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dc.contributor.authorChen, YCen_US
dc.contributor.authorHwang, JKen_US
dc.date.accessioned2014-12-08T15:18:03Z-
dc.date.available2014-12-08T15:18:03Z-
dc.date.issued2005-11-15en_US
dc.identifier.issn0887-3585en_US
dc.identifier.urihttp://dx.doi.org/10.1002/prot.20627en_US
dc.identifier.urihttp://hdl.handle.net/11536/13059-
dc.description.abstractThe difficulties in predicting disulfide connectivity from protein sequences lie in the nonlocal properties of the disulfide bridges that involve cysteine pairs at large sequence separation. Though some progress has been recently made in the prediction of disulfide connectivity, the current methods predict less than half of the disulfide patterns for the data set sharing less than 30% sequence identity. In this report, we use the support vector machines based on sequence features such as the coupling between the local sequence environments of cysteine pair, the cysteines sequence separations, and the global sequence descriptor, such as amino acid content. Our approach is able to predict 55% of the disulfide patterns of proteins with two to five disulfide bridges, which is 11-26% higher than other methods in the literature. Proteins 2005;61:507-512. (c) 2005 Wiley-Liss, Inc.en_US
dc.language.isoen_USen_US
dc.subjectdisulfide connectivityen_US
dc.subjectdisulfide patternsen_US
dc.subjectsupport vector machinesen_US
dc.titlePrediction of disulfide connectivity from protein sequencesen_US
dc.typeArticleen_US
dc.identifier.doi10.1002/prot.20627en_US
dc.identifier.journalPROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICSen_US
dc.citation.volume61en_US
dc.citation.issue3en_US
dc.citation.spage507en_US
dc.citation.epage512en_US
dc.contributor.department生物科技學系zh_TW
dc.contributor.department生物資訊及系統生物研究所zh_TW
dc.contributor.departmentDepartment of Biological Science and Technologyen_US
dc.contributor.departmentInstitude of Bioinformatics and Systems Biologyen_US
dc.identifier.wosnumberWOS:000233029800007-
dc.citation.woscount24-
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