From a300b8f40faf0d808ce4ae24cabfc2d42ca68e33 Mon Sep 17 00:00:00 2001 From: dominiquesydow Date: Mon, 1 Aug 2022 11:32:06 +0100 Subject: [PATCH 1/9] T029: Add talktorial draft --- .../talktorials/T029_query_gpcrdb/README.md | 55 + .../T029_query_gpcrdb/talktorial.ipynb | 5774 +++++++++++++++++ 2 files changed, 5829 insertions(+) create mode 100644 teachopencadd/talktorials/T029_query_gpcrdb/README.md create mode 100644 teachopencadd/talktorials/T029_query_gpcrdb/talktorial.ipynb diff --git a/teachopencadd/talktorials/T029_query_gpcrdb/README.md b/teachopencadd/talktorials/T029_query_gpcrdb/README.md new file mode 100644 index 00000000..e9356a41 --- /dev/null +++ b/teachopencadd/talktorials/T029_query_gpcrdb/README.md @@ -0,0 +1,55 @@ +# T029 · GPCR data acquisition (GPCRdb) + +**Note:** This talktorial is a part of TeachOpenCADD, a platform that aims to teach domain-specific skills and to provide pipeline templates as starting points for research projects. + +Authors: + +- Dominique Sydow, 2022, [Volkamer lab, Charité](https://volkamerlab.org/) + + +## Aim of this talktorial + +Add a short summary of this talktorial's content. + + +### Contents in *Theory* + +_Add Table of Contents (TOC) for Theory section._ + +* ChEMBL database +* Compound activity measures + + +
+ +Sync TOC with section titles: These points should refer to the headlines of your Theory section. + +
+ + +### Contents in *Practical* + +_Add Table of Contents (TOC) for Practical section._ + +* Connect to ChEMBL database +* Load and draw molecules + + +
+ +Sync TOC with section titles: These points should refer to the headlines of your Practical section. + +
+ + +### References + +* Paper +* Tutorial links +* Other useful resources + +*We suggest the following citation style:* +* Keyword describing resource: Journal (year), volume, pages (link to resource) + +*Example:* +* ChEMBL web services: [Nucleic Acids Res. (2015), 43, 612-620](https://academic.oup.com/nar/article/43/W1/W612/2467881) diff --git a/teachopencadd/talktorials/T029_query_gpcrdb/talktorial.ipynb b/teachopencadd/talktorials/T029_query_gpcrdb/talktorial.ipynb new file mode 100644 index 00000000..a0bff8b1 --- /dev/null +++ b/teachopencadd/talktorials/T029_query_gpcrdb/talktorial.ipynb @@ -0,0 +1,5774 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# T029 · GPCR data acquisition (GPCRdb)\n", + "\n", + "**Note:** This talktorial is a part of TeachOpenCADD, a platform that aims to teach domain-specific skills and to provide pipeline templates as starting points for research projects.\n", + "\n", + "Authors:\n", + "\n", + "- Dominique Sydow, 2022, [Volkamer lab, Charité](https://volkamerlab.org/)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Aim of this talktorial\n", + "\n", + "Add a short summary of this talktorial's content." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Contents in *Theory*\n", + "\n", + "_Add Table of Contents (TOC) for Theory section._\n", + "\n", + "* ChEMBL database\n", + "* Compound activity measures" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "Sync TOC with section titles: These points should refer to the headlines of your Theory section.\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Contents in *Practical*\n", + "\n", + "_Add Table of Contents (TOC) for Practical section._\n", + "\n", + "* Connect to ChEMBL database\n", + "* Load and draw molecules" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "Sync TOC with section titles: These points should refer to the headlines of your Practical section.\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### References\n", + "\n", + "* Paper \n", + "* Tutorial links\n", + "* Other useful resources\n", + "\n", + "*We suggest the following citation style:*\n", + "* Keyword describing resource: Journal (year), volume, pages (link to resource) \n", + "\n", + "*Example:*\n", + "* ChEMBL web services: [Nucleic Acids Res. (2015), 43, 612-620](https://academic.oup.com/nar/article/43/W1/W612/2467881) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Theory" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### GPCRs\n", + "\n", + "TODO" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### GPCRdb overview \n", + "\n", + "The GPCRdb is a GPCR data resource, similar to the kinase resource KLIFS, full of receptor-level annotations:\n", + "- Links to ChEMBL, Guide to Pharmacology (GtoPDB), and UniProt\n", + "- Endogenous ligands\n", + "- Residue mutations\n", + "- Homology models (active, inactive, and/or intermediate state)\n", + "- PDB structures\n", + "- Structure-based sequence alignments across the GPCRome (using a [GPCRdb numbering scheme](https://docs.gpcrdb.org/generic_numbering.html))\n", + "- Tons of analysis tools, e.g. different tools to visualize residues within the transmembrane helices (TMs), structure comparison tools, and phylogenetic trees" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### GPCRdb programmatic access\n", + "\n", + "The GPCRdb has --- just like KLIFS --- a Swagger API (https://gpcrdb.org/services/), allowing us to fetch some of the data programmatically, e.g. using Python. I could not find a Swagger definitions that would allow us to dynamically generate a Python client with `bravado` but we can easily send requests with `requests`. \n", + "\n", + "We can send queries (e.g. for β2-adrenoceptor) based on the following terms:\n", + "- `entry_name`: UniProt entry name (adrb2_human)\n", + "- `slug`: GPCRdb slugs (001_001_003_008, i.e. *class_ligand_subfamily_subtype*)\n", + "\n", + "Let's get receptor data on different levels:\n", + "- Protein\n", + "- Drugs\n", + "- Residues\n", + "- Structure\n", + "- Interactions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Practical" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import requests\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's define some example receptor and structure:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "query_receptor = \"adrb2_human\"\n", + "query_structure = \"3SN6\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Protein annotations for input receptor" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def protein_by_entry_name(entry_name):\n", + " \"\"\"\n", + " From the GPCRdb, get protein annotations associated with the input UniProt entry name.\n", + "\n", + " Parameters\n", + " ----------\n", + " entry_name : str\n", + " UniProt entry name for GPCR of interest.\n", + "\n", + " Returns\n", + " -------\n", + " dict\n", + " Protein annotations deposited in the GPCRdb.\n", + " \"\"\"\n", + "\n", + " url = f\"https://gpcrdb.org/services/protein/{entry_name}/\"\n", + " response = requests.get(url)\n", + " data = response.json()\n", + " return data" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'entry_name': 'adrb2_human',\n", + " 'name': 'β2-adrenoceptor',\n", + " 'accession': 'P07550',\n", + " 'family': '001_001_003_008',\n", + " 'species': 'Homo sapiens',\n", + " 'source': 'SWISSPROT',\n", + " 'residue_numbering_scheme': 'GPCRdb(A)',\n", + " 'sequence': 'MGQPGNGSAFLLAPNGSHAPDHDVTQERDEVWVVGMGIVMSLIVLAIVFGNVLVITAIAKFERLQTVTNYFITSLACADLVMGLAVVPFGAAHILMKMWTFGNFWCEFWTSIDVLCVTASIETLCVIAVDRYFAITSPFKYQSLLTKNKARVIILMVWIVSGLTSFLPIQMHWYRATHQEAINCYANETCCDFFTNQAYAIASSIVSFYVPLVIMVFVYSRVFQEAKRQLQKIDKSEGRFHVQNLSQVEQDGRTGHGLRRSSKFCLKEHKALKTLGIIMGTFTLCWLPFFIVNIVHVIQDNLIRKEVYILLNWIGYVNSGFNPLIYCRSPDFRIAFQELLCLRRSSLKAYGNGYSSNGNTGEQSGYHVEQEKENKLLCEDLPGTEDFVGHQGTVPSDNIDSQGRNCSTNDSLL',\n", + " 'genes': ['ADRB2', 'ADRB2R', 'B2AR']}" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "protein_by_entry_name(query_receptor)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Drug annotations for input receptor" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "def drugs_by_entry_name(entry_name):\n", + " \"\"\"\n", + " From the GPCRdb, get drugs associated with input UniProt entry name.\n", + "\n", + " Parameters\n", + " ----------\n", + " entry_name : str\n", + " UniProt entry name for GPCR of interest.\n", + "\n", + " Returns\n", + " -------\n", + " pandas.DataFrame\n", + " Drug annotations deposited in the GPCRdb.\n", + " \"\"\"\n", + "\n", + " url = f\"https://gpcrdb.org/services/drugs/{entry_name}/\"\n", + " response = requests.get(url)\n", + " data = response.json()\n", + " data = pd.DataFrame(data)\n", + " return data" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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0albuterol1981chronic obstructive pulmonary disease (COPD)approvedsmall moleculeagonistestablished
1alprenolol1966Anti-Arrhythmia ; Antihypertensiveapprovedsmall moleculeantagonistestablished
2arformoterol2006Bronchodilatorapprovedsmall moleculeagonistestablished
3bitolterol1984obstructive lung diseaseapprovedsmall moleculeagonistestablished
4carteolol1988cardiovascular diseaseapprovedsmall moleculeantagonistestablished
........................
70procaterol-Bronchodilatorin trial (recruiting, 2015)small moleculeagonistestablished
71pw2101-Antihypertensivein trial (discontinued, 2005)small moleculeantagonistestablished
72syl040012-Antiglaucomicin trial (completed, 2014)sirnaantagonistestablished
73vilanterol-chronic obstructive pulmonary disease (COPD)in trial (ongoing, 2016)small moleculeagonistestablished
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pdb_codeproteinfamilyspeciespreferred_chainresolutionpublication_datetypestatedistancepublicationligandssignalling_protein
02RH1adrb2_human001_001_003_008Homo sapiensA2.42007-10-30X-ray diffractionInactiveNonehttps://dx.doi.org/10.1126/SCIENCE.1150577[{'name': 'Carazolol', 'type': 'small-molecule...NaN
12R4Radrb2_human001_001_003_008Homo sapiensA3.42007-11-06X-ray diffractionInactiveNonehttps://dx.doi.org/10.1038/NATURE06325[]NaN
22R4Sadrb2_human001_001_003_008Homo sapiensA3.42007-11-06X-ray diffractionInactiveNoneNone[]NaN
33D4Sadrb2_human001_001_003_008Homo sapiensA2.82008-06-17X-ray diffractionInactiveNonehttps://dx.doi.org/10.1016/J.STR.2008.05.001[{'name': 'timolol', 'type': 'small-molecule',...NaN
43KJ6adrb2_human001_001_003_008Homo sapiensA3.42010-02-16X-ray diffractionInactiveNonehttps://dx.doi.org/10.1038/NATURE08650[]NaN
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" + ], + "text/plain": [ + " pdb_code protein family species preferred_chain \\\n", + "0 2RH1 adrb2_human 001_001_003_008 Homo sapiens A \n", + "1 2R4R adrb2_human 001_001_003_008 Homo sapiens A \n", + "2 2R4S adrb2_human 001_001_003_008 Homo sapiens A \n", + "3 3D4S adrb2_human 001_001_003_008 Homo sapiens A \n", + "4 3KJ6 adrb2_human 001_001_003_008 Homo sapiens A \n", + "\n", + " resolution publication_date type state distance \\\n", + "0 2.4 2007-10-30 X-ray diffraction Inactive None \n", + "1 3.4 2007-11-06 X-ray diffraction Inactive None \n", + "2 3.4 2007-11-06 X-ray diffraction Inactive None \n", + "3 2.8 2008-06-17 X-ray diffraction Inactive None \n", + "4 3.4 2010-02-16 X-ray diffraction Inactive None \n", + "\n", + " publication \\\n", + "0 https://dx.doi.org/10.1126/SCIENCE.1150577 \n", + "1 https://dx.doi.org/10.1038/NATURE06325 \n", + "2 None \n", + "3 https://dx.doi.org/10.1016/J.STR.2008.05.001 \n", + "4 https://dx.doi.org/10.1038/NATURE08650 \n", + "\n", + " ligands signalling_protein \n", + "0 [{'name': 'Carazolol', 'type': 'small-molecule... NaN \n", + "1 [] NaN \n", + "2 [] NaN \n", + "3 [{'name': 'timolol', 'type': 'small-molecule',... NaN \n", + "4 [] NaN " + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "structures = structures_by_entry_name(query_receptor)\n", + "print(f\"Number of structures: {len(structures)}\")\n", + "structures.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "state\n", + "Active 13\n", + "Inactive 24\n", + "dtype: int64" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "structures.groupby(\"state\").size()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Interaction annotations for input structure" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "def interactions_by_pdb_id(pdb_id):\n", + " \"\"\"\n", + " From the GPCRdb, get interactions between structure and ligand for input PDB ID.\n", + "\n", + " Parameters\n", + " ----------\n", + " pdb_id : str\n", + " PDB ID for GPCR structure of interest.\n", + "\n", + " Returns\n", + " -------\n", + " pandas.DataFrame\n", + " Interactions reported in the GPCRdb for input structure.\n", + " \"\"\"\n", + "\n", + " url = f\"https://gpcrdb.org/services/structure/{pdb_id}/interaction/\"\n", + " response = requests.get(url)\n", + " data = response.json()\n", + " data = pd.DataFrame(data)\n", + " return data" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of structures: 36\n" + ] + }, + { + "data": { + "text/html": [ + "
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pdb_codeligand_nameamino_acidsequence_numberdisplay_generic_numberinteraction_type
03SN6BI-167107W1093.28x28accessible
13SN6BI-167107W1093.28x28hydrophobic
23SN6BI-167107T1103.29x29accessible
33SN6BI-167107D1133.32x32polar (charge-charge)
43SN6BI-167107D1133.32x32accessible
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" + ], + "text/plain": [ + " pdb_code ligand_name amino_acid sequence_number display_generic_number \\\n", + "0 3SN6 BI-167107 W 109 3.28x28 \n", + "1 3SN6 BI-167107 W 109 3.28x28 \n", + "2 3SN6 BI-167107 T 110 3.29x29 \n", + "3 3SN6 BI-167107 D 113 3.32x32 \n", + "4 3SN6 BI-167107 D 113 3.32x32 \n", + "\n", + " interaction_type \n", + "0 accessible \n", + "1 hydrophobic \n", + "2 accessible \n", + "3 polar (charge-charge) \n", + "4 accessible " + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "interactions = interactions_by_pdb_id(query_structure)\n", + "print(f\"Number of structures: {len(interactions)}\")\n", + "interactions.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Case study: Receptor interaction profile for β2-adrenoceptor" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "def interactions_by_entry_name(entry_name, state=None):\n", + " \"\"\"\n", + " From the GPCRdb, get interactions for all structures associated with\n", + " the input UniProt entry name.\n", + "\n", + " Parameters\n", + " ----------\n", + " pdb_id : str\n", + " PDB ID for GPCR structure of interest.\n", + " state : None or str\n", + " Optional: Filter for structures in a specific receptor state (active, inactive).\n", + "\n", + " Returns\n", + " -------\n", + " pandas.DataFrame\n", + " Interactions reported in the GPCRdb for all structures for the input GPCR.\n", + " \"\"\"\n", + "\n", + " # Get all structures\n", + " structures = structures_by_entry_name(entry_name)\n", + " structures[\"state\"] = structures[\"state\"].str.lower()\n", + " states = structures[\"state\"].unique().tolist()\n", + "\n", + " # Select state\n", + " if state is None:\n", + " pass\n", + " elif state.lower() in states:\n", + " structures = structures[structures[\"state\"] == state.lower()]\n", + " else:\n", + " raise KeyError(f\"Unknown state {state}; choose from {states}\")\n", + "\n", + " # Get interactions for all structures\n", + " pdb_ids = structures[\"pdb_code\"].to_list()\n", + " receptor_interactions = []\n", + " for pdb_id in pdb_ids:\n", + " structure_interactions = interactions_by_pdb_id(pdb_id)\n", + " receptor_interactions.append(structure_interactions)\n", + " receptor_interactions = pd.concat(receptor_interactions)\n", + "\n", + " # Discard residues without generic numbering\n", + " receptor_interactions = receptor_interactions[\n", + " receptor_interactions[\"display_generic_number\"].notna()\n", + " ]\n", + "\n", + " return receptor_interactions" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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pdb_codeligand_nameamino_acidsequence_numberdisplay_generic_numberinteraction_type
02RH1CarazololW1093.28x28accessible
12RH1CarazololT1103.29x29accessible
22RH1CarazololD1133.32x32polar (charge-charge)
32RH1CarazololD1133.32x32accessible
42RH1CarazololD1133.32x32polar (charge-assisted hydrogen bond)
.....................
177DHRLEVISOPRENALINEF2906.52x52accessible
187DHRLEVISOPRENALINEN2936.55x55accessible
207DHRLEVISOPRENALINEN3127.39x38polar (hydrogen bond)
217DHRLEVISOPRENALINEN3127.39x38accessible
227DHRLEVISOPRENALINEY3167.43x42accessible
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1149 rows × 6 columns

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" + ], + "text/plain": [ + " pdb_code ligand_name amino_acid sequence_number \\\n", + "0 2RH1 Carazolol W 109 \n", + "1 2RH1 Carazolol T 110 \n", + "2 2RH1 Carazolol D 113 \n", + "3 2RH1 Carazolol D 113 \n", + "4 2RH1 Carazolol D 113 \n", + ".. ... ... ... ... \n", + "17 7DHR LEVISOPRENALINE F 290 \n", + "18 7DHR LEVISOPRENALINE N 293 \n", + "20 7DHR LEVISOPRENALINE N 312 \n", + "21 7DHR LEVISOPRENALINE N 312 \n", + "22 7DHR LEVISOPRENALINE Y 316 \n", + "\n", + " display_generic_number interaction_type \n", + "0 3.28x28 accessible \n", + "1 3.29x29 accessible \n", + "2 3.32x32 polar (charge-charge) \n", + "3 3.32x32 accessible \n", + "4 3.32x32 polar (charge-assisted hydrogen bond) \n", + ".. ... ... \n", + "17 6.52x52 accessible \n", + "18 6.55x55 accessible \n", + "20 7.39x38 polar (hydrogen bond) \n", + "21 7.39x38 accessible \n", + "22 7.43x42 accessible \n", + "\n", + "[1149 rows x 6 columns]" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "receptor_interactions = interactions_by_entry_name(\"adrb2_human\", state=None)\n", + "receptor_interactions" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Found the following interaction types:\n", + "- accessible\n", + "- aromatic (edge-to-face)\n", + "- aromatic (face-to-edge)\n", + "- aromatic (face-to-face)\n", + "- hydrophobic\n", + "- polar (charge-assisted hydrogen bond)\n", + "- polar (charge-charge)\n", + "- polar (hydrogen bond with backbone)\n", + "- polar (hydrogen bond)\n" + ] + } + ], + "source": [ + "# Report interaction types\n", + "print(\"\\nFound the following interaction types:\")\n", + "interaction_types = receptor_interactions[\"interaction_type\"].unique().tolist()\n", + "for interaction_type in sorted(interaction_types):\n", + " print(f\"- {interaction_type}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Interactions count per structure and residue (heatmap)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "def get_interactions_for_structures_vs_residues(interactions_df, interactions_type=None):\n", + " \"\"\"\n", + " Get interaction count matrix per structure and residue.\n", + "\n", + " Parameters\n", + " ----------\n", + " interactions_df : pandas.DataFrame\n", + " Output DataFrame from `interactions_by_entry_name` function.\n", + " interactions_type : None or str\n", + " Optional: Filter by a certain interaction type (can be a substring).\n", + "\n", + " Returns\n", + " -------\n", + " pandas.DataFrame\n", + " Structures vs residue matrix counting number of interactions for input interaction type.\n", + " \"\"\"\n", + "\n", + " if interactions_type is None:\n", + " pass\n", + " else:\n", + " interactions_type = interactions_type.lower()\n", + " interactions_df = interactions_df[\n", + " interactions_df[\"interaction_type\"].str.contains(interactions_type)\n", + " ]\n", + "\n", + " structures_vs_interactions_df = (\n", + " interactions_df.groupby([\"pdb_code\", \"display_generic_number\"])[\"interaction_type\"]\n", + " .apply(len)\n", + " .reset_index()\n", + " .pivot(index=\"pdb_code\", columns=\"display_generic_number\", values=\"interaction_type\")\n", + " .fillna(0)\n", + " .astype(int)\n", + " )\n", + " return structures_vs_interactions_df" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " 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display_generic_number12.49x492.40x402.64x633.32x323.33x333.37x373.41x414.41x4145.51x5145.52x525.39x405.42x435.45x465.46x4616.36x366.55x557.39x387.43x428.47x478.49x49
pdb_code                    
2RH100020000000100002100
3D4S00020100000000012100
3NY800010000000000003100
3NY900020000000000023000
3NYA00020000000000003100
3P0G00020000000201003000
3PDS00110000000301013000
3SN600010000000201013000
4GBR00020000000100001100
4LDE00020000000301013000
4LDL00020000100101002100
4LDO00020000000100013000
4QKX00220000000201002100
5D5A00020000000100002100
5D5B00020000000100002000
5D6L00020000000100002100
5JQH00020000000100002000
5X7D11020000000100103011
6E6700020000001301012000
6MXT00020000010301002000
6N4800020001000201013100
6NI300020000000101011000
6OBA00010010000010003000
6PRZ00020000000000003000
6PS000020000000100002000
6PS100021100000000012000
6PS200020000000000003000
6PS300020000000100004000
6PS400020000000000003000
6PS500020000000000003000
6PS600020100000000022000
7BZ200000000000200000200
7DHI00020000000201001000
7DHR00020000000001001000
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "structures_vs_interactions_df = get_interactions_for_structures_vs_residues(\n", + " receptor_interactions, interactions_type=\"polar\"\n", + ")\n", + "structures_vs_interactions_df.style.background_gradient()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Interaction type count per residue (barplot)\n", + "\n", + "Just like we did in TeachOpenCADD for KLIFS: https://projects.volkamerlab.org/teachopencadd/talktorials/T012_query_klifs.html#2.-Average-interaction-fingerprint" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "def get_residues_vs_interactions(interactions_df):\n", + " \"\"\"\n", + " Get interaction type count per residue (summarized over all structures for a given receptor).\n", + "\n", + " Parameters\n", + " ----------\n", + " interactions_df : pandas.DataFrame\n", + " Output DataFrame from `interactions_by_entry_name` function.\n", + "\n", + " Returns\n", + " -------\n", + " pandas.DataFrame\n", + " Residues vs interaction type matrix counting the number of structures showing interactions.\n", + " \"\"\"\n", + "\n", + " # Simplify interaction types\n", + " interactions_df[\"interaction_type\"] = interactions_df[\"interaction_type\"].apply(\n", + " lambda x: x.split(\" (\")[0]\n", + " )\n", + "\n", + " # Is interaction present per structure and residue (do not keep count)\n", + " interactions_deduplicated_df = (\n", + " interactions_df[[\"pdb_code\", \"display_generic_number\", \"interaction_type\"]]\n", + " .drop_duplicates()\n", + " .copy()\n", + " )\n", + " interactions_deduplicated_df\n", + "\n", + " # Transform DataFrame into presence/absence matrix residues vs interaction types\n", + " residues_vs_interactions_df = (\n", + " interactions_deduplicated_df.groupby([\"display_generic_number\", \"interaction_type\"])\n", + " .apply(len)\n", + " .reset_index()\n", + " .rename(columns={0: \"count\"})\n", + " .pivot(index=\"display_generic_number\", columns=\"interaction_type\", values=\"count\")\n", + " .fillna(0)\n", + " .astype(int)\n", + " )\n", + "\n", + " return residues_vs_interactions_df" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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interaction_typeaccessiblearomatichydrophobicpolar
display_generic_number
1.53x531000
1.56x561000
1.57x571000
1.60x601000
12.49x491001
\n", + "
" + ], + "text/plain": [ + "interaction_type accessible aromatic hydrophobic polar\n", + "display_generic_number \n", + "1.53x53 1 0 0 0\n", + "1.56x56 1 0 0 0\n", + "1.57x57 1 0 0 0\n", + "1.60x60 1 0 0 0\n", + "12.49x49 1 0 0 1" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "residues_vs_interactions_df = get_residues_vs_interactions(receptor_interactions)\n", + "residues_vs_interactions_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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stateactiveinactive
interaction_typeaccessiblearomatichydrophobicpolaraccessiblearomatichydrophobicpolar
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...........................
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8.53x5300001000
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72 rows × 8 columns

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" + ], + "text/plain": [ + "state active inactive \\\n", + "interaction_type accessible aromatic hydrophobic polar accessible \n", + "display_generic_number \n", + "2.42x42 1 0 0 0 0 \n", + "2.61x60 1 0 0 0 2 \n", + "2.64x63 8 1 0 1 2 \n", + "2.65x64 1 0 0 0 2 \n", + "23.50x50 1 0 0 0 1 \n", + "... ... ... ... ... ... \n", + "8.48x48 0 0 0 0 1 \n", + "8.49x49 0 0 0 0 1 \n", + "8.50x50 0 0 0 0 1 \n", + "8.52x52 0 0 0 0 1 \n", + "8.53x53 0 0 0 0 1 \n", + "\n", + "state \n", + "interaction_type aromatic hydrophobic polar \n", + "display_generic_number \n", + "2.42x42 0 0 0 \n", + "2.61x60 0 0 0 \n", + "2.64x63 1 0 1 \n", + "2.65x64 0 0 0 \n", + "23.50x50 0 0 0 \n", + "... ... ... ... \n", + "8.48x48 0 0 0 \n", + "8.49x49 0 0 1 \n", + "8.50x50 0 1 0 \n", + "8.52x52 0 0 0 \n", + "8.53x53 0 0 0 \n", + "\n", + "[72 rows x 8 columns]" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "receptor_interactions_by_state = {}\n", + "for state in [\"active\", \"inactive\"]:\n", + " _df = interactions_by_entry_name(query_receptor, state=state)\n", + " _df2 = get_residues_vs_interactions(_df)\n", + " # Add multicolumn (state and interaction type)\n", + " _df2 = pd.concat([_df2.transpose()], keys=[state], names=[\"state\"]).transpose()\n", + " receptor_interactions_by_state[state] = _df2\n", + "\n", + "# Concat both DataFrames in order to align residues\n", + "_tmp = pd.concat(list(receptor_interactions_by_state.values()), axis=1).fillna(0).astype(int)\n", + "_tmp" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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stateactiveinactive
interaction_typeaccessiblearomatichydrophobicpolaraccessiblearomatichydrophobicpolar
10.53x5300001000
10.56x5600001000
10.57x5700001000
10.60x6000001000
12.49x4900001001
...........................
80.48x4800001000
80.49x4900001001
80.50x5000001010
80.52x5200001000
80.53x5300001000
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72 rows × 8 columns

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" + ], + "text/plain": [ + "state active inactive \\\n", + "interaction_type accessible aromatic hydrophobic polar accessible aromatic \n", + "10.53x53 0 0 0 0 1 0 \n", + "10.56x56 0 0 0 0 1 0 \n", + "10.57x57 0 0 0 0 1 0 \n", + "10.60x60 0 0 0 0 1 0 \n", + "12.49x49 0 0 0 0 1 0 \n", + "... ... ... ... ... ... ... \n", + "80.48x48 0 0 0 0 1 0 \n", + "80.49x49 0 0 0 0 1 0 \n", + "80.50x50 0 0 0 0 1 0 \n", + "80.52x52 0 0 0 0 1 0 \n", + "80.53x53 0 0 0 0 1 0 \n", + "\n", + "state \n", + "interaction_type hydrophobic polar \n", + "10.53x53 0 0 \n", + "10.56x56 0 0 \n", + "10.57x57 0 0 \n", + "10.60x60 0 0 \n", + "12.49x49 0 1 \n", + "... ... ... \n", + "80.48x48 0 0 \n", + "80.49x49 0 1 \n", + "80.50x50 1 0 \n", + "80.52x52 0 0 \n", + "80.53x53 0 0 \n", + "\n", + "[72 rows x 8 columns]" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Sort residues (index)\n", + "# Requires the trick of replacing 1. with 10. etc for correct sorting\n", + "_index_tmp = []\n", + "for i in _tmp.index:\n", + " i1, i2 = i.split(\".\")[0], i.split(\".\")[1]\n", + " if len(i1) == 1:\n", + " i_new = f\"{i1}0.{i2}\"\n", + " else:\n", + " i_new = i\n", + " _index_tmp.append(i_new)\n", + "_tmp.index = _index_tmp\n", + "_tmp = _tmp.sort_index()\n", + "_tmp" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "f, (ax1, ax2) = plt.subplots(2, 1, figsize=(15, 10), sharex=True)\n", + "for ax, state in zip([ax1, ax2], [\"active\", \"inactive\"]):\n", + " _tmp[state].plot.bar(\n", + " ax=ax,\n", + " stacked=True,\n", + " xlabel=\"GPCR residues (GPCRdb generic numbering)\",\n", + " ylabel=f\"Number of {state.upper()} structures\",\n", + " color=[\"grey\", \"cornflowerblue\", \"gold\", \"limegreen\"],\n", + " );" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Discussion\n", + "\n", + "Wrap up the talktorial's content here and discuss pros/cons and open questions/challenges." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Quiz\n", + "\n", + "Ask three questions that the user should be able to answer after doing this talktorial. Choose important take-aways from this talktorial for your questions.\n", + "\n", + "1. Question\n", + "2. Question\n", + "3. Question" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "Useful checks at the end: \n", + " \n", + "
    \n", + "
  • Clear output and rerun your complete notebook. Does it finish without errors?
    • \n", + "
  • Check if your talktorial's runtime is as excepted. If not, try to find out which step(s) take unexpectedly long.
    • \n", + "
  • Flag code cells with # NBVAL_CHECK_OUTPUT that have deterministic output and should be tested within our Continuous Integration (CI) framework.
    • \n", + "
\n", + "\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.9.13" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From bc8aa2e0c4cd01683e6c636631300f14b8cf1933 Mon Sep 17 00:00:00 2001 From: dominiquesydow Date: Mon, 1 Aug 2022 11:32:36 +0100 Subject: [PATCH 2/9] CI: Temporarily restrict CI to T029 --- .github/workflows/ci.yml | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index c1d5e230..263dbbf4 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -76,11 +76,12 @@ jobs: shell: bash -l {0} run: | PYTEST_ARGS="--nbval-lax --current-env --dist loadscope --numprocesses 2" - if [ "$RUNNER_OS" != "Windows" ]; then - pytest $PYTEST_ARGS teachopencadd/talktorials/T*/talktorial.ipynb - else - pytest $PYTEST_ARGS teachopencadd/talktorials/ --ignore=teachopencadd/talktorials/T008_md_simulation/talktorial.ipynb --ignore=teachopencadd/talktorials/T019_md_simulation/talktorial.ipynb - fi + pytest $PYTEST_ARGS teachopencadd/talktorials/T029_query_gpcrdb/talktorial.ipynb + #if [ "$RUNNER_OS" != "Windows" ]; then + # pytest $PYTEST_ARGS teachopencadd/talktorials/T*/talktorial.ipynb + #else + # pytest $PYTEST_ARGS teachopencadd/talktorials/ --ignore=teachopencadd/talktorials/T008_md_simulation/talktorial.ipynb --ignore=teachopencadd/talktorials/T019_md_simulation/talktorial.ipynb + #fi format: name: Black From adebcd59813c37d73454a12a1be6b105d7e8e5ab Mon Sep 17 00:00:00 2001 From: dominiquesydow Date: Wed, 3 Aug 2022 14:46:47 +0100 Subject: [PATCH 3/9] Test (verified commit?) From 06b7e8c9ee84ff03bafffdcd445658c1ffd5db9b Mon Sep 17 00:00:00 2001 From: dominiquesydow Date: Wed, 3 Aug 2022 15:17:47 +0100 Subject: [PATCH 4/9] Test From 5c522ca3eb14024d6fdf7776b6e6d06a6275cd0c Mon Sep 17 00:00:00 2001 From: dominiquesydow Date: Wed, 3 Aug 2022 15:19:52 +0100 Subject: [PATCH 5/9] Test [skip ci] From 7f246a35b4ad1ae9ffe4c93de6f8f6d497dab71e Mon Sep 17 00:00:00 2001 From: dominiquesydow Date: Wed, 3 Aug 2022 15:23:11 +0100 Subject: [PATCH 6/9] Test [skip ci] From 607351c6116d005f92e0a2404a2309d5269eb20e Mon Sep 17 00:00:00 2001 From: dominiquesydow Date: Wed, 3 Aug 2022 15:27:12 +0100 Subject: [PATCH 7/9] Test [skip ci] From 7caaeb1e14e0748d88695a8b3b727f15800e892e Mon Sep 17 00:00:00 2001 From: dominiquesydow Date: Wed, 3 Aug 2022 15:31:13 +0100 Subject: [PATCH 8/9] Test [skip ci] From 3f38ae2d21fdd7bca3b0ad67177d8edc87436e20 Mon Sep 17 00:00:00 2001 From: dominiquesydow Date: Thu, 4 Aug 2022 10:06:57 +0100 Subject: [PATCH 9/9] Trigger CI