsHL/wuyuYWfFLLROD3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\n68Ngfw7AxX3ZFUI8DMDTANwsp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VMnNptfw0GkXg+4BPNS2yBJgxwKqSJEmSpIJaKfw+APwEmBsR3wH2AV7TSvCI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\nEUdm5iVrE0uSRgsLP0kaJiL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GcynX/di8K0h4SymF27bRuyZCsL9zgXOczaqKWPLmuVwxYidrDn+VuK21O3AW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\n5JKKOQUtWzML3eF4NXCv53nDsl7Y+lio3ogC8zsjCszvjCgwvzOiwPy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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plots_model(SrcErl,SrcErlLig,'Src_Erl_spectra')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "DeltaG_trace_Src_Erl_spectra = np.load('DeltaG_trace_Src_Erl_spectra.npy')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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LB04484IFC+c2NK35pnV37NLf339ctVrtpOcY1tVemnWO1/xmdU/3/CFu3zx/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\nQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(DeltaG_trace_Src_Erl_spectra, 40, alpha=0.75, label='SrcErl')\n", + "#plt.plot([DeltaG_trace_Src_Erl_spectra.mean(),DeltaG_trace_Src_Erl_spectra.mean()],[0, 45],'b-')\n", + "#plt.plot([literature_Bos,literature_Bos],[0, 350],'r-',label='IUPHARM data')\n", + "plt.xlabel('$\\Delta G$ ($k_B T$)');\n", + "plt.ylabel('$P(\\Delta G)$');\n", + "plt.title('histogram of estimates for binding free energy');\n", + "plt.legend(loc=0);" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36mAbl\u001b[m\u001b[m/ Simple Spectra Import Clean w Dictionary.ipynb\r\n", + "\u001b[36mAblT334I\u001b[m\u001b[m/ Simple Spectra Import Clean.ipynb\r\n", + "Analysis.ipynb Simple Spectra Import.ipynb\r\n", + "DeltaG_Src_Bsi_spectra.npy Spectra-Analysis.ipynb\r\n", + "DeltaG_Src_Erl_spectra.npy \u001b[36mSrc\u001b[m\u001b[m/\r\n", + "DeltaG_Src_Gef_spectra.npy \u001b[36mSrcT338I\u001b[m\u001b[m/\r\n", + "DeltaG_trace_Src_Bsi_spectra.npy Test.ipynb\r\n", + "DeltaG_trace_Src_Erl_spectra.npy Thoughts.ipynb\r\n", + "DeltaG_trace_Src_Gef_spectra.npy pymc_model_Src_Erl_spectra.pickle\r\n", + "README.md pymc_model_Src_Gef_spectra.pickle\r\n" + ] + } + ], + "source": [ + "ls" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "DeltaG = np.load('DeltaG_Src_Erl_spectra.npy')" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array(-14.422382614092243)" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "DeltaG " + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "-13.834389287608321" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "DeltaG_trace_Src_Erl_spectra.mean()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Hmmm...." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "SrcBsi = platereader.select_data(Src_Spectra, 'em280', 'C', wavelength='480')\n", + "SrcBsiLig = platereader.select_data(Src_Spectra, 'em280', 'D', wavelength='480')" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Src_Bsi_spectra\n", + "MAP fitting cycle 5/5\n", + "DeltaG = -19.0 +- 5.8 kT\n", + "Kd = 5.7 nM +- 38.5 nM\n", + " [-----------------100%-----------------] 10 of 10 complete in 0.0 sec" + ] + }, + { + "data": { + "image/png": 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0xGJtL2Y2FfgP8EXymotL0XFnQwv5Rn5NY69jK7lncpny+DJ5PqSTl2vbh5xQ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\nUjoAHbkwEHgMkZ7hPfQAhiFyZPDOwLaIjC3481C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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "quick_model(SrcBsi,SrcBsiLig,'Src_Bsi_spectra')" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "DeltaG_trace_Src_Bsi_spectra = np.load('DeltaG_trace_Src_Bsi_spectra.npy')" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(DeltaG_trace_Src_Bsi_spectra, 40, alpha=0.75, label='SrcBsi')\n", + "#plt.hist(DeltaG_trace_Src_Erl_spectra, 40, alpha=0.75, label='SrcErl', color='g')\n", + "#plt.plot([DeltaG_trace_Src_Bsi_spectra.mean(),DeltaG_trace_Src_Bsi_spectra.mean()],[0, 45],'b-')\n", + "#plt.plot([literature_Bos,literature_Bos],[0, 350],'r-',label='IUPHARM data')\n", + "plt.xlabel('$\\Delta G$ ($k_B T$)');\n", + "plt.ylabel('$P(\\Delta G)$');\n", + "plt.title('histogram of estimates for binding free energy');\n", + "plt.legend(loc=0);" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#plt.hist(DeltaG_trace_Src_Bsi_spectra, 40, alpha=0.75, label='SrcBsi')\n", + "plt.hist(DeltaG_trace_Src_Erl_spectra, 40, alpha=0.75, label='SrcErl', color='g')\n", + "#plt.plot([DeltaG_trace_Src_Bsi_spectra.mean(),DeltaG_trace_Src_Bsi_spectra.mean()],[0, 45],'b-')\n", + "#plt.plot([literature_Bos,literature_Bos],[0, 350],'r-',label='IUPHARM data')\n", + "plt.xlabel('$\\Delta G$ ($k_B T$)');\n", + "plt.ylabel('$P(\\Delta G)$');\n", + "plt.title('histogram of estimates for binding free energy');\n", + "plt.legend(loc=0);" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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1usPpDGYy5wmcHFsCJUmSJHWUMlnr2iR4snwmUJIkSZK6iEmgJEmSJHWRlnQHjYgTKebU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\nX5+Z346IRwD7l/+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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(DeltaG_trace_Src_Bsi_spectra, 40, alpha=0.75, label='SrcBsi')\n", + "plt.hist(DeltaG_trace_Src_Erl_spectra, 40, alpha=0.75, label='SrcErl', color='g')\n", + "#plt.plot([DeltaG_trace_Src_Bsi_spectra.mean(),DeltaG_trace_Src_Bsi_spectra.mean()],[0, 45],'b-')\n", + "#plt.plot([literature_Bos,literature_Bos],[0, 350],'r-',label='IUPHARM data')\n", + "plt.xlabel('$\\Delta G$ ($k_B T$)');\n", + "plt.ylabel('$P(\\Delta G)$');\n", + "plt.title('histogram of estimates for binding free energy');\n", + "plt.legend(loc=0);" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "SrcBos = platereader.select_data(Src_Spectra, 'em280', 'A', wavelength='480')\n", + "SrcBosLig = platereader.select_data(Src_Spectra, 'em280', 'B', wavelength='480')" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "ename": "ValueError", + "evalue": "could not convert string to float: OVER", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mquick_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mSrcBos\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mSrcBosLig\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'Src_Bos_spectra'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m\u001b[0m in \u001b[0;36mquick_model\u001b[0;34m(protein_data, buffer_data, name)\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mquick_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprotein_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuffer_data\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mreorder_protein\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mreorder2list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprotein_data\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mwell\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mreorder_buffer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mreorder2list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbuffer_data\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mwell\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m\u001b[0m in \u001b[0;36mreorder2list\u001b[0;34m(data, wells)\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0mreorder_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreorder_data\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat64\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mreorder_data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/Users/hansons/anaconda/lib/python2.7/site-packages/numpy/core/numeric.pyc\u001b[0m in \u001b[0;36masarray\u001b[0;34m(a, dtype, order)\u001b[0m\n\u001b[1;32m 480\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 481\u001b[0m \"\"\"\n\u001b[0;32m--> 482\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 483\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 484\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0masanyarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mValueError\u001b[0m: could not convert string to float: OVER" + ] + } + ], + "source": [ + "quick_model(SrcBos,SrcBosLig,'Src_Bos_spectra')" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "SrcGef = platereader.select_data(Src_Spectra, 'em280', 'G', wavelength='480')\n", + "SrcGefLig = platereader.select_data(Src_Spectra, 'em280', 'H', wavelength='480')" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Src_Gef_spectra\n", + "MAP fitting cycle 5/5\n", + "DeltaG = -12.5 +- 0.2 kT\n", + "Kd = 3.8 uM +- 0.8 uM\n", + " [-----------------100%-----------------] 10 of 10 complete in 0.0 sec" + ] + }, + { + "data": { + "image/png": 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KF6lKsSKNULxIVYqVIc7MTqV9nuEYYFzd07PL7WsRcVaFyzUcL2a2NrBeRPyibL9IXvPx\nOeD95MTQyNVYFwdmlFM3jIj/M7Na8jCc9oTz0YhY1cwWJi/jsUhEzCxFdiYBi0dE7ToySDVzqOoO\nQFtKaSPgMPJ46ZOAQ1NKm5ADcid3XxI4iDxpdxvgOHcfRe7mvr8ce165BsCZwB7luuu5+xpNfA8i\nIiIiIn3CzD5EXuj9JeAiOiaNkHv3DgIub1YbIuKuWtJY7A7sBqxZhrvWqrL+MyJm1h13budLlWOH\nAR8s136NvAbkKeWY+YAblTQODZ0n4faZlNJf3P2vZfP95EpRE1JKt5R9VwNbk79VmZhSmgnMdPcn\ngNWBDYEfl2OvAQ4v66KMSinV1kW5lrzA6X3Neh8iIiIiInPLzEaR1/Y7HPgN0NW8v43q1k9siYj4\nW6ddnyQPUb2j0/7TO20vCbxcHhu8MywW8udzyMV9Pmlm69aqsMrg1dTiOCml2e5+DnAqcCEdu9Kn\nkCffjiN3hXe1f3IP++r3i4iIiIgMSGb2PvJ6izcAH+XdSeNk4PFWJ41diYgrIuIbEXFx2fUVcgGf\nxzsdugHvrghaW/9xxXL/z3J/h5ld3+eNrcrseszaym2u2+Hue7v7Ge7+875oXhfX/7m7b9qMa8+N\npvU41qSU9nb3Jchd7/PVPTWO/C3EZNqrOVEed97f1b76a/RmyFcAkj6leJGqFCvSCMWLVKVYGWIu\nuOACrr76atZcc80VjjjiiA3efPPNDs/fd99941JK45izf/umxktbWxsTJ05kgw02uAJgjTXW4JFH\nHuHJJ5+8ZOmll2bYsGEANm3atNhll1249NJLAYZvtdVW9e0yYMKyyy4bl19+OWuuuea7X6hZttqq\n854JLLtscPnlMIftOP7445k0aRLf+MY3IBc46lN77rkn22yzTV9fd67nTjezOM7ngWVTSse5+zjy\ncNLHgR+llG529zOBG4FbgOuBdciJ5R3kCcMHAmNTSke5++7AximlA939XuBT5K7+vwJHppTu7KEp\nmmQujVC8SFWKFWmE4kWqUqwMIWZmERFlCOefyMtudP73vToitp/Dl2h5vJjZKsDPyFPOIBesfIvc\noXMYcDQwDRjdTduej4hlW9DUzKytu3Ywh+1w973Ic1Xfn1L6mLvvABxFHkX5Grmn9YfkNTqXBZYC\nLk8pHV5GY04jT+VbCtg7pXSvux9ALhb6MrlH+rCU0s1z0r5maeZQ1UuANdz9ZvIcxa8DXwWOcvf/\nI/d2XpJSeokcfLeSE8lDU0rTgTOAVd39VmBf8j8G5OqsF5IXTL2nl6RRRERERKS/nG5mPyWvNDCb\njglMG3nJi0P6o2FzKiIeIS+/EZF7oL4E3FMeX1IOu6HfGtha4e7DyNPytk0pbUFeCzOA9wK3p5S2\nBdYj5zCU554u+08D9nf3xYGDy3Hbl2MG3MiDZhbHeZtcoamzzbo49mzg7C7O/0wXx/4d+FjftFJE\nREREpGnuBrYA7gT+p9Nz55GXqniq80kDXXQcsvgI8K3y+DHgBfIIwvloL5JT8zywY9Mb2NGNTW7H\n4sDklNIrZftWcuGg/wLruPvm5Ol2o+vOubfcP0suCPoB4JFSLBR3n8gAHHnQ9DmOIiIiIiLzGjMz\nciGcT/Puz9wREV9sfav6XkRMrNtsIydM10fEsWb2EvCe8lxrh6i2N3ArzJ4Dlqm1Y06HqHbByENL\nx7r7YimlV8kdXE8BewOvp5QOcPcPkIehdnU+5Ol8q5Z17KcB65JXoBhQmlpVVURERERkXmJmK5nZ\nn8ij6W4gJ431vUfTgAE1d62vlJ7IDeqW3vgqecjlC7S+p7HejuRexr7u8YyUUpDf51Xufj15iOoM\nck/ntmXfd4G73H3p2nl191ESzmOA24DrgPr1MweMphXHGUA0yVwaoXiRqhQr0gjFi1SlWBnkzGwE\ncBZ5GOphdFxVIMi1O+6LiHv64OUGdLyY2fuB/SLi+/3clKZy9+8CJ6eUZrj7+cC1KaUL+rtdfU1D\nVUVERERE+s4S5DodI+iYNAIcFRG/aX2T+kdEPA0M6aSxmALc4e5vkYepXtTP7WkK9TiKdKR4kaoU\nK9IIxYtUpVgZpMzsB+RCMasDKwB71D0dwFRg2YiY3Icvq3iRllGPo4iIiIjI3PsH8D1gDLBWp+d+\nCzzSx0mjSEspcRQRERERmXtLk3sbx3TaPysi9umH9oj0KSWOIiIiIiJzwMzeS57P+AowEVgQGF53\nyEzg/H5omkif03IcIiIiIiJzZhZ5ncZNgAfpmDQG8FPg2/3QLpE+px5HEREREZE5EBEvmNnj5AXb\nh3d6+piIOKIfmiXSFEocRUREREQaYGZ7ALcDKwOjgNU6HTIVOLLFzRJpKiWOIiIiIiKNWRX4BrAi\nsFCn5yYCP42Itpa3SqSJKq3j6O6jU0rT3X0l8jcrV6eUBssvg9a3kUYoXqQqxYo0QvEiVSlWBgEz\nGwFcDmxJ7nGsmQWMitYtlK54kZbptcfR3Y8AVnL3w4CbgYeBnYH9mtw2EREREZEBwcwWIi+38Rrw\nH2BrOs5rbAOOaGHSKNJSVaqq7gTsC+wBXJhSmgCs2dRWiYiIiIgMLCsCfwR+AjzDu4vh/BL4casb\nJdIqVRLH4Sml6cAOwFXuPpx3L2wqIiIiIjJkRcTdwD7ADN49au/XEfFlzWuUoaxKcZwb3P1B4G3y\nUNWbgSt6O8ndRwK/AZYDRgPHAM8BfwUeK4ednlK62N33A/Ynjws/JqV0pbvPD1wALA5MAfZKKb3q\n7usDp5Rjr0spHV353YqIiIiINMDM1gTuB94DfArYsdMhU9EULpkHVOlx/BHwcWD9UhDnq8DpFc77\nHPBKSmkTYFvgF+QhriellDYvt4vdfUngIGADYBvgOHcfBXwZuL+cfx5wWLnumcAeKaWNgPXcfY2q\nb1ZEREREpCozM+BYcqfJE8DenQ75D7Cu5jXKvKDbHkd3fy85sbwS2B5Yxt0hf6tyDfDBXq59MXBJ\neTwMmAmslS/tOwGPAweTF0ydmFKaCcx09yfIE483pH2c+DXA4e4+FhiVUnqq7L8WmADcV/UNi4iI\niIhUERFhZjsC3yYXw6mvYNoGLBsR0/qlcSIt1tNQ1aOBzYClycNTa2aRh5v2KKX0JkBJ9i4Gvg/M\nB/wqpXSvux8K/ICc9L1Rd+oUYDwwDpjcw77a/hV6a4uIiIiISFVmNhpYhPwZdTZwKB2L4QTwPSWN\nMi/pNnFMKX0RwN2/k1KaowpRpdfyUuAXKaU/uPv4lFItSbwMOA24BRhbd9pY4HVygji2h32QE8nX\nKzRFwwekEYoXqUqxIo1QvEhVipV+dsUVV3DAAQew3HLLcffddzN9+vQOzx944IH285///McMjCqq\nihepYq7X+7TuhmS7+/4ppV+6+w/oGJAGRG9Fadx9CeBvwFdSSjeVfbcDX0sp3enuBwHLAD8FrgfW\nIfdI3gGsARwIjE0pHeXuuwMbp5QOdPd7yROTnyL3fB6ZUrqzh6ZoYVRphOJFqlKsSCMUL1KVYmWA\nMLNPkYeortvpqZsiYot+aFJXFC/SMj0NVbVu7qs6lDy89Ah3P6LsOxj4qbvPBF4A9k8pTXX3nwG3\nkudCHppSmu7uZwDnuvutwHTgs+UaBwAXkocLXNtL0igiIiIiUomZLRQRr5vZMPJ0qHU6HTKdd1dV\nFZkndNvjOITomxhphOJFqlKsSCMUL1KVYqWflGTxPuBGcofFQsCoukNmAotExNR+aF53FC/SMr2u\n4+juewMnkicI10RKaXjXZ4iIiIiIDC4R0WZm25OXiVucjglZAB8cYEmjSEv12uPo7k8BnwAeSikN\nxu5JfRMjjVC8SFWKFWmE4kWqUqy0WOlpHBYRs8xsJPAasECnw/4aEZ9ofet6pXiRlum1xxF4LqX0\nYNNbIiIiIiLSep8HvmBmrwIb8+6k8byI2Kv1zRIZWKr0OJ4CLAtcR54QDHmo6nlNbltf0Tcx0gjF\ni1SlWJFGKF6kKsVKi5nZcOBwYD/y+uX1noiIlVrfqsoUL9IyVXocFwKmAh8r20YO0sGSOIqIiIiI\ndGBmFtnsMkS1c9IYwBf7oWkiA5Kqqop0pHiRqhQr0gjFi1SlWGkBMxtFXm/8VHJv4/uAsXWHBDB/\nREx/99kDiuJFWqZKVdWnutgdKaUVmtAeEREREZGmiogZZnYkeY3xVbs45OODIGkUaakqQ1U3r3s8\nEtgZmK85zRERERERaYn/Bf7Sxf7ngGta3BaRAW+Ohqq6+90ppbWa0J5mUBe+NELxIlUpVqQRihep\nSrHSRGb2BeAD5J/xtsDanQ65A/hkRLzY6rbNIcWLtEyVoaqbkoMScmB+GPU4ioiIiMjgcy3wBWAD\nYP5Oz70BbBIRM1veKpFBoMpQ1aNoTxwDeBXQWjYiIiIiMqhExEtm9iSwRRdPH6ykUaR7qqoq0pHi\nRapSrEgjFC9SlWKlj5nZgsAZwBHAL8lLzC3Q6bA9gesi4pUWN29uKV6kZar0OIqIiIiIDEoRMdXM\nnicXwlmti0POiogLW9wskUFHPY4iHSlepCrFijRC8SJVKVb6iJktHRH/Lo8XA17g3Z0mLwAfiIi3\nWt2+PqJ4kZYZ1t8NEBERERHpS2Y2FrjLzLYys2nAy7w7aXwO+A3wdqvbJzIY9drj6O7vB34FLA9s\nAlwI/L+U0lO9nDeS/Mu4HDAaOAZ4BDgHaAMeBA5MKYW77wfsD8wCjkkpXenu8wMXAIsDU4C9Ukqv\nuvv6wCnl2OtSSkf38h71TYw0QvEiVSlWpBGKF6lKsdJHzGwH4GK6Xg1gNrBGRDzY2lb1OcWLtEyV\nHsezgBPJyduL5MTx3ArnfQ54JaW0CXmdnF8AJwGHln0G7OTuSwIHkcsibwMc5+6jgC8D95djzwMO\nK9c9E9gjpbQRsJ67r1HpnYqIiIjIkGVm85nZ181smJkdQB6G2t0ScpOHQNIo0lJVEsfFUkrXAqSU\n2lJKZwPjK5x3Mbl6Ve11ZgJrppRuKfuuBiYA6wATU0ozU0qTgSeA1YENgWvKsdcAE9x9LDCqrrfz\n2nINEREREZm3BfAZ4G/kKqr/6OFY9dKJNKhK4viWuy9b23D3jYBpvZ2UUnozpTS1JHsXk3sM619v\nCjkBHUdecLWr/ZN72Fe/X0RERETmQWZWSwLbgG+TR7FB959znwe2bHa7RIaaKstx/A9wJbCCu98P\nLALsWuXi7v5e4FLgFyml37v7T+qeHge8Tk4Ex9btH9vF/q721V+jN0O+dKz0KcWLVKVYkUYoXqQq\nxUpFr732GmuvvTbHHnssZsYCCyzA1KlTuz1+iSWW4MUXX1wGuLt1rWw6xYtUMde97L0mjimlO919\nbcDJ39w8XYaU9sjdlwCuA76SUrqp7L7X3TdNKd0MbAfcSB5GcKy7jyaPQ1+FXDhnIrA9cGc59paU\n0hR3n+HuKwBPAVsDR1Z4nxqOIFVpkrlUpViRRihepCrFSgMWXnhh7rrrrgvuuuuuTwOje0gapwJj\nXnrppU8AV7Wsgc2neJGW6XWoqrt/BrgnpfQg8BbwsLvvXOHah5KHkR7h7je5+03k4apHufv/kZPW\nS1JKLwE/A24lJ5KHppSmk8emr+rutwL7AkeV6x5ALtDz99KuO6u/XREREREZ7MxsdTPb3sz+DGxB\nruDflbeACRExFlggIoZS0ijSUlWW43gAmFASPNz9PcD1KaWPtKB9fUHfxEgjFC9SlWJFGqF4kaoU\nK70ws0WAx8nJ4gLdHDabnDQ+FhFrt6pt/UDxIi1TpTjOyFrSCJBSermJ7RERERER6ZKZObAs8Brd\nJ42zyCPXlgH2aFHTRIa8KsVxJrr778nDQ41c5vj2prZKRERERKQws4WBY4FPAYsCw7s4LMiVVfeK\niN+VfVNa00KRoa9K4nggcBDwJfJajLcApzezUSIiIiIiAGY2Cngb2I1c3b8rAfyJvPb3PS1qmsg8\npdc5jgDuPp5c6KY2hjpSSv9qZsP6kMZ+SyMUL1KVYkUaoXiRqhQrdcxsNPAsueL+5t0cNg1YOyIe\nalnDBg7Fi7RMrz2O7n4o8F3gv3RcJ2b5ZjVKREREROZtZrYEMAmYn+6Txj+Rh62+0qp2icyrqgxV\n3RdYMaWcz7xcAAAgAElEQVSkX0gRERERaSozWxD4PfAqeY3vrnrU3iIv0fbHiJjewuaJzLOqJI7P\nkCtXiYiIiIg0jZkNB04DPk73QzBfBB6KiPNb1jARqZQ4PgHc5u7/C9S+0YmU0tHNa5aIiIiIzCvM\nbAywBvBtYEe6ThoDuBrYNyJeaGHzRIRqiePz5Vab36gJuCIiIiLSlw4GjimPu/qs2QYcHhE/al2T\nRKRe1aqqCwIrAg8AY1JKU5vdsD6kalPSCMWLVKVYkUYoXqSqeSZWzGwYeU3GA4Hv032Hxn+BNYF/\nRZUPrvOWeSZepP8N6+0Ad98SuA/4C7AU8LS7b9PshomIiIjIkHYGcBVwBF0njbOBbwFLRcQzShpF\n+leviSNwHLAx8FpK6XlgU+CEprZKRERERIYkM1vQzFYFPgesTdc9Zk8D+0TEiRExo5XtE5GuVUkc\nh6WU3pmAnFJ6iI7rOYqIiIiI9MrMTiPPZbwdWKCLQ2YB1wB7RcS5rWybiPSsSnGcZ939EwDuvhB5\nHPq/mtoqERERERkyzGwEsDDwWWAhuu68eBv4cUQc1cq2iUg1VRLHA4BTgfcCk4D/BfZvZqNERERE\nZPAzMwM2A74JjAIW6ebQ/wDfiYhft6hpItKgqlVV10wp3VN6HNdKKd3Y/Kb1GVWbkkYoXqQqxYo0\nQvEiVQ2pWDGzccDL5M6K4V0cEsDXI+K0ljZs6BhS8SIDW689ju5+PLAWsBUwP3C4u2+SUvpBlRdw\n9/WA41NKm7v7R4ErgMfL06enlC529/3IvZizgGNSSle6+/zABcDiwBRgr5TSq+6+PnBKOfa6lNLR\njbxhEREREWkuM1uCXGBxSWB0N4fNBj4ZEVe0rGEiMseqFMf5BLAtQCmSMwH4VJWLu/u3gV/R/gdj\nLeDklNLm5Xaxuy8JHARsAGwDHOfuo4AvA/enlDYBzgMOK9c4E9gjpbQRsJ67r1GlLSIiIiLSfGa2\nBXAXsCewXTeH3Q6sDPy1Ve0SkblTZY7jcGAMudcPchLYVvH6TwC7AOeX7bWAld19J3Kv48HAusDE\nlNJMYKa7PwGsDmwI/Licdw25p3MsMCql9FTZfy05kb2vYntEREREpAnMbDlyMvh9YBm6HkI5C/hy\nRJzdyraJyNyr0uN4FnC3u5/o7icBd5J7/XqVUrqU/Aei5u/AN1NKm5IL7fwAGAu8UXfMFGA8MA6Y\n3MO++v0iIiIi0k/MbHfgKOBy8prfXSWNM4CzgYta2DQR6SO99jimlH7q7hOBTci/8J9LKd07h693\nWUqpliReBpwG3EJOHmvGAq+TE8SxPeyDnEi+XuF1te6kNELxIlUpVqQRihepatDEyosvvsi4cePY\neOONufXWW7s9bu+99+bUU08dNW7cuAPIFful7wyaeJF+NddFlHrtcXT3RYHxKaUTgQWBQ939Q3P4\nete4+zrl8QTy+Pd/ABu7+2h3Hw+sAjwITAS2L8duB9ySUpoCzHD3FdzdgK3JiWdvTDfdKt4YAG3Q\nbXDcGABt0G3w3BgAbdBtcNwYAG2odDOzTZdaaqm/LrDAAn/qIWkM4PFzzjln+Lhx4/q9zUPwxgBo\ng26D4zbXqsxx/D1whbsH8GlyRdMzyT2QVdW+CTkA+IW7zwReAPZPKU11958Bt5IT2UNTStPd/Qzg\nXHe/FZhOXjC2do0LyXMvr00p3dlAO0RERERkLpjZsIhoI38mXJW8PmNXHiGPLjszqqz/JiIDWq/r\nOLr7nSmlddz9NOCJlNKp7n5XSmnt1jRxrml9G2mE4kWqUqxIIxQvUtWAjpWyLuNtwEPA7j0cendE\nDJbPioPZgI4XGVqq9Diau68F7AxsVpa/qHKeiIiIiAwRJWl8FFiC3NPYlTfJ05AOblW7RKQ1qlRV\n/Q5wAnBSSulJ4HTgf5raKhEREREZEMzsw2Y2iVyQcCny58fOnyED+DfwGWD7iPhna1spIs3W61BV\nAHdfEFgReAAYk1Ka2uyG9SF14UsjFC9SlWJFGqF4kaoGVKyY2TrAVcBiPRwWwGvA1yPigpY0TGoG\nVLzI0FalquqWwH3AX8jfMj3t7ts0u2EiIiIi0j/MbAszWxP4Az0njVOAe4CdlTSKDG1VhqoeB2wM\nvJZSep68qOsJTW2ViIiIiPQLM9sUOBy4Flihl8N3B9aNiO4XcRSRIaFK4jgspfRCbSOl9BBaaFRE\nRERkSDGz1czsQ8DZ5GXXeuppbAN+GBFXlaU5RGSIq1Id9Tl3/wSAuy8EHAj8q6mtEhEREZGWMbOV\ngeuB+YFxvRz+Orlq6vnNbpeIDBxVEscvAacC7wUmAf8L7N/MRomIiIhI85nZGOCjwHXAmB4OnQGM\nBKYDW0fEnS1onogMIFUSx4NSSj0t8CoiIiIig4yZbQRcCLyvh8OC3MP4J2AZ4CsR8XTzWyciA02V\nOY47unuV40RERERkEDCz3clDU7tLGqcD04D7gc8D+0fE9koaReZdVXoc/wM86u73AG+XfZFS+n/N\na5aIiIiI9DUz+yG5Qv7GPRw2A7gZGAWcGBFXtqJtIjKwVUkczy33tUqqhqqqioiIiAwaZmbk5dQO\nBoZ3c9jMcvs6cH5ETG9R80RkELCI3nNAd18N2IycaN6UUrqvye3qS0FOdkWqULxIVYoVaYTiRarq\n01gxs+HApcBGwCI9vOaLwEXAK8BJShoHDf1tkZbpde6iu38e+DOwPLAccJm779PshomIiIjInDOz\nUcAfgR3pOmmcDbxFThg3i4hDIuJHShpFpCtVhqp+E1g3pfQfAHc/hjzu/dfNbJiIiIiINM7MVgOO\nJieM3XUSvA08B1wC/DQiXmlR80RkkKqSOA6rJY0AKaVX3X12E9skIiIiInPAzJYDrgKW7eaQWeR5\njB8HJkbEjFa1TUQGtyqJ4z/d/RRyD6MB+5BLM1fi7usBx6eUNnf3DwDnAG3Ag8CBKaVw9/2A/cl/\nzI5JKV3p7vMDFwCLA1OAvUrSuj5wSjn2upTS0VXbIiIiIjIUmdnJ5HmM63RzyEzgGfISHL+KiHtb\n1TYRGRqqrM+4H7ks82+A35bHX6lycXf/NvArYHTZdTJwaEppE3ISupO7LwkcBGwAbAMc5+6jgC8D\n95djzwMOK9c4E9gjpbQRsJ67r1GlLSIiIiJDkZltDxxC10ljkOcxng9sARykpFFE5kSvPY4ppbeA\nb8/h9Z8AdiH/sQJYM6V0S3l8NbA1eWL2xJTSTGCmuz8BrA5sCPy4HHsNcLi7jwVGpZSeKvuvBSYA\ng6nKq4iIiMhcMbM1yV+qbwfM181h08hJ4+cj4qpWtU1EhqZeE0d3b+ti979TSt2NnX9HSulSd39/\n3a76csFTgPHAOOCNbvZP7mFfbf8KvbVDREREZKgws1XJX65P6OaQNvJnq42ANyLi+Va1TUSGrio9\nju8MZ3X3kcDO5GGlc6I+CR0HvE5OBMfW7R/bxf6u9tVfoze9L1Yp0k7xIlUpVqQRihepqstY2Xnn\nnXn22Wd7PHGFFVZgvfXWG3b++ecvPHz48Iea0joZaPS3RaqY6/U+qxTHeUcZTnqxux/W68Fdu9fd\nN00p3UweWnEj8A/gWHcfTR5qsQq5cM5EYHvgznLsLSmlKe4+w91XAJ4iD3U9ssLramFUqUoL6UpV\nihVphOJFquoQK2ZmwBLAbsBJwPBuzpsGnD9p0qTvTZo0aczvfve7njNMGSr0t0VapspQ1b3qNg1Y\nFWh0YdjaNyHfAH5Vit88DFxSqqr+DLiVXKzn0JTSdHc/AzjX3W8tr/fZco0DgAvJfzivTSnd2WBb\nRERERAY8M9sOOAZYDRjZzWGzgKnAVhFxV9n3n26OFRGZYxbRc++2u59De+IXwKvAGXUFagY6fRMj\njVC8SFWKFWmE4kUqmT17dowYMWIEeRm0jem5lsPr5ZgxwD8jYloLmigDi/62SMv0mjgOAfqFkkYo\nXqQqxYo0QvEivTKzpVdeeeXnH3vssSeBFXs49HXgrojYqkVNk4FLf1ukZbodquruPfUoRkpJ1UxF\nRERE5pKZLQAcDEx47LHHoPukcRbwJLlI4UataZ2ISNbTHMezgD8AiwOvtKY5IiIiIvOGkjBeDKwB\nvIfuC98EufjN9hHxt7Lv8qY3UESkTk+J4xeBE4FLU0prtqg9IiIiIkNWqZL6HvJnsPvJBQCXpPvh\nhtOBq4AzgJdb0UYRka70lDhOJP+xMndv6/RcpJS6+1ZMRERERLp2GvAZcpXUhXo4LoBnKMVxYh4o\nSiEiA1uVqqqXp5R2bFF7mkGThqURihepSrEijVC8zKPM7APAzsDngI+U3W10PywV4C3gEHLP5Imq\nlio9GPh/W8wMffExJKiqqkhHihepSrEijVC8zEPMbAywBLAOsD+wCd2vw1iv7eCDDx52yimnLA68\nHhGzmthMGRoG3t+W+kTRbAXg90Ss17+Nkr4wrL8bICIiIjIUmNlIM9sW+BdwL/BzYEt6TxoDmAz8\nz7rrrktEvKqkUQaNPG+39ngMMAmzWswvCKyM2YL90TTpW0ocRUREROZQSRYPNbPbgc8DV5OHoo4j\nV6bvzXRgq3LumXvssUfT2irSJ8zsnWQx3z+E2VLl2VHAAsB7y/ZG5Lm8o1rdTOl7ShxFREREGmBm\n85vZ3ma2PDAF2BNYG/hVOWRxeh8+GMDdwBXAYxFxeURMb1abReZKfa9irvK7fu0ZYDywetlelBz/\ntbVI9yr3izW7idJ8ShxFREREemFmI8xsITP7A3AecAJwB7knZRVypfqqn6tmATsA/xsRu0bEs81o\ns8gc6zj89Exg97pnV6E9cRwOLA1MKNv7lPvanMYVyn2tMJQMYiqOI9KR4kWqUqxIIxQvg5SZfYac\nFB4C/AfYnDxnsZF/zyBXSp2/PL4yInbq4VjFilTVN/FiNoyItvL4+/nKcWzZfhK4gYgvYTaM/MXH\nP4hYH7MPAQ8BjxOxMma3ABsDDxOxKmbTgNGlnTcSsdVct1X6jRJHkY4UL1KVYkUaoXgZJMrw00WB\nVYENyB+CV6Lnta+7EsDrwBjgWWBl4Fjgioi4vZfzFCtS1ZzFS8dEcV9gIyL2LtsJ+DcRm5eex9nA\nf4lYrBS/eROYRcRIzI4DvgvMJGIUZk8DywEPAC/R3hNZ8zywIxH3NNxm6XeN/hEUERERGTIs96As\nQe5N3JGcJH6TXNDDaOxD+QxgZjnnA+T5Xa8C50T+pv7Qvmu5SAPMhhMxuzz+NHAQsGl59lBqcZ4T\nxZVpH2K6WHlu0bJdW9u9tg7pcuV+drl/vOybSa4o3NkywOXAsnPzdqR/KHEUERGReYqZWUSEmY0C\nPgr8iTz87r3ANHIvYRVBroo6jNybOAVYAzgkIv4LHN/XbReppGOiuCfwI+B9JTG8AJhVHo8ClifH\nMrRXAh5RhqW+r9OVXyj3tUTxVGAP8u8N5IT0EeBc8u+WDCEaqirSkeJFqlKsSCMULwOE5fXkHgb+\nSl4CYxKwGtX/fdrKbSLwKLAO8PuIOLGPmqhYkY7Mrqe9967zPMHAbBngZSJqyeAU8pcZC5N7wdvI\nid5CwNvkL0mC9vVFa2uGLk3uIZ9RtkeQY3Em0EbE8HL9NuAtIhbAbER5flciLiF/GTON3Gt5Bhqq\nOqT0S4+ju98DvFE2JwHHAeeQA/FB4MCUUrj7fsD+5IA+JqV0pbvPT/6mZHHyL8ZeKaVXW/wWRERE\nZJAws0vIFVD3AW4mD009gPyhePUeTq1pA6aSh7POT/78sXk5P2Ie+BZe+klOGuuTrwmYtZE/Ox9O\nDr3ngZmYrUjEs5gtQE4MlyFiErlA6nAg90LmbQM2JuJvtBdQfZuImWU7gBFETC/bUwGIiFLwZtey\nPQuzt6j1OEbMwGwf4BUitsLsOfLw1NzOCA1RHcRavhyHu88HkFLavNz2AU4GDk0pbUIO5J3cfUly\nd/cGwDbAce4+CvgycH859jzgsFa/BxERERm4zGw/M/ummV1lZrsAG5I/aH+Q/IX0KKqtsziTXA01\nyENQVwFWjIiVI2tT0ih9zuw7mC1dtrqaJ2jkeYkb1u0bSft8w9oxUzudt0+n7c6/A4uU+5nk4ji1\ndUWfI1cVrnmB9nUaISe2t76zFfFbImodRDuSE9vnaZ8fKYNUf/Q4fgQY4+7Xltf/PrBmSumW8vzV\nwNbkLvWJKaWZwEx3f4L8reCGwI/LsdcAh7ey8SIiIjKwmNkqwM7khPC88ngb8hfkE2gfcge9J4xv\nAfMB2wNLAteX859VkihNkXvEryLiN2XP8cARmC3Zy5krdNp+ptP2acBuddtLdXp+4XJ/D7l3cVLZ\nfpwc9zWPkedB1mwGvPLOVk9VgvOQVPUyDhH9kTi+CZyQUvq1u69ETv7qTQHGA+NoH87aef/kTvtE\nRERkHmFmI4Gvkj/gPgnsQC5O8zZ5aYCRtFd9HNnVNerMJvcoXkgekvoHckGQFyLi2j5vvIjZA8Az\nROxQ9nwK2AWzm4l4suwbQ+4lv5F3zxOseb3c1+bF3kzHZLLWKzidvJbiQmX7eGBzIi4t22+TP5/X\n3FT21Xyiw3bEv3p5hzJE9Ufi+BjwBEBK6XF3/w8dqy6NI/8iTAbG1u0f28X+2r7e6BtCaYTiRapS\nrEgjFC9zaNasWXzta19jp512YpFFFuH6669n6623ZplllmHEiBG89NJLvP3221C9GiqjR4/mgx/8\nINtvv/3wadOmMWzYsL1OPPFEgC826300QLEy2M2eDcPLdxeLLAKzZsHkyfVHfJirrw622662bWy4\n4RMdrrHNNgdyzTXUzUGEZZaB55/Pj0eM+PM75wKMGZOro26yCdxyC3zkI2sBwQknwC9/CQ8+uC+w\nL5/6FDz6KNTi7Oyz4emn27fb2mqv+V0A1NE+VMx10a2WV1V19y8Bq6eUDnT3pcnfpEwCfpJSutnd\nzyz7biF3k69DHjJyB3l+wYHA2JTSUe6+O7BxSunAHl5S1cmkEYoXqUqxIo1QvPTCzBYH3oyItywX\n97gd+A25QN6lwLPkXo8F6k5r5OdaWzrjdnI11C2BjYE3on0u10CgWBlszJYAPkrENWX7TWA+IoaX\n7dqH7W8RcWLd9otELFWK3Ri14jFms8nDrN8gYiHMZpF70ANYG/gzeemYNqZPH8bo0dsA1wL/JWJR\nzA4AvgN8nojbMDsK+AIRy5f2rAdsRkRt6pdIJf2ROI4Afkv7BN5vk6uU/Yo8N+FhYL9SVXVf8iT2\nYcCxKaXLSlXVc8njtKcDn00pvdzDS+oPsDRC8SJVKVakEYqXTszs4+T13p4mfyh+BbiEPJzuIHIx\njcnkoaaPAmvOwcsEOdm8DdioXGNN4I/AKhHx4Fy9ieZQrAx0ZjsAexKxe9meDowiwsp2LRH8MxGf\nrEsEZxIxqi4RnEXEyFKldDR5qPQo4F/kpTGC/PvwFfKwVQAnT+V6EZjF9OkjGD16R+Bs4GfkOiA7\nAL8k4j2lPUsBuxFxSvN+KDIv0DqOIh0pXqQqxYo0Yp6MFzOzWkEZM/sFuVfkXnKVxunk+YkLAz8l\nfzBuI3/Ans3cTad5mtwj8xtyorgucARwakS8NhfXbYV5MlYGNLPvAt8jYnzZfou8LMsCRLyF2Uxy\nvL5ITvjeLM9T7p8FFqO2xEX+HVi9bC8OfJ32Yo+rkjtUXizPr0ue4vUqufd9OWBR4C7gdzzzzD4s\nt9zy5I6XBYloK2sp7llXbEekT7R8OQ4REREZesxsMTN7b3lsZnYb8GszO8LM9gT2I/coPkN7T+IH\nyWsqHk9OloaX+0aTxmnl/u1y7S3Jw/kujoi1y7IZRw6CpFGawex6zNrK7founre65S/A7CeYBWa1\nAksHA+Mwq1Uora0fPhmzMbQvRbEkObE7r2wH+bP212pXJq8usF3d9jrAUeQvTdrK+ZPJSeIb5CTx\nzXLsleX4x8r53+V974OIp8kFbPIXDhEzlDRKMyhxFBERkcpKUjjezHYys13NbC0zOwO4H7jLzN4g\nrx/3MXKhmaOA88mJ4kjyh9sF+7BJ04Efkj9UnwBsQh7m+pmIeHeSIPOWnChOIMedARMwew6z32Lv\nVJ05DHges03K9krlvjaU+axy/wfMVgK+VLaHk+cc/r+6VxxO+5qHBnycXKm3Zisi/k3+smMmtbmK\nuZf9n8CSRLxdts8mD2edSe61PJKIvxIxC1gNaP8iJOJGImY39LMRaVB/VFUVERGRAcrMRgDLR8Tj\nZdmLrcnDPO8gz69altxTWF+oxmjuEMsZ5Llf9WaSh6SeTB6mdyEwPNrXopN5mdlC5C8wtuzi2WWA\nvcmxvBW5YBLAjZjNT55TuDPwQcwmAEeTfwegfY5szSJE3FJX+XQfIg4t8xhHAB8j4uIyvHUqOW4h\n/75cDMwmIjB7Dvg57esnHgecR8RTZXuVklCWs99ZtkOkZdTjKCIiMsiZ2bC6x2a5ymNte6S196Rg\nZgua2b5127ub2ctm9iEz+wJ57tRjZvY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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "quick_model(SrcGef,SrcGefLig,'Src_Gef_spectra')" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "DeltaG_trace_Src_Gef_spectra = np.load('DeltaG_trace_Src_Gef_spectra.npy')" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#plt.hist(DeltaG_trace_Src_Bsi_spectra, 40, alpha=0.75, label='SrcBsi')\n", + "#plt.hist(DeltaG_trace_Src_Erl_spectra, 40, alpha=0.75, label='SrcErl', color='g')\n", + "plt.hist(DeltaG_trace_Src_Gef_spectra, 40, alpha=0.75, label='SrcGef', color='r')\n", + "#plt.plot([DeltaG_trace_Src_Bsi_spectra.mean(),DeltaG_trace_Src_Bsi_spectra.mean()],[0, 45],'b-')\n", + "#plt.plot([literature_Bos,literature_Bos],[0, 350],'r-',label='IUPHARM data')\n", + "plt.xlabel('$\\Delta G$ ($k_B T$)');\n", + "plt.ylabel('$P(\\Delta G)$');\n", + "plt.title('histogram of estimates for binding free energy');\n", + "plt.legend(loc=0);" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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3UKIGrybgLHfvMrNHgXvMrB14DniAqHWwZI1gQRiWtsHNzOYA5wHzgPcBxwDvcPdt4/1H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\nTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(DeltaG_trace_Src_Bsi_spectra, 40, alpha=0.75, label='SrcBsi')\n", + "plt.hist(DeltaG_trace_Src_Erl_spectra, 40, alpha=0.75, label='SrcErl', color='g')\n", + "plt.hist(DeltaG_trace_Src_Gef_spectra, 40, alpha=0.75, label='SrcGef', color='r')\n", + "#plt.plot([DeltaG_trace_Src_Bsi_spectra.mean(),DeltaG_trace_Src_Bsi_spectra.mean()],[0, 45],'b-')\n", + "#plt.plot([literature_Bos,literature_Bos],[0, 350],'r-',label='IUPHARM data')\n", + "plt.xlabel('$\\Delta G$ ($k_B T$)');\n", + "plt.ylabel('$P(\\Delta G)$');\n", + "plt.title('histogram of estimates for binding free energy');\n", + "plt.legend(loc=0);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Why are these multi modal?" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Test_DG = -17.43" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "2.6930671376578519e-08" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.exp(Test_DG)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "-17.43" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.log(np.exp(Test_DG))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "###Let's see how these values compare to values from iupharm database" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "Src_erlotinib = 700e-9 # 700 nM" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Src_gefitinib = 3800e-9 # 3800 nM" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "#Note these values are in the csv files in data/literature_affinity, but I haven't found the best way of parsing them yet." + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "-14.172185501903007" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.log(Src_erlotinib)" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "-12.480509491231935" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.log(Src_gefitinib)" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "SrcErl_dG = np.log(Src_erlotinib)" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "SrcGef_dG = np.log(Src_gefitinib)" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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sdQK7cRCwrbu3FKD4IiIiIlJOtF6g5GlIB4EJWA6kzOzrwL3u/jcz28nMHgLe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\nxdRjrpjd38RqxZQB6rMVvbe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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(DeltaG_trace_Src_Bsi_spectra, 40, alpha=0.75, label='SrcBsi')\n", + "plt.hist(DeltaG_trace_Src_Erl_spectra, 40, alpha=0.75, label='SrcErl', color='g')\n", + "plt.hist(DeltaG_trace_Src_Gef_spectra, 40, alpha=0.75, label='SrcGef', color='r')\n", + "plt.plot([SrcErl_dG,SrcErl_dG],[0, 90],'g--',label='IUPHARM Erl')\n", + "plt.plot([SrcGef_dG,SrcGef_dG],[0, 90],'r--',label='IUPHARM Gef')\n", + "plt.xlabel('$\\Delta G$ ($k_B T$)');\n", + "plt.ylabel('$P(\\Delta G)$');\n", + "plt.title('histogram of estimates for binding free energy');\n", + "plt.legend(loc=0);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# LOOK AT US!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```\n", + "$$$$$$$$$$$$$$$$$$$$$$$$$\n", + "$$$$$$$$$$$$$$$$$$$$$$$$$\n", + "$$$$$'`$$$$$$$$$$$$$'`$$$\n", + "$$$$$$ $$$$$$$$$$$ $$$$\n", + "$$$$$$$ '$/ `/ `$' .$$$$\n", + "$$$$$$$$. i i /! .$$$$$\n", + "$$$$$$$$$.--'--' $$$$$$\n", + "$$^^$$$$$' J$$$$$$\n", + "$$$ ~\"\" `. .$$$$$$$\n", + "$$$$$e, ; .$$$$$$$$\n", + "$$$$$$$$$$$.' $$$$$$$$$\n", + "$$$$$$$$$$$$. $$$$$$$$\n", + "$$$$$$$$$$$$$ $by&TL$\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/data/spectra/Src/Src_BosI_20151215_bw2020_gain120_163633.xml b/data/spectra/Src/Src_BosI_20151215_bw2020_gain120_163633.xml new file mode 100755 index 0000000..6080165 --- /dev/null +++ b/data/spectra/Src/Src_BosI_20151215_bw2020_gain120_163633.xml @@ -0,0 +1,5851 @@ + + + + + + + + + + +
+ + + + + + infinite M1000 PRO + 1311007572 + + + MEX, V 1.20 Safire2 MCR (V 1.20 Safire2 MCR ) + MEM, V 1.20 Safire2 MCR (V 1.20 Safire2 MCR ) + FPO, V_1.03_Safire2 FP (V_1.03_Safire2 FP ) + ABS, V 1.00 MCR Abs 4 Channel (V 1.00 MCR Abs 4 Channel) + LUM, V_1.05_02/2015_LUMINESCENCE (Feb 24 2015/17.31.16) + BCR, V_1.00_02/2008_S3FTCAN + INA, V_1.05_11/2011_S3LCE_ALPHA (Nov 3 2011/09.27.24) + INB, V_1.05_11/2011_S3LCE_ALPHA (Nov 3 2011/09.27.24) + TCAN, V_1.00_02/2008_S3FTCAN (Feb 21 2008/17.19.16) + + + + + + +
+ + + + + +
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+ \ No newline at end of file diff --git a/data/spectra/Src/Src_Bos_20151215_bw2020_gain120_161211.xml b/data/spectra/Src/Src_Bos_20151215_bw2020_gain120_161211.xml new file mode 100755 index 0000000..f9a17ac --- /dev/null +++ b/data/spectra/Src/Src_Bos_20151215_bw2020_gain120_161211.xml @@ -0,0 +1,5851 @@ + + + + + + + + + + +
+ + + + + + infinite M1000 PRO + 1311007572 + + + MEX, V 1.20 Safire2 MCR (V 1.20 Safire2 MCR ) + MEM, V 1.20 Safire2 MCR (V 1.20 Safire2 MCR ) + FPO, V_1.03_Safire2 FP (V_1.03_Safire2 FP ) + ABS, V 1.00 MCR Abs 4 Channel (V 1.00 MCR Abs 4 Channel) + LUM, V_1.05_02/2015_LUMINESCENCE (Feb 24 2015/17.31.16) + BCR, V_1.00_02/2008_S3FTCAN + INA, V_1.05_11/2011_S3LCE_ALPHA (Nov 3 2011/09.27.24) + INB, V_1.05_11/2011_S3LCE_ALPHA (Nov 3 2011/09.27.24) + TCAN, V_1.00_02/2008_S3FTCAN (Feb 21 2008/17.19.16) + + + + + + +
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+ \ No newline at end of file diff --git a/data/spectra/Src/Src_Erl_20151215_bw2020_gain120_170056.xml b/data/spectra/Src/Src_Erl_20151215_bw2020_gain120_170056.xml new file mode 100755 index 0000000..2d87a9c --- /dev/null +++ b/data/spectra/Src/Src_Erl_20151215_bw2020_gain120_170056.xml @@ -0,0 +1,5851 @@ + + + + + + + + + + +
+ + + + + + infinite M1000 PRO + 1311007572 + + + MEX, V 1.20 Safire2 MCR (V 1.20 Safire2 MCR ) + MEM, V 1.20 Safire2 MCR (V 1.20 Safire2 MCR ) + FPO, V_1.03_Safire2 FP (V_1.03_Safire2 FP ) + ABS, V 1.00 MCR Abs 4 Channel (V 1.00 MCR Abs 4 Channel) + LUM, V_1.05_02/2015_LUMINESCENCE (Feb 24 2015/17.31.16) + BCR, V_1.00_02/2008_S3FTCAN + INA, V_1.05_11/2011_S3LCE_ALPHA (Nov 3 2011/09.27.24) + INB, V_1.05_11/2011_S3LCE_ALPHA (Nov 3 2011/09.27.24) + TCAN, V_1.00_02/2008_S3FTCAN (Feb 21 2008/17.19.16) + + + + + + +
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+ \ No newline at end of file diff --git a/data/spectra/Src/Src_Gef_20151215_bw2020_gain120_172518.xml b/data/spectra/Src/Src_Gef_20151215_bw2020_gain120_172518.xml new file mode 100755 index 0000000..2c5430b --- /dev/null +++ b/data/spectra/Src/Src_Gef_20151215_bw2020_gain120_172518.xml @@ -0,0 +1,8613 @@ + + + + + + + + + + +
+ + + + + + infinite M1000 PRO + 1311007572 + + + MEX, V 1.20 Safire2 MCR (V 1.20 Safire2 MCR ) + MEM, V 1.20 Safire2 MCR (V 1.20 Safire2 MCR ) + FPO, V_1.03_Safire2 FP (V_1.03_Safire2 FP ) + ABS, V 1.00 MCR Abs 4 Channel (V 1.00 MCR Abs 4 Channel) + LUM, V_1.05_02/2015_LUMINESCENCE (Feb 24 2015/17.31.16) + BCR, V_1.00_02/2008_S3FTCAN + INA, V_1.05_11/2011_S3LCE_ALPHA (Nov 3 2011/09.27.24) + INB, V_1.05_11/2011_S3LCE_ALPHA (Nov 3 2011/09.27.24) + TCAN, V_1.00_02/2008_S3FTCAN (Feb 21 2008/17.19.16) + + + + + + +
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+ + + + + + infinite M1000 PRO + 1311007572 + + + MEX, V 1.20 Safire2 MCR (V 1.20 Safire2 MCR ) + MEM, V 1.20 Safire2 MCR (V 1.20 Safire2 MCR ) + FPO, V_1.03_Safire2 FP (V_1.03_Safire2 FP ) + ABS, V 1.00 MCR Abs 4 Channel (V 1.00 MCR Abs 4 Channel) + LUM, V_1.05_02/2015_LUMINESCENCE (Feb 24 2015/17.31.16) + BCR, V_1.00_02/2008_S3FTCAN + INA, V_1.05_11/2011_S3LCE_ALPHA (Nov 3 2011/09.27.24) + INB, V_1.05_11/2011_S3LCE_ALPHA (Nov 3 2011/09.27.24) + TCAN, V_1.00_02/2008_S3FTCAN (Feb 21 2008/17.19.16) + + + + + + +
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+ + + + + + infinite M1000 PRO + 1311007572 + + + MEX, V 1.20 Safire2 MCR (V 1.20 Safire2 MCR ) + MEM, V 1.20 Safire2 MCR (V 1.20 Safire2 MCR ) + FPO, V_1.03_Safire2 FP (V_1.03_Safire2 FP ) + ABS, V 1.00 MCR Abs 4 Channel (V 1.00 MCR Abs 4 Channel) + LUM, V_1.05_02/2015_LUMINESCENCE (Feb 24 2015/17.31.16) + BCR, V_1.00_02/2008_S3FTCAN + INA, V_1.05_11/2011_S3LCE_ALPHA (Nov 3 2011/09.27.24) + INB, V_1.05_11/2011_S3LCE_ALPHA (Nov 3 2011/09.27.24) + TCAN, V_1.00_02/2008_S3FTCAN (Feb 21 2008/17.19.16) + + + + + + +
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+ \ No newline at end of file