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dc.contributor.authorVreven, Thom
dc.contributor.authorHwang, Howook
dc.contributor.authorWeng, Zhiping
dc.date2022-08-11T08:08:29.000
dc.date.accessioned2022-08-23T15:56:39Z
dc.date.available2022-08-23T15:56:39Z
dc.date.issued2013-02-21
dc.date.submitted2013-07-26
dc.identifier.citation<p>PLoS One. 2013;8(2):e56645. doi: 10.1371/journal.pone.0056645. Epub 2013 Feb 21. <a href="http://dx.doi.org/10.1371/journal.pone.0056645">Link to article on publisher's site</a></p>
dc.identifier.issn1932-6203 (Linking)
dc.identifier.doi10.1371/journal.pone.0056645
dc.identifier.pmid23437194
dc.identifier.urihttp://hdl.handle.net/20.500.14038/30017
dc.description.abstractWe 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.
dc.language.isoen_US
dc.relation<p><a href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=pubmed&cmd=Retrieve&list_uids=23437194&dopt=Abstract">Link to Article in PubMed</a></p>
dc.rightsCopyright: 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.
dc.subjectAmino Acids, Peptides, and Proteins
dc.subjectBioinformatics
dc.subjectComputational Biology
dc.subjectStructural Biology
dc.titleExploring angular distance in protein-protein docking algorithms
dc.typeJournal Article
dc.source.journaltitlePloS one
dc.source.volume8
dc.source.issue2
dc.identifier.legacyfulltexthttps://escholarship.umassmed.edu/cgi/viewcontent.cgi?article=1249&amp;context=faculty_pubs&amp;unstamped=1
dc.identifier.legacycoverpagehttps://escholarship.umassmed.edu/faculty_pubs/250
dc.identifier.contextkey4352261
refterms.dateFOA2022-08-23T15:56:39Z
html.description.abstract<p>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.</p>
dc.identifier.submissionpathfaculty_pubs/250
dc.contributor.departmentProgram in Bioinformatics and Integrative Biology
dc.source.pagese56645


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