ComplexContact: a web server for inter-protein contact prediction using deep learning
Online Publication Date2018-05-22
Print Publication Date2018-07-02
Permanent link to this recordhttp://hdl.handle.net/10754/627970
MetadataShow full item record
AbstractComplexContact (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.
CitationZeng 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.
SponsorsNational Institutes of Health (NIH) [R01GM089753 to J.X.]; National Science Foundation (NSF) [DBI-1564955 to J.X.]. Funding for open access charge: NIH; NSF.
PublisherOxford University Press (OUP)
JournalNucleic Acids Research