ComplexContact: a web server for inter-protein contact prediction using deep learning

Type
Article

Authors
Zeng, Hong
Wang, Sheng
Zhou, Tianming
Zhao, Feifeng
Li, Xiufeng
Wu, Qing
Xu, Jinbo

KAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Online Publication Date
2018-05-22

Print Publication Date
2018-07-02

Date
2018-05-22

Abstract
ComplexContact (http://raptorx2.uchicago.edu/ComplexContact/) is a web server for sequence-based interfacial residue-residue contact prediction of a putative protein complex. Interfacial residue-residue contacts are critical for understanding how proteins form complex and interact at residue level. When receiving a pair of protein sequences, ComplexContact first searches for their sequence homologs and builds two paired multiple sequence alignments (MSA), then it applies co-evolution analysis and a CASP-winning deep learning (DL) method to predict interfacial contacts from paired MSAs and visualizes the prediction as an image. The DL method was originally developed for intra-protein contact prediction and performed the best in CASP12. Our large-scale experimental test further shows that ComplexContact greatly outperforms pure co-evolution methods for inter-protein contact prediction, regardless of the species.

Citation
Zeng H, Wang S, Zhou T, Zhao F, Li X, et al. (2018) ComplexContact: a web server for inter-protein contact prediction using deep learning. Nucleic Acids Research. Available: http://dx.doi.org/10.1093/nar/gky420.

Acknowledgements
National Institutes of Health (NIH) [R01GM089753 to J.X.]; National Science Foundation (NSF) [DBI-1564955 to J.X.]. Funding for open access charge: NIH; NSF.

Publisher
Oxford University Press (OUP)

Journal
Nucleic Acids Research

DOI
10.1093/nar/gky420

Additional Links
https://academic.oup.com/nar/advance-article/doi/10.1093/nar/gky420/5001161#116851220

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