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      LPS-annotate: complete annotation of compositionally biased regions in the protein knowledgebase

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          Abstract

          Compositional bias (i.e. a skew in the composition of a biological sequence towards a subset of residue types) can occur at a wide variety of scales, from compositional biases of whole genomes, down to short regions in individual protein and gene–DNA sequences that are compositionally biased (CB regions). Such CB regions are made from a subset of residue types that are strewn along the length of the region in an irregular way. Here, we have developed the database server LPS-annotate, for the analysis of such CB regions, and protein disorder in protein sequences. The algorithm defines compositional bias through a thorough search for lowest-probability subsequences (LPSs) (i.e., the least likely sequence regions in terms of composition). Users can (i) initially annotate CB regions in input protein or nucleotide sequences of interest, and then (ii) query a database of greater than 1 500 000 pre-calculated protein-CB regions, for investigation of further functional hypotheses and inferences, about the specific CB regions that were discovered, and their protein disorder propensities. We demonstrate how a user can search for CB regions of similar compositional bias and protein disorder, with a worked example. We show that our annotations substantially augment the CB-region annotations that already exist in the UniProt database, with more comprehensive annotation of more complex CB regions. Our analysis indicates tens of thousands of CB regions that do not comprise globular domains or transmembrane domains, and that do not have a propensity to protein disorder, indicating a large cohort of protein-CB regions of biophysically uncharacterized types. This server and database is a conceptually novel addition to the workbench of tools now available to molecular biologists to generate hypotheses and inferences about the proteins that they are investigating. It can be accessed at http://libaio.biol.mcgill.ca/lps-annotate.html.

          Database URL: http://libaio.biol.mcgill.ca/lps-annotate.html

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          Most cited references19

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          Understanding protein non-folding.

          This review describes the family of intrinsically disordered proteins, members of which fail to form rigid 3-D structures under physiological conditions, either along their entire lengths or only in localized regions. Instead, these intriguing proteins/regions exist as dynamic ensembles within which atom positions and backbone Ramachandran angles exhibit extreme temporal fluctuations without specific equilibrium values. Many of these intrinsically disordered proteins are known to carry out important biological functions which, in fact, depend on the absence of a specific 3-D structure. The existence of such proteins does not fit the prevailing structure-function paradigm, which states that a unique 3-D structure is a prerequisite to function. Thus, the protein structure-function paradigm has to be expanded to include intrinsically disordered proteins and alternative relationships among protein sequence, structure, and function. This shift in the paradigm represents a major breakthrough for biochemistry, biophysics and molecular biology, as it opens new levels of understanding with regard to the complex life of proteins. This review will try to answer the following questions: how were intrinsically disordered proteins discovered? Why don't these proteins fold? What is so special about intrinsic disorder? What are the functional advantages of disordered proteins/regions? What is the functional repertoire of these proteins? What are the relationships between intrinsically disordered proteins and human diseases? Copyright 2010 Elsevier B.V. All rights reserved.
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            Function and structure of inherently disordered proteins.

            The application of bioinformatics methodologies to proteins inherently lacking 3D structure has brought increased attention to these macromolecules. Here topics concerning these proteins are discussed, including their prediction from amino acid sequence, their enrichment in eukaryotes compared to prokaryotes, their more rapid evolution compared to structured proteins, their organization into specific groups, their structural preferences, their half-lives in cells, their contributions to signaling diversity (via high contents of multiple-partner binding sites, post-translational modifications, and alternative splicing), their distinct functional repertoire compared to that of structured proteins, and their involvement in diseases.
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              Analysis of compositionally biased regions in sequence databases.

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                Author and article information

                Journal
                Database (Oxford)
                database
                databa
                Database: The Journal of Biological Databases and Curation
                Oxford University Press
                1758-0463
                2011
                7 January 2011
                7 January 2011
                : 2011
                : baq031
                Affiliations
                Department of Biology, McGill University, Stewart Biology Building, 1205 Dr. Penfield Ave., Montreal, QC, H3A 1B1, Canada
                Author notes
                * Corresponding author: Tel: +1 514 398 6420; Fax: +1 514 398 5069; Email: paul.harrison@ 123456mcgill.ca
                Article
                baq031
                10.1093/database/baq031
                3017391
                21216786
                5b06b412-5f74-44a8-b9e2-d49876f64955
                © The Author(s) 2011. Published by Oxford University Press.

                This is Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 10 June 2010
                : 24 November 2010
                Page count
                Pages: 8
                Categories
                Original Article

                Bioinformatics & Computational biology
                Bioinformatics & Computational biology

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