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dc.contributor.authorYang, Yuedong
dc.contributor.authorFaraggi, Eshel
dc.contributor.authorZhao, Huiying
dc.contributor.authorZhou, Yaoqi
dc.date.accessioned2017-08-25T12:32:21Z
dc.date.available2017-08-25T12:32:21Z
dc.date.issued2011
dc.date.modified2014-01-29T22:51:45Z
dc.identifier.issn1367-4803
dc.identifier.doi10.1093/bioinformatics/btr350
dc.identifier.urihttp://hdl.handle.net/10072/56124
dc.description.abstractMotivation: In recent years, development of a single-method fold-recognition server lags behind consensus and multiple template techniques. However, a good consensus prediction relies on the accuracy of individual methods. This article reports our efforts to further improve a single-method fold recognition technique called SPARKS by changing the alignment scoring function and incorporating the SPINE-X techniques that make improved prediction of secondary structure, backbone torsion angle and solvent accessible surface area. Results: The new method called SPARKS-X was tested with the SALIGN benchmark for alignment accuracy, Lindahl and SCOP benchmarks for fold recognition, and CASP 9 blind test for structure prediction. The method is compared to several state-of-the-art techniques such as HHPRED and BoostThreader. Results show that SPARKS-X is one of the best single-method fold recognition techniques. We further note that incorporating multiple templates and refinement in model building will likely further improve SPARKS-X.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.languageEnglish
dc.publisherOxford University Press
dc.publisher.placeUnited Kingdom
dc.relation.ispartofstudentpublicationN
dc.relation.ispartofpagefrom2076
dc.relation.ispartofpageto2082
dc.relation.ispartofissue15
dc.relation.ispartofjournalBioinformatics
dc.relation.ispartofvolume27
dc.rights.retentionY
dc.subject.fieldofresearchBioinformatics
dc.subject.fieldofresearchMathematical Sciences
dc.subject.fieldofresearchBiological Sciences
dc.subject.fieldofresearchInformation and Computing Sciences
dc.subject.fieldofresearchcode060102
dc.subject.fieldofresearchcode01
dc.subject.fieldofresearchcode06
dc.subject.fieldofresearchcode08
dc.titleImproving protein fold recognition and template-based modeling by employing probabilistic-based matching between predicted one-dimensional structural properties of query and corresponding native properties of templates
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.date.issued2011
gro.hasfulltextNo Full Text
gro.griffith.authorZhou, Yaoqi
gro.griffith.authorYang, Yuedong


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