Use of a structural alphabet for analysis of short loops connecting repetitive structures

<p>Abstract</p> <p>Background</p> <p>Because loops connect regular secondary structures, analysis of the former depends directly on the definition of the latter. The numerous assignment methods, however, can offer different definitions. In a previous study, we defined a...

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Main Authors: de Brevern Alexandre G, Benros Cristina, Fourrier Laurent
Format: Article
Language:English
Published: BMC 2004-05-01
Series:BMC Bioinformatics
Online Access:http://www.biomedcentral.com/1471-2105/5/58
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author de Brevern Alexandre G
Benros Cristina
Fourrier Laurent
author_facet de Brevern Alexandre G
Benros Cristina
Fourrier Laurent
author_sort de Brevern Alexandre G
collection DOAJ
description <p>Abstract</p> <p>Background</p> <p>Because loops connect regular secondary structures, analysis of the former depends directly on the definition of the latter. The numerous assignment methods, however, can offer different definitions. In a previous study, we defined a structural alphabet composed of 16 average protein fragments, which we called Protein Blocks (PBs). They allow an accurate description of every region of 3D protein backbones and have been used in local structure prediction. In the present study, we use this structural alphabet to analyze and predict the loops connecting two repetitive structures.</p> <p>Results</p> <p>We first analyzed the secondary structure assignments. Use of five different assignment methods (DSSP, DEFINE, PCURVE, STRIDE and PSEA) showed the absence of consensus: 20% of the residues were assigned to different states. The discrepancies were particularly important at the extremities of the repetitive structures. We used PBs to describe and predict the short loops because they can help analyze and in part explain these discrepancies. An analysis of the PB distribution in these regions showed some specificities in the sequence-structure relationship. Of the amino acid over- or under-representations observed in the short loop databank, 20% did not appear in the entire databank. Finally, predicting 3D structure in terms of PBs with a Bayesian approach yielded an accuracy rate of 36.0% for all loops and 41.2% for the short loops. Specific learning in the short loops increased the latter by 1%.</p> <p>Conclusion</p> <p>This work highlights the difficulties of assigning repetitive structures and the advantages of using more precise descriptions, that is, PBs. We observed some new amino acid distributions in the short loops and used this information to enhance local prediction. Instead of describing entire loops, our approach predicts each position in the loops locally. It can thus be used to propose many different structures for the loops and to probe and sample their flexibility. It can be a useful tool in <it>ab initio </it>loop prediction.</p>
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spelling doaj.art-d1d7f7105a66415eb60439bccb1c5b3f2022-12-22T01:44:08ZengBMCBMC Bioinformatics1471-21052004-05-01515810.1186/1471-2105-5-58Use of a structural alphabet for analysis of short loops connecting repetitive structuresde Brevern Alexandre GBenros CristinaFourrier Laurent<p>Abstract</p> <p>Background</p> <p>Because loops connect regular secondary structures, analysis of the former depends directly on the definition of the latter. The numerous assignment methods, however, can offer different definitions. In a previous study, we defined a structural alphabet composed of 16 average protein fragments, which we called Protein Blocks (PBs). They allow an accurate description of every region of 3D protein backbones and have been used in local structure prediction. In the present study, we use this structural alphabet to analyze and predict the loops connecting two repetitive structures.</p> <p>Results</p> <p>We first analyzed the secondary structure assignments. Use of five different assignment methods (DSSP, DEFINE, PCURVE, STRIDE and PSEA) showed the absence of consensus: 20% of the residues were assigned to different states. The discrepancies were particularly important at the extremities of the repetitive structures. We used PBs to describe and predict the short loops because they can help analyze and in part explain these discrepancies. An analysis of the PB distribution in these regions showed some specificities in the sequence-structure relationship. Of the amino acid over- or under-representations observed in the short loop databank, 20% did not appear in the entire databank. Finally, predicting 3D structure in terms of PBs with a Bayesian approach yielded an accuracy rate of 36.0% for all loops and 41.2% for the short loops. Specific learning in the short loops increased the latter by 1%.</p> <p>Conclusion</p> <p>This work highlights the difficulties of assigning repetitive structures and the advantages of using more precise descriptions, that is, PBs. We observed some new amino acid distributions in the short loops and used this information to enhance local prediction. Instead of describing entire loops, our approach predicts each position in the loops locally. It can thus be used to propose many different structures for the loops and to probe and sample their flexibility. It can be a useful tool in <it>ab initio </it>loop prediction.</p>http://www.biomedcentral.com/1471-2105/5/58
spellingShingle de Brevern Alexandre G
Benros Cristina
Fourrier Laurent
Use of a structural alphabet for analysis of short loops connecting repetitive structures
BMC Bioinformatics
title Use of a structural alphabet for analysis of short loops connecting repetitive structures
title_full Use of a structural alphabet for analysis of short loops connecting repetitive structures
title_fullStr Use of a structural alphabet for analysis of short loops connecting repetitive structures
title_full_unstemmed Use of a structural alphabet for analysis of short loops connecting repetitive structures
title_short Use of a structural alphabet for analysis of short loops connecting repetitive structures
title_sort use of a structural alphabet for analysis of short loops connecting repetitive structures
url http://www.biomedcentral.com/1471-2105/5/58
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