diff --git a/tutorials/AlphaFold2/AlphaFold2.ipynb b/tutorials/AlphaFold2/AlphaFold2.ipynb new file mode 100644 index 0000000..8610db0 --- /dev/null +++ b/tutorials/AlphaFold2/AlphaFold2.ipynb @@ -0,0 +1,2174 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "tags": [], + "user_expressions": [] + }, + "source": [ + "## Notebooks Hub AlphaFold2 Tutorial Using Jupyter Widgets\n", + "This is an adapted notebook of the original [ColabFold AlphaFold2 notebook](https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/AlphaFold2.ipynb) to serve as an example for Notebooks Hub using interactive Jupyter widgets." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [], + "user_expressions": [] + }, + "source": [ + "Here are some helpful links to learn more about using AlphaFold and its impact in computational biology. \n", + "  \\- AlphaFold Protein Structure Database [FAQ](https://alphafold.ebi.ac.uk/faw) \n", + "  \\- neurosnap.ai's guides [Part 1](https://neurosnap.ai/blog/post/641a34a1148354cbab382afe) [Part 2](https://neurosnap.ai/blog/post/64222437a55063d26e9c069e) [Part 3](https://neurosnap.ai/blog/post/6422432aa55063d26e9c06a1) \n", + "  \\- Jumper et al (2021). Highly accurate protein structure prediction with AlphaFold. [doi: 10.1038/s41586-021-03819-2](https://doi.org/10.1038/s41586-021-03819-2) \n", + "  \\- Mirdita et al (2022). ColabFold: Making protein folding accessible to all. [doi: 10.1038/s41592-022-01488-1](https://doi.org/10.1038/s41592-022-01488-1) \n", + "  \\- Bertoline et al (2023). Before and after AlphaFold2: An overview of protein structure prediction. [doi: 10.3389/fbinf.2023.1120370](https://doi.org/10.3389/fbinf.2023.1120370) \n", + "  \\- Fang et al (2023). A method for multiple-sequence-alignment-free protein structure prediction using a protein language model. [doi: 10.1038/s42256-023-00721-6](https://doi.org/10.1038/s42256-023-00721-6) \n", + "\n", + "To learn more about Notebooks Hub or Jupyter Widgets, check out their documentation [here](https://polusai.github.io/notebooks-hub/) and [here](https://ipywidgets.readthedocs.io/en/8.1.2/index.html), respectively." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [], + "user_expressions": [] + }, + "source": [ + "### Setup\n", + "Import appropriate packages into your program to get started. These are necessary to run the AlphaFold predictions later on." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import os\n", + "import re\n", + "import hashlib\n", + "import random\n", + "import shutil\n", + "\n", + "from sys import version_info\n", + "python_version = f\"{version_info.major}.{version_info.minor}\"" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "The following packages will enable the build of interactive widgets to provide a better user experience. More information on building interactive widgets can be found in the [Jupyter Widgets documentation](https://ipywidgets.readthedocs.io/en/8.1.2/index.html)." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from ipywidgets import interact, interactive, fixed, interact_manual, FileUpload, GridBox, Layout, VBox\n", + "import ipywidgets as widgets\n", + "from IPython.display import display, HTML\n", + "from ipyfilechooser import FileChooser" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [], + "user_expressions": [] + }, + "source": [ + "### Generate interactive widgets for query session inputs\n", + "An interactive widget will help the user select the necessary inputs that are required to run the analysis. Examples of additional styles and formats can be found in the [documentation's](https://ipywidgets.readthedocs.io/en/8.1.2/index.html) [widget list](https://ipywidgets.readthedocs.io/en/8.1.2/examples/Widget%20List.html). \n", + "1. The core parameters to define before running AlphaFold predictions include the **query sequence(s)**, **Amber relaxed model(s)**, and **template(s)**, as well as **jobname** to keep track of each query session. \n", + "2. Set up individual widget types for each prediction parameter. These are widget *children* that will be grouped into a *family* in the next step. For visual convenience, a border will be added to outline each family container." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "The `.HTML` widget type will display text that can be used as headers or descriptions. These strings can be formatted using html tags (e.g., ``). The `grid_area` attribute will help with widget placement inside the family container." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "html_input = widgets.HTML(description=\"Input Protein Sequences:\",\n", + " value=\"\",\n", + " style={'description_width': 'initial'},\n", + " layout=Layout(width='auto', grid_area='html_input'))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "Variables can be assigned values (or, as shown below, concatenated f-strings for large blocks of text) that can then be used to more easily attribute values inside each widget." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "qtips = (f\"Helpful Tips
\"\n", + " f\"Query: Use `:` to specify inter-protein chainbreaks for modeling complexes (supports homo- and hetro-oligomers). \"\n", + " f\"For example `PI...SK:PI...SK` for a homodimer.
\"\n", + " f\"Template Mode: Select the desired template to run predictions against.
\"\n", + " f\"Amber Relaxes: Specify how many of the top ranked structures to relax using Amber.
\"\n", + " f\"Amber` is a suite of programs that apply AMBER forcefields to simulations of biomolecules and molecular dynamics. \"\n", + " f\"Amber relaxed models relax acid side chain positions and are usually required for users who need accurate side-chain positions.\"\n", + " )\n", + "\n", + "html_qtips = widgets.HTML(description=\"\",\n", + " value=qtips,\n", + " style={'description_width': 'initial'},\n", + " layout=Layout(width='auto', grid_area='html_qtips'))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "The `.Text` widget type will provide a box that allows the user to input text strings while `.Textarea` will provide an adjustable box. In this case, the adjustable box was selected for **query sequence**. This will be useful if the query is long or complex, because the user can view the input query in its entirety. If specified, the attribute `placeholder` will be visible in the text box when empty. An initial value can be assigned to the `value` attribute to pre-populate the box." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "text_queryseq = widgets.Textarea(value='PIAQIHILEGRSDEQKETLIREVSEAISRSLDAPLTSVRVIITEMAKGHFGIGGELASK',\n", + " placeholder='Input Amino Acid Sequence for Protein of Interest',\n", + " description='Query Sequence:',\n", + " disabled=False,\n", + " style={'description_width': '170px'},\n", + " layout=Layout(width='700px', grid_area='text_queryseq'))\n", + "\n", + "text_jobname = widgets.Text(value='test',\n", + " placeholder='Type Jobname',\n", + " description='Jobname:',\n", + " disabled=False,\n", + " style={'description_width': '170px'},\n", + " layout=Layout(width='auto', grid_area='text_jobname'))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "The next two widgets need to offer pre-determined options for user selection. These options can be defined for each necessary parameters using the `options` attribute within each appropriate individual. The default selection within these options can be specified using the `value` attribute. \n", + "\n", + "The `RadioButtons` widget type will list the possible options with buttons for a single selection." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "buttons_num_relax = widgets.RadioButtons(options=[('0',0),('1',1),('5',5)],\n", + " value=0,\n", + " description='Number of Amber Relaxes:',\n", + " disabled=False,\n", + " style={'description_width': '170px'},\n", + " layout=Layout(width='auto', grid_area='buttons_num_relax'))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "The `.ToggleButtons` widget will display buttons that allow the user to make one choice of the given options. This widget type is very helpful, because the attribute `tooltips` enables descriptions for each option upon hovering over with the mouse pointer." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "buttons_template_mode = widgets.ToggleButtons(options=['none','pdb100','custom'],\n", + " description='Template Mode:',\n", + " disabled=False,\n", + " button_style='',\n", + " style={'description_width': '170px'},\n", + " layout=Layout(width='auto', grid_area='buttons_template_mode'),\n", + " tooltips=['no template information is used',\n", + " 'detect templates in pdb100',\n", + " 'upload and search own templates (PDB or mmCIF format, see notes) to bias AlphaFold\\'s predictions'])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "Now, set up the widget family. Jupyter's `Box` widgets utilize the CSS [flexbox spec](https://css-tricks.com/snippets/css/a-guide-to-flexbox/) for gathering individual widgets within a container. The family below will use the `GridBox` container that can be customized according to the CSS [Grid layouts](https://css-tricks.com/snippets/css/complete-guide-grid). \n", + "1. Using the `GridBox` container, define its children from the code above.\n", + "2. Lay out children as desired within container. Each child's `Layout.grid_area` attribute will need to have a matching label inside the container's `Layout.grid_template_areas` attribute.\n", + "3. `display()` will display the interactive widget family." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "8577dc777ce547329f08335363ab1bfa", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "GridBox(children=(HTML(value='', description='Input Protein Sequences:', layout=Layout(grid_area='html_…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "controls_query = GridBox(children=[html_input, html_qtips, text_queryseq, text_jobname, buttons_num_relax, buttons_template_mode],\n", + " layout=Layout(\n", + " border='solid 1.5px',\n", + " width='1255px',\n", + " grid_template_rows='auto auto auto auto auto',\n", + " grid_template_columns='300px 450px 500px',\n", + " grid_template_areas='''\n", + " \"html_input html_input html_qtips\"\n", + " \"text_queryseq text_queryseq html_qtips\"\n", + " \"text_jobname . html_qtips\"\n", + " \"buttons_template_mode buttons_template_mode html_qtips\"\n", + " \"buttons_num_relax buttons_num_relax html_qtips\"\n", + " ''')\n", + " )\n", + "\n", + "display(controls_query)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [], + "user_expressions": [] + }, + "source": [ + "### Access widget outputs to generate a new directory for saving prediction results and queries" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "The following code defines functions to help augment jobnames to minimize the risk of files being overwritten if the same sequence was queried multiple times using different parameter values. The functions below will append an underscore and integer at the end of the jobname with each sequential run (e.g., `_0`, `_1`, ...)." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "def add_hash(x,y):\n", + " return x+\"_\"+hashlib.sha1(y.encode()).hexdigest()[:5]\n", + "\n", + "def update_jobname(jobname):\n", + " basejobname = \"\".join(jobname.split())\n", + " basejobname = re.sub(r'\\W+', '', basejobname)\n", + " jobname_new = add_hash(basejobname, query_sequence)\n", + " \n", + " return jobname_new" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "User-defined and selected values from the widget can be accessed through each children's `.value` to save as accessible variables. This is shown in the top portion of the following code block. \n", + "  • With the rest of the code block, the system will then check in the working path for a directory sharing the same jobname. If one does not exist, it will create one. If one does, it will create an iteration (e.g., `_0`, `_1`, ...). \n", + "  • ***Note:*** *these code chunks are required to be in the same cell, otherwise iterative numbering does not work as intended (i.e., recursively appends `_0` instead of increasing in value).*" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "jobname: test_a5e17_3\n", + "sequence: PIAQIHILEGRSDEQKETLIREVSEAISRSLDAPLTSVRVIITEMAKGHFGIGGELASK\n", + "length: 59\n", + "relax: 0\n", + "template: none\n" + ] + } + ], + "source": [ + "# save outputs as accessible variables\n", + "jobname = text_jobname.value\n", + "query_sequence = text_queryseq.value\n", + "num_relax = buttons_num_relax.value\n", + "template_mode = buttons_template_mode.value\n", + "\n", + "use_amber = num_relax > 0\n", + "length = len(query_sequence.replace(\":\",\"\"))\n", + "\n", + "# remove whitespaces and update jobname\n", + "query_sequence = \"\".join(query_sequence.split())\n", + "jobname = update_jobname(jobname)\n", + "\n", + "# check if directory with jobname exists\n", + "def check(folder):\n", + " if os.path.exists(folder):\n", + " return False\n", + " else:\n", + " return True\n", + " \n", + "if not check(jobname):\n", + " n = 0\n", + " while not check(f\"{jobname}_{n}\"): n += 1\n", + " jobname = f\"{jobname}_{n}\"\n", + "\n", + "# make directory to save results\n", + "os.makedirs(jobname, exist_ok=True)\n", + "\n", + "# save a copy of the query sequence in the newly generated folder\n", + "queries_path = os.path.join(jobname, f\"{jobname}.csv\")\n", + "with open(queries_path, \"w\") as text_file:\n", + " text_file.write(f\"id,sequence\\n{jobname},{query_sequence}\")\n", + "\n", + "# for verification purposes, return the session's information.\n", + "print(f\"jobname: {jobname}\" \"\\n\"\n", + " f\"sequence: {query_sequence}\" \"\\n\"\n", + " f\"length: {length}\" \"\\n\"\n", + " f\"relax: {num_relax}\" \"\\n\"\n", + " f\"template: {template_mode}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "### Generate file upload widgets to select custom templates for predictions" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "**Local File Upload:** The `FileUpload` widget allows the user to select local computer files for upload to the current working directory on the server. AlphaFold allows for multiple custom templates, so `multiple=TRUE` was set. *One* specific file extension can be specified inside the attribute `accept=''`. \n", + "  • ***Note:*** *This will replace pre-existing files in the current directory with the same name. Please rename if necessary.* \n", + "  • ***Note:*** *The counter shown will increase despite re-selecting a file. The cell containing `display(upload)` must be rerun to reset the counter.* " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "upload = FileUpload(accept='', multiple=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "**Server File Upload:** The `FileChooser` widget allows the user to select a single file that is already present on the server. Default files can be shown by defining `fc.filter_pattern` with one or more specific extensions. For AlphaFold, templates should be PDB or PDBx/mmCIF format. \n", + "  • **Note:** ipyfilechooser is a separate package that works in conjunction with ipywidgets. \n", + "  • If a file from the server was selected, `os.rename` will move the file into the template folder inside the current query session's directory (i.e., */\\/template/\\*) \n", + "  • If a file from the server was selected, `os.rename` will move the file into the template folder inside the current query session's directory (i.e., */\\/template/\\*) " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "fc = FileChooser(\n", + " os.getcwd(),\n", + " filename='',\n", + " title='Select custom template(s)
Note: must follow four letter PDB naming with lower case letters',\n", + " show_hidden=False,\n", + " select_default=False,\n", + " show_only_dirs=False\n", + " )\n", + "\n", + "fc.filter_pattern = ['*.pdb', '*.pdbx', '*.txt'] " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "The following statements set variables for downstream analysis and will display the upload widgets only if the `template_mode` was set to *custom*. A template folder will also be generated inside the current jobname's directory to store template files. \n", + "  • ***Note:*** *In order to cancel file selection from the server, the previous cell must be rerun.*" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# set variables and display file uploaders if template mode is set to custom\n", + "if template_mode == \"pdb100\":\n", + " use_templates = True\n", + " custom_template_path = None\n", + "elif template_mode == \"custom\":\n", + " custom_template_path = os.path.join(jobname,f\"template\")\n", + " os.makedirs(custom_template_path, exist_ok=True)\n", + " use_templates = True\n", + " display(fc)\n", + " display(upload)\n", + "else:\n", + " custom_template_path = None\n", + " use_templates = False" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "File selected from the server: None\n", + "File(s) selected from the local host for upload: None\n" + ] + } + ], + "source": [ + "# move server file to session's template folder\n", + "if template_mode == \"custom\":\n", + " if fc.selected is not None:\n", + " for fn in fc.selected:\n", + " os.rename(fn,os.path.join(custom_template_path,fn))\n", + "\n", + "# return filenames of all files selected for custom template use.\n", + "if not upload.value:\n", + " fps = \"None\"\n", + "print(f\"File selected from the server: {fc.selected}\")\n", + "print(f\"File(s) selected from the local host for upload: {fps}\")\n", + "\n", + "# upload local files to server and place inside session's template folder\n", + "if upload.value:\n", + " fps = []\n", + " for fp in upload.value:\n", + " fps.append(f\"{fp}\")\n", + " with open(fp, 'wb') as output_file:\n", + " content = upload.value[fp]['content']\n", + " output_file.write(content)\n", + " os.rename(fp,os.path.join(custom_template_path,fp))\n", + " print(f\">> {fp} successfully uploaded\")\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [], + "user_expressions": [] + }, + "source": [ + "### Generate interactive widget for multiple sequence alignment options" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "AlphaFold's AI was trained with multiple sequence alignment (MSA), paired residues, and experimentally validated protein structures from the [RSCB Protein Data Bank (PDB)](https://www.rcsb.org/). AlphaFold2 uses MMseq2 [(Many-against-Many searching)](https://mmseqs.com/latest/userguide.pdf) software to search and cluster huge sequence sets from databases that comprise of UniRef [(UniProt Reference Clusters)](https://www.uniprot.org/help/uniref) and its own novel [environmental database](https://colabfold.mmseqs.com/), referred to as _env_ inside widget options. MSA pairing can also be controlled to improve prediction accuracy for protein complexes. A new family of widgets will be created below for these options." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "Use the `.HTML` widget type to create a descriptive header." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "html_msaopts = widgets.HTML(description=\"Multiple Sequence Alignment Options (custom MSA upload, single sequence, pairing mode)\",\n", + " value=\"\",\n", + " style= {'description_width': 'initial'},\n", + " layout=Layout(width='auto', grid_area='html_msaopts'),)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "The `.Select` widget type will display a box with all possible options for selection by row." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "select_msa_mode = widgets.Select(options=['mmseqs2_uniref_env', 'mmseqs2_uniref', 'single_sequence', 'custom'],\n", + " value='mmseqs2_uniref_env',\n", + " description='MSA mode:',\n", + " rows=5,\n", + " disabled=False,\n", + " layout=Layout(width='auto', grid_area='select_msa_mode'))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "The `.ToggleButtons` type will display options with the helpful description upon hover." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "buttons_pair_mode = widgets.ToggleButtons(options=['unpaired_paired', 'paired', 'unpaired'],\n", + " description='Pair Mode:',\n", + " disabled=False,\n", + " button_style='',\n", + " layout=Layout(width='auto', grid_area='buttons_pair_mode'),\n", + " tooltips=['pair sequences from same species + unpaired MSA',\n", + " 'seperate MSA for each chain',\n", + " 'only use paired sequences'])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "Now, set up the widget family.\n", + "1. Using the `GridBox` container, define its children from the code above.\n", + "2. `display()` will display the interactive widget family." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "7baf411722a34bba9adffdd71e629436", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "GridBox(children=(HTML(value='', description='Multiple Sequence Alignment Options (custom MSA upload, singl…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "controls_msa = GridBox(children=[html_msaopts, select_msa_mode, buttons_pair_mode],\n", + " layout=Layout(\n", + " border='solid 1.5px',\n", + " width='605px',\n", + " grid_template_rows='auto auto',\n", + " grid_template_columns='300px 300px',\n", + " grid_template_areas='''\n", + " \"html_msaopts html_msaopts\"\n", + " \"select_msa_mode buttons_pair_mode\"\n", + " ''')\n", + " )\n", + "\n", + "display(controls_msa)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "Next, save the widget selections as accessible variables." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "msa_mode = select_msa_mode.value\n", + "pair_mode = buttons_pair_mode.value" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "#### Custom MSA file (.a3m formatted)\n", + "##### DISCLAIMER: HAVE NOT TESTED FUNCTIONALITY OF USING CUSTOM A3M FILE AFTER SUCCESSFUL UPLOAD\n", + "Custom MSA allows users to provide their own alignment files for multiple sequence alignment. Any kind of alignment tool can be used to generate the MSA, including the [HHblits Toolkit server](https://toolkit.tuebingen.mpg.de/tools/hhblits)." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "cellView": "form", + "id": "C2_sh2uAonJH", + "tags": [] + }, + "outputs": [], + "source": [ + "# create additional file uploaders to use for custom MSA\n", + "upload_msa = FileUpload(accept='.a3m', multiple=False)\n", + "\n", + "# decide which a3m to use\n", + "if \"mmseqs2\" in msa_mode:\n", + " a3m_file = os.path.join(jobname,f\"{jobname}.a3m\")\n", + "\n", + "elif msa_mode == \"custom\":\n", + " a3m_file = os.path.join(jobname,f\"{jobname}.custom.a3m\")\n", + " if not os.path.isfile(a3m_file):\n", + " print(\"The first FASTA entry of the A3M file must be the query sequence without gaps.\")\n", + " display(upload_msa)\n", + "else:\n", + " a3m_file = os.path.join(jobname,f\"{jobname}.single_sequence.a3m\")\n", + " with open(a3m_file, \"w\") as text_file:\n", + " text_file.write(\">1\\n%s\" % query_sequence)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "The following code cell will save the selected local file to the server and create a renamed copy for the program to access." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "# upload local file to session's folder on server\n", + "if upload_msa.value:\n", + " up_msa = upload_msa.value\n", + " fpmsa = []\n", + " for fn, fd in up_msa.items():\n", + " fpmsa.append(f\"{fn}\")\n", + " with open(fn, 'wb') as output_file:\n", + " content = fd['content']\n", + " output_file.write(content)\n", + " os.rename(fn,os.path.join(jobname,fn))\n", + " print(f\"{fn} successfully uploaded. Don't forget to cite your custom MSA generation method!\")\n", + "\n", + "if upload_msa.value:\n", + " orig_msa = f\"{jobname}/{fpmsa[0]}\"\n", + " custom_msa = shutil.copy2(orig_msa,f\"{jobname}/strip_{fpmsa[0]}\") # copy file as backup or for preservation purposes\n", + "\n", + " header = 0\n", + " import fileinput\n", + " for line in fileinput.FileInput(custom_msa,inplace=True):\n", + " if line.startswith(\">\"):\n", + " header = header + 1\n", + " if not line.rstrip():\n", + " continue\n", + " if line.startswith(\">\") == False and header == 1:\n", + " query_sequence = line.rstrip()\n", + " print(line, end='')\n", + " \n", + " os.rename(custom_msa, a3m_file)\n", + " queries_path=a3m_file\n", + " print(f\"Moving {custom_msa} to {a3m_file} for use by AlphaFold.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "### Advanced Settings" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "Create the widget header and a tips box using the `.HTML` widget type." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "html_advset = widgets.HTML(description=\"Advanced Settings:\",\n", + " value=\"\",\n", + " style={'description_width': 'initial'},\n", + " layout=Layout(width='auto', grid_area='html_advset'))\n", + "\n", + "advtips = (f\"Helpful Tips
\"\n", + " f\"Model Type: Choose the structural dimentions for prediction (i.e., oligomeric or multimeric). For monomer predictions, choose alphafold2-ptm. \"\n", + " f\"Auto permits the model to decide and will use alphafold2_multimer_v3 for complex prediction.
\"\n", + " f\"Number of Recycles: Enables multiple reiterations through the sequence by building off its own predictions. \"\n", + " f\"The default is 3, but 6+ will enable a more accurate prediction despite longer runtimes.
\"\n", + " f\"Recycle Early Stop Tolerance: Auto tolerance will be 0.0, unless using alphafold2_multimer_v3.
\"\n", + " f\"Alphafold2_multimer_v3: For complex predictions using this model, `auto` will result in recycles = 20 and tolerance = 0.05.\"\n", + " )\n", + "\n", + "html_advtips = widgets.HTML(description=\"\",\n", + " value=advtips,\n", + " style={'description_width': 'initial'},\n", + " layout=Layout(width='auto', grid_area='html_advtips'))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "The `.Dropdown` widget type will display a single selection list in dropdown format. Create dropdown widgets to select AlphaFold **model types**, **number of recycles**, and **recycle early stop tolerance** values." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "drop_model_type = widgets.Dropdown(options=['auto','alphafold2_ptm','alphafold2_multimer_v1','alphafold2_multimer_v2','alphafold2_multimer_v3'],\n", + " value='auto',\n", + " description='Model Type:',\n", + " disabled=False,\n", + " style={'description_width': '170px'},\n", + " layout=Layout(width='auto', grid_area='drop_model_type'))\n", + "\n", + "drop_num_recycles = widgets.Dropdown(options=[('auto','auto'),('0',0),('1',1),('3',3),('6',6),('12',12),('24',24),('48',48)],\n", + " value='auto',\n", + " description='Number of Recycles:',\n", + " disabled=False,\n", + " style={'description_width': '170px'},\n", + " layout=Layout(width='auto', grid_area='drop_num_recycles'))\n", + "\n", + "drop_tol = widgets.Dropdown(options=[('auto','auto'),('0.0',0.0),('0.5',0.5),('1.0',1.0)],\n", + " value='auto',\n", + " description='Recycle Early Stop Tolerance:',\n", + " disabled=False,\n", + " style={'description_width': '170px'},\n", + " layout=Layout(width='auto', grid_area='drop_tol'))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "Use `.ToggleButtons` to create toggle buttons to select **pairing strategy** and have descriptions display upon hovering over each button." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "buttons_pairing_strategy = widgets.ToggleButtons(options=['greedy','complete'],\n", + " description='Pairing Strategy:',\n", + " disabled=False,\n", + " button_style='',\n", + " style={'description_width': '170px'},\n", + " layout=Layout(width='auto', grid_area='buttons_pairing_strategy'),\n", + " tooltips=['pair any taxonomically matching subsets',\n", + " ' all sequences have to match in one line'])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "Now, set up the widget family, once again using `GridBox` and `display()`." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "cca6239d6f5c4c3cbeb3a9b35dc0fc3a", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "GridBox(children=(HTML(value='', description='Advanced Settings:', layout=Layout(grid_area='html_advset…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "controls_advset = GridBox(children=[html_advset, html_advtips, drop_model_type, drop_num_recycles, drop_tol, buttons_pairing_strategy],\n", + " layout=Layout(\n", + " border='solid 1.5px',\n", + " width='1305px',\n", + " grid_template_rows='auto auto auto auto auto',\n", + " grid_template_columns='350px 200px 750px',\n", + " grid_template_areas='''\n", + " \"html_advset html_advset html_advtips\"\n", + " \"drop_model_type . html_advtips\"\n", + " \"drop_num_recycles . html_advtips\"\n", + " \"drop_tol . html_advtips\"\n", + " \"buttons_pairing_strategy buttons_pairing_strategy html_advtips\"\n", + " ''')\n", + " )\n", + "\n", + "display(controls_advset)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "Lastly, save the widget selections as accessible variables." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "model_type = drop_model_type.value\n", + "pairing_strategy = buttons_pairing_strategy.value\n", + "\n", + "if drop_model_type.value != 'alphafold2_multimer_v3':\n", + " if drop_num_recycles.value == 'auto':\n", + " num_recycles = 3\n", + " else:\n", + " num_recycles = drop_num_recycles.value\n", + " \n", + " if drop_tol.value == 'auto':\n", + " recycle_early_stop_tolerance = 0.0\n", + " else:\n", + " recycle_early_stop_tolerance = drop_tol.value\n", + "\n", + "elif drop_model_type.value == 'alphafold2_multimer_v3':\n", + " if drop_num_recycles.value == 'auto':\n", + " num_recycles = 20\n", + " else:\n", + " num_recycles = drop_num_recycles.value\n", + "\n", + " if drop_tol.value == 'auto':\n", + " recycle_early_stop_tolerance = 0.5\n", + " else:\n", + " recycle_early_stop_tolerance = drop_tol.value" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "### Generate interactive widget to define sample settings" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "`.HTML` widgets can be used again to add headers as well as additional text to provide setting tips." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "user_expressions": [] + }, + "outputs": [], + "source": [ + "html_sampset = widgets.HTML(description=\"Sample Settings:\",\n", + " value=\"\",\n", + " style= {'description_width': 'initial'})\n", + "\n", + "msatips = (f\"Helpful Tips
\"\n", + " f\"- Decrease Max MSA to increase uncertainty.
\"\n", + " f\"- Enable dropouts and increase # seeds to sample predictions from uncertainty of the model.\"\n", + " )\n", + "\n", + "html_msatips = widgets.HTML(description=\"\",\n", + " value=msatips,\n", + " style={'description_width': 'initial'},\n", + " layout=Layout(width='auto', grid_area='html_msatips'))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "A `.SelectionSlider` widget will be used alongside the standard `.Dropdown` type used previously. The selection slider offers a range of custom values without conforming to a uniform increment." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "drop_max_msa = widgets.Dropdown(options=['auto','512:1024','256:512','64:128','32:64', '16:32'],\n", + " value='auto',\n", + " description='Max MSA:',\n", + " disabled=False)\n", + "\n", + "slider_num_seeds = widgets.SelectionSlider(options=[('1',1),('2',2),('4',4),('8',8),('16',16)],\n", + " value=1,\n", + " description='# seeds:',\n", + " disabled=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "Here, a `.Checkbox` widget type that can be selected or unselected is introduced." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "cb_dropout = widgets.Checkbox(value=False, description='Use Dropout', disabled=False, indent=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [], + "user_expressions": [] + }, + "source": [ + "Now, set up the widget family, once again using `GridBox` and `display()`." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "65d0fc31364e4abba96f20175e67a29e", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "GridBox(children=(HTML(value='', description='Sample Settings:', style=DescriptionStyle(description_wid…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "controls_sampset = GridBox(children=[html_sampset, html_msatips, drop_max_msa, slider_num_seeds, cb_dropout],\n", + " layout=Layout(\n", + " border='solid 1.5px',\n", + " width='805px',\n", + " grid_template_rows='auto auto auto auto auto',\n", + " grid_template_columns='400px 400px',\n", + " grid_template_areas='''\n", + " \"h_sampset html_msatips\"\n", + " \"drop_max_msa html_msatips\"\n", + " \"slider_num_seeds html_msatips\"\n", + " \"cb_dropout .\"\n", + " ''')\n", + " )\n", + "\n", + "display(controls_sampset)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "Save the widget selections as accessible variables. These will also be used to assign other values as depicted in the bottom half of the code cell." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "max_msa = drop_max_msa.value\n", + "num_seeds = slider_num_seeds.value\n", + "use_dropout = cb_dropout.value\n", + "\n", + "num_recycles = None if num_recycles == \"auto\" else int(num_recycles)\n", + "recycle_early_stop_tolerance = None if recycle_early_stop_tolerance == \"auto\" else float(recycle_early_stop_tolerance)\n", + "if max_msa == \"auto\": max_msa = None" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "### Generate interactive widget to toggle save settings" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "The `.IntText` widget can be used to allow the user to specify any integer inside its given text box." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "text_save_dpi = widgets.IntText(value=200,\n", + " description='dpi:',\n", + " disabled=False,\n", + " layout=Layout(width='auto', grid_area='text_save_dpi'))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [], + "user_expressions": [] + }, + "source": [ + "Set up individual `.HTML` widgets to display text and `.Checkboxes` for toggles." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "html_saveset = widgets.HTML(description=\"Save Settings:\",\n", + " value=\"\",\n", + " style= {'description_width': 'initial'},\n", + " layout=Layout(width='auto', grid_area='html_saveset'))\n", + "\n", + "html_savedpi = widgets.HTML(description=\"Set dpi for image resolution:\",\n", + " value=\"\",\n", + " style= {'description_width': 'initial'},\n", + " layout=Layout(width='auto', grid_area='html_savedpi'))\n", + "\n", + "cb_savefull = widgets.Checkbox(value=False,description='Save All', disabled=False, layout=Layout(width='auto', grid_area='cb_savefull'))\n", + "\n", + "cb_saverecyc = widgets.Checkbox(value=False, description='Save Recycles', disabled=False, layout=Layout(width='auto', grid_area='cb_saverecyc'))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "Set up the widget family, once again using `GridBox` and `display()`. This time columns are also utilized. For proper layout assignment into the family container, `grid_area` was defined for each widget child." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "d23d5928e4b34ba9a7093ab87e156f7d", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "GridBox(children=(HTML(value='', description='Save Settings:', layout=Layout(grid_area='html_saveset', …" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "controls_saveset = GridBox(children=[html_saveset, html_savedpi, text_save_dpi, cb_savefull, cb_saverecyc],\n", + " layout=Layout(\n", + " border='solid 1.5px',\n", + " width='455px',\n", + " grid_template_rows='auto auto auto auto',\n", + " grid_template_columns='150px 300px',\n", + " grid_template_areas='''\n", + " \"html_saveset .\"\n", + " \"html_savedpi text_save_dpi\"\n", + " \". cb_savefull\"\n", + " \". cb_saverecyc\"\n", + " ''')\n", + " )\n", + "\n", + "display(controls_saveset)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "Save the widget selections as accessible variables. " + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "save_all = cb_savefull.value\n", + "save_recycles = cb_saverecyc.value\n", + "dpi = text_save_dpi.value" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [], + "user_expressions": [] + }, + "source": [ + "### Prepare prediction run" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "Use a simple `.Checkbox` widget to allow the user to toggle whether or not images should be displayed during the prediction run." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "6a2fdce0120147198404fc82a7ac1be7", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Checkbox(value=True, description='Display Images', layout=Layout(border='solid 1.5px'))" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cb_displayimg = widgets.Checkbox(value=True, description='Display Images', disabled=False, indent=True, layout=Layout(border='solid 1.5px'))\n", + "display(cb_displayimg)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "Assign the selection as an accessible variable." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "display_images = cb_displayimg" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "The following package imports are necessary to finally run the structural predictions." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "import warnings\n", + "warnings.simplefilter(action='ignore', category=FutureWarning)\n", + "from Bio import BiopythonDeprecationWarning\n", + "#warnings.simplefilter(action='ignore', category=BiopythonDeprecationWarning)\n", + "from pathlib import Path\n", + "from colabfold.download import download_alphafold_params, default_data_dir\n", + "from colabfold.utils import setup_logging\n", + "from colabfold.batch import get_queries, run, set_model_type\n", + "from colabfold.plot import plot_msa_v2\n", + "\n", + "from colabfold.colabfold import plot_protein\n", + "#from colabfold.cf import plot_protein\n", + "from pathlib import Path\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import os\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "Add system paths to the new dependencies that were installed previously." + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "# pdbfixer 1.8.1\n", + "if use_amber and f\"/opt/conda/pkgs/pdbfixer-1.8.1-pyh6c4a22f_0/site-packages/\" not in sys.path:\n", + " sys.path.insert(0, f\"/opt/conda/pkgs/pdbfixer-1.8.1-pyh6c4a22f_0/site-packages/\")\n", + "\n", + "# openmm 7.7.0\n", + "if use_amber and f\"/opt/conda/pkgs/openmm-7.7.0-py39h15fbce5_1/lib/python3.9/site-packages\" not in sys.path:\n", + " sys.path.insert(0, f\"/opt/conda/pkgs/openmm-7.7.0-py39h15fbce5_1/lib/python3.9/site-packages\")\n", + "\n", + "# kalign2 2.0.4\n", + "if use_templates and f\"/opt/conda/pkgs/kalign2-2.04-h031d066_5/bin\" not in sys.path:\n", + " sys.path.insert(0, f\"/opt/conda/pkgs/kalign2-2.04-h031d066_5/bin\")\n", + "\n", + "# hhsuite 3.3.0\n", + "if use_templates and f\"/opt/conda/pkgs/hhsuite-3.3.0-py39pl5321he10ea66_9/bin\" not in sys.path:\n", + " sys.path.insert(0, f\"/opt/conda/pkgs/hhsuite-3.3.0-py39pl5321he10ea66_9/bin\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "Additionally, the following cell defines a few necessary functions for ColabFold/AlphaFold." + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "def input_features_callback(input_features):\n", + " if display_images:\n", + " plot_msa_v2(input_features)\n", + " plt.show()\n", + " plt.close()\n", + "\n", + "def prediction_callback(protein_obj, length,\n", + " prediction_result, input_features, mode):\n", + " model_name, relaxed = mode\n", + " if not relaxed:\n", + " if display_images:\n", + " fig = plot_protein(protein_obj, Ls=length, dpi=150)\n", + " plt.show()\n", + " plt.close()\n", + "\n", + "result_dir = jobname\n", + "log_filename = os.path.join(jobname,\"log.txt\")\n", + "if not os.path.isfile(log_filename) or 'logging_setup' not in globals():\n", + " setup_logging(Path(log_filename))\n", + " logging_setup = True\n", + "\n", + "queries, is_complex = get_queries(queries_path)\n", + "model_type = set_model_type(is_complex, model_type)\n", + "\n", + "if \"multimer\" in model_type and max_msa is not None:\n", + " use_cluster_profile = False\n", + "else:\n", + " use_cluster_profile = True" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "### Run AlphaFold2 predictions" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "**Note:** IF USING AMBER RELAXATION: User may receive the following error during pLDDT reranking and may be unable to continue forward. Adding `run_relax=false` somewhere inside may help with this issue [see here](https://github.com/google-deepmind/alphafold/issues/112). \n", + "> ValueError: Minimization failed after 100 attempts." + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "cellView": "form", + "collapsed": true, + "id": "mbaIO9pWjaN0", + "jupyter": { + "outputs_hidden": true + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2023-12-05 20:28:49,371 Unable to initialize backend 'cuda': module 'jaxlib.xla_extension' has no attribute 'GpuAllocatorConfig'\n", + "2023-12-05 20:28:49,374 Unable to initialize backend 'rocm': module 'jaxlib.xla_extension' has no attribute 'GpuAllocatorConfig'\n", + "2023-12-05 20:28:49,379 Unable to initialize backend 'tpu': INVALID_ARGUMENT: TpuPlatform is not available.\n", + "2023-12-05 20:28:49,383 No GPU/TPU found, falling back to CPU. (Set TF_CPP_MIN_LOG_LEVEL=0 and rerun for more info.)\n", + "2023-12-05 20:28:49,383 WARNING: no GPU detected, will be using CPU\n", + "2023-12-05 20:28:59,023 Found 4 citations for tools or databases\n", + "2023-12-05 20:28:59,024 Query 1/1: test_a5e17_3 (length 59)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "COMPLETE: 100%|██████████| 150/150 [elapsed: 00:02 remaining: 00:00]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2023-12-05 20:29:02,320 Setting max_seq=512, max_extra_seq=5120\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-12-05 20:29:15.291663: W external/local_tsl/tsl/framework/cpu_allocator_impl.cc:83] Allocation of 43410312 exceeds 10% of free system memory.\n", + "2023-12-05 20:29:15.297120: W external/local_tsl/tsl/framework/cpu_allocator_impl.cc:83] Allocation of 42604372 exceeds 10% of free system memory.\n", + "2023-12-05 20:29:15.920536: W external/local_tsl/tsl/framework/cpu_allocator_impl.cc:83] Allocation of 42604372 exceeds 10% of free system memory.\n", + "2023-12-05 20:29:16.028863: W external/local_tsl/tsl/framework/cpu_allocator_impl.cc:83] Allocation of 42604372 exceeds 10% of free system memory.\n", + "2023-12-05 20:29:16.135384: W external/local_tsl/tsl/framework/cpu_allocator_impl.cc:83] Allocation of 42604372 exceeds 10% of free system memory.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2023-12-05 20:32:33,551 alphafold2_ptm_model_1_seed_000 recycle=0 pLDDT=96.6 pTM=0.754\n", + "2023-12-05 20:35:27,454 alphafold2_ptm_model_1_seed_000 recycle=1 pLDDT=96.5 pTM=0.758 tol=0.233\n", + "2023-12-05 20:38:21,705 alphafold2_ptm_model_1_seed_000 recycle=2 pLDDT=96.4 pTM=0.757 tol=0.0374\n", + "2023-12-05 20:41:15,995 alphafold2_ptm_model_1_seed_000 recycle=3 pLDDT=96.1 pTM=0.756 tol=0.0339\n", + "2023-12-05 20:41:15,997 alphafold2_ptm_model_1_seed_000 took 719.7s (3 recycles)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2023-12-05 20:44:09,965 alphafold2_ptm_model_2_seed_000 recycle=0 pLDDT=96.9 pTM=0.76\n", + "2023-12-05 20:47:03,429 alphafold2_ptm_model_2_seed_000 recycle=1 pLDDT=96.9 pTM=0.765 tol=0.284\n", + "2023-12-05 20:49:57,554 alphafold2_ptm_model_2_seed_000 recycle=2 pLDDT=96.9 pTM=0.766 tol=0.124\n", + "2023-12-05 20:52:50,860 alphafold2_ptm_model_2_seed_000 recycle=3 pLDDT=96.8 pTM=0.767 tol=0.0565\n", + "2023-12-05 20:52:50,862 alphafold2_ptm_model_2_seed_000 took 694.4s (3 recycles)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2023-12-05 20:55:44,737 alphafold2_ptm_model_3_seed_000 recycle=0 pLDDT=97.2 pTM=0.775\n", + "2023-12-05 20:58:38,388 alphafold2_ptm_model_3_seed_000 recycle=1 pLDDT=97.4 pTM=0.783 tol=0.273\n", + "2023-12-05 21:01:32,093 alphafold2_ptm_model_3_seed_000 recycle=2 pLDDT=97.4 pTM=0.782 tol=0.116\n", + "2023-12-05 21:04:25,697 alphafold2_ptm_model_3_seed_000 recycle=3 pLDDT=97.4 pTM=0.784 tol=0.0477\n", + "2023-12-05 21:04:25,699 alphafold2_ptm_model_3_seed_000 took 694.7s (3 recycles)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2023-12-05 21:07:19,449 alphafold2_ptm_model_4_seed_000 recycle=0 pLDDT=97.4 pTM=0.774\n", + "2023-12-05 21:10:12,890 alphafold2_ptm_model_4_seed_000 recycle=1 pLDDT=97.4 pTM=0.781 tol=0.302\n", + "2023-12-05 21:13:06,725 alphafold2_ptm_model_4_seed_000 recycle=2 pLDDT=97.2 pTM=0.777 tol=0.0837\n", + "2023-12-05 21:16:00,285 alphafold2_ptm_model_4_seed_000 recycle=3 pLDDT=96.9 pTM=0.777 tol=0.033\n", + "2023-12-05 21:16:00,287 alphafold2_ptm_model_4_seed_000 took 694.5s (3 recycles)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2023-12-05 21:18:54,305 alphafold2_ptm_model_5_seed_000 recycle=0 pLDDT=97.4 pTM=0.784\n", + "2023-12-05 21:21:47,797 alphafold2_ptm_model_5_seed_000 recycle=1 pLDDT=96.9 pTM=0.784 tol=0.248\n", + "2023-12-05 21:24:41,418 alphafold2_ptm_model_5_seed_000 recycle=2 pLDDT=96.3 pTM=0.776 tol=0.188\n", + "2023-12-05 21:27:35,677 alphafold2_ptm_model_5_seed_000 recycle=3 pLDDT=96.3 pTM=0.778 tol=0.0931\n", + "2023-12-05 21:27:35,679 alphafold2_ptm_model_5_seed_000 took 695.2s (3 recycles)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2023-12-05 21:27:35,815 reranking models by 'plddt' metric\n", + "2023-12-05 21:27:35,816 rank_001_alphafold2_ptm_model_3_seed_000 pLDDT=97.4 pTM=0.784\n", + "2023-12-05 21:27:35,828 rank_002_alphafold2_ptm_model_4_seed_000 pLDDT=96.9 pTM=0.777\n", + "2023-12-05 21:27:35,837 rank_003_alphafold2_ptm_model_2_seed_000 pLDDT=96.8 pTM=0.767\n", + "2023-12-05 21:27:35,846 rank_004_alphafold2_ptm_model_5_seed_000 pLDDT=96.3 pTM=0.778\n", + "2023-12-05 21:27:35,852 rank_005_alphafold2_ptm_model_1_seed_000 pLDDT=96.1 pTM=0.756\n", + "2023-12-05 21:27:38,005 Done\n" + ] + } + ], + "source": [ + "download_alphafold_params(model_type, Path(\".\"))\n", + "results = run(\n", + " queries=queries,\n", + " result_dir=result_dir,\n", + " use_templates=use_templates,\n", + " custom_template_path=custom_template_path,\n", + " num_relax=num_relax,\n", + " msa_mode=msa_mode,\n", + " model_type=model_type,\n", + " num_models=5,\n", + " num_recycles=num_recycles,\n", + " recycle_early_stop_tolerance=recycle_early_stop_tolerance,\n", + " num_seeds=num_seeds,\n", + " use_dropout=use_dropout,\n", + " model_order=[1,2,3,4,5],\n", + " is_complex=is_complex,\n", + " data_dir=Path(\".\"),\n", + " keep_existing_results=False,\n", + " rank_by=\"auto\",\n", + " pair_mode=pair_mode,\n", + " pairing_strategy=pairing_strategy,\n", + " stop_at_score=float(100),\n", + " prediction_callback=prediction_callback,\n", + " dpi=dpi,\n", + " zip_results=False,\n", + " save_all=save_all,\n", + " max_msa=max_msa,\n", + " use_cluster_profile=use_cluster_profile,\n", + " input_features_callback=input_features_callback,\n", + " save_recycles=save_recycles,\n", + " user_agent=\"colabfold/google-colab-main\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "Once predictions are successfully generated, the results can be saved into the current directory as a zip file." + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'/home/jovyan/work/AF2 preinstall test/test_a5e17_3.result.zip'" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results = f\"{jobname}.result\"\n", + "\n", + "if not check(f\"{results}.zip\"):\n", + " n = 0\n", + " while not check(f\"{results}_{n}.zip\"): n += 1\n", + " results = f\"{results}_{n}\"\n", + " \n", + "shutil.make_archive(results, 'zip', jobname)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "**Result zip file contents** \n", + "1. PDB formatted structures sorted by avg. pLDDT and complexes are sorted by pTMscore. (unrelaxed and relaxed if `use_amber` is enabled).\n", + "2. Plots of the model quality.\n", + "3. Plots of the MSA coverage.\n", + "4. Parameter log file.\n", + "5. A3M formatted input MSA.\n", + "6. A `predicted_aligned_error_v1.json` using [AlphaFold-DB's format](https://alphafold.ebi.ac.uk/faq#faq-7) and a `scores.json` for each model which contains an array (list of lists) for PAE, a list with the average pLDDT and the pTMscore.\n", + "7. BibTeX file with citations for all used tools and databases." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [], + "user_expressions": [] + }, + "source": [ + "### Generate interactive widget to display 3D structure" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [], + "user_expressions": [] + }, + "source": [ + "Import additional packages to enable visualization in 3D. \n", + "**Note:** *`colabfold.colabfold.py` was renamed to `colabfold.cf.py` directly inside the package contents, and `colabfold.colabfold` was renamed to `colabfold.cf` in the next cell and inside `colabfold.batch.py`.*" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "import py3Dmol\n", + "import glob\n", + "import matplotlib.pyplot as plt\n", + "from colabfold.colabfold import plot_plddt_legend\n", + "from colabfold.colabfold import pymol_color_list, alphabet_list" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "Generate an interactive widget to select which ranked prediction to display, which specific color scheme, and whether to show sidechains and/or mainchains. The same widget types used earlier (`.HTML`, `.Dropdown`, `.Select`, `.Checkbox`) will be used for this family. \n", + "\n", + "***Note:*** *this will not update the 3D image in real time, so each time a different selection is selected, the cell containing the following functions will need to be rerun.* \n", + "    `show_pdb(rank_num, show_sidechains, show_mainchains, color).show()` \n", + "    `if color == \"pLDDT\": plot_plddt_legend().show()`" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "html_disp3d = widgets.HTML(description=\"Display 3D Structure:\",\n", + " value=\"\",\n", + " style= {'description_width': 'initial'},\n", + " layout=Layout(width='auto', grid_area='html_disp3d'))\n", + "\n", + "drop_rank_num = widgets.Dropdown(options=[('1',1),('2',2),('3',3),('4',4),('5',5)],\n", + " value=1,\n", + " description='rank_num:',\n", + " disabled=False,\n", + " layout=Layout(width='auto', grid_area='drop_rank_num'))\n", + "\n", + "select_color = widgets.Select(options=['chain','pLDDT','rainbow'],\n", + " value='pLDDT',\n", + " description='Color:',\n", + " rows=3,\n", + " disabled=False,\n", + " layout=Layout(width='auto', grid_area='select_color'))\n", + "\n", + "cb_sidechains = widgets.Checkbox(value=False,\n", + " description='show_sidechains',\n", + " disabled=False,\n", + " indent=True,\n", + " layout=Layout(width='auto', grid_area='cb_sidechains'))\n", + "\n", + "cb_mainchains = widgets.Checkbox(value=False,\n", + " description='show_mainchains',\n", + " disabled=False,\n", + " indent=True,\n", + " layout=Layout(width='auto', grid_area='cb_mainchains'))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "Display widget family." + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "d944ce870f6841fbb48637bf676c96a1", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "GridBox(children=(HTML(value='', description='Display 3D Structure:', layout=Layout(grid_area='html_dis…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "controls_disp3d = GridBox(children=[html_disp3d, drop_rank_num, select_color, cb_sidechains, cb_mainchains],\n", + " layout=Layout(\n", + " border='solid 1.5px',\n", + " grid_template_rows='auto auto',\n", + " grid_template_columns='20% 20% 20%',\n", + " grid_template_areas='''\n", + " \"html_disp3d html_disp3d .\"\n", + " \"drop_rank_num select_color cb_sidechains\"\n", + " \". select_color cb_mainchains\"\n", + " ''')\n", + " )\n", + "\n", + "display(controls_disp3d)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "The following code cell helps apply the proper display settings for the predicted protein structure selected in the widget." + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "cellView": "form", + "id": "KK7X9T44pWb7" + }, + "outputs": [], + "source": [ + "def show_pdb(rank_num=1, show_sidechains=False, show_mainchains=False, color=\"pLDDT\"):\n", + " model_name = f\"rank_{rank_num}\"\n", + " view = py3Dmol.view(js='https://3dmol.org/build/3Dmol.js',)\n", + " #view.addModel(open(pdb_file[0],'r').read(),'pdb')\n", + " view.addModel(open(pdb_file[rank_num -1],'r').read(),'pdb')\n", + "\n", + " if color == \"pLDDT\":\n", + " view.setStyle({'cartoon': {'colorscheme': {'prop':'b','gradient': 'roygb','min':50,'max':90}}})\n", + " elif color == \"rainbow\":\n", + " view.setStyle({'cartoon': {'color':'spectrum'}})\n", + " elif color == \"chain\":\n", + " chains = len(queries[0][1]) + 1 if is_complex else 1\n", + " for n,chain,color in zip(range(chains),alphabet_list,pymol_color_list):\n", + " view.setStyle({'chain':chain},{'cartoon': {'color':color}})\n", + "\n", + " if show_sidechains:\n", + " BB = ['C','O','N']\n", + " view.addStyle({'and':[{'resn':[\"GLY\",\"PRO\"],'invert':True},{'atom':BB,'invert':True}]},\n", + " {'stick':{'colorscheme':f\"WhiteCarbon\",'radius':0.3}})\n", + " view.addStyle({'and':[{'resn':\"GLY\"},{'atom':'CA'}]},\n", + " {'sphere':{'colorscheme':f\"WhiteCarbon\",'radius':0.3}})\n", + " view.addStyle({'and':[{'resn':\"PRO\"},{'atom':['C','O'],'invert':True}]},\n", + " {'stick':{'colorscheme':f\"WhiteCarbon\",'radius':0.3}})\n", + " if show_mainchains:\n", + " BB = ['C','O','N','CA']\n", + " view.addStyle({'atom':BB},{'stick':{'colorscheme':f\"WhiteCarbon\",'radius':0.3}})\n", + "\n", + " view.zoomTo()\n", + " return view" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "Assign widget selections as accessible variables and show 3D protein structure." + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "application/3dmoljs_load.v0": "
\n

You appear to be running in JupyterLab (or JavaScript failed to load for some other reason). You need to install the 3dmol extension:
\n jupyter labextension install jupyterlab_3dmol

\n
\n", + "text/html": [ + "
\n", + "

You appear to be running in JupyterLab (or JavaScript failed to load for some other reason). You need to install the 3dmol extension:
\n", + " jupyter labextension install jupyterlab_3dmol

\n", + "
\n", + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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BpaWl6Mz3vHnz4OvrK/y9du1a4YqNzv1nx9PT00VlAn8fm3TjgNjYWNjY2ODPP/8UldOuXTt8+umn9a5LfW9L8/T0xLJly/TSY2Ji4ODgADMzMxAR/Pz8RFe6HmV/31iGGk/aOKMhbURX95MnTwrLMzMzRW0TqLpyEx4eLvxdWloKIqr1dtua2phOeXk59u7di9GjRwvPd65evVp05dTPzw8BAQG4evUqtFotvv76a0ilUnTo0AFA1W3URIT8/HxR2WPGjEFISIgo7a233kJgYGCN9WmIJnXlpk+fPiSRSIS//fz8KDMzs8EzFQEQlVNbPiKqV14XFxe6cOEC9e7du0F1aew6depEffv2pS+++IKIiC5evEixsbHC2c+UlBT68ssvydraWngFBQVRZWUlZWVlCeVUP/tgYWFB48ePF8o9ffo0nT17liZNmlSvehUXF1N+fj7169dPlN6vXz9KS0sjIqKuXbtSixYtSK1WU2xsLHl7e9OIESNIrVYTUd1n7svKykihUOi1j/DwcAoPD6du3bpRbm4u7dixg4YOHUpSqeGv66JFi6hfv37k7e1NCxYsoPnz59Pq1avr9T7vpzvrfffu3Qava2rS0tLIz89PtG/69etHpaWlwowuuit3ACg2NpZeeOEF8vDwoLi4OFKr1eTs7Ezu7u41bqOsrEzvjFdlZSVJJBL65ptvqHfv3jRs2DD66KOP6KuvvhJdvakPpVJJlZWVJn9mVkfX39aHv78/WVtb06JFi0ir1daZ39C+qr48MjJSdNWmoZRKZZP57tV3X6WlpVHr1q1FVzc6d+5MzZs3F/phoqpZzZo1ayb87eTkZPDMdm3bMTc3px49eghpnTp1Eq4gEVUdi0pLS+mpp54SHY+ysrLo0qVLRquLjqE2V1BQQFOnTqWJEyfSyZMnSa1Wk1wup9GjRzeo/RM1rf7+SRtnNKSNpKenk7m5Ofn4+Ahp7du3Jzs7O7283bt3F/5vZWVFNjY2tba9uvo1c3Nzev755+mHH36grKwscnR0pHnz5tEHH3wg5Pn6668JALm4uJBCoaCPP/6YXn755RrHK7UxZh/YpIIbYygsLKQ//viD2rRpU2deXedbn7xNWWhoKP34449UUlJC27Zto3bt2lFAQAAREZWWltK0adMoOTlZeKWkpFBmZia1a9dOKMPQrQ2vvvoqHT58mPLy8mjbtm00aNAgUqlURqu3RCKhAQMGUExMjBDIdO/ene7du0dnz56l48ePC+/DEHt7e7p79y799ddfovTw8HB6++23KTExkTp06EAzZsxo0O0qvr6+lJeXJwxqHR0d6dq1a6I8165dIxsbG9FtPLrL1y1btqz3tpqywMBAiouLo5SUFJLJZNSpUycKDAwU2kNt+56oav/fvHlTlObk5EQuLi6i2w49PDwIgBBU1bQ/HR0dRWlFRUVkZWWld6uWqWnfvj1JJBLRYPd+aWlpZGdnJ2rX3bp1o+joaDp69CiNHTu2zgDH0L66365du+ju3bs0YcIEUXpN+0q37H5FRUVN5rvn7u5OEomELly4YJTydLdk6UgkEqqsrDRK2TqlpaXk5OQkOhYlJydTeno6zZs3z+h1MdTmNm7cSLa2trRq1Sry9vamAQMG0I4dOyg6Olo4RnB/b9iTNM74p9prQ8utq18DQMeOHaOpU6eSh4cHXbx4kRYvXkyzZ88W8rRr147UajWVlpZSbm4unThxgsrLy6lt27ZE9Hc/V99jlrHaY5MKbqoPEH///Xdyd3cnMzOzepexfv16kkqlNGrUqFrzVVZW0scff0xt2rQhb2/vB6lukxESEkJSqZQiIyNp+/btNGXKFOGMuY+PD50/f57at2+v95LL5bWW261bN+rZsydt2bKFIiMjacqUKfWuk42NDTk7O1N8fLwoPT4+njp37iz8rTt7HxMTQ4GBgSSVSmnAgAG0evVqunfvnt6Vn/vp5ro/f/68KL19+/b0wQcfUE5ODkVGRtLNmzdp4MCB1KFDB1q+fLnoTJIhycnJZGdnRwqFgoiqrlBGR0eL8hw+fJj8/PxEaWfPniVXV1eyt7evtfymwMPDgxISEkRnQ+Pj46lZs2bk6upKRH8/d7N27VrhIKkLbnTtoTbe3t56+75fv36Un59PpaWlQlpGRgZJpVJhuw3Zn02h73nqqafomWeeoU2bNuld3SooKKBvvvmGxo4dq3eF1MvLi6Kjo+nYsWMUEhJC5eXlNW7D29ubCgoKahwIbN26lYKDg/UOzH5+fnTmzBnR2dPDhw+TjY2NqB+5dOkS/fnnn01ifxERtWjRgoKCgmjjxo10584dveW3bt0ioqrvYW5uLuXm5grLzp8/T7du3RJ9fg+rU6dOpNVqRc8VpKenC/UgqjoWFRQUkLm5ud6xqCF9plwur9fdIob6h7t37+qdEdeNX3SDWO7vDXsSxxn10bFjR9JqtcLznURVV55qC0rqy1AbI6o65ixatIjatm1Lw4cPJ61WS1FRUXT58mWKiIggNzc3vXWsrKzIycmJbt68SQcPHqSRI0cSUdXJfUdHR1GbLC4upsTExH/2mGWUm9sagYCAAFhbW+Ott97ChQsXEBkZCSsrK3zyyScAap8KOicnRzQV9MqVK4V8hqaC3rNnjzAV9G+//aZXF37mRl9oaCjs7OxgZmaGq1evCukpKSlQKpUICwtDUlISMjIyEBUVJZqhpLbnoz777DPI5XLY2dnpTblbXfVnbtauXQsbGxt89913uHDhAhYsWACZTCZ6jiI5ORkSiQQKhUKY/nXt2rUwMzNDnz596nzfPj4+2LBhQ535bt++jS1btsDf3x9SqRS3b98GUDVTzpYtW3DmzBlkZmZi06ZNsLS0xOLFi4V1dVODzps3D2lpadi4caPBqUEnTpxohHnmG8td/1XvNzAwEElJSaJXTk4O8vLyYGlpibCwMKSlpSEqKgr29vZYsmSJqAwvLy+YmZkJM+4UFhZCJpOBiIQZFWvy8ccfo0ePHqK0kpISuLq6YvTo0Th37hzUajXc3d3x6quvCnni4+Nhbm6ONWvWIC0tDUuWLNGbChqo6vNqu5+6PhrDMzcAkJGRAXt7e/Tv3x9qtRo5OTnYv38/unbtWudU0KmpqWjZsiVGjRpV48xAWq0WLVu2xM8//6y3LDMzExKJxOC97bqpoIcOHYrk5GQcOHAALVu21JsKetu2bXU+01MfjefbVzV9t6OjozAVdEZGBs6fP4/169ejU6dOAKpmWvLy8kL//v1x6tQpJCYmokePHqLfhTM0tW71Z2zqeuYGqJpxz9vbG7///jv++9//wt/fH0qlUji2VFZWwt/fH56enjh48CCysrIQHx+PhQsXCs9D1KcuU6dORa9evZCVlYU//vijxmfi9u7dCwcHB9Fsq9HR0ZBIJIiIiEBGRgZOnTqFoKAgqFQq4XmaR9nfN7ahxpM4zgD020j1tjlkyBD4+PggMTERp0+fFsaX69atE/KQgecObW1t9X7j6H6G2lh2djakUikGDRqEr776yuBshvc7cOAA9u/fj8uXL+PQoUPw9PSEr6+vqC9duXIlmjdvjj179iA1NRUjR47Umwr6zp07UCqVRvm9L+Dh+6dGIyAgANOnT8frr78OGxsb2NnZYeHChbVOBU1UNVWjXC6Hm5sbQkJC9IKV6j/kZmlpCQ8PD0yfPh2ZmZkG62IouNGl3f/AW1Ny/PhxEBGGDRumt+zEiRN45plnYG1tDSsrK3Tv3l30oH9tnU5JSQksLS2FSQBqY2gq6KVLl8LFxQUymUxvKmhdHjs7O9HDgLppgd9+++06t7lp06Z6BUH3u3jxotBx7N+/H15eXsJn4+npiU8++UTvgHn06FF4eXlBLpejbdu2eh1eWVkZbG1tkZCQ0KC6NGaGpmUlIoSGhgKofSponerTfwNVD2k6OjrWuf3CwkJYWFjoBUFpaWkYMmQIlEolXF1dMXv2bL0pgr///nt06NABcrkcXbp00fsRz7y8PMhkMoO/7WCqrly5gokTJ6JVq1aQyWRo3bo13nzzzXr9iOeZM2fg4OCAkSNH1vj7FfPnz8dLL72kl/7OO++gdevWNQ5Sr1y5gueeew5KpRL29vaYM2eOXjsaOnSo6Dd0mor8/HyEhYVBpVJBLpfDxcUFwcHBouNgfaeCvt+DBDcajQbDhw+HQqGAm5ubMFXv/ceW4uJivPnmm3B2dhba2Lhx44QJD+pTl/T0dPTp0wdKpRJENU8FXV5eDmdnZ72g5Ntvv4W3tzesrKzQsmVLBAcH602Mwf29YU/iOAOoO7jJz8/Hc889B4VCAZVKhcjISDg4OAgn54EHC24MtbE7d+4gOzu7zvehs3PnTrRt21Y4ToaFhen9OKzuRzxbtWoFhUKBwYMHIz09XZQnMjLSqL9vJQHuu+/ChAUGBpKXlxetW7fucVeFPUJXrlyhdu3a0cmTJ0UP5D0pysrKqGPHjrRz5069S7SP0ubNm2n37t106NChx1aHpmjevHlUXFxMn376qVHLXbBgAd28eZM+++wzo5bblBUUFFCXLl3o9OnTRn1279y5czRo0CDKyMjQm+KdNW0bN26kvXv30sGDB41aLvf3xvWoxxl5eXnUunVrOnLkCA0ePPihyvqn2lhD9enTh2bOnEn//ve/jVKeuVFKYewJU15eToWFhRQeHk59+vR5IgMboqrZQbZv3043btx4rPWQyWS0YcOGx1qHpujdd9+lTZs2UWVl5QPNLlMTBwcH0UOf7OE5OjrS1q1bKScnx6jBjUajoe3bt3Ngw/RMmzaNbt26RSUlJaLZtR4W9/fG8ajGGb/99huVlpZSt27dSKPR0Pz58+npp5+mAQMGPHTZ/1Qba4gbN27QCy+8QC+//LLRyuQrN8wkxcTECA/h79q1i7p16/a4q8QYY4wxE/GoxhkHDx6kOXPm0OXLl6lZs2bUt29fWrdunVFPspiaJhPcMMYYY4wxxkxbk5oKmjHGGGOMMWa6OLhhjDHGGGOMmQQObhhjjDHGGGMmgYMbxhhjjDHGmEng4IYxxhhjjDFmEji4YYwxxhhjjJkEDm4YY4wxxhhjJoGDG8YYY4wxxphJ4OCGMcYYY4wxZhI4uGGMMcYYY4yZBA5uGGOMMcYYYyaBgxvGGGOMMcaYSeDghjHGGGOMMWYSOLhhjDHGGGOMmYT/BwIEulkpbuv7AAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "rank_num = drop_rank_num.value\n", + "color = select_color.value\n", + "show_sidechains = cb_sidechains.value\n", + "show_mainchains = cb_mainchains.value\n", + "\n", + "jobname_prefix = \".custom\" if msa_mode == \"custom\" else \"\"\n", + "pdb_file = sorted(glob.glob(f\"./{jobname}\"+\"/*.pdb\"))\n", + "\n", + "# show result\n", + "show_pdb(rank_num, show_sidechains, show_mainchains, color).show()\n", + "if color == \"pLDDT\":\n", + " plot_plddt_legend().show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [], + "user_expressions": [] + }, + "source": [ + "##### Plots" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "Generate master plot of mini plots generated from the prediction." + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "cellView": "form", + "id": "11l8k--10q0C" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "

Plots for test_a5e17_3

\n", + " \n", + " \n", + " \n", + "
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import base64\n", + "from html import escape\n", + "\n", + "# see: https://stackoverflow.com/a/53688522\n", + "def image_to_data_url(filename):\n", + " ext = filename.split('.')[-1]\n", + " prefix = f'data:image/{ext};base64,'\n", + " with open(filename, 'rb') as f:\n", + " img = f.read()\n", + " return prefix + base64.b64encode(img).decode('utf-8')\n", + "\n", + "pae = image_to_data_url(os.path.join(jobname,f\"{jobname}{jobname_prefix}_pae.png\"))\n", + "cov = image_to_data_url(os.path.join(jobname,f\"{jobname}{jobname_prefix}_coverage.png\"))\n", + "plddt = image_to_data_url(os.path.join(jobname,f\"{jobname}{jobname_prefix}_plddt.png\"))\n", + "display(HTML(f\"\"\"\n", + "\n", + "
\n", + "

Plots for {escape(jobname)}

\n", + " \n", + " \n", + " \n", + "
\n", + "\"\"\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "G4yBrceuFbf3", + "tags": [], + "user_expressions": [] + }, + "source": [ + "
\n", + "\n", + "**ColabFold v1.5.2-patch: AlphaFold2 using MMseqs2** \n", + "Easy-to-use protein structure and complex prediction using [AlphaFold2](https://www.nature.com/articles/s41586-021-03819-2) and [Alphafold2-multimer](https://www.biorxiv.org/content/10.1101/2021.10.04.463034v1). Sequence alignments/templates are generated through [MMseqs2](mmseqs.com) and [HHsearch](https://github.com/soedinglab/hh-suite). For more details, see [bottom](#Instructions) of the notebook, checkout the [ColabFold GitHub](https://github.com/sokrypton/ColabFold) and read the authors' manuscript: [Mirdita M, Schütze K, Moriwaki Y, Heo L, Ovchinnikov S, Steinegger M. ColabFold: Making protein folding accessible to all.\n", + "*Nature Methods*, 2022](https://www.nature.com/articles/s41592-022-01488-1).\n", + "Old versions: [v1.4](https://colab.research.google.com/github/sokrypton/ColabFold/blob/v1.4.0/AlphaFold2.ipynb), [v1.5.1](https://colab.research.google.com/github/sokrypton/ColabFold/blob/v1.5.1/AlphaFold2.ipynb)\n", + "\n", + "**LICENSE** \n", + "The source code of ColabFold is licensed under [MIT](https://raw.githubusercontent.com/sokrypton/ColabFold/main/LICENSE). Additionally, this notebook uses the AlphaFold2 source code and its parameters licensed under [Apache 2.0](https://raw.githubusercontent.com/deepmind/alphafold/main/LICENSE) and [CC BY 4.0](https://creativecommons.org/licenses/by-sa/4.0/) respectively. Read more about the AlphaFold license [here](https://github.com/deepmind/alphafold). \n", + "\n", + "**PDB100** \n", + "As of 23/06/08, ColabFold has transitioned from using the PDB70 to a 100% clustered PDB, the PDB100. The construction methodology of PDB100 differs from that of PDB70. \n", + "The PDB70 was constructed by running each PDB70 representative sequence through [HHblits](https://github.com/soedinglab/hh-suite) against the [Uniclust30](https://uniclust.mmseqs.com/). \n", + "On the other hand, the PDB100 is built by searching each PDB100 representative structure with [Foldseek](https://github.com/steineggerlab/foldseek) against the [AlphaFold Database](https://alphafold.ebi.ac.uk). \n", + "*To maintain compatibility with older Notebook versions and local installations, the generated files and API responses will continue to be named \"PDB70\", even though we're now using the PDB100.* \n", + "\n", + "**USING CUSTOM TEMPLATES** \n", + "\\- Custom templates must follow the four letter PDB naming with lower case letters. \n", + "\\- Templates in mmCIF format must contain `_entity_poly_seq`. An error is thrown if this field is not present. The field `_pdbx_audit_revision_history.revision_date` is automatically generated if it is not present. \n", + "\\- Templates in PDB format are automatically converted to the mmCIF format. `_entity_poly_seq` and `_pdbx_audit_revision_history.revision_date` are automatically generated. \n", + "\\- If you encounter problems, please report them to this [issue](https://github.com/sokrypton/ColabFold/issues/177).\n", + "\n", + "**COMPARISON TO THE FULL ALPHAFOLD2 AND ALPHAFOLD2 COLAB** \n", + "This notebook replaces the homology detection and MSA pairing of AlphaFold2 with MMseqs2. For a comparison against the [AlphaFold2 Colab](https://colab.research.google.com/github/deepmind/alphafold/blob/main/notebooks/AlphaFold.ipynb) and the full [AlphaFold2](https://github.com/deepmind/alphafold) system read our [paper](https://www.nature.com/articles/s41592-022-01488-1).\n", + "\n", + "**BUGS** \n", + "If you encounter any bugs in the original notebook, please report the issue to https://github.com/sokrypton/ColabFold/issues\n", + "\n", + "**LIMITATIONS** \n", + "*The ColabFold's authors recommend to additionally use the full [AlphaFold2 pipeline](https://github.com/deepmind/alphafold).* \n", + "\\- **Computing resources:** The original [ColabFold AlphaFold2 notebook](https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/AlphaFold2.ipynb) MMseqs2 API can handle ~20-50k requests per day. \n", + "\\- **MSAs:** MMseqs2 is very precise and sensitive but might find less hits compared to HHblits/HMMer searched against BFD or MGnify.\n", + "\n", + "**DESCRIPTION OF PLOTS** \n", + "\\- **Number of sequences per position** - We want to see at least 30 sequences per position, for best performance, ideally 100 sequences. \n", + "\\- **Predicted lDDT per position** - model confidence (out of 100) at each position. The higher the better. \n", + "\\- **Predicted Alignment Error** - For homooligomers, this could be a useful metric to assess how confident the model is about the interface. The lower the better. \n", + "\n", + "**COLABFOLD ACKNOWLEDGEMENTS** \n", + "\\- We thank the AlphaFold team for developing an excellent model and open sourcing the software. \n", + "\\- [KOBIC](https://kobic.re.kr) and [Söding Lab](https://www.mpinat.mpg.de/soeding) for providing the computational resources for the MMseqs2 MSA server. \n", + "\\- Richard Evans for helping to benchmark the ColabFold's Alphafold-multimer support. \n", + "\\- [David Koes](https://github.com/dkoes) for his awesome [py3Dmol](https://3dmol.csb.pitt.edu/) plugin, without whom these notebooks would be quite boring! \n", + "\\- Do-Yoon Kim for creating the ColabFold logo. \n", + "\\- A colab by Sergey Ovchinnikov ([@sokrypton](https://twitter.com/sokrypton)), Milot Mirdita ([@milot_mirdita](https://twitter.com/milot_mirdita)) and Martin Steinegger ([@thesteinegger](https://twitter.com/thesteinegger))." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "af2-env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/tutorials/AlphaFold2/ignore/AlphaFold2_CPU.ipynb b/tutorials/AlphaFold2/ignore/AlphaFold2_CPU.ipynb new file mode 100644 index 0000000..529d4b0 --- /dev/null +++ b/tutorials/AlphaFold2/ignore/AlphaFold2_CPU.ipynb @@ -0,0 +1,2247 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "tags": [], + "user_expressions": [] + }, + "source": [ + "## Notebooks Hub AlphaFold2 Tutorial Using Jupyter Widgets\n", + "This is an adapted notebook of the original [ColabFold AlphaFold2 notebook](https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/AlphaFold2.ipynb) to serve as an example for Notebooks Hub using interactive Jupyter widgets. \n", + "**Note: This particular notebook runs on CPU and a GPU version is in process.**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [], + "user_expressions": [] + }, + "source": [ + "Here are some helpful links to learn more about using AlphaFold and its impact in computational biology. \n", + "  \\- AlphaFold Protein Structure Database [FAQ](https://alphafold.ebi.ac.uk/faw) \n", + "  \\- neurosnap.ai's guides [Part 1](https://neurosnap.ai/blog/post/641a34a1148354cbab382afe) [Part 2](https://neurosnap.ai/blog/post/64222437a55063d26e9c069e) [Part 3](https://neurosnap.ai/blog/post/6422432aa55063d26e9c06a1) \n", + "  \\- Jumper et al (2021). Highly accurate protein structure prediction with AlphaFold. [doi: 10.1038/s41586-021-03819-2](https://doi.org/10.1038/s41586-021-03819-2) \n", + "  \\- Mirdita et al (2022). ColabFold: Making protein folding accessible to all. [doi: 10.1038/s41592-022-01488-1](https://doi.org/10.1038/s41592-022-01488-1) \n", + "  \\- Bertoline et al (2023). Before and after AlphaFold2: An overview of protein structure prediction. [doi: 10.3389/fbinf.2023.1120370](https://doi.org/10.3389/fbinf.2023.1120370) \n", + "  \\- Fang et al (2023). A method for multiple-sequence-alignment-free protein structure prediction using a protein language model. [doi: 10.1038/s42256-023-00721-6](https://doi.org/10.1038/s42256-023-00721-6) \n", + "\n", + "To learn more about Notebooks Hub or Jupyter Widgets, check out their documentation [here](https://polusai.github.io/notebooks-hub/) and [here](https://ipywidgets.readthedocs.io/en/8.1.2/index.html), respectively." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [], + "user_expressions": [] + }, + "source": [ + "### Setup\n", + "Import appropriate packages into your program to get started. These are necessary to run the AlphaFold predictions later on." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import os\n", + "import re\n", + "import hashlib\n", + "import random\n", + "import shutil\n", + "\n", + "from sys import version_info\n", + "python_version = f\"{version_info.major}.{version_info.minor}\"" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "The following packages will enable the build of interactive widgets to provide a better user experience. More information on building interactive widgets can be found in the [Jupyter Widgets documentation](https://ipywidgets.readthedocs.io/en/8.1.2/index.html)." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from ipywidgets import interact, interactive, fixed, interact_manual, FileUpload, GridBox, Layout, VBox\n", + "import ipywidgets as widgets\n", + "from IPython.display import display, HTML\n", + "from ipyfilechooser import FileChooser" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [], + "user_expressions": [] + }, + "source": [ + "### Generate interactive widgets for query session inputs\n", + "An interactive widget will help the user select the necessary inputs that are required to run the analysis. Examples of additional styles and formats can be found in the [documentation's](https://ipywidgets.readthedocs.io/en/8.1.2/index.html) [widget list](https://ipywidgets.readthedocs.io/en/8.1.2/examples/Widget%20List.html). \n", + "1. The core parameters to define before running AlphaFold predictions include the **query sequence(s)**, **Amber relaxed model(s)**, and **template(s)**, as well as **jobname** to keep track of each query session. \n", + "2. Set up individual widget types for each prediction parameter. These are widget *children* that will be grouped into a *family* in the next step. For visual convenience, a border will be added to outline each family container." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "The `.HTML` widget type will display text that can be used as headers or descriptions. These strings can be formatted using html tags (e.g., ``). The `grid_area` attribute will help with widget placement inside the family container." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "html_input = widgets.HTML(description=\"Input Protein Sequences:\",\n", + " value=\"\",\n", + " style={'description_width': 'initial'},\n", + " layout=Layout(width='auto', grid_area='html_input'))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "Variables can be assigned values (or, as shown below, concatenated f-strings for large blocks of text) that can then be used to more easily attribute values inside each widget." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "qtips = (f\"Helpful Tips
\"\n", + " f\"Query: Use `:` to specify inter-protein chainbreaks for modeling complexes (supports homo- and hetro-oligomers). \"\n", + " f\"For example `PI...SK:PI...SK` for a homodimer.
\"\n", + " f\"Template Mode: Select the desired template to run predictions against.
\"\n", + " f\"Amber Relaxes: Specify how many of the top ranked structures to relax using Amber.
\"\n", + " f\"Amber` is a suite of programs that apply AMBER forcefields to simulations of biomolecules and molecular dynamics. \"\n", + " f\"Amber relaxed models relax acid side chain positions and are usually required for users who need accurate side-chain positions.\"\n", + " )\n", + "\n", + "html_qtips = widgets.HTML(description=\"\",\n", + " value=qtips,\n", + " style={'description_width': 'initial'},\n", + " layout=Layout(width='auto', grid_area='html_qtips'))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "The `.Text` widget type will provide a box that allows the user to input text strings while `.Textarea` will provide an adjustable box. In this case, the adjustable box was selected for **query sequence**. This will be useful if the query is long or complex, because the user can view the input query in its entirety. If specified, the attribute `placeholder` will be visible in the text box when empty. An initial value can be assigned to the `value` attribute to pre-populate the box." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "text_queryseq = widgets.Textarea(value='PIAQIHILEGRSDEQKETLIREVSEAISRSLDAPLTSVRVIITEMAKGHFGIGGELASK',\n", + " placeholder='Input Amino Acid Sequence for Protein of Interest',\n", + " description='Query Sequence:',\n", + " disabled=False,\n", + " style={'description_width': '170px'},\n", + " layout=Layout(width='700px', grid_area='text_queryseq'))\n", + "\n", + "text_jobname = widgets.Text(value='test',\n", + " placeholder='Type Jobname',\n", + " description='Jobname:',\n", + " disabled=False,\n", + " style={'description_width': '170px'},\n", + " layout=Layout(width='auto', grid_area='text_jobname'))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "The next two widgets need to offer pre-determined options for user selection. These options can be defined for each necessary parameters using the `options` attribute within each appropriate individual. The default selection within these options can be specified using the `value` attribute. \n", + "\n", + "The `RadioButtons` widget type will list the possible options with buttons for a single selection." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "buttons_num_relax = widgets.RadioButtons(options=[('0',0),('1',1),('5',5)],\n", + " value=0,\n", + " description='Number of Amber Relaxes:',\n", + " disabled=False,\n", + " style={'description_width': '170px'},\n", + " layout=Layout(width='auto', grid_area='buttons_num_relax'))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "The `.ToggleButtons` widget will display buttons that allow the user to make one choice of the given options. This widget type is very helpful, because the attribute `tooltips` enables descriptions for each option upon hovering over with the mouse pointer." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "buttons_template_mode = widgets.ToggleButtons(options=['none','pdb100','custom'],\n", + " description='Template Mode:',\n", + " disabled=False,\n", + " button_style='',\n", + " style={'description_width': '170px'},\n", + " layout=Layout(width='auto', grid_area='buttons_template_mode'),\n", + " tooltips=['no template information is used',\n", + " 'detect templates in pdb100',\n", + " 'upload and search own templates (PDB or mmCIF format, see notes) to bias AlphaFold\\'s predictions'])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "Now, set up the widget family. Jupyter's `Box` widgets utilize the CSS [flexbox spec](https://css-tricks.com/snippets/css/a-guide-to-flexbox/) for gathering individual widgets within a container. The family below will use the `GridBox` container that can be customized according to the CSS [Grid layouts](https://css-tricks.com/snippets/css/complete-guide-grid). \n", + "1. Using the `GridBox` container, define its children from the code above.\n", + "2. Lay out children as desired within container. Each child's `Layout.grid_area` attribute will need to have a matching label inside the container's `Layout.grid_template_areas` attribute.\n", + "3. `display()` will display the interactive widget family." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "9cd2ade9f4bd4a91aa565d6426431699", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "GridBox(children=(HTML(value='', description='Input Protein Sequences:', layout=Layout(grid_area='html_…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "controls_query = GridBox(children=[html_input, html_qtips, text_queryseq, text_jobname, buttons_num_relax, buttons_template_mode],\n", + " layout=Layout(\n", + " border='solid 1.5px',\n", + " width='1255px',\n", + " grid_template_rows='auto auto auto auto auto',\n", + " grid_template_columns='300px 450px 500px',\n", + " grid_template_areas='''\n", + " \"html_input html_input html_qtips\"\n", + " \"text_queryseq text_queryseq html_qtips\"\n", + " \"text_jobname . html_qtips\"\n", + " \"buttons_template_mode buttons_template_mode html_qtips\"\n", + " \"buttons_num_relax buttons_num_relax html_qtips\"\n", + " ''')\n", + " )\n", + "\n", + "display(controls_query)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [], + "user_expressions": [] + }, + "source": [ + "### Access widget outputs to generate a new directory for saving prediction results and queries" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "The following code defines functions to help augment jobnames to minimize the risk of files being overwritten if the same sequence was queried multiple times using different parameter values. The functions below will append an underscore and integer at the end of the jobname with each sequential run (e.g., `_0`, `_1`, ...)." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "def add_hash(x,y):\n", + " return x+\"_\"+hashlib.sha1(y.encode()).hexdigest()[:5]\n", + "\n", + "def update_jobname(jobname):\n", + " basejobname = \"\".join(jobname.split())\n", + " basejobname = re.sub(r'\\W+', '', basejobname)\n", + " jobname_new = add_hash(basejobname, query_sequence)\n", + " \n", + " return jobname_new" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "User-defined and selected values from the widget can be accessed through each children's `.value` to save as accessible variables. This is shown in the top portion of the following code block. \n", + "  • With the rest of the code block, the system will then check in the working path for a directory sharing the same jobname. If one does not exist, it will create one. If one does, it will create an iteration (e.g., `_0`, `_1`, ...). \n", + "  • ***Note:*** *these code chunks are required to be in the same cell, otherwise iterative numbering does not work as intended (i.e., recursively appends `_0` instead of increasing in value).*" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "jobname: test_a5e17_0\n", + "sequence: PIAQIHILEGRSDEQKETLIREVSEAISRSLDAPLTSVRVIITEMAKGHFGIGGELASK\n", + "length: 59\n", + "relax: 0\n", + "template: none\n" + ] + } + ], + "source": [ + "# save outputs as accessible variables\n", + "jobname = text_jobname.value\n", + "query_sequence = text_queryseq.value\n", + "num_relax = buttons_num_relax.value\n", + "template_mode = buttons_template_mode.value\n", + "\n", + "use_amber = num_relax > 0\n", + "length = len(query_sequence.replace(\":\",\"\"))\n", + "\n", + "# remove whitespaces and update jobname\n", + "query_sequence = \"\".join(query_sequence.split())\n", + "jobname = update_jobname(jobname)\n", + "\n", + "# check if directory with jobname exists\n", + "def check(folder):\n", + " if os.path.exists(folder):\n", + " return False\n", + " else:\n", + " return True\n", + " \n", + "if not check(jobname):\n", + " n = 0\n", + " while not check(f\"{jobname}_{n}\"): n += 1\n", + " jobname = f\"{jobname}_{n}\"\n", + "\n", + "# make directory to save results\n", + "os.makedirs(jobname, exist_ok=True)\n", + "\n", + "# save a copy of the query sequence in the newly generated folder\n", + "queries_path = os.path.join(jobname, f\"{jobname}.csv\")\n", + "with open(queries_path, \"w\") as text_file:\n", + " text_file.write(f\"id,sequence\\n{jobname},{query_sequence}\")\n", + "\n", + "# for verification purposes, return the session's information.\n", + "print(f\"jobname: {jobname}\" \"\\n\"\n", + " f\"sequence: {query_sequence}\" \"\\n\"\n", + " f\"length: {length}\" \"\\n\"\n", + " f\"relax: {num_relax}\" \"\\n\"\n", + " f\"template: {template_mode}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "### Generate file upload widgets to select custom templates for predictions" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "**Local File Upload:** The `FileUpload` widget allows the user to select local computer files for upload to the current working directory on the server. AlphaFold allows for multiple custom templates, so `multiple=TRUE` was set. *One* specific file extension can be specified inside the attribute `accept=''`. \n", + "  • ***Note:*** *This will replace pre-existing files in the current directory with the same name. Please rename if necessary.* \n", + "  • ***Note:*** *The counter shown will increase despite re-selecting a file. The cell containing `display(upload)` must be rerun to reset the counter.* " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "upload = FileUpload(accept='', multiple=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "**Server File Upload:** The `FileChooser` widget allows the user to select a single file that is already present on the server. Default files can be shown by defining `fc.filter_pattern` with one or more specific extensions. For AlphaFold, templates should be PDB or PDBx/mmCIF format. \n", + "  • **Note:** ipyfilechooser is a separate package that works in conjunction with ipywidgets. \n", + "  • If a file from the server was selected, `os.rename` will move the file into the template folder inside the current query session's directory (i.e., */\\/template/\\*) \n", + "  • If a file from the server was selected, `os.rename` will move the file into the template folder inside the current query session's directory (i.e., */\\/template/\\*) " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "fc = FileChooser(\n", + " os.getcwd(),\n", + " filename='',\n", + " title='Select custom template(s)
Note: must follow four letter PDB naming with lower case letters',\n", + " show_hidden=False,\n", + " select_default=False,\n", + " show_only_dirs=False\n", + " )\n", + "\n", + "fc.filter_pattern = ['*.pdb', '*.pdbx', '*.txt'] " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "The following statements set variables for downstream analysis and will display the upload widgets only if the `template_mode` was set to *custom*. A template folder will also be generated inside the current jobname's directory to store template files. \n", + "  • ***Note:*** *In order to cancel file selection from the server, the previous cell must be rerun.*" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# set variables and display file uploaders if template mode is set to custom\n", + "if template_mode == \"pdb100\":\n", + " use_templates = True\n", + " custom_template_path = None\n", + "elif template_mode == \"custom\":\n", + " custom_template_path = os.path.join(jobname,f\"template\")\n", + " os.makedirs(custom_template_path, exist_ok=True)\n", + " use_templates = True\n", + " display(fc)\n", + " display(upload)\n", + "else:\n", + " custom_template_path = None\n", + " use_templates = False" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "File selected from the server: None\n", + "File(s) selected from the local host for upload: None\n" + ] + } + ], + "source": [ + "# move server file to session's template folder\n", + "if template_mode == \"custom\":\n", + " if fc.selected is not None:\n", + " for fn in fc.selected:\n", + " os.rename(fn,os.path.join(custom_template_path,fn))\n", + "\n", + "# return filenames of all files selected for custom template use.\n", + "if not upload.value:\n", + " fps = \"None\"\n", + "print(f\"File selected from the server: {fc.selected}\")\n", + "print(f\"File(s) selected from the local host for upload: {fps}\")\n", + "\n", + "# upload local files to server and place inside session's template folder\n", + "if upload.value:\n", + " fps = []\n", + " for fp in upload.value:\n", + " fps.append(f\"{fp}\")\n", + " with open(fp, 'wb') as output_file:\n", + " content = upload.value[fp]['content']\n", + " output_file.write(content)\n", + " os.rename(fp,os.path.join(custom_template_path,fp))\n", + " print(f\">> {fp} successfully uploaded\")\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [], + "user_expressions": [] + }, + "source": [ + "### Install dependencies" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "Duplicate variables (with capital letters) for the program to find so that it can install any necessary dependencies." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "USE_AMBER = use_amber\n", + "USE_TEMPLATES = use_templates\n", + "PYTHON_VERSION = python_version" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "The following code was minimally adapted from the original code and is based on various combinations of parameters selected for analysis. It will scan the current working directory for files indicating the appropriate modules are installed, create a symbolic link between the package location and current working directory, add patches, and create/run appropriate files needed to run AlphaFold predictions." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "cellView": "form", + "id": "AzIKiDiCaHAn", + "tags": [] + }, + "outputs": [], + "source": [ + "if not os.path.isfile(\"COLABFOLD_READY\"):\n", + " print(\"installing colabfold...\")\n", + " os.system(\"ln -s /opt/modules/my/conda-envs/alphafold-test/lib/python3.9/site-packages/colabfold colabfold\")\n", + " os.system(\"ln -s /opt/modules/my/conda-envs/alphafold-test/lib/python3.9/site-packages/alphafold alphafold\")\n", + " # patch for jax > 0.3.25\n", + " os.system(\"sed -i 's/weights = jax.nn.softmax(logits)/logits=jnp.clip(logits,-1e8,1e8);weights=jax.nn.softmax(logits)/g' alphafold/model/modules.py\")\n", + " os.system(\"touch COLABFOLD_READY\")\n", + " print(\"colabfold ready!\")\n", + "\n", + "if USE_AMBER or USE_TEMPLATES:\n", + " if not os.path.isfile(\"CONDA_READY\"):\n", + " print(\"installing conda...\")\n", + " os.system(\"wget -qnc https://github.com/conda-forge/miniforge/releases/latest/download/Mambaforge-Linux-x86_64.sh\")\n", + " os.system(\"bash Mambaforge-Linux-x86_64.sh -bfp /usr/local\")\n", + " os.system(\"mamba config --set auto_update_conda false\")\n", + " os.system(\"touch CONDA_READY\")\n", + " print(\"conda ready!\")\n", + "\n", + "if USE_TEMPLATES and not os.path.isfile(\"HH_READY\") and USE_AMBER and not os.path.isfile(\"AMBER_READY\"):\n", + " print(\"installing hhsuite and amber...\")\n", + " os.system(f\"mamba install -y -c conda-forge -c bioconda kalign2=2.04 hhsuite=3.3.0 openmm=7.7.0 python='{PYTHON_VERSION}' pdbfixer\")\n", + " os.system(\"touch HH_READY\")\n", + " os.system(\"touch AMBER_READY\")\n", + " print(\"hhsuite & amber ready!\")\n", + " \n", + "else:\n", + " if USE_TEMPLATES and not os.path.isfile(\"HH_READY\"):\n", + " print(\"installing hhsuite...\")\n", + " os.system(f\"mamba install -y -c conda-forge -c bioconda kalign2=2.04 hhsuite=3.3.0 python='{PYTHON_VERSION}'\")\n", + " os.system(\"touch HH_READY\")\n", + " print(\"hhsuite ready!\")\n", + " if USE_AMBER and not os.path.isfile(\"AMBER_READY\"):\n", + " print(\"installing amber...\")\n", + " os.system(f\"mamba install -y -c conda-forge openmm=7.7.0 python='{PYTHON_VERSION}' pdbfixer\")\n", + " os.system(\"touch AMBER_READY\")\n", + " print(\"amber ready!\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [], + "user_expressions": [] + }, + "source": [ + "### Generate interactive widget for multiple sequence alignment options" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "AlphaFold's AI was trained with multiple sequence alignment (MSA), paired residues, and experimentally validated protein structures from the [RSCB Protein Data Bank (PDB)](https://www.rcsb.org/). AlphaFold2 uses MMseq2 [(Many-against-Many searching)](https://mmseqs.com/latest/userguide.pdf) software to search and cluster huge sequence sets from databases that comprise of UniRef [(UniProt Reference Clusters)](https://www.uniprot.org/help/uniref) and its own novel [environmental database](https://colabfold.mmseqs.com/), referred to as _env_ inside widget options. MSA pairing can also be controlled to improve prediction accuracy for protein complexes. A new family of widgets will be created below for these options." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "Use the `.HTML` widget type to create a descriptive header." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "html_msaopts = widgets.HTML(description=\"Multiple Sequence Alignment Options (custom MSA upload, single sequence, pairing mode)\",\n", + " value=\"\",\n", + " style= {'description_width': 'initial'},\n", + " layout=Layout(width='auto', grid_area='html_msaopts'),)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "The `.Select` widget type will display a box with all possible options for selection by row." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "select_msa_mode = widgets.Select(options=['mmseqs2_uniref_env', 'mmseqs2_uniref', 'single_sequence', 'custom'],\n", + " value='mmseqs2_uniref_env',\n", + " description='MSA mode:',\n", + " rows=5,\n", + " disabled=False,\n", + " layout=Layout(width='auto', grid_area='select_msa_mode'))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "The `.ToggleButtons` type will display options with the helpful description upon hover." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "buttons_pair_mode = widgets.ToggleButtons(options=['unpaired_paired', 'paired', 'unpaired'],\n", + " description='Pair Mode:',\n", + " disabled=False,\n", + " button_style='',\n", + " layout=Layout(width='auto', grid_area='buttons_pair_mode'),\n", + " tooltips=['pair sequences from same species + unpaired MSA',\n", + " 'seperate MSA for each chain',\n", + " 'only use paired sequences'])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "Now, set up the widget family.\n", + "1. Using the `GridBox` container, define its children from the code above.\n", + "2. `display()` will display the interactive widget family." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "7030ed90c3f44ae482d2e4faf9114729", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "GridBox(children=(HTML(value='', description='Multiple Sequence Alignment Options (custom MSA upload, singl…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "controls_msa = GridBox(children=[html_msaopts, select_msa_mode, buttons_pair_mode],\n", + " layout=Layout(\n", + " border='solid 1.5px',\n", + " width='605px',\n", + " grid_template_rows='auto auto',\n", + " grid_template_columns='300px 300px',\n", + " grid_template_areas='''\n", + " \"html_msaopts html_msaopts\"\n", + " \"select_msa_mode buttons_pair_mode\"\n", + " ''')\n", + " )\n", + "\n", + "display(controls_msa)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "Next, save the widget selections as accessible variables." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "msa_mode = select_msa_mode.value\n", + "pair_mode = buttons_pair_mode.value" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "#### Custom MSA file (.a3m formatted)\n", + "##### DISCLAIMER: HAVE NOT TESTED FUNCTIONALITY OF USING CUSTOM A3M FILE AFTER SUCCESSFUL UPLOAD\n", + "Custom MSA allows users to provide their own alignment files for multiple sequence alignment. Any kind of alignment tool can be used to generate the MSA, including the [HHblits Toolkit server](https://toolkit.tuebingen.mpg.de/tools/hhblits)." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "cellView": "form", + "id": "C2_sh2uAonJH", + "tags": [] + }, + "outputs": [], + "source": [ + "# create additional file uploaders to use for custom MSA\n", + "upload_msa = FileUpload(accept='.a3m', multiple=False)\n", + "\n", + "# decide which a3m to use\n", + "if \"mmseqs2\" in msa_mode:\n", + " a3m_file = os.path.join(jobname,f\"{jobname}.a3m\")\n", + "\n", + "elif msa_mode == \"custom\":\n", + " a3m_file = os.path.join(jobname,f\"{jobname}.custom.a3m\")\n", + " if not os.path.isfile(a3m_file):\n", + " print(\"The first FASTA entry of the A3M file must be the query sequence without gaps.\")\n", + " display(upload_msa)\n", + "else:\n", + " a3m_file = os.path.join(jobname,f\"{jobname}.single_sequence.a3m\")\n", + " with open(a3m_file, \"w\") as text_file:\n", + " text_file.write(\">1\\n%s\" % query_sequence)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "The following code cell will save the selected local file to the server and create a renamed copy for the program to access." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "# upload local file to session's folder on server\n", + "if upload_msa.value:\n", + " up_msa = upload_msa.value\n", + " fpmsa = []\n", + " for fn, fd in up_msa.items():\n", + " fpmsa.append(f\"{fn}\")\n", + " with open(fn, 'wb') as output_file:\n", + " content = fd['content']\n", + " output_file.write(content)\n", + " os.rename(fn,os.path.join(jobname,fn))\n", + " print(f\"{fn} successfully uploaded. Don't forget to cite your custom MSA generation method!\")\n", + "\n", + "if upload_msa.value:\n", + " orig_msa = f\"{jobname}/{fpmsa[0]}\"\n", + " custom_msa = shutil.copy2(orig_msa,f\"{jobname}/strip_{fpmsa[0]}\") # copy file as backup or for preservation purposes\n", + "\n", + " header = 0\n", + " import fileinput\n", + " for line in fileinput.FileInput(custom_msa,inplace=True):\n", + " if line.startswith(\">\"):\n", + " header = header + 1\n", + " if not line.rstrip():\n", + " continue\n", + " if line.startswith(\">\") == False and header == 1:\n", + " query_sequence = line.rstrip()\n", + " print(line, end='')\n", + " \n", + " os.rename(custom_msa, a3m_file)\n", + " queries_path=a3m_file\n", + " print(f\"Moving {custom_msa} to {a3m_file} for use by AlphaFold.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "### Advanced Settings" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "Create the widget header and a tips box using the `.HTML` widget type." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "html_advset = widgets.HTML(description=\"Advanced Settings:\",\n", + " value=\"\",\n", + " style={'description_width': 'initial'},\n", + " layout=Layout(width='auto', grid_area='html_advset'))\n", + "\n", + "advtips = (f\"Helpful Tips
\"\n", + " f\"Model Type: Choose the structural dimentions for prediction (i.e., oligomeric or multimeric). For monomer predictions, choose alphafold2-ptm. \"\n", + " f\"Auto permits the model to decide and will use alphafold2_multimer_v3 for complex prediction.
\"\n", + " f\"Number of Recycles: Enables multiple reiterations through the sequence by building off its own predictions. \"\n", + " f\"The default is 3, but 6+ will enable a more accurate prediction despite longer runtimes.
\"\n", + " f\"Recycle Early Stop Tolerance: Auto tolerance will be 0.0, unless using alphafold2_multimer_v3.
\"\n", + " f\"Alphafold2_multimer_v3: For complex predictions using this model, `auto` will result in recycles = 20 and tolerance = 0.05.\"\n", + " )\n", + "\n", + "html_advtips = widgets.HTML(description=\"\",\n", + " value=advtips,\n", + " style={'description_width': 'initial'},\n", + " layout=Layout(width='auto', grid_area='html_advtips'))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "The `.Dropdown` widget type will display a single selection list in dropdown format. Create dropdown widgets to select AlphaFold **model types**, **number of recycles**, and **recycle early stop tolerance** values." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "drop_model_type = widgets.Dropdown(options=['auto','alphafold2_ptm','alphafold2_multimer_v1','alphafold2_multimer_v2','alphafold2_multimer_v3'],\n", + " value='auto',\n", + " description='Model Type:',\n", + " disabled=False,\n", + " style={'description_width': '170px'},\n", + " layout=Layout(width='auto', grid_area='drop_model_type'))\n", + "\n", + "drop_num_recycles = widgets.Dropdown(options=[('auto','auto'),('0',0),('1',1),('3',3),('6',6),('12',12),('24',24),('48',48)],\n", + " value='auto',\n", + " description='Number of Recycles:',\n", + " disabled=False,\n", + " style={'description_width': '170px'},\n", + " layout=Layout(width='auto', grid_area='drop_num_recycles'))\n", + "\n", + "drop_tol = widgets.Dropdown(options=[('auto','auto'),('0.0',0.0),('0.5',0.5),('1.0',1.0)],\n", + " value='auto',\n", + " description='Recycle Early Stop Tolerance:',\n", + " disabled=False,\n", + " style={'description_width': '170px'},\n", + " layout=Layout(width='auto', grid_area='drop_tol'))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "Use `.ToggleButtons` to create toggle buttons to select **pairing strategy** and have descriptions display upon hovering over each button." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "buttons_pairing_strategy = widgets.ToggleButtons(options=['greedy','complete'],\n", + " description='Pairing Strategy:',\n", + " disabled=False,\n", + " button_style='',\n", + " style={'description_width': '170px'},\n", + " layout=Layout(width='auto', grid_area='buttons_pairing_strategy'),\n", + " tooltips=['pair any taxonomically matching subsets',\n", + " ' all sequences have to match in one line'])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "Now, set up the widget family, once again using `GridBox` and `display()`." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "9253f05178e24e71aac5d7ce4e9a6024", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "GridBox(children=(HTML(value='', description='Advanced Settings:', layout=Layout(grid_area='html_advset…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "controls_advset = GridBox(children=[html_advset, html_advtips, drop_model_type, drop_num_recycles, drop_tol, buttons_pairing_strategy],\n", + " layout=Layout(\n", + " border='solid 1.5px',\n", + " width='1305px',\n", + " grid_template_rows='auto auto auto auto auto',\n", + " grid_template_columns='350px 200px 750px',\n", + " grid_template_areas='''\n", + " \"html_advset html_advset html_advtips\"\n", + " \"drop_model_type . html_advtips\"\n", + " \"drop_num_recycles . html_advtips\"\n", + " \"drop_tol . html_advtips\"\n", + " \"buttons_pairing_strategy buttons_pairing_strategy html_advtips\"\n", + " ''')\n", + " )\n", + "\n", + "display(controls_advset)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "Lastly, save the widget selections as accessible variables." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "model_type = drop_model_type.value\n", + "pairing_strategy = buttons_pairing_strategy.value\n", + "\n", + "if drop_model_type.value != 'alphafold2_multimer_v3':\n", + " if drop_num_recycles.value == 'auto':\n", + " num_recycles = 3\n", + " else:\n", + " num_recycles = drop_num_recycles.value\n", + " \n", + " if drop_tol.value == 'auto':\n", + " recycle_early_stop_tolerance = 0.0\n", + " else:\n", + " recycle_early_stop_tolerance = drop_tol.value\n", + "\n", + "elif drop_model_type.value == 'alphafold2_multimer_v3':\n", + " if drop_num_recycles.value == 'auto':\n", + " num_recycles = 20\n", + " else:\n", + " num_recycles = drop_num_recycles.value\n", + "\n", + " if drop_tol.value == 'auto':\n", + " recycle_early_stop_tolerance = 0.5\n", + " else:\n", + " recycle_early_stop_tolerance = drop_tol.value" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "### Generate interactive widget to define sample settings" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "`.HTML` widgets can be used again to add headers as well as additional text to provide setting tips." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "user_expressions": [] + }, + "outputs": [], + "source": [ + "html_sampset = widgets.HTML(description=\"Sample Settings:\",\n", + " value=\"\",\n", + " style= {'description_width': 'initial'})\n", + "\n", + "msatips = (f\"Helpful Tips
\"\n", + " f\"- Decrease Max MSA to increase uncertainty.
\"\n", + " f\"- Enable dropouts and increase # seeds to sample predictions from uncertainty of the model.\"\n", + " )\n", + "\n", + "html_msatips = widgets.HTML(description=\"\",\n", + " value=msatips,\n", + " style={'description_width': 'initial'},\n", + " layout=Layout(width='auto', grid_area='html_msatips'))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "A `.SelectionSlider` widget will be used alongside the standard `.Dropdown` type used previously. The selection slider offers a range of custom values without conforming to a uniform increment." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "drop_max_msa = widgets.Dropdown(options=['auto','512:1024','256:512','64:128','32:64', '16:32'],\n", + " value='auto',\n", + " description='Max MSA:',\n", + " disabled=False)\n", + "\n", + "slider_num_seeds = widgets.SelectionSlider(options=[('1',1),('2',2),('4',4),('8',8),('16',16)],\n", + " value=1,\n", + " description='# seeds:',\n", + " disabled=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "Here, a `.Checkbox` widget type that can be selected or unselected is introduced." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "cb_dropout = widgets.Checkbox(value=False, description='Use Dropout', disabled=False, indent=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [], + "user_expressions": [] + }, + "source": [ + "Now, set up the widget family, once again using `GridBox` and `display()`." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "ff6a53f08157477fac47dfc1cee7761d", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "GridBox(children=(HTML(value='', description='Sample Settings:', style=DescriptionStyle(description_wid…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "controls_sampset = GridBox(children=[html_sampset, html_msatips, drop_max_msa, slider_num_seeds, cb_dropout],\n", + " layout=Layout(\n", + " border='solid 1.5px',\n", + " width='805px',\n", + " grid_template_rows='auto auto auto auto auto',\n", + " grid_template_columns='400px 400px',\n", + " grid_template_areas='''\n", + " \"h_sampset html_msatips\"\n", + " \"drop_max_msa html_msatips\"\n", + " \"slider_num_seeds html_msatips\"\n", + " \"cb_dropout .\"\n", + " ''')\n", + " )\n", + "\n", + "display(controls_sampset)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "Save the widget selections as accessible variables. These will also be used to assign other values as depicted in the bottom half of the code cell." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "max_msa = drop_max_msa.value\n", + "num_seeds = slider_num_seeds.value\n", + "use_dropout = cb_dropout.value\n", + "\n", + "num_recycles = None if num_recycles == \"auto\" else int(num_recycles)\n", + "recycle_early_stop_tolerance = None if recycle_early_stop_tolerance == \"auto\" else float(recycle_early_stop_tolerance)\n", + "if max_msa == \"auto\": max_msa = None" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "### Generate interactive widget to toggle save settings" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "The `.IntText` widget can be used to allow the user to specify any integer inside its given text box." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "text_save_dpi = widgets.IntText(value=200,\n", + " description='dpi:',\n", + " disabled=False,\n", + " layout=Layout(width='auto', grid_area='text_save_dpi'))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [], + "user_expressions": [] + }, + "source": [ + "Set up individual `.HTML` widgets to display text and `.Checkboxes` for toggles." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "html_saveset = widgets.HTML(description=\"Save Settings:\",\n", + " value=\"\",\n", + " style= {'description_width': 'initial'},\n", + " layout=Layout(width='auto', grid_area='html_saveset'))\n", + "\n", + "html_savedpi = widgets.HTML(description=\"Set dpi for image resolution:\",\n", + " value=\"\",\n", + " style= {'description_width': 'initial'},\n", + " layout=Layout(width='auto', grid_area='html_savedpi'))\n", + "\n", + "cb_savefull = widgets.Checkbox(value=False,description='Save All', disabled=False, layout=Layout(width='auto', grid_area='cb_savefull'))\n", + "\n", + "cb_saverecyc = widgets.Checkbox(value=False, description='Save Recycles', disabled=False, layout=Layout(width='auto', grid_area='cb_saverecyc'))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "Set up the widget family, once again using `GridBox` and `display()`. This time columns are also utilized. For proper layout assignment into the family container, `grid_area` was defined for each widget child." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "2fb08726f7d047f48aca368ea456cc4b", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "GridBox(children=(HTML(value='', description='Save Settings:', layout=Layout(grid_area='html_saveset', …" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "controls_saveset = GridBox(children=[html_saveset, html_savedpi, text_save_dpi, cb_savefull, cb_saverecyc],\n", + " layout=Layout(\n", + " border='solid 1.5px',\n", + " width='455px',\n", + " grid_template_rows='auto auto auto auto',\n", + " grid_template_columns='150px 300px',\n", + " grid_template_areas='''\n", + " \"html_saveset .\"\n", + " \"html_savedpi text_save_dpi\"\n", + " \". cb_savefull\"\n", + " \". cb_saverecyc\"\n", + " ''')\n", + " )\n", + "\n", + "display(controls_saveset)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "Save the widget selections as accessible variables. " + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "save_all = cb_savefull.value\n", + "save_recycles = cb_saverecyc.value\n", + "dpi = text_save_dpi.value" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [], + "user_expressions": [] + }, + "source": [ + "### Prepare prediction run" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "Use a simple `.Checkbox` widget to allow the user to toggle whether or not images should be displayed during the prediction run." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "755dbc2dad2e452a859217e852a90d3c", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Checkbox(value=True, description='Display Images', layout=Layout(border='solid 1.5px'))" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cb_displayimg = widgets.Checkbox(value=True, description='Display Images', disabled=False, indent=True, layout=Layout(border='solid 1.5px'))\n", + "display(cb_displayimg)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "Assign the selection as an accessible variable." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "display_images = cb_displayimg" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "The following package imports are necessary to finally run the structural predictions. \n", + "**Note:** *`colabfold.colabfold.py` was renamed to `colabfold.cf.py` directly inside the package contents, and `colabfold.colabfold` was renamed to `colabfold.cf` in the next cell and inside `colabfold.batch.py`.*" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "import warnings\n", + "warnings.simplefilter(action='ignore', category=FutureWarning)\n", + "from Bio import BiopythonDeprecationWarning\n", + "#warnings.simplefilter(action='ignore', category=BiopythonDeprecationWarning)\n", + "from pathlib import Path\n", + "from colabfold.download import download_alphafold_params, default_data_dir\n", + "from colabfold.utils import setup_logging\n", + "from colabfold.batch import get_queries, run, set_model_type\n", + "from colabfold.plot import plot_msa_v2\n", + "\n", + "from colabfold.cf import plot_protein\n", + "from pathlib import Path\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import os\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "Add system paths to the new dependencies that were installed previously." + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "# pdbfixer 1.8.1\n", + "if use_amber and f\"/opt/conda/pkgs/pdbfixer-1.8.1-pyh6c4a22f_0/site-packages/\" not in sys.path:\n", + " sys.path.insert(0, f\"/opt/conda/pkgs/pdbfixer-1.8.1-pyh6c4a22f_0/site-packages/\")\n", + "\n", + "# openmm 7.7.0\n", + "if use_amber and f\"/opt/conda/pkgs/openmm-7.7.0-py39h15fbce5_1/lib/python3.9/site-packages\" not in sys.path:\n", + " sys.path.insert(0, f\"/opt/conda/pkgs/openmm-7.7.0-py39h15fbce5_1/lib/python3.9/site-packages\")\n", + "\n", + "# kalign2 2.0.4\n", + "if use_templates and f\"/opt/conda/pkgs/kalign2-2.04-h031d066_5/bin\" not in sys.path:\n", + " sys.path.insert(0, f\"/opt/conda/pkgs/kalign2-2.04-h031d066_5/bin\")\n", + "\n", + "# hhsuite 3.3.0\n", + "if use_templates and f\"/opt/conda/pkgs/hhsuite-3.3.0-py39pl5321he10ea66_9/bin\" not in sys.path:\n", + " sys.path.insert(0, f\"/opt/conda/pkgs/hhsuite-3.3.0-py39pl5321he10ea66_9/bin\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "Additionally, the following cell defines a few necessary functions for ColabFold/AlphaFold." + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "def input_features_callback(input_features):\n", + " if display_images:\n", + " plot_msa_v2(input_features)\n", + " plt.show()\n", + " plt.close()\n", + "\n", + "def prediction_callback(protein_obj, length,\n", + " prediction_result, input_features, mode):\n", + " model_name, relaxed = mode\n", + " if not relaxed:\n", + " if display_images:\n", + " fig = plot_protein(protein_obj, Ls=length, dpi=150)\n", + " plt.show()\n", + " plt.close()\n", + "\n", + "result_dir = jobname\n", + "log_filename = os.path.join(jobname,\"log.txt\")\n", + "if not os.path.isfile(log_filename) or 'logging_setup' not in globals():\n", + " setup_logging(Path(log_filename))\n", + " logging_setup = True\n", + "\n", + "queries, is_complex = get_queries(queries_path)\n", + "model_type = set_model_type(is_complex, model_type)\n", + "\n", + "if \"multimer\" in model_type and max_msa is not None:\n", + " use_cluster_profile = False\n", + "else:\n", + " use_cluster_profile = True" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "### Run AlphaFold2 predictions" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "**Note:** IF USING AMBER RELAXATION: User may receive the following error during pLDDT reranking and may be unable to continue forward. Adding `run_relax=false` somewhere inside may help with this issue [see here](https://github.com/google-deepmind/alphafold/issues/112). \n", + "> ValueError: Minimization failed after 100 attempts." + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "cellView": "form", + "collapsed": true, + "id": "mbaIO9pWjaN0", + "jupyter": { + "outputs_hidden": true + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2023-10-12 15:30:50,210 Unable to initialize backend 'cuda': module 'jaxlib.xla_extension' has no attribute 'GpuAllocatorConfig'\n", + "2023-10-12 15:30:50,228 Unable to initialize backend 'rocm': module 'jaxlib.xla_extension' has no attribute 'GpuAllocatorConfig'\n", + "2023-10-12 15:30:50,229 Unable to initialize backend 'tpu': INVALID_ARGUMENT: TpuPlatform is not available.\n", + "2023-10-12 15:30:50,229 No GPU/TPU found, falling back to CPU. (Set TF_CPP_MIN_LOG_LEVEL=0 and rerun for more info.)\n", + "2023-10-12 15:30:50,229 WARNING: no GPU detected, will be using CPU\n", + "2023-10-12 15:30:58,115 Found 4 citations for tools or databases\n", + "2023-10-12 15:30:58,116 Query 1/1: test_a5e17_0 (length 59)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "COMPLETE: 100%|██████████| 150/150 [elapsed: 00:02 remaining: 00:00]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2023-10-12 15:31:01,304 Setting max_seq=512, max_extra_seq=5120\n", + "2023-10-12 15:33:18,608 alphafold2_ptm_model_1_seed_000 recycle=0 pLDDT=96.6 pTM=0.755\n", + "2023-10-12 15:35:13,837 alphafold2_ptm_model_1_seed_000 recycle=1 pLDDT=96.6 pTM=0.758 tol=0.297\n", + "2023-10-12 15:37:08,659 alphafold2_ptm_model_1_seed_000 recycle=2 pLDDT=96.4 pTM=0.757 tol=0.0433\n", + "2023-10-12 15:39:03,605 alphafold2_ptm_model_1_seed_000 recycle=3 pLDDT=96.2 pTM=0.757 tol=0.0395\n", + "2023-10-12 15:39:03,607 alphafold2_ptm_model_1_seed_000 took 476.0s (3 recycles)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2023-10-12 15:40:58,848 alphafold2_ptm_model_2_seed_000 recycle=0 pLDDT=96.9 pTM=0.76\n", + "2023-10-12 15:42:53,596 alphafold2_ptm_model_2_seed_000 recycle=1 pLDDT=97 pTM=0.765 tol=0.366\n", + "2023-10-12 15:44:47,713 alphafold2_ptm_model_2_seed_000 recycle=2 pLDDT=96.9 pTM=0.766 tol=0.124\n", + "2023-10-12 15:46:42,150 alphafold2_ptm_model_2_seed_000 recycle=3 pLDDT=96.8 pTM=0.767 tol=0.0802\n", + "2023-10-12 15:46:42,152 alphafold2_ptm_model_2_seed_000 took 458.4s (3 recycles)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2023-10-12 15:48:38,692 alphafold2_ptm_model_3_seed_000 recycle=0 pLDDT=97.2 pTM=0.775\n", + "2023-10-12 15:50:36,467 alphafold2_ptm_model_3_seed_000 recycle=1 pLDDT=97.4 pTM=0.782 tol=0.293\n", + "2023-10-12 15:52:34,415 alphafold2_ptm_model_3_seed_000 recycle=2 pLDDT=97.4 pTM=0.782 tol=0.116\n", + "2023-10-12 15:54:31,265 alphafold2_ptm_model_3_seed_000 recycle=3 pLDDT=97.4 pTM=0.784 tol=0.055\n", + "2023-10-12 15:54:31,266 alphafold2_ptm_model_3_seed_000 took 469.0s (3 recycles)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2023-10-12 15:56:25,472 alphafold2_ptm_model_4_seed_000 recycle=0 pLDDT=97.4 pTM=0.775\n", + "2023-10-12 15:58:19,697 alphafold2_ptm_model_4_seed_000 recycle=1 pLDDT=97.4 pTM=0.782 tol=0.29\n", + "2023-10-12 16:00:13,941 alphafold2_ptm_model_4_seed_000 recycle=2 pLDDT=97.2 pTM=0.778 tol=0.069\n", + "2023-10-12 16:02:08,141 alphafold2_ptm_model_4_seed_000 recycle=3 pLDDT=97.1 pTM=0.779 tol=0.0458\n", + "2023-10-12 16:02:08,143 alphafold2_ptm_model_4_seed_000 took 456.8s (3 recycles)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2023-10-12 16:04:01,285 alphafold2_ptm_model_5_seed_000 recycle=0 pLDDT=97.4 pTM=0.784\n", + "2023-10-12 16:05:55,664 alphafold2_ptm_model_5_seed_000 recycle=1 pLDDT=97 pTM=0.784 tol=0.234\n", + "2023-10-12 16:07:50,563 alphafold2_ptm_model_5_seed_000 recycle=2 pLDDT=96.4 pTM=0.777 tol=0.166\n", + "2023-10-12 16:09:45,743 alphafold2_ptm_model_5_seed_000 recycle=3 pLDDT=96.2 pTM=0.777 tol=0.128\n", + "2023-10-12 16:09:45,744 alphafold2_ptm_model_5_seed_000 took 457.5s (3 recycles)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2023-10-12 16:09:45,856 reranking models by 'plddt' metric\n", + "2023-10-12 16:09:45,857 rank_001_alphafold2_ptm_model_3_seed_000 pLDDT=97.4 pTM=0.784\n", + "2023-10-12 16:09:45,864 rank_002_alphafold2_ptm_model_4_seed_000 pLDDT=97.1 pTM=0.779\n", + "2023-10-12 16:09:45,871 rank_003_alphafold2_ptm_model_2_seed_000 pLDDT=96.8 pTM=0.767\n", + "2023-10-12 16:09:45,879 rank_004_alphafold2_ptm_model_5_seed_000 pLDDT=96.2 pTM=0.777\n", + "2023-10-12 16:09:45,886 rank_005_alphafold2_ptm_model_1_seed_000 pLDDT=96.2 pTM=0.757\n", + "2023-10-12 16:09:47,878 Done\n" + ] + } + ], + "source": [ + "download_alphafold_params(model_type, Path(\".\"))\n", + "results = run(\n", + " queries=queries,\n", + " result_dir=result_dir,\n", + " use_templates=use_templates,\n", + " custom_template_path=custom_template_path,\n", + " num_relax=num_relax,\n", + " msa_mode=msa_mode,\n", + " model_type=model_type,\n", + " num_models=5,\n", + " num_recycles=num_recycles,\n", + " recycle_early_stop_tolerance=recycle_early_stop_tolerance,\n", + " num_seeds=num_seeds,\n", + " use_dropout=use_dropout,\n", + " model_order=[1,2,3,4,5],\n", + " is_complex=is_complex,\n", + " data_dir=Path(\".\"),\n", + " keep_existing_results=False,\n", + " rank_by=\"auto\",\n", + " pair_mode=pair_mode,\n", + " pairing_strategy=pairing_strategy,\n", + " stop_at_score=float(100),\n", + " prediction_callback=prediction_callback,\n", + " dpi=dpi,\n", + " zip_results=False,\n", + " save_all=save_all,\n", + " max_msa=max_msa,\n", + " use_cluster_profile=use_cluster_profile,\n", + " input_features_callback=input_features_callback,\n", + " save_recycles=save_recycles,\n", + " user_agent=\"colabfold/google-colab-main\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "Once predictions are successfully generated, the results can be saved into the current directory as a zip file." + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'/home/jovyan/work/AlphaFold2/test_a5e17_0.result.zip'" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results = f\"{jobname}.result\"\n", + "\n", + "if not check(f\"{results}.zip\"):\n", + " n = 0\n", + " while not check(f\"{results}_{n}.zip\"): n += 1\n", + " results = f\"{results}_{n}\"\n", + " \n", + "shutil.make_archive(results, 'zip', jobname)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "**Result zip file contents** \n", + "1. PDB formatted structures sorted by avg. pLDDT and complexes are sorted by pTMscore. (unrelaxed and relaxed if `use_amber` is enabled).\n", + "2. Plots of the model quality.\n", + "3. Plots of the MSA coverage.\n", + "4. Parameter log file.\n", + "5. A3M formatted input MSA.\n", + "6. A `predicted_aligned_error_v1.json` using [AlphaFold-DB's format](https://alphafold.ebi.ac.uk/faq#faq-7) and a `scores.json` for each model which contains an array (list of lists) for PAE, a list with the average pLDDT and the pTMscore.\n", + "7. BibTeX file with citations for all used tools and databases." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [], + "user_expressions": [] + }, + "source": [ + "### Generate interactive widget to display 3D structure" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [], + "user_expressions": [] + }, + "source": [ + "Import additional packages to enable visualization in 3D. \n", + "**Note:** *`colabfold.colabfold.py` was renamed to `colabfold.cf.py` directly inside the package contents, and `colabfold.colabfold` was renamed to `colabfold.cf` in the next cell and inside `colabfold.batch.py`.*" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "import py3Dmol\n", + "import glob\n", + "import matplotlib.pyplot as plt\n", + "from colabfold.cf import plot_plddt_legend\n", + "from colabfold.cf import pymol_color_list, alphabet_list" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "Generate an interactive widget to select which ranked prediction to display, which specific color scheme, and whether to show sidechains and/or mainchains. The same widget types used earlier (`.HTML`, `.Dropdown`, `.Select`, `.Checkbox`) will be used for this family. \n", + "\n", + "***Note:*** *this will not update the 3D image in real time, so each time a different selection is selected, the cell containing the following functions will need to be rerun.* \n", + "    `show_pdb(rank_num, show_sidechains, show_mainchains, color).show()` \n", + "    `if color == \"pLDDT\": plot_plddt_legend().show()`" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "html_disp3d = widgets.HTML(description=\"Display 3D Structure:\",\n", + " value=\"\",\n", + " style= {'description_width': 'initial'},\n", + " layout=Layout(width='auto', grid_area='html_disp3d'))\n", + "\n", + "drop_rank_num = widgets.Dropdown(options=[('1',1),('2',2),('3',3),('4',4),('5',5)],\n", + " value=1,\n", + " description='rank_num:',\n", + " disabled=False,\n", + " layout=Layout(width='auto', grid_area='drop_rank_num'))\n", + "\n", + "select_color = widgets.Select(options=['chain','pLDDT','rainbow'],\n", + " value='pLDDT',\n", + " description='Color:',\n", + " rows=3,\n", + " disabled=False,\n", + " layout=Layout(width='auto', grid_area='select_color'))\n", + "\n", + "cb_sidechains = widgets.Checkbox(value=False,\n", + " description='show_sidechains',\n", + " disabled=False,\n", + " indent=True,\n", + " layout=Layout(width='auto', grid_area='cb_sidechains'))\n", + "\n", + "cb_mainchains = widgets.Checkbox(value=False,\n", + " description='show_mainchains',\n", + " disabled=False,\n", + " indent=True,\n", + " layout=Layout(width='auto', grid_area='cb_mainchains'))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "Display widget family." + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "5b5008b9917b4c95a243e972bfc43c48", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "GridBox(children=(HTML(value='', description='Display 3D Structure:', layout=Layout(grid_area='html_dis…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "controls_disp3d = GridBox(children=[html_disp3d, drop_rank_num, select_color, cb_sidechains, cb_mainchains],\n", + " layout=Layout(\n", + " border='solid 1.5px',\n", + " grid_template_rows='auto auto',\n", + " grid_template_columns='20% 20% 20%',\n", + " grid_template_areas='''\n", + " \"html_disp3d html_disp3d .\"\n", + " \"drop_rank_num select_color cb_sidechains\"\n", + " \". select_color cb_mainchains\"\n", + " ''')\n", + " )\n", + "\n", + "display(controls_disp3d)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "The following code cell helps apply the proper display settings for the predicted protein structure selected in the widget." + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "cellView": "form", + "id": "KK7X9T44pWb7" + }, + "outputs": [], + "source": [ + "def show_pdb(rank_num=1, show_sidechains=False, show_mainchains=False, color=\"pLDDT\"):\n", + " model_name = f\"rank_{rank_num}\"\n", + " view = py3Dmol.view(js='https://3dmol.org/build/3Dmol.js',)\n", + " #view.addModel(open(pdb_file[0],'r').read(),'pdb')\n", + " view.addModel(open(pdb_file[rank_num -1],'r').read(),'pdb')\n", + "\n", + " if color == \"pLDDT\":\n", + " view.setStyle({'cartoon': {'colorscheme': {'prop':'b','gradient': 'roygb','min':50,'max':90}}})\n", + " elif color == \"rainbow\":\n", + " view.setStyle({'cartoon': {'color':'spectrum'}})\n", + " elif color == \"chain\":\n", + " chains = len(queries[0][1]) + 1 if is_complex else 1\n", + " for n,chain,color in zip(range(chains),alphabet_list,pymol_color_list):\n", + " view.setStyle({'chain':chain},{'cartoon': {'color':color}})\n", + "\n", + " if show_sidechains:\n", + " BB = ['C','O','N']\n", + " view.addStyle({'and':[{'resn':[\"GLY\",\"PRO\"],'invert':True},{'atom':BB,'invert':True}]},\n", + " {'stick':{'colorscheme':f\"WhiteCarbon\",'radius':0.3}})\n", + " view.addStyle({'and':[{'resn':\"GLY\"},{'atom':'CA'}]},\n", + " {'sphere':{'colorscheme':f\"WhiteCarbon\",'radius':0.3}})\n", + " view.addStyle({'and':[{'resn':\"PRO\"},{'atom':['C','O'],'invert':True}]},\n", + " {'stick':{'colorscheme':f\"WhiteCarbon\",'radius':0.3}})\n", + " if show_mainchains:\n", + " BB = ['C','O','N','CA']\n", + " view.addStyle({'atom':BB},{'stick':{'colorscheme':f\"WhiteCarbon\",'radius':0.3}})\n", + "\n", + " view.zoomTo()\n", + " return view" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "Assign widget selections as accessible variables and show 3D protein structure." + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "application/3dmoljs_load.v0": "
\n

You appear to be running in JupyterLab (or JavaScript failed to load for some other reason). You need to install the 3dmol extension:
\n jupyter labextension install jupyterlab_3dmol

\n
\n", + "text/html": [ + "
\n", + "

You appear to be running in JupyterLab (or JavaScript failed to load for some other reason). You need to install the 3dmol extension:
\n", + " jupyter labextension install jupyterlab_3dmol

\n", + "
\n", + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "rank_num = drop_rank_num.value\n", + "color = select_color.value\n", + "show_sidechains = cb_sidechains.value\n", + "show_mainchains = cb_mainchains.value\n", + "\n", + "jobname_prefix = \".custom\" if msa_mode == \"custom\" else \"\"\n", + "pdb_file = sorted(glob.glob(f\"./{jobname}\"+\"/*.pdb\"))\n", + "\n", + "# show result\n", + "show_pdb(rank_num, show_sidechains, show_mainchains, color).show()\n", + "if color == \"pLDDT\":\n", + " plot_plddt_legend().show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [], + "user_expressions": [] + }, + "source": [ + "##### Plots" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "user_expressions": [] + }, + "source": [ + "Generate master plot of mini plots generated from the prediction." + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "cellView": "form", + "id": "11l8k--10q0C" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "

Plots for test_a5e17_0

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\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import base64\n", + "from html import escape\n", + "\n", + "# see: https://stackoverflow.com/a/53688522\n", + "def image_to_data_url(filename):\n", + " ext = filename.split('.')[-1]\n", + " prefix = f'data:image/{ext};base64,'\n", + " with open(filename, 'rb') as f:\n", + " img = f.read()\n", + " return prefix + base64.b64encode(img).decode('utf-8')\n", + "\n", + "pae = image_to_data_url(os.path.join(jobname,f\"{jobname}{jobname_prefix}_pae.png\"))\n", + "cov = image_to_data_url(os.path.join(jobname,f\"{jobname}{jobname_prefix}_coverage.png\"))\n", + "plddt = image_to_data_url(os.path.join(jobname,f\"{jobname}{jobname_prefix}_plddt.png\"))\n", + "display(HTML(f\"\"\"\n", + "\n", + "
\n", + "

Plots for {escape(jobname)}

\n", + " \n", + " \n", + " \n", + "
\n", + "\"\"\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "G4yBrceuFbf3", + "tags": [], + "user_expressions": [] + }, + "source": [ + "
\n", + "\n", + "**ColabFold v1.5.2-patch: AlphaFold2 using MMseqs2** \n", + "Easy-to-use protein structure and complex prediction using [AlphaFold2](https://www.nature.com/articles/s41586-021-03819-2) and [Alphafold2-multimer](https://www.biorxiv.org/content/10.1101/2021.10.04.463034v1). Sequence alignments/templates are generated through [MMseqs2](mmseqs.com) and [HHsearch](https://github.com/soedinglab/hh-suite). For more details, see [bottom](#Instructions) of the notebook, checkout the [ColabFold GitHub](https://github.com/sokrypton/ColabFold) and read the authors' manuscript: [Mirdita M, Schütze K, Moriwaki Y, Heo L, Ovchinnikov S, Steinegger M. ColabFold: Making protein folding accessible to all.\n", + "*Nature Methods*, 2022](https://www.nature.com/articles/s41592-022-01488-1).\n", + "Old versions: [v1.4](https://colab.research.google.com/github/sokrypton/ColabFold/blob/v1.4.0/AlphaFold2.ipynb), [v1.5.1](https://colab.research.google.com/github/sokrypton/ColabFold/blob/v1.5.1/AlphaFold2.ipynb)\n", + "\n", + "**LICENSE** \n", + "The source code of ColabFold is licensed under [MIT](https://raw.githubusercontent.com/sokrypton/ColabFold/main/LICENSE). Additionally, this notebook uses the AlphaFold2 source code and its parameters licensed under [Apache 2.0](https://raw.githubusercontent.com/deepmind/alphafold/main/LICENSE) and [CC BY 4.0](https://creativecommons.org/licenses/by-sa/4.0/) respectively. Read more about the AlphaFold license [here](https://github.com/deepmind/alphafold). \n", + "\n", + "**PDB100** \n", + "As of 23/06/08, ColabFold has transitioned from using the PDB70 to a 100% clustered PDB, the PDB100. The construction methodology of PDB100 differs from that of PDB70. \n", + "The PDB70 was constructed by running each PDB70 representative sequence through [HHblits](https://github.com/soedinglab/hh-suite) against the [Uniclust30](https://uniclust.mmseqs.com/). \n", + "On the other hand, the PDB100 is built by searching each PDB100 representative structure with [Foldseek](https://github.com/steineggerlab/foldseek) against the [AlphaFold Database](https://alphafold.ebi.ac.uk). \n", + "*To maintain compatibility with older Notebook versions and local installations, the generated files and API responses will continue to be named \"PDB70\", even though we're now using the PDB100.* \n", + "\n", + "**USING CUSTOM TEMPLATES** \n", + "\\- Custom templates must follow the four letter PDB naming with lower case letters. \n", + "\\- Templates in mmCIF format must contain `_entity_poly_seq`. An error is thrown if this field is not present. The field `_pdbx_audit_revision_history.revision_date` is automatically generated if it is not present. \n", + "\\- Templates in PDB format are automatically converted to the mmCIF format. `_entity_poly_seq` and `_pdbx_audit_revision_history.revision_date` are automatically generated. \n", + "\\- If you encounter problems, please report them to this [issue](https://github.com/sokrypton/ColabFold/issues/177).\n", + "\n", + "**COMPARISON TO THE FULL ALPHAFOLD2 AND ALPHAFOLD2 COLAB** \n", + "This notebook replaces the homology detection and MSA pairing of AlphaFold2 with MMseqs2. For a comparison against the [AlphaFold2 Colab](https://colab.research.google.com/github/deepmind/alphafold/blob/main/notebooks/AlphaFold.ipynb) and the full [AlphaFold2](https://github.com/deepmind/alphafold) system read our [paper](https://www.nature.com/articles/s41592-022-01488-1).\n", + "\n", + "**BUGS** \n", + "If you encounter any bugs in the original notebook, please report the issue to https://github.com/sokrypton/ColabFold/issues\n", + "\n", + "**LIMITATIONS** \n", + "*The ColabFold's authors recommend to additionally use the full [AlphaFold2 pipeline](https://github.com/deepmind/alphafold).* \n", + "\\- **Computing resources:** The original [ColabFold AlphaFold2 notebook](https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/AlphaFold2.ipynb) MMseqs2 API can handle ~20-50k requests per day. \n", + "\\- **MSAs:** MMseqs2 is very precise and sensitive but might find less hits compared to HHblits/HMMer searched against BFD or MGnify.\n", + "\n", + "**DESCRIPTION OF PLOTS** \n", + "\\- **Number of sequences per position** - We want to see at least 30 sequences per position, for best performance, ideally 100 sequences. \n", + "\\- **Predicted lDDT per position** - model confidence (out of 100) at each position. The higher the better. \n", + "\\- **Predicted Alignment Error** - For homooligomers, this could be a useful metric to assess how confident the model is about the interface. The lower the better. \n", + "\n", + "**COLABFOLD ACKNOWLEDGEMENTS** \n", + "\\- We thank the AlphaFold team for developing an excellent model and open sourcing the software. \n", + "\\- [KOBIC](https://kobic.re.kr) and [Söding Lab](https://www.mpinat.mpg.de/soeding) for providing the computational resources for the MMseqs2 MSA server. \n", + "\\- Richard Evans for helping to benchmark the ColabFold's Alphafold-multimer support. \n", + "\\- [David Koes](https://github.com/dkoes) for his awesome [py3Dmol](https://3dmol.csb.pitt.edu/) plugin, without whom these notebooks would be quite boring! \n", + "\\- Do-Yoon Kim for creating the ColabFold logo. \n", + "\\- A colab by Sergey Ovchinnikov ([@sokrypton](https://twitter.com/sokrypton)), Milot Mirdita ([@milot_mirdita](https://twitter.com/milot_mirdita)) and Martin Steinegger ([@thesteinegger](https://twitter.com/thesteinegger))." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "alphafold-test", + "language": "python", + "name": "alphafold-test" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/tutorials/AlphaFold2/ignore/environment.yml b/tutorials/AlphaFold2/ignore/environment.yml new file mode 100644 index 0000000..693f74a --- /dev/null +++ b/tutorials/AlphaFold2/ignore/environment.yml @@ -0,0 +1,220 @@ +name: /opt/modules/my/conda-envs/alphafold-test +channels: + - conda-forge + - bioconda + - defaults +dependencies: + - _libgcc_mutex=0.1=conda_forge + - _openmp_mutex=4.5=2_gnu + - asttokens=2.4.0=pyhd8ed1ab_0 + - backcall=0.2.0=pyh9f0ad1d_0 + - backports=1.0=pyhd8ed1ab_3 + - backports.functools_lru_cache=1.6.5=pyhd8ed1ab_0 + - bzip2=1.0.8=h7f98852_4 + - ca-certificates=2023.7.22=hbcca054_0 + - comm=0.1.4=pyhd8ed1ab_0 + - cudatoolkit=11.8.0=h4ba93d1_12 + - debugpy=1.8.0=py39h3d6467e_1 + - decorator=5.1.1=pyhd8ed1ab_0 + - exceptiongroup=1.1.3=pyhd8ed1ab_0 + - icu=73.2=h59595ed_0 + - importlib_metadata=6.8.0=hd8ed1ab_0 + - ipykernel=6.25.2=pyh2140261_0 + - ipython=8.16.1=pyh0d859eb_0 + - jedi=0.19.1=pyhd8ed1ab_0 + - jupyter_client=8.3.1=pyhd8ed1ab_0 + - jupyter_core=5.3.2=py39hf3d152e_0 + - ld_impl_linux-64=2.40=h41732ed_0 + - libexpat=2.5.0=hcb278e6_1 + - libffi=3.4.2=h7f98852_5 + - libgcc-ng=13.2.0=h807b86a_2 + - libgomp=13.2.0=h807b86a_2 + - libnsl=2.0.0=hd590300_1 + - libsodium=1.0.18=h36c2ea0_1 + - libsqlite=3.43.0=h2797004_0 + - libstdcxx-ng=13.2.0=h7e041cc_2 + - libuuid=2.38.1=h0b41bf4_0 + - libuv=1.46.0=hd590300_0 + - libzlib=1.2.13=hd590300_5 + - matplotlib-inline=0.1.6=pyhd8ed1ab_0 + - ncurses=6.4=hcb278e6_0 + - nodejs=20.8.0=hb753e55_0 + - openssl=3.1.3=hd590300_0 + - packaging=23.2=pyhd8ed1ab_0 + - parso=0.8.3=pyhd8ed1ab_0 + - pexpect=4.8.0=pyh1a96a4e_2 + - pickleshare=0.7.5=py_1003 + - pip=23.2.1=pyhd8ed1ab_0 + - platformdirs=3.11.0=pyhd8ed1ab_0 + - prompt-toolkit=3.0.39=pyha770c72_0 + - prompt_toolkit=3.0.39=hd8ed1ab_0 + - psutil=5.9.5=py39hd1e30aa_1 + - ptyprocess=0.7.0=pyhd3deb0d_0 + - pure_eval=0.2.2=pyhd8ed1ab_0 + - pygments=2.16.1=pyhd8ed1ab_0 + - python=3.9.18=h0755675_0_cpython + - python-dateutil=2.8.2=pyhd8ed1ab_0 + - python_abi=3.9=4_cp39 + - readline=8.2=h8228510_1 + - setuptools=68.2.2=pyhd8ed1ab_0 + - six=1.16.0=pyh6c4a22f_0 + - stack_data=0.6.2=pyhd8ed1ab_0 + - tk=8.6.13=h2797004_0 + - tornado=6.3.3=py39hd1e30aa_1 + - traitlets=5.11.2=pyhd8ed1ab_0 + - typing-extensions=4.8.0=hd8ed1ab_0 + - typing_extensions=4.8.0=pyha770c72_0 + - tzdata=2023c=h71feb2d_0 + - wcwidth=0.2.8=pyhd8ed1ab_0 + - wheel=0.41.2=pyhd8ed1ab_0 + - xz=5.2.6=h166bdaf_0 + - zeromq=4.3.4=h9c3ff4c_1 + - zipp=3.17.0=pyhd8ed1ab_0 + - zlib=1.2.13=hd590300_5 + - pip: + - absl-py==1.4.0 + - alphafold-colabfold==2.3.5 + - anyio==4.0.0 + - appdirs==1.4.4 + - argon2-cffi==23.1.0 + - argon2-cffi-bindings==21.2.0 + - arrow==1.3.0 + - astunparse==1.6.3 + - async-lru==2.0.4 + - attrs==23.1.0 + - babel==2.13.0 + - beautifulsoup4==4.12.2 + - biopython==1.81 + - bleach==6.0.0 + - bokeh==3.2.2 + - branca==0.6.0 + - cachetools==5.3.1 + - certifi==2023.7.22 + - cffi==1.16.0 + - charset-normalizer==3.3.0 + - chex==0.1.83 + - colabfold==1.5.2 + - contextlib2==21.6.0 + - contourpy==1.1.1 + - cycler==0.12.0 + - defusedxml==0.7.1 + - dm-haiku==0.0.9 + - dm-tree==0.1.8 + - docker==6.1.3 + - executing==2.0.0 + - fastjsonschema==2.18.1 + - flatbuffers==23.5.26 + - fonttools==4.43.0 + - fqdn==1.5.1 + - gast==0.5.4 + - google-auth==2.23.2 + - google-auth-oauthlib==1.0.0 + - google-pasta==0.2.0 + - grpcio==1.59.0 + - h5py==3.9.0 + - idna==3.4 + - immutabledict==3.0.0 + - importlib-metadata==4.13.0 + - importlib-resources==6.1.0 + - ipyfilechooser==0.6.0 + - ipyleaflet==0.17.4 + - ipython-genutils==0.2.0 + - ipywidgets==7.7.0 + - isoduration==20.11.0 + - jax==0.4.13 + - jaxlib==0.4.13 + - jinja2==3.1.2 + - jmp==0.0.4 + - json5==0.9.14 + - jsonpointer==2.4 + - jsonschema==4.19.1 + - jsonschema-specifications==2023.7.1 + - jupyter-bokeh==3.0.5 + - jupyter-events==0.7.0 + - jupyter-lsp==2.2.0 + - jupyter-server==2.7.3 + - jupyter-server-terminals==0.4.4 + - jupyterlab==4.0.6 + - jupyterlab-pygments==0.2.2 + - jupyterlab-server==2.25.0 + - jupyterlab-widgets==3.0.9 + - keras==2.14.0 + - kiwisolver==1.4.5 + - libclang==16.0.6 + - markdown==3.4.4 + - markupsafe==2.1.3 + - matplotlib==3.8.0 + - mistune==3.0.2 + - ml-collections==0.1.1 + - ml-dtypes==0.2.0 + - nbclient==0.8.0 + - nbconvert==7.9.2 + - nbformat==5.9.2 + - nest-asyncio==1.5.8 + - notebook==7.0.4 + - notebook-shim==0.2.3 + - numpy==1.26.0 + - nvidia-cublas-cu11==11.11.3.6 + - nvidia-cuda-cupti-cu11==11.8.87 + - nvidia-cuda-nvcc-cu11==11.8.89 + - nvidia-cuda-nvrtc-cu11==11.8.89 + - nvidia-cuda-runtime-cu11==11.8.89 + - nvidia-cudnn-cu11==8.9.4.25 + - nvidia-cufft-cu11==10.9.0.58 + - nvidia-cusolver-cu11==11.4.1.48 + - nvidia-cusparse-cu11==11.7.5.86 + - oauthlib==3.2.2 + - opt-einsum==3.3.0 + - overrides==7.4.0 + - pandas==1.5.3 + - pandocfilters==1.5.0 + - pillow==10.0.1 + - prometheus-client==0.17.1 + - protobuf==4.24.3 + - py3dmol==2.0.4 + - pyasn1==0.5.0 + - pyasn1-modules==0.3.0 + - pycparser==2.21 + - pyparsing==3.1.1 + - python-json-logger==2.0.7 + - pytz==2023.3.post1 + - pyyaml==6.0.1 + - pyzmq==24.0.1 + - referencing==0.30.2 + - requests==2.31.0 + - requests-oauthlib==1.3.1 + - rfc3339-validator==0.1.4 + - rfc3986-validator==0.1.1 + - rpds-py==0.10.4 + - rsa==4.9 + - scipy==1.11.3 + - sde==1.1.9 + - send2trash==1.8.2 + - sniffio==1.3.0 + - soupsieve==2.5 + - stack-data==0.6.3 + - tabulate==0.9.0 + - tensorboard==2.14.1 + - tensorboard-data-server==0.7.1 + - tensorflow-cpu==2.14.0 + - tensorflow-estimator==2.14.0 + - tensorflow-io-gcs-filesystem==0.34.0 + - termcolor==2.3.0 + - terminado==0.17.1 + - tinycss2==1.2.1 + - tomli==2.0.1 + - toolz==0.12.0 + - tqdm==4.66.1 + - traittypes==0.2.1 + - types-python-dateutil==2.8.19.14 + - uri-template==1.3.0 + - urllib3==2.0.6 + - webcolors==1.13 + - webencodings==0.5.1 + - websocket-client==1.6.3 + - werkzeug==3.0.0 + - widgetsnbextension==3.6.6 + - wrapt==1.14.1 + - xyzservices==2023.7.0 + - y-py==0.6.2 +prefix: /opt/modules/my/conda-envs/alphafold-test diff --git a/tutorials/AlphaFold2/ignore/jupyter_labextensions.txt b/tutorials/AlphaFold2/ignore/jupyter_labextensions.txt new file mode 100644 index 0000000..3dfa861 --- /dev/null +++ b/tutorials/AlphaFold2/ignore/jupyter_labextensions.txt @@ -0,0 +1,40 @@ +JupyterLab v4.0.6 +/opt/modules/my/conda-envs/alphafold-test/share/jupyter/labextensions + jupyterlab-datawidgets v7.1.2 enabled OK + jupyter-vue v1.8.0 enabled OK + jupyterlab_pygments v0.2.2 enabled X (python, jupyterlab_pygments) + jupyter-matplotlib v0.11.3 enabled OK + jupyter-vuetify v1.8.4 enabled X + bqplot v0.5.37 enabled X (python, bqplot) + ipyvolume v0.6.1 enabled OK + jupyter-threejs v2.4.0 enabled OK (python, pythreejs) + jupyter-leaflet v0.17.4 enabled OK + jupyterlab-plotly v5.12.0 enabled X + jupyter-webrtc v0.6.0 enabled OK + @beakerx/beakerx-tabledisplay v2.3.13 enabled X + @pyviz/jupyterlab_pyviz v2.3.2 enabled X (python, pyviz_comms) + @jupyter-notebook/lab-extension v7.0.4 enabled OK + @bokeh/jupyter_bokeh v3.0.5 enabled X (python, jupyter_bokeh) + @voila-dashboards/jupyterlab-preview v2.1.6 enabled X (python, voila) + @jupyter-widgets/jupyterlab-manager v3.1.3 enabled X (python, jupyterlab_widgets) + + + The following extensions are outdated: + jupyterlab_pygments + jupyter-vuetify + bqplot + jupyterlab-plotly + @beakerx/beakerx-tabledisplay + @pyviz/jupyterlab_pyviz + @bokeh/jupyter_bokeh + @voila-dashboards/jupyterlab-preview + @jupyter-widgets/jupyterlab-manager + + Consider checking if an update is available for these packages. + +Other labextensions (built into JupyterLab) + app dir: /opt/modules/my/conda-envs/alphafold-test/share/jupyter/lab + + +The following source extensions are overshadowed by older prebuilt extensions: + @jupyter-widgets/jupyterlab-manager \ No newline at end of file diff --git a/tutorials/AlphaFold2/requirements.txt b/tutorials/AlphaFold2/requirements.txt new file mode 100644 index 0000000..98f6f75 --- /dev/null +++ b/tutorials/AlphaFold2/requirements.txt @@ -0,0 +1,24 @@ +### list of packages installed into new environment + +### conda install +git + +### pip install the following +ipywidgets==7.7.1 +ipyfilechooser +ipykernel +alphafold-colabfold +colabfold @ git+https://github.com/sokrypton/ColabFold@36afbad707ea7401982e24c1cddd05e03c55c001 + +### ensure jax and jaxlib get installed using the following line +### these packages MUST be version 0.4.13 +pip install --upgrade jax==0.4.13 jaxlib==0.4.13+cuda12.cudnn89 -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html + +### conda install -c conda-forge +openmm==7.7.0 +pdbfixer + +### conda install -c bioconda +kalign2 +hhsuite +