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

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Book cover Protein-Protein Interaction Networks

Part of the book series: Methods in Molecular Biology ((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.

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References

  1. Jones S, Thornton JM (1996) Principles of protein-protein interactions. Proc Natl Acad Sci 93:13–20

    Article  CAS  Google Scholar 

  2. Alberts B (1998) The cell as a collection of protein machines: preparing the next generation of molecular biologists. Cell 92:291–294

    Article  CAS  Google Scholar 

  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 84:323–348

    Article  Google Scholar 

  4. Lensink MF, Velankar S, Wodak SJ (2017) Modeling protein–protein and protein–peptide complexes: CAPRI 6th edition. Proteins 85:359–377

    Article  CAS  Google Scholar 

  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 82:208–218

    Article  CAS  Google Scholar 

  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:e1005324

    Article  Google Scholar 

  7. Ovchinnikov S, Kamisetty H, Baker D (2014) Robust and accurate prediction of residue–residue interactions across protein interfaces using evolutionary information. elife 3:e02030

    Article  Google Scholar 

  8. Hopf TA, Schärfe CP, Rodrigues JP et al (2014) Sequence co-evolution gives 3D contacts and structures of protein complexes. elife 3:e03430

    Article  Google Scholar 

  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 85:378–390

    Article  CAS  Google Scholar 

  10. Gromiha MM, Selvaraj S (2004) Inter-residue interactions in protein folding and stability. Prog Biophys Mol Biol 86:235–277

    Article  CAS  Google Scholar 

  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 28:184–190

    Article  Google Scholar 

  12. Marks DS, Hopf TA, Sander C (2012) Protein structure prediction from sequence variation. Nat Biotechnol 30:1072

    Article  CAS  Google Scholar 

  13. Seemayer S, Gruber M, Söding J (2014) CCMpred—fast and precise prediction of protein residue–residue contacts from correlated mutations. Bioinformatics 30:3128–3130

    Article  CAS  Google Scholar 

  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 113:12186–12191

    Article  Google Scholar 

  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 106:67–72

    Article  CAS  Google Scholar 

  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 113:15018–15023

    Article  CAS  Google Scholar 

  17. Wang S, Li Z, Yu Y, Xu J (2017) Folding membrane proteins by deep transfer learning. Cell Syst 5:202–211.e3

    Article  CAS  Google Scholar 

  18. Wang S, Sun S, Xu J (2018) Analysis of deep learning methods for blind protein contact prediction in CASP12. Proteins 86:67–77

    Article  CAS  Google Scholar 

  19. Xu J (2018) Distance-based protein folding powered by deep learning. arXiv preprint arXiv:181103481

    Google Scholar 

  20. Remmert M, Biegert A, Hauser A, Söding J (2012) HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment. Nat Methods 9:173

    Article  CAS  Google Scholar 

  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:e0149166

    Article  Google Scholar 

  22. Federhen S (2011) The NCBI taxonomy database. Nucleic Acids Res 40:D136–D143

    Article  Google Scholar 

  23. Zhou T, Wang S, Xu J (2017) Deep learning reveals many more inter-protein residue-residue contacts than direct coupling analysis. bioRxiv:240754

    Google Scholar 

  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 770–778

    Google Scholar 

  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–W435

    Article  CAS  Google Scholar 

  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–W437

    Article  CAS  Google Scholar 

  27. Yachdav G, Wilzbach S, Rauscher B et al (2016) MSAViewer: interactive JavaScript visualization of multiple sequence alignments. Bioinformatics 32:3501–3503

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Levy ED, Pereira-Leal JB, Chothia C, Teichmann SA (2006) 3D complex: a structural classification of protein complexes. PLoS Comput Biol 2:e155

    Article  Google Scholar 

  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):30361–30366

    Article  CAS  Google Scholar 

  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 93:14355–14360

    Article  CAS  Google Scholar 

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Acknowledgments

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

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Author notes

  1. Xiaoyang Jing, Hong Zeng and Sheng Wang contributed equally to this work.

Authors and Affiliations

  1. Toyota Technological Institute at Chicago, Chicago, IL, USA

    Xiaoyang Jing, Sheng Wang & Jinbo Xu

  2. School of Computer Science, Fudan University, Shanghai, China

    Xiaoyang Jing

  3. School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China

    Hong Zeng

Authors
  1. Xiaoyang Jing
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  2. Hong Zeng
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  3. Sheng Wang
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  4. Jinbo Xu
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Editor information

Editors and Affiliations

  1. Gene Center, Ludwig-Maximilians-Universität München, München, Germany

    Stefan Canzar

  2. Gene Center, Ludwig-Maximilians-Universität München, München, Germany

    Francisca Rojas Ringeling

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Jing, X., Zeng, H., Wang, S., Xu, J. (2020). A Web-Based Protocol for Interprotein Contact Prediction by Deep Learning. In: Canzar, S., Ringeling, F. (eds) Protein-Protein Interaction Networks. Methods in Molecular Biology, vol 2074. Humana, New York, NY. https://doi.org/10.1007/978-1-4939-9873-9_6

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  • DOI: https://doi.org/10.1007/978-1-4939-9873-9_6

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-4939-9872-2

  • Online ISBN: 978-1-4939-9873-9

  • eBook Packages: Springer Protocols

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