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• W2105961968 abstract "An estimated 25% of all eukaryotic proteins contain repeats, which underlines the importance of duplication for evolving new protein functions. Internal repeats often correspond to structural or functional units in proteins. Methods capable of identifying diverged repeated segments or domains at the sequence level can therefore assist in predicting domain structures, inferring hypotheses about function and mechanism, and investigating the evolution of proteins from smaller fragments.We present HHrepID, a method for the de novo identification of repeats in protein sequences. It is able to detect the sequence signature of structural repeats in many proteins that have not yet been known to possess internal sequence symmetry, such as outer membrane beta-barrels. HHrepID uses HMM-HMM comparison to exploit evolutionary information in the form of multiple sequence alignments of homologs. In contrast to a previous method, the new method (1) generates a multiple alignment of repeats; (2) utilizes the transitive nature of homology through a novel merging procedure with fully probabilistic treatment of alignments; (3) improves alignment quality through an algorithm that maximizes the expected accuracy; (4) is able to identify different kinds of repeats within complex architectures by a probabilistic domain boundary detection method and (5) improves sensitivity through a new approach to assess statistical significance.Server: http://toolkit.tuebingen.mpg.de/hhrepid; Executables: ftp://ftp.tuebingen.mpg.de/pub/protevo/HHrepID" @default.
• W2105961968 created "2016-06-24" @default.
• W2105961968 creator A5010030898 @default.
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• W2105961968 date "2008-02-01" @default.
• W2105961968 modified "2023-10-01" @default.
• W2105961968 title "<i>De novo</i> identification of highly diverged protein repeats by probabilistic consistency" @default.
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• W2105961968 doi "https://doi.org/10.1093/bioinformatics/btn039" @default.
• W2105961968 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/18245125" @default.
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