Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/3011
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dc.contributor.authorWilliams, A-
dc.contributor.authorGilbert, D-
dc.contributor.authorWesthead, DR-
dc.coverage.spatial11en
dc.date.accessioned2009-02-05T15:50:10Z-
dc.date.available2009-02-05T15:50:10Z-
dc.date.issued2003-
dc.identifier.citationProtein Engineering. 16(12): 913-923en
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/3011-
dc.description.abstractTopsalign is a method that will structurally align diverse protein structures, for example, structural alignment of protein superfolds. All proteins within a superfold share the same fold but often have very low sequence identity and different biological and biochemical functions. There is often signi®cant structural diversity around the common scaffold of secondary structure elements of the fold. Topsalign uses topological descriptions of proteins. A pattern discovery algorithm identi®es equivalent secondary structure elements between a set of proteins and these are used to produce an initial multiple structure alignment. Simulated annealing is used to optimize the alignment. The output of Topsalign is a multiple structure-based sequence alignment and a 3D superposition of the structures. This method has been tested on three superfolds: the b jelly roll, TIM (a/b) barrel and the OB fold. Topsalign outperforms established methods on very diverse structures. Despite the pattern discovery working only on b strand secondary structure elements, Topsalign is shown to align TIM (a/b) barrel superfamilies, which contain both a helices and b strands.en
dc.format.extent639055 bytes-
dc.format.mimetypeapplication/pdf-
dc.language.isoen-
dc.publisherOxford University Pressen
dc.subjectPattern discoveryen
dc.subjectProtein topology-
dc.subjectSimulated annealing-
dc.subjectStructural alignment-
dc.subjectSuperfolds-
dc.titleMultiple structural alignment for distantly related all b structures using TOPS pattern discovery and simulated annealingen
dc.typeResearch Paperen
dc.identifier.doihttp://dx.doi.org/10.1093/protein/gzg116-
Appears in Collections:Computer Science
Dept of Computer Science Research Papers

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