ComplexContact: a web server for inter-protein contact prediction using deep learning. Issue Volume 46:Issue W1(2018) (22nd May 2018)

• Record Type: Journal Article
• Authors: Zeng, Hong; Wang, Sheng; Zhou, Tianming; Zhao, Feifeng; Li, Xiufeng; Wu, Qing; Xu, Jinbo
• Is Part Of: Nucleic acids research. Volume 46:Issue W1(2018)
• Page Start: W432
• Page End: W437
• Publication Date: 2018-05-22
• Subjects: Nucleic acids -- Periodicals; Molecular biology -- Periodicals; 572.805
• DOI: 10.1093/nar/gky420
• Languages: English
• ISSNs: 0305-1048

Abstract

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.