SCA for the Eukaryotic Protein Kinases.
*** Summary: This analysis accompanies: Pincus D., Pandey J.P, Creixell P., Resnekov O., Reynolds K.A. Evolution and Engineering of Allsoteric Regulation in Protein Kinases bioRxiv: https://doi.org/10.1101/189761
The analysis was performed using the pySCA toolbox (v.6.3) using a series of shell scripts. This results in a pickle database (*.db) containing the calculations. This database is loaded into a jupyter notebook for further analysis and plotting. To run the analysis completely from scratch, a user will first need to install pySCA (https://github.com/reynoldsk/pySCA, for docs see: http://reynoldsk.github.io/pySCA/) Once this is done, the user runs runAnalysis.sh to reproduce all SCA calculations. The jupyter notebook (SCAKinaseAnalysis_Kss1ERK.ipynb) also describes how to repeat all of the calculations (and generate the accompanying figures) from Pincus et al. Due to some stochasticity in SCA (for example, the alignment is subsampled for computational efficiency) the results may vary slightly from run to run.
To ensure exact reproducibility of the results in Pincus et al, we also provide .db files (pickle database files) of our calculated results. These results can be found in Outputs_1703/. This permits a user to re-run the analysis in the ipython notebook (and interact with the data) without installing SCA or running the calculations from scratch (runAnalysis.sh is not needed in this case).
If the user wishes to view the results (but not interact with them/modify plots), we supply an html version of the notebook (SCAKinaseAnalysis_Kss1ERK.html)
Inputs: Directory containing input alignments for analysis LICENSE: BSD 3-Clause License File Matlab_SurfDist: Directory containing Matlab scripts necessary to compute surface accessible positions contacting the sector or conserved positions. These results are pre-calculated (and can be found in RefTxtFiles); the are read in by the jupyter notebook when needed. Outputs: Contains the outputs of running the SCA analysis (the pickle *.db files). (runAnalysis.sh must be run first, until then this is an empty directory) Outputs_1703: Contains the outputs from the SCA analysis described in Pincus et al. Enables exact reproduction of the results in the paper. Plots: Contains plotted outputs of the jupyter notebook. To write plots here, you will need to uncomment the plt.savefig() commands in the jupyter notebook. Pymol: Contains pymol scripts for color coding the ERK2 data on the structure (generated by the jupyter notebook) README.md: this file RefTxtFiles: Assorted text files that record useful indices and lists of positions, these are used as inputs for SCAKinaseAnalysis_Kss2ERK.ipynb Refpos: Lists of reference positions for each kinase. These are used to map between homologous positions in the kinase alignment. SCAKinaseAnalaysis_Kss1ERK.html: html version of the jupyter notebook, for viewing the data in a web browser (without running the code) SCAKinaseAnalysis_Kss1ERK.ipynb: jupyter notebook, the starting place for this analysis runAnalysis.sh: a shell script that runs all the SCA calculations (for two kinase alignments, and with a range of cutoffs) scaMakeATS.py: a script that assists in mapping positions from one kinase structure to another homolog (ATS = alignment-to-structure) scaTools.py: the sca toolbox, functions from this are used in the jupyter notebook