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Cited by 0Protein Structure and DynamicsMachine Learning in BioinformaticsComputational Drug Discovery Methods

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Protein language model-embedded geometric graphs power inter-protein contact prediction

Open access
Si, Yunda, Yan, Chengfei·2024·eLife
DOI 10.7554/elife.92184.3

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Abstract

Accurate prediction of contacting residue pairs between interacting proteins is very useful for structural characterization of protein–protein interactions. Although significant improvement has been made in inter-protein contact prediction recently, there is still a large room for improving the prediction accuracy. Here we present a new deep learning method referred to as PLMGraph-Inter for inter-protein contact prediction. Specifically, we employ rotationally and translationally invariant geometric graphs obtained from structures of interacting proteins to integrate multiple protein language models, which are successively transformed by graph encoders formed by geometric vector perceptrons and residual networks formed by dimensional hybrid residual blocks to predict inter-protein contacts. Extensive evaluation on multiple test sets illustrates that PLMGraph-Inter outperforms five top inter-protein contact prediction methods, including DeepHomo, GLINTER, CDPred, DeepHomo2, and DRN-1D2D_Inter, by large margins. In addition, we also show that the prediction of PLMGraph-Inter can complement the result of AlphaFold-Multimer. Finally, we show leveraging the contacts predicted by PLMGraph-Inter as constraints for protein–protein docking can dramatically improve its performance for protein complex structure prediction.

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Research works printed but not linked: 43

  1. Alberts, 1998, The cell as a collection of protein machines: preparing the next generation of molecular biologists, Cell, 92, 291, 10.1016/s0092-8674(00)80922-8 · DOI · Paper page
  2. Berman, 2000, The protein data bank, Nucleic Acids Research, 28, 235, 10.1093/nar/28.1.235 · DOI · Paper page
  3. Bonvin, 2006, Flexible protein-protein docking, Current Opinion in Structural Biology, 16, 194, 10.1016/j.sbi.2006.02.002 · DOI
  4. Dominguez, 2003, HADDOCK: A protein-protein docking approach based on biochemical or biophysical information, Journal of the American Chemical Society, 125, 1731, 10.1021/ja026939x · DOI · Paper page
  5. Goodsell, 2000, Structural symmetry and protein function, Annual Review of Biophysics and Biomolecular Structure, 29, 105, 10.1146/annurev.biophys.29.1.105 · DOI · Paper page
  6. Guo, 2022, Prediction of inter-chain distance maps of protein complexes with 2D attention-based deep neural networks, Nature Communications, 13, 10.1038/s41467-022-34600-2 · DOI
  7. Hanson, 2018, Accurate prediction of protein contact maps by coupling residual two-dimensional bidirectional long short-term memory with convolutional neural networks, Bioinformatics, 34, 4039, 10.1093/bioinformatics/bty481 · DOI
  8. Honorato, 2021, Structural biology in the clouds: The WeNMR-EOSC ecosystem, Frontiers in Molecular Biosciences, 8, 10.3389/fmolb.2021.729513 · DOI
  9. Hopf, 2014, Sequence co-evolution gives 3D contacts and structures of protein complexes, eLife, 3, 10.7554/eLife.03430 · DOI · Paper page
  10. Hsu, 2022, Learning Inverse Folding from Millions of Predicted Structures, 10.1101/2022.04.10.487779 · DOI
  11. Jing B, Eismann S, Soni PN, Dror RO. 2021a. Equivariant graph neural networks for 3D macromolecular structure. arXiv. http://arxiv.org/abs/2106.03843. · arXiv
  12. Jing B, Eismann S, Suriana P, Townshend RJL, Dror RO. 2021b. Learning from Protein Structure with Geometric Vector Perceptrons. arXiv. https://arxiv.org/abs/2009.01411. · arXiv
  13. Jones, 2015, MetaPSICOV: combining coevolution methods for accurate prediction of contacts and long range hydrogen bonding in proteins, Bioinformatics, 31, 999, 10.1093/bioinformatics/btu791 · DOI
  14. Ju, 2021, CopulaNet: Learning residue co-evolution directly from multiple sequence alignment for protein structure prediction, Nature Communications, 12, 10.1038/s41467-021-22869-8 · DOI
  15. Li, 2001, Clustering of highly homologous sequences to reduce the size of large protein databases, Bioinformatics, 17, 282, 10.1093/bioinformatics/17.3.282 · DOI
  16. Li, 2021, Protein–protein docking with interface residue restraints*, Chinese Physics B, 30, 10.1088/1674-1056/abc14e · DOI
  17. Lin, 2023, DeepHomo2.0: improved protein–protein contact prediction of homodimers by transformer-enhanced deep learning, Briefings in Bioinformatics, 24, 10.1093/bib/bbac499 · DOI
  18. Martino, 2021, Mapping, structure and modulation of PPI, Frontiers in Chemistry, 9, 10.3389/fchem.2021.718405 · DOI
  19. Ovchinnikov, 2014, Robust and accurate prediction of residue-residue interactions across protein interfaces using evolutionary information, eLife, 3, 10.7554/eLife.02030 · DOI · Paper page
  20. Pagès, 2019, Protein model quality assessment using 3D oriented convolutional neural networks, Bioinformatics, 35, 3313, 10.1093/bioinformatics/btz122 · DOI
  21. Rao R, Liu J, Verkuil R, Meier J, Canny JF, Abbeel P, Sercu T, Rives A. 2021b. MSA Transformer. Proceedings of the 38th International Conference on Machine Learning.
  22. Roy, 2022, A deep dilated convolutional residual network for predicting interchain contacts of protein homodimers, Bioinformatics, 38, 1904, 10.1093/bioinformatics/btac063 · DOI · Paper page
  23. Seemayer, 2014, CCMpred--fast and precise prediction of protein residue-residue contacts from correlated mutations, Bioinformatics, 30, 3128, 10.1093/bioinformatics/btu500 · DOI
  24. Si, 2021, Improved protein contact prediction using dimensional hybrid residual networks and singularity enhanced loss function, Briefings in Bioinformatics, 22, 10.1093/bib/bbab341 · DOI
  25. Si, 2022, Protein complex structure prediction powered by multiple sequence alignments of interologs from multiple taxonomic ranks and AlphaFold2, Briefings in Bioinformatics, 23, 10.1093/bib/bbac208 · DOI
  26. Si, 2023, Improved inter-protein contact prediction using dimensional hybrid residual networks and protein language models, Briefings in Bioinformatics, 24, 10.1093/bib/bbad039 · DOI · Paper page
  27. Sledzieski, 2021, D-SCRIPT translates genome to phenome with sequence-based, structure-aware, genome-scale predictions of protein-protein interactions, Cell Systems, 12, 969, 10.1016/j.cels.2021.08.010 · DOI
  28. Spirin, 2003, Protein complexes and functional modules in molecular networks, PNAS, 100, 12123, 10.1073/pnas.2032324100 · DOI
  29. Steinegger, 2018, Clustering huge protein sequence sets in linear time, Nature Communications, 9, 10.1038/s41467-018-04964-5 · DOI · Paper page
  30. Steinegger, 2019, HH-suite3 for fast remote homology detection and deep protein annotation, BMC Bioinformatics, 20, 10.1186/s12859-019-3019-7 · DOI · Paper page
  31. Sun, 2020, Review of multimer protein–protein interaction complex topology and structure prediction*, Chinese Physics B, 29, 10.1088/1674-1056/abb659 · DOI
  32. Uguzzoni, 2017, Large-scale identification of coevolution signals across homo-oligomeric protein interfaces by direct coupling analysis, PNAS, 114, E2662, 10.1073/pnas.1615068114 · DOI
  33. van Zundert, 2016, The HADDOCK2.2 Web Server: User-Friendly Integrative Modeling of Biomolecular Complexes, Journal of Molecular Biology, 428, 720, 10.1016/j.jmb.2015.09.014 · DOI · Paper page
  34. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I. 2017. Attention is all you need. Advances in Neural Information Processing Systems.
  35. Weigt, 2009, Identification of direct residue contacts in protein–protein interaction by message passing, PNAS, 106, 67, 10.1073/pnas.0805923106 · DOI · Paper page
  36. Wu T, Huang H, Li J, Wang W, Gong X. 2022. Inter-chain contact map prediction for protein complex based on graph attention network and triangular multiplication update. 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).
  37. Wu, 2023, Integration of pre-trained protein language models into geometric deep learning networks, Communications Biology, 6, 10.1038/s42003-023-05133-1 · DOI
  38. Xie, 2022, Deep graph learning of inter-protein contacts, Bioinformatics, 38, 947, 10.1093/bioinformatics/btab761 · DOI · Paper page
  39. Yan, 2021, Accurate prediction of inter-protein residue–residue contacts for homo-oligomeric protein complexes, Briefings in Bioinformatics, 22, 1, 10.1093/bib/bbab038 · DOI
  40. Yan C. 2023. Plmgraph-inter. Software Heritage. swh:1:rev:b8afdd3dae15e1ddeec4b3d57f8d84d3668c7619. https://archive.softwareheritage.org/swh:1:dir:b462a9746bb7a2eed509375fedd35f613c2256dd;origin=https://github.com/ChengfeiYan/PLMGraph-Inter;visit=swh:1:snp:6ba464f147c15b7c528e1efeec52387c273f1770;anchor=swh:1:rev:b8afdd3dae15e1ddeec4b3d57f8d84d3668c7619.
  41. Zeng, 2018, ComplexContact: A web server for inter-protein contact prediction using deep learning, Nucleic Acids Research, 46, W432, 10.1093/nar/gky420 · DOI
  42. Zhang, 2004, Scoring function for automated assessment of protein structure template quality, Proteins, 57, 702, 10.1002/prot.20264 · DOI · Paper page
  43. Zhang, 2005, TM-align: A protein structure alignment algorithm based on the TM-score, Nucleic Acids Research, 33, 2302, 10.1093/nar/gki524 · DOI · Paper page

Metadata

Authors
Si, Y., & Yan, C.
Affiliations
Huazhong University of Science and Technology (CN)
ORCID
Venue
eLife
Publisher
eLife Sciences Publications, Ltd
Published
2024-04-02
Volume
12
ISSN
2050-084X (ISSN-L)
DOI
10.7554/elife.92184.3
OpenAlex
W4393526713
Cited by
0
References
14
FWCI
0.00
Type
journal article
Language
en
Open access
Licence
Locations
eLife (publishedVersion, cc-by)
Funders
  • National Natural Science Foundation of China (32101001)
  • Huazhong University of Science and Technology (3004012167)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
  • Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
Keywords
protein-protein interaction, protein language models, geometric graphs, graph neural networks, AlphaFold-Multimer, protein complex structure prediction, protein-protein docking

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