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

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    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
    Date
    2018-05-22
    Online Publication Date
    2018-05-22
    Print Publication Date
    2018-07-02
    Permanent link to this record
    http://hdl.handle.net/10754/627970
    
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    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.
    Sponsors
    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
    ae974a485f413a2113503eed53cd6c53
    10.1093/nar/gky420
    Scopus Count
    Collections
    Articles; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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