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    Exploring angular distance in protein-protein docking algorithms

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    Authors
    Vreven, Thom
    Hwang, Howook
    Weng, Zhiping
    UMass Chan Affiliations
    Program in Bioinformatics and Integrative Biology
    Document Type
    Journal Article
    Publication Date
    2013-02-21
    Keywords
    Amino Acids, Peptides, and Proteins
    Bioinformatics
    Computational Biology
    Structural Biology
    
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    Abstract
    We present a two-stage hybrid-resolution approach for rigid-body protein-protein docking. The first stage is carried out at low-resolution (15 degrees ) angular sampling. In the second stage, we sample promising regions from the first stage at a higher resolution of 6 degrees . The hybrid-resolution approach produces the same results as a 6 degrees uniform sampling docking run, but uses only 17% of the computational time. We also show that the angular distance can be used successfully in clustering and pruning algorithms, as well as the characterization of energy funnels. Traditionally the root-mean-square-distance is used in these algorithms, but the evaluation is computationally expensive as it depends on both the rotational and translational parameters of the docking solutions. In contrast, the angular distances only depend on the rotational parameters, which are generally fixed for all docking runs. Hence the angular distances can be pre-computed, and do not add computational time to the post-processing of rigid-body docking results.
    Source

    PLoS One. 2013;8(2):e56645. doi: 10.1371/journal.pone.0056645. Epub 2013 Feb 21. Link to article on publisher's site

    DOI
    10.1371/journal.pone.0056645
    Permanent Link to this Item
    http://hdl.handle.net/20.500.14038/30017
    PubMed ID
    23437194
    Related Resources

    Link to Article in PubMed

    Rights
    Copyright: 2013 Vreven et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
    ae974a485f413a2113503eed53cd6c53
    10.1371/journal.pone.0056645
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    UMass Chan Faculty and Researcher Publications
    Program in Bioinformatics and Integrative Biology Publications

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