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BCSearch: fast structural fragment mining over large collections of protein structures
Author(s) -
Frédéric Guyon,
François Martz,
Marek Vavruša,
Jérôme Becot,
Julien Rey,
Pierre Tufféry
Publication year - 2015
Publication title -
nucleic acids research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 9.008
H-Index - 537
eISSN - 1362-4954
pISSN - 0305-1048
DOI - 10.1093/nar/gkv492
Subject(s) - biology , fragment (logic) , discriminative model , protein data bank , kernel (algebra) , protein structure , data mining , computational biology , protein structure database , protein design , computer science , bioinformatics , algorithm , mathematics , combinatorics , machine learning , genetics , biochemistry , gene , sequence database
Resources to mine the large amount of protein structures available today are necessary to better understand how amino acid variations are compatible with conformation preservation, to assist protein design, engineering and, further, the development of biologic therapeutic compounds. BCSearch is a versatile service to efficiently mine large collections of protein structures. It relies on a new approach based on a Binet-Cauchy kernel that is more discriminative than the widely used root mean square deviation criterion. It has statistics independent of size even for short fragments, and is fast. The systematic mining of large collections of structures such as the complete SCOPe protein structural classification or comprehensive subsets of the Protein Data Bank can be performed in few minutes. Based on this new score, we propose four innovative applications: BCFragSearch and BCMirrorSearch, respectively, search for fragments similar and anti-similar to a query and return information on the diversity of the sequences of the hits. BCLoopSearch identifies candidate fragments of fixed size matching the flanks of a gaped structure. BCSpecificitySearch analyzes a complete protein structure and returns information about sites having few similar fragments. BCSearch is available at http://bioserv.rpbs.univ-paris-diderot.fr/services/BCSearch.

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