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

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

Research output: Contribution to journalArticlepeer-review

90 Scopus citations


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.
Original languageEnglish (US)
Pages (from-to)W432-W437
Number of pages1
JournalNucleic Acids Research
Issue numberW1
StatePublished - May 22 2018

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
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.


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