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    Integrating Cross-Linking Experiments with Ab Initio Protein-Protein Docking

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    Authors
    Vreven, Thom
    Schweppe, Devin K.
    Chavez, Juan D.
    Weisbrod, Chad R.
    Shibata, Sayaka
    Zheng, Chunxiang
    Bruce, James E.
    Weng, Zhiping
    UMass Chan Affiliations
    Program in Bioinformatics and Integrative Biology
    Document Type
    Journal Article
    Publication Date
    2018-06-08
    Keywords
    ZDOCK
    mass spectrometry
    protein–protein complex
    structure
    symmetry
    Biochemistry, Biophysics, and Structural Biology
    Bioinformatics
    Computational Biology
    Molecular Biology
    Structural Biology
    
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    Link to Full Text
    https://doi.org/10.1016/j.jmb.2018.04.010
    Abstract
    Ab initio protein-protein docking algorithms often rely on experimental data to identify the most likely complex structure. We integrated protein-protein docking with the experimental data of chemical cross-linking followed by mass spectrometry. We tested our approach using 19 cases that resulted from an exhaustive search of the Protein Data Bank for protein complexes with cross-links identified in our experiments. We implemented cross-links as constraints based on Euclidean distance or void-volume distance. For most test cases, the rank of the top-scoring near-native prediction was improved by at least twofold compared with docking without the cross-link information, and the success rate for the top 5 predictions nearly tripled. Our results demonstrate the delicate balance between retaining correct predictions and eliminating false positives. Several test cases had multiple components with distinct interfaces, and we present an approach for assigning cross-links to the interfaces. Employing the symmetry information for these cases further improved the performance of complex structure prediction.
    Source

    J Mol Biol. 2018 Jun 8;430(12):1814-1828. doi: 10.1016/j.jmb.2018.04.010. Epub 2018 Apr 14. Link to article on publisher's site

    DOI
    10.1016/j.jmb.2018.04.010
    Permanent Link to this Item
    http://hdl.handle.net/20.500.14038/25836
    PubMed ID
    29665372
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    Link to Article in PubMed

    ae974a485f413a2113503eed53cd6c53
    10.1016/j.jmb.2018.04.010
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