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    CMsearch: simultaneous exploration of protein sequence space and structure space improves not only protein homology detection but also protein structure prediction

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    Type
    Article
    Authors
    Cui, Xuefeng
    Lu, Zhiwu
    wang, sheng
    Wang, Jim Jing-Yan
    Gao, Xin cc
    KAUST Department
    Computational Bioscience Research Center (CBRC)
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    KAUST Grant Number
    URF/1/1976-04
    Date
    2016-06-15
    Online Publication Date
    2016-06-15
    Print Publication Date
    2016-06-15
    Permanent link to this record
    http://hdl.handle.net/10754/615924
    
    Metadata
    Show full item record
    Abstract
    Motivation: Protein homology detection, a fundamental problem in computational biology, is an indispensable step toward predicting protein structures and understanding protein functions. Despite the advances in recent decades on sequence alignment, threading and alignment-free methods, protein homology detection remains a challenging open problem. Recently, network methods that try to find transitive paths in the protein structure space demonstrate the importance of incorporating network information of the structure space. Yet, current methods merge the sequence space and the structure space into a single space, and thus introduce inconsistency in combining different sources of information. Method: We present a novel network-based protein homology detection method, CMsearch, based on cross-modal learning. Instead of exploring a single network built from the mixture of sequence and structure space information, CMsearch builds two separate networks to represent the sequence space and the structure space. It then learns sequence–structure correlation by simultaneously taking sequence information, structure information, sequence space information and structure space information into consideration. Results: We tested CMsearch on two challenging tasks, protein homology detection and protein structure prediction, by querying all 8332 PDB40 proteins. Our results demonstrate that CMsearch is insensitive to the similarity metrics used to define the sequence and the structure spaces. By using HMM–HMM alignment as the sequence similarity metric, CMsearch clearly outperforms state-of-the-art homology detection methods and the CASP-winning template-based protein structure prediction methods.
    Citation
    CMsearch: simultaneous exploration of protein sequence space and structure space improves not only protein homology detection but also protein structure prediction 2016, 32 (12):i332 Bioinformatics
    Sponsors
    The research reported in this publication was supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. URF/1/1976-04, National Natural Science Foundation of China (61573363), the Fundamental Research Funds for the Central Universities and the Research Funds of Renmin University of China (15XNLQ01), and IBM Global SUR Award Program. This research made use of the resources of the computer clusters at KAUST.
    Publisher
    Oxford University Press (OUP)
    Journal
    Bioinformatics
    DOI
    10.1093/bioinformatics/btw271
    PubMed ID
    27307635
    Additional Links
    http://bioinformatics.oxfordjournals.org/lookup/doi/10.1093/bioinformatics/btw271
    ae974a485f413a2113503eed53cd6c53
    10.1093/bioinformatics/btw271
    Scopus Count
    Collections
    Articles; Computer Science Program; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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