Advertisement

A Web-Based Protocol for Interprotein Contact Prediction by Deep Learning

  • Xiaoyang Jing
  • Hong Zeng
  • Sheng Wang
  • Jinbo Xu
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 2074)

Abstract

Identifying residue–residue contacts in protein–protein interactions or complex is crucial for understanding protein and cell functions. DCA (direct-coupling analysis) methods shed some light on this, but they need many sequence homologs to yield accurate prediction. Inspired by the success of our deep-learning method for intraprotein contact prediction, we have developed RaptorX-ComplexContact, a web server for interprotein residue–residue contact prediction. Given a pair of interacting protein sequences, RaptorX-ComplexContact first searches for their sequence homologs and builds two paired multiple sequence alignments (MSA) based on genomic distance and phylogeny information, respectively. Then, RaptorX-ComplexContact uses two deep convolutional residual neural networks (ResNet) to predict interprotein contacts from sequential features and coevolution information of paired MSAs. RaptorX-ComplexContact shall be useful for protein docking, protein–protein interaction prediction, and protein interaction network construction.

Key words

Interprotein contact prediction Protein–protein interaction (PPI) prediction Protein interaction network Protein complex Deep learning (DL) Direct-coupling analysis (DCA) Multiple sequence alignment (MSA) Protein docking 

Notes

Acknowledgments

This work was supported by National Institutes of Health grant R01GM089753 to JX and National Science Foundation grant DBI-1564955 to JX.

References

  1. 1.
    Jones S, Thornton JM (1996) Principles of protein-protein interactions. Proc Natl Acad Sci 93:13–20CrossRefGoogle Scholar
  2. 2.
    Alberts B (1998) The cell as a collection of protein machines: preparing the next generation of molecular biologists. Cell 92:291–294CrossRefGoogle Scholar
  3. 3.
    Lensink MF, Velankar S, Kryshtafovych A et al (2016) Prediction of homoprotein and heteroprotein complexes by protein docking and template-based modeling: a CASP-CAPRI experiment. Proteins 84:323–348CrossRefGoogle Scholar
  4. 4.
    Lensink MF, Velankar S, Wodak SJ (2017) Modeling protein–protein and protein–peptide complexes: CAPRI 6th edition. Proteins 85:359–377CrossRefGoogle Scholar
  5. 5.
    Kim DE, DiMaio F, Yu-Ruei Wang R et al (2014) One contact for every twelve residues allows robust and accurate topology-level protein structure modeling. Proteins 82:208–218CrossRefGoogle Scholar
  6. 6.
    Wang S, Sun S, Li Z et al (2017) Accurate de novo prediction of protein contact map by ultra-deep learning model. PLoS Comput Biol 13:e1005324CrossRefGoogle Scholar
  7. 7.
    Ovchinnikov S, Kamisetty H, Baker D (2014) Robust and accurate prediction of residue–residue interactions across protein interfaces using evolutionary information. elife 3:e02030CrossRefGoogle Scholar
  8. 8.
    Hopf TA, Schärfe CP, Rodrigues JP et al (2014) Sequence co-evolution gives 3D contacts and structures of protein complexes. elife 3:e03430CrossRefGoogle Scholar
  9. 9.
    Yu J, Andreani J, Ochsenbein F, Guerois R (2017) Lessons from (co-) evolution in the docking of proteins and peptides for CAPRI rounds 28–35. Proteins 85:378–390CrossRefGoogle Scholar
  10. 10.
    Gromiha MM, Selvaraj S (2004) Inter-residue interactions in protein folding and stability. Prog Biophys Mol Biol 86:235–277CrossRefGoogle Scholar
  11. 11.
    Jones DT, Buchan DW, Cozzetto D, Pontil M (2011) PSICOV: precise structural contact prediction using sparse inverse covariance estimation on large multiple sequence alignments. Bioinformatics 28:184–190CrossRefGoogle Scholar
  12. 12.
    Marks DS, Hopf TA, Sander C (2012) Protein structure prediction from sequence variation. Nat Biotechnol 30:1072CrossRefGoogle Scholar
  13. 13.
    Seemayer S, Gruber M, Söding J (2014) CCMpred—fast and precise prediction of protein residue–residue contacts from correlated mutations. Bioinformatics 30:3128–3130CrossRefGoogle Scholar
  14. 14.
    Gueudré T, Baldassi C, Zamparo M et al (2016) Simultaneous identification of specifically interacting paralogs and interprotein contacts by direct coupling analysis. Proc Natl Acad Sci 113:12186–12191CrossRefGoogle Scholar
  15. 15.
    Weigt M, White RA, Szurmant H et al (2009) Identification of direct residue contacts in protein–protein interaction by message passing. Proc Natl Acad Sci 106:67–72CrossRefGoogle Scholar
  16. 16.
    Rodriguez-Rivas J, Marsili S, Juan D, Valencia A (2016) Conservation of coevolving protein interfaces bridges prokaryote–eukaryote homologies in the twilight zone. Proc Natl Acad Sci 113:15018–15023CrossRefGoogle Scholar
  17. 17.
    Wang S, Li Z, Yu Y, Xu J (2017) Folding membrane proteins by deep transfer learning. Cell Syst 5:202–211.e3CrossRefGoogle Scholar
  18. 18.
    Wang S, Sun S, Xu J (2018) Analysis of deep learning methods for blind protein contact prediction in CASP12. Proteins 86:67–77CrossRefGoogle Scholar
  19. 19.
    Xu J (2018) Distance-based protein folding powered by deep learning. arXiv preprint arXiv:181103481Google Scholar
  20. 20.
    Remmert M, Biegert A, Hauser A, Söding J (2012) HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment. Nat Methods 9:173CrossRefGoogle Scholar
  21. 21.
    Feinauer C, Szurmant H, Weigt M, Pagnani A (2016) Inter-protein sequence co-evolution predicts known physical interactions in bacterial ribosomes and the Trp operon. PLoS One 11:e0149166CrossRefGoogle Scholar
  22. 22.
    Federhen S (2011) The NCBI taxonomy database. Nucleic Acids Res 40:D136–D143CrossRefGoogle Scholar
  23. 23.
    Zhou T, Wang S, Xu J (2017) Deep learning reveals many more inter-protein residue-residue contacts than direct coupling analysis. bioRxiv:240754Google Scholar
  24. 24.
    He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778Google Scholar
  25. 25.
    Wang S, Li W, Liu S, Xu J (2016) RaptorX-property: a web server for protein structure property prediction. Nucleic Acids Res 44:W430–W435CrossRefGoogle Scholar
  26. 26.
    Zeng H, Wang S, Zhou T et al (2018) ComplexContact: a web server for inter-protein contact prediction using deep learning. Nucleic Acids Res 46(W1):W432–W437CrossRefGoogle Scholar
  27. 27.
    Yachdav G, Wilzbach S, Rauscher B et al (2016) MSAViewer: interactive JavaScript visualization of multiple sequence alignments. Bioinformatics 32:3501–3503PubMedPubMedCentralGoogle Scholar
  28. 28.
    Levy ED, Pereira-Leal JB, Chothia C, Teichmann SA (2006) 3D complex: a structural classification of protein complexes. PLoS Comput Biol 2:e155CrossRefGoogle Scholar
  29. 29.
    Toogood HS, van Thiel A, Scrutton NS, Leys D (2005) Stabilisation of non-productive conformations underpins rapid electron transfer to ETF. J Biol Chem 280(34):30361–30366CrossRefGoogle Scholar
  30. 30.
    Roberts DL, Frerman FE, Kim J-JP (1996) Three-dimensional structure of human electron transfer flavoprotein to 2.1-Å resolution. Proc Natl Acad Sci 93:14355–14360CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2020

Authors and Affiliations

  • Xiaoyang Jing
    • 1
    • 2
  • Hong Zeng
    • 3
  • Sheng Wang
    • 1
  • Jinbo Xu
    • 1
  1. 1.Toyota Technological Institute at ChicagoChicagoUSA
  2. 2.School of Computer ScienceFudan UniversityShanghaiChina
  3. 3.School of Computer Science and TechnologyHangzhou Dianzi UniversityHangzhouChina

Personalised recommendations