DeepRank-GNN: a graph neural network framework to learn patterns in protein–protein interfaces
Abstract Motivation Gaining structural insights into the protein–protein interactome is essential to understand biological phenomena and extract knowledge for rational drug design or protein engineering. We have previously developed DeepRank, a deep-learning framework to facilitate pattern learning...
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| Published in: | Bioinformatics (Oxford, England) Vol. 39; no. 1 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
England
Oxford University Press
01.01.2023
Oxford Publishing Limited (England) |
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| ISSN: | 1367-4811, 1367-4803, 1367-4811 |
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| Abstract | Abstract
Motivation
Gaining structural insights into the protein–protein interactome is essential to understand biological phenomena and extract knowledge for rational drug design or protein engineering. We have previously developed DeepRank, a deep-learning framework to facilitate pattern learning from protein–protein interfaces using convolutional neural network (CNN) approaches. However, CNN is not rotation invariant and data augmentation is required to desensitize the network to the input data orientation which dramatically impairs the computation performance. Representing protein–protein complexes as atomic- or residue-scale rotation invariant graphs instead enables using graph neural networks (GNN) approaches, bypassing those limitations.
Results
We have developed DeepRank-GNN, a framework that converts protein–protein interfaces from PDB 3D coordinates files into graphs that are further provided to a pre-defined or user-defined GNN architecture to learn problem-specific interaction patterns. DeepRank-GNN is designed to be highly modularizable, easily customized and is wrapped into a user-friendly python3 package. Here, we showcase DeepRank-GNN’s performance on two applications using a dedicated graph interaction neural network: (i) the scoring of docking poses and (ii) the discriminating of biological and crystal interfaces. In addition to the highly competitive performance obtained in those tasks as compared to state-of-the-art methods, we show a significant improvement in speed and storage requirement using DeepRank-GNN as compared to DeepRank.
Availability and implementation
DeepRank-GNN is freely available from https://github.com/DeepRank/DeepRank-GNN.
Supplementary information
Supplementary data are available at Bioinformatics online. |
|---|---|
| AbstractList | Abstract
Motivation
Gaining structural insights into the protein–protein interactome is essential to understand biological phenomena and extract knowledge for rational drug design or protein engineering. We have previously developed DeepRank, a deep-learning framework to facilitate pattern learning from protein–protein interfaces using convolutional neural network (CNN) approaches. However, CNN is not rotation invariant and data augmentation is required to desensitize the network to the input data orientation which dramatically impairs the computation performance. Representing protein–protein complexes as atomic- or residue-scale rotation invariant graphs instead enables using graph neural networks (GNN) approaches, bypassing those limitations.
Results
We have developed DeepRank-GNN, a framework that converts protein–protein interfaces from PDB 3D coordinates files into graphs that are further provided to a pre-defined or user-defined GNN architecture to learn problem-specific interaction patterns. DeepRank-GNN is designed to be highly modularizable, easily customized and is wrapped into a user-friendly python3 package. Here, we showcase DeepRank-GNN’s performance on two applications using a dedicated graph interaction neural network: (i) the scoring of docking poses and (ii) the discriminating of biological and crystal interfaces. In addition to the highly competitive performance obtained in those tasks as compared to state-of-the-art methods, we show a significant improvement in speed and storage requirement using DeepRank-GNN as compared to DeepRank.
Availability and implementation
DeepRank-GNN is freely available from https://github.com/DeepRank/DeepRank-GNN.
Supplementary information
Supplementary data are available at Bioinformatics online. Gaining structural insights into the protein-protein interactome is essential to understand biological phenomena and extract knowledge for rational drug design or protein engineering. We have previously developed DeepRank, a deep-learning framework to facilitate pattern learning from protein-protein interfaces using convolutional neural network (CNN) approaches. However, CNN is not rotation invariant and data augmentation is required to desensitize the network to the input data orientation which dramatically impairs the computation performance. Representing protein-protein complexes as atomic- or residue-scale rotation invariant graphs instead enables using graph neural networks (GNN) approaches, bypassing those limitations. We have developed DeepRank-GNN, a framework that converts protein-protein interfaces from PDB 3D coordinates files into graphs that are further provided to a pre-defined or user-defined GNN architecture to learn problem-specific interaction patterns. DeepRank-GNN is designed to be highly modularizable, easily customized and is wrapped into a user-friendly python3 package. Here, we showcase DeepRank-GNN's performance on two applications using a dedicated graph interaction neural network: (i) the scoring of docking poses and (ii) the discriminating of biological and crystal interfaces. In addition to the highly competitive performance obtained in those tasks as compared to state-of-the-art methods, we show a significant improvement in speed and storage requirement using DeepRank-GNN as compared to DeepRank. DeepRank-GNN is freely available from https://github.com/DeepRank/DeepRank-GNN. Supplementary data are available at Bioinformatics online. Gaining structural insights into the protein-protein interactome is essential to understand biological phenomena and extract knowledge for rational drug design or protein engineering. We have previously developed DeepRank, a deep-learning framework to facilitate pattern learning from protein-protein interfaces using convolutional neural network (CNN) approaches. However, CNN is not rotation invariant and data augmentation is required to desensitize the network to the input data orientation which dramatically impairs the computation performance. Representing protein-protein complexes as atomic- or residue-scale rotation invariant graphs instead enables using graph neural networks (GNN) approaches, bypassing those limitations.MOTIVATIONGaining structural insights into the protein-protein interactome is essential to understand biological phenomena and extract knowledge for rational drug design or protein engineering. We have previously developed DeepRank, a deep-learning framework to facilitate pattern learning from protein-protein interfaces using convolutional neural network (CNN) approaches. However, CNN is not rotation invariant and data augmentation is required to desensitize the network to the input data orientation which dramatically impairs the computation performance. Representing protein-protein complexes as atomic- or residue-scale rotation invariant graphs instead enables using graph neural networks (GNN) approaches, bypassing those limitations.We have developed DeepRank-GNN, a framework that converts protein-protein interfaces from PDB 3D coordinates files into graphs that are further provided to a pre-defined or user-defined GNN architecture to learn problem-specific interaction patterns. DeepRank-GNN is designed to be highly modularizable, easily customized and is wrapped into a user-friendly python3 package. Here, we showcase DeepRank-GNN's performance on two applications using a dedicated graph interaction neural network: (i) the scoring of docking poses and (ii) the discriminating of biological and crystal interfaces. In addition to the highly competitive performance obtained in those tasks as compared to state-of-the-art methods, we show a significant improvement in speed and storage requirement using DeepRank-GNN as compared to DeepRank.RESULTSWe have developed DeepRank-GNN, a framework that converts protein-protein interfaces from PDB 3D coordinates files into graphs that are further provided to a pre-defined or user-defined GNN architecture to learn problem-specific interaction patterns. DeepRank-GNN is designed to be highly modularizable, easily customized and is wrapped into a user-friendly python3 package. Here, we showcase DeepRank-GNN's performance on two applications using a dedicated graph interaction neural network: (i) the scoring of docking poses and (ii) the discriminating of biological and crystal interfaces. In addition to the highly competitive performance obtained in those tasks as compared to state-of-the-art methods, we show a significant improvement in speed and storage requirement using DeepRank-GNN as compared to DeepRank.DeepRank-GNN is freely available from https://github.com/DeepRank/DeepRank-GNN.AVAILABILITY AND IMPLEMENTATIONDeepRank-GNN is freely available from https://github.com/DeepRank/DeepRank-GNN.Supplementary data are available at Bioinformatics online.SUPPLEMENTARY INFORMATIONSupplementary data are available at Bioinformatics online. Motivation Gaining structural insights into the protein–protein interactome is essential to understand biological phenomena and extract knowledge for rational drug design or protein engineering. We have previously developed DeepRank, a deep-learning framework to facilitate pattern learning from protein–protein interfaces using convolutional neural network (CNN) approaches. However, CNN is not rotation invariant and data augmentation is required to desensitize the network to the input data orientation which dramatically impairs the computation performance. Representing protein–protein complexes as atomic- or residue-scale rotation invariant graphs instead enables using graph neural networks (GNN) approaches, bypassing those limitations. Results We have developed DeepRank-GNN, a framework that converts protein–protein interfaces from PDB 3D coordinates files into graphs that are further provided to a pre-defined or user-defined GNN architecture to learn problem-specific interaction patterns. DeepRank-GNN is designed to be highly modularizable, easily customized and is wrapped into a user-friendly python3 package. Here, we showcase DeepRank-GNN’s performance on two applications using a dedicated graph interaction neural network: (i) the scoring of docking poses and (ii) the discriminating of biological and crystal interfaces. In addition to the highly competitive performance obtained in those tasks as compared to state-of-the-art methods, we show a significant improvement in speed and storage requirement using DeepRank-GNN as compared to DeepRank. Availability and implementation DeepRank-GNN is freely available from https://github.com/DeepRank/DeepRank-GNN. Supplementary information Supplementary data are available at Bioinformatics online. |
| Author | Réau, Manon Renaud, Nicolas Xue, Li C Bonvin, Alexandre M J J |
| Author_xml | – sequence: 1 givenname: Manon surname: Réau fullname: Réau, Manon – sequence: 2 givenname: Nicolas surname: Renaud fullname: Renaud, Nicolas – sequence: 3 givenname: Li C surname: Xue fullname: Xue, Li C – sequence: 4 givenname: Alexandre M J J orcidid: 0000-0001-7369-1322 surname: Bonvin fullname: Bonvin, Alexandre M J J email: a.m.j.j.bonvin@uu.nl |
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| Cites_doi | 10.1145/3065386 10.1021/acs.jcim.9b00927 10.1002/wcms.1410 10.1002/prot.21804 10.1021/acs.jcim.6b00740 10.1016/j.jmb.2015.07.016 10.1002/prot.26222 10.1093/bioinformatics/btaa714 10.1093/nar/gkz991 10.1038/s41592-019-0666-6 10.1101/2021.01.29.425727 10.1002/prot.25888 10.1093/bioinformatics/btz496 10.1186/1471-2105-13-334 10.1002/prot.24678 10.1093/bioinformatics/btab118 10.1371/journal.pone.0249404 10.1093/bioinformatics/btz122 10.3389/fenvs.2015.00080 10.1021/acs.jcim.9b00628 10.1021/acs.jcim.7b00650 10.1002/prot.25007 10.1186/s12900-014-0022-0 10.1021/acs.jcim.0c00411 10.1021/acsomega.9b04162 10.3389/fmolb.2021.647915 10.1038/s41586-021-03819-2 10.1016/j.jmb.2015.09.014 10.1093/bioinformatics/btz870 |
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| References | Igashov (2023010107544976300_btac759-B13) 2021; 37 Renaud (2023010107544976300_btac759-B30) 2020 Mahbub (2023010107544976300_btac759-B24) 2022 Geng (2023010107544976300_btac759-B12) 2020; 36 Lensink (2023010107544976300_btac759-B19) 2007; 69 Gainza (2023010107544976300_btac759-B10) 2020; 17 Jiménez (2023010107544976300_btac759-B14) 2018; 58 Ragoza (2023010107544976300_btac759-B28) 2017; 57 Vreven (2023010107544976300_btac759-B37) 2015; 427 Fout (2023010107544976300_btac759-B8) 2017 Wang (2023010107544976300_btac759-B38) 2020; 36 Krizhevsky (2023010107544976300_btac759-B18) 2017; 60 Pagès (2023010107544976300_btac759-B27) 2019; 35 Renaud (2023010107544976300_btac759-B33) 2021 Cao (2023010107544976300_btac759-B4) 2020; 88 Son (2023010107544976300_btac759-B35) 2021; 16 Geng (2023010107544976300_btac759-B11) 2019; 9 Kingma (2023010107544976300_btac759-B17) 2017 Morrone (2023010107544976300_btac759-B26) 2020; 60 Paszke (2023010107544976300_btac759-B7) 2017 Renaud (2023010107544976300_btac759-B31) 2021 Lensink (2023010107544976300_btac759-B21) 2021; 89 van Zundert (2023010107544976300_btac759-B40) 2016; 428 Réau (2023010107544976300_btac759-B29) 2021 Hagberg (2023010107544976300_btac759-B1200) 2008 Lensink (2023010107544976300_btac759-B22) 2014; 82 Francoeur (2023010107544976300_btac759-B9) 2020; 60 Duarte (2023010107544976300_btac759-B5) 2012; 13 Lu (2023010107544976300_btac759-B23) 2020; 48 Wang (2023010107544976300_btac759-B39) 2021; 8 Lensink (2023010107544976300_btac759-B20) 2016; 84 Mayr (2023010107544976300_btac759-B25) 2016; 3 Baskaran (2023010107544976300_btac759-B2) 2014; 14 Jumper (2023010107544976300_btac759-B15) 2021; 596 Torng (2023010107544976300_btac759-B36) 2019; 59 Baldassarre (2023010107544976300_btac759-B1) 2021; 37 Renaud (2023010107544976300_btac759-B32) 2021 Karlov (2023010107544976300_btac759-B16) 2020; 5 |
| References_xml | – volume: 60 start-page: 84 year: 2017 ident: 2023010107544976300_btac759-B18 article-title: ImageNet classification with deep convolutional neural networks publication-title: Commun. ACM doi: 10.1145/3065386 – volume: 60 start-page: 4170 year: 2020 ident: 2023010107544976300_btac759-B26 article-title: Combining docking pose rank and structure with deep learning improves protein-ligand binding mode prediction publication-title: J. Chem. Inf. Model doi: 10.1021/acs.jcim.9b00927 – volume: 9 start-page: e1410 year: 2019 ident: 2023010107544976300_btac759-B11 article-title: Finding the ΔΔG spot: are predictors of binding affinity changes upon mutations in protein–protein interactions ready for it? publication-title: WIREs Comput. Mol. Sci doi: 10.1002/wcms.1410 – volume: 69 start-page: 704 year: 2007 ident: 2023010107544976300_btac759-B19 article-title: Docking and scoring protein complexes: CAPRI 3rd edition publication-title: Proteins doi: 10.1002/prot.21804 – volume: 57 start-page: 942 year: 2017 ident: 2023010107544976300_btac759-B28 article-title: Protein–ligand scoring with convolutional neural networks publication-title: J. Chem. Inf. Model doi: 10.1021/acs.jcim.6b00740 – year: 2021 ident: 2023010107544976300_btac759-B32 – volume: 427 start-page: 3031 year: 2015 ident: 2023010107544976300_btac759-B37 article-title: Updates to the integrated protein-protein interaction benchmarks: docking benchmark version 5 and affinity benchmark version 2 publication-title: J. Mol. Biol doi: 10.1016/j.jmb.2015.07.016 – year: 2021 ident: 2023010107544976300_btac759-B33 – volume: 89 start-page: 1800 year: 2021 ident: 2023010107544976300_btac759-B21 article-title: Prediction of protein assemblies, the next frontier: the CASP14-CAPRI experiment publication-title: Proteins doi: 10.1002/prot.26222 – volume: 37 start-page: 360 year: 2021 ident: 2023010107544976300_btac759-B1 article-title: GraphQA: protein model quality assessment using graph convolutional networks publication-title: Bioinformatics doi: 10.1093/bioinformatics/btaa714 – volume: 48 start-page: D265 year: 2020 ident: 2023010107544976300_btac759-B23 article-title: CDD/SPARCLE: the conserved domain database in 2020 publication-title: Nucleic Acids Res doi: 10.1093/nar/gkz991 – volume: 17 start-page: 184 year: 2020 ident: 2023010107544976300_btac759-B10 article-title: Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning publication-title: Nat. Methods doi: 10.1038/s41592-019-0666-6 – year: 2021 ident: 2023010107544976300_btac759-B31 doi: 10.1101/2021.01.29.425727 – volume: 88 start-page: 1091 year: 2020 ident: 2023010107544976300_btac759-B4 article-title: Energy-based graph convolutional networks for scoring protein docking models publication-title: Proteins: Struct., Funct. Bioinformatics doi: 10.1002/prot.25888 – volume: 36 start-page: 112 year: 2020 ident: 2023010107544976300_btac759-B12 article-title: iScore: a novel graph kernel-based function for scoring protein-protein docking models publication-title: Bioinformatics doi: 10.1093/bioinformatics/btz496 – volume: 13 start-page: 334 year: 2012 ident: 2023010107544976300_btac759-B5 article-title: Protein interface classification by evolutionary analysis publication-title: BMC Bioinformatics doi: 10.1186/1471-2105-13-334 – year: 2020 ident: 2023010107544976300_btac759-B30 – volume: 82 start-page: 3163 year: 2014 ident: 2023010107544976300_btac759-B22 article-title: Score_set: a CAPRI benchmark for scoring protein complexes publication-title: Proteins: Struct., Funct. Bioinformatics doi: 10.1002/prot.24678 – volume: 37 start-page: 2332 year: 2021 ident: 2023010107544976300_btac759-B13 article-title: VoroCNN: deep convolutional neural network built on 3D voronoi tessellation of protein structures publication-title: Bioinformatics doi: 10.1093/bioinformatics/btab118 – volume: 16 start-page: e0249404 year: 2021 ident: 2023010107544976300_btac759-B35 article-title: Development of a graph convolutional neural network model for efficient prediction of protein-ligand binding affinities publication-title: PLoS One doi: 10.1371/journal.pone.0249404 – volume: 35 start-page: 3313 year: 2019 ident: 2023010107544976300_btac759-B27 article-title: Protein model quality assessment using 3D oriented convolutional neural networks publication-title: Bioinformatics doi: 10.1093/bioinformatics/btz122 – year: 2008 ident: 2023010107544976300_btac759-B1200 – volume: 3 year: 2016 ident: 2023010107544976300_btac759-B25 article-title: DeepTox: toxicity prediction using deep learning publication-title: Front. Environ. Sci doi: 10.3389/fenvs.2015.00080 – volume: 59 start-page: 4131 year: 2019 ident: 2023010107544976300_btac759-B36 article-title: Graph convolutional neural networks for predicting drug-target interactions publication-title: J. Chem. Inf. Model doi: 10.1021/acs.jcim.9b00628 – volume: 58 start-page: 287 year: 2018 ident: 2023010107544976300_btac759-B14 article-title: KDEEP: protein–ligand absolute binding affinity prediction via 3D-Convolutional neural networks publication-title: J. Chem. Inf. Model doi: 10.1021/acs.jcim.7b00650 – volume: 84 start-page: 323 year: 2016 ident: 2023010107544976300_btac759-B20 article-title: Prediction of homoprotein and heteroprotein complexes by protein docking and template-based modeling: a CASP-CAPRI experiment publication-title: Proteins: Struct., Funct., Bioinformatics doi: 10.1002/prot.25007 – volume: 14 start-page: 22 year: 2014 ident: 2023010107544976300_btac759-B2 article-title: A PDB-wide, evolution-based assessment of protein-protein interfaces publication-title: BMC Struct. Biol doi: 10.1186/s12900-014-0022-0 – volume: 60 start-page: 4200 year: 2020 ident: 2023010107544976300_btac759-B9 article-title: Three-dimensional convolutional neural networks and a cross-docked data set for structure-based drug design publication-title: J. Chem. Inf. Model doi: 10.1021/acs.jcim.0c00411 – volume: 5 start-page: 5150 year: 2020 ident: 2023010107544976300_btac759-B16 article-title: graphDelta: MPNN scoring function for the affinity prediction of protein–ligand complexes publication-title: ACS Omega doi: 10.1021/acsomega.9b04162 – year: 2017 ident: 2023010107544976300_btac759-B8 – volume: 8 start-page: 647915 year: 2021 ident: 2023010107544976300_btac759-B39 article-title: Protein docking model evaluation by graph neural networks publication-title: Front. Mol. Biosci doi: 10.3389/fmolb.2021.647915 – year: 2021 ident: 2023010107544976300_btac759-B29 – volume: 596 start-page: 583 year: 2021 ident: 2023010107544976300_btac759-B15 article-title: Highly accurate protein structure prediction with AlphaFold publication-title: Nature doi: 10.1038/s41586-021-03819-2 – year: 2017 ident: 2023010107544976300_btac759-B17 – year: 2022 ident: 2023010107544976300_btac759-B24 – year: 2017 ident: 2023010107544976300_btac759-B7 – volume: 428 start-page: 720 year: 2016 ident: 2023010107544976300_btac759-B40 article-title: The HADDOCK2.2 web server: user-friendly integrative modeling of biomolecular complexes publication-title: J. Mol. Biol doi: 10.1016/j.jmb.2015.09.014 – volume: 36 start-page: 2113 year: 2020 ident: 2023010107544976300_btac759-B38 article-title: Protein docking model evaluation by 3D deep convolutional neural networks publication-title: Bioinformatics doi: 10.1093/bioinformatics/btz870 |
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Motivation
Gaining structural insights into the protein–protein interactome is essential to understand biological phenomena and extract knowledge for... Gaining structural insights into the protein-protein interactome is essential to understand biological phenomena and extract knowledge for rational drug design... Motivation Gaining structural insights into the protein–protein interactome is essential to understand biological phenomena and extract knowledge for rational... |
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| SubjectTerms | Artificial neural networks Availability Bioinformatics Data augmentation Deep learning Drug development Graph neural networks Graphical representations Graphical user interface Graphs Interfaces Invariants Machine learning Neural networks Neural Networks, Computer Original Paper Protein engineering Proteins Proteins - chemistry Rotation |
| Title | DeepRank-GNN: a graph neural network framework to learn patterns in protein–protein interfaces |
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