Template-based Recognition of Natively Disordered Regions in Proteins
|Title:||Template-based Recognition of Natively Disordered Regions in Proteins||Authors:||Vullo, Alessandro; Roche, Cliona P.; Pollastri, Gianluca||Permanent link:||http://hdl.handle.net/10197/12405||Date:||Apr-2012||Online since:||2021-08-11T11:14:30Z||Abstract:||Disordered proteins are increasingly recognised as a fundamental component of the cellular machinery. Parallel to this, the prediction of protein disorder by computational means has emerged as an aid to the investigation of protein functions. Although predictors of disorder have met with considerable success, it is increasingly clear that further improvements are most likely to come from additional sources of information, to complement patterns extracted from the primary sequence of a protein. In this article, a system for the prediction of protein disorder that relies both on sequence information and on structural information from homologous proteins of known structure (templates) is described. Structural information is introduced directly (as a further input to the predictor) and indirectly through highly reliable template-based predictions of structural features of the protein. The predictive system, based on Support Vector Machines, is tested by rigorous 5-fold cross validation on a large, non-redundant set of proteins extracted from the Protein Data Bank. In these tests the introduction of structural information, which is carefully weighed based on sequence identity between homologues and query, results in large improvements in prediction accuracy. The method, when re-trained on a 2004 version of the PDB, clearly outperforms the algorithms that ranked top at the 2006 CASP competition.||Funding Details:||Health Research Board
Science Foundation Ireland
|Type of material:||Technical Report||Publisher:||University College Dublin. School of Computer Science and Informatics||Series/Report no.:||UCD CSI Technical Reports; ucd-csi-2012-01||Copyright (published version):||2012 the Authors||Keywords:||Intrinsicaly disordered proteins (IDPs); Functional proteomics; Protein classification; Machine learning||Other versions:||https://web.archive.org/web/20080226040105/http:/csiweb.ucd.ie/Research/TechnicalReports.html||Language:||en||Status of Item:||Not peer reviewed||This item is made available under a Creative Commons License:||https://creativecommons.org/licenses/by-nc-nd/3.0/ie/|
|Appears in Collections:||CASL Research Collection|
Computer Science and Informatics Technical Reports
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