Exploiting residue-level and profile-level interface propensities for usage in binding sites prediction of proteins

<p>Abstract</p> <p>Background</p> <p>Recognition of binding sites in proteins is a direct computational approach to the characterization of proteins in terms of biological and biochemical function. Residue preferences have been widely used in many studies but the result...

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Main Authors: Lin Lei, Wang Xiaolong, Dong Qiwen, Guan Yi
Format: Article
Language:English
Published: BMC 2007-05-01
Series:BMC Bioinformatics
Online Access:http://www.biomedcentral.com/1471-2105/8/147
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spelling doaj-8dccff94bf0247c389a5dcbad926a4cd2020-11-25T02:18:36ZengBMCBMC Bioinformatics1471-21052007-05-018114710.1186/1471-2105-8-147Exploiting residue-level and profile-level interface propensities for usage in binding sites prediction of proteinsLin LeiWang XiaolongDong QiwenGuan Yi<p>Abstract</p> <p>Background</p> <p>Recognition of binding sites in proteins is a direct computational approach to the characterization of proteins in terms of biological and biochemical function. Residue preferences have been widely used in many studies but the results are often not satisfactory. Although different amino acid compositions among the interaction sites of different complexes have been observed, such differences have not been integrated into the prediction process. Furthermore, the evolution information has not been exploited to achieve a more powerful propensity.</p> <p>Result</p> <p>In this study, the residue interface propensities of four kinds of complexes (homo-permanent complexes, homo-transient complexes, hetero-permanent complexes and hetero-transient complexes) are investigated. These propensities, combined with sequence profiles and accessible surface areas, are inputted to the support vector machine for the prediction of protein binding sites. Such propensities are further improved by taking evolutional information into consideration, which results in a class of novel propensities at the profile level, i.e. the binary profiles interface propensities. Experiment is performed on the 1139 non-redundant protein chains. Although different residue interface propensities among different complexes are observed, the improvement of the classifier with residue interface propensities can be negligible in comparison with that without propensities. The binary profile interface propensities can significantly improve the performance of binding sites prediction by about ten percent in term of both precision and recall.</p> <p>Conclusion</p> <p>Although there are minor differences among the four kinds of complexes, the residue interface propensities cannot provide efficient discrimination for the complicated interfaces of proteins. The binary profile interface propensities can significantly improve the performance of binding sites prediction of protein, which indicates that the propensities at the profile level are more accurate than those at the residue level.</p> http://www.biomedcentral.com/1471-2105/8/147
collection DOAJ
language English
format Article
sources DOAJ
author Lin Lei
Wang Xiaolong
Dong Qiwen
Guan Yi
spellingShingle Lin Lei
Wang Xiaolong
Dong Qiwen
Guan Yi
Exploiting residue-level and profile-level interface propensities for usage in binding sites prediction of proteins
BMC Bioinformatics
author_facet Lin Lei
Wang Xiaolong
Dong Qiwen
Guan Yi
author_sort Lin Lei
title Exploiting residue-level and profile-level interface propensities for usage in binding sites prediction of proteins
title_short Exploiting residue-level and profile-level interface propensities for usage in binding sites prediction of proteins
title_full Exploiting residue-level and profile-level interface propensities for usage in binding sites prediction of proteins
title_fullStr Exploiting residue-level and profile-level interface propensities for usage in binding sites prediction of proteins
title_full_unstemmed Exploiting residue-level and profile-level interface propensities for usage in binding sites prediction of proteins
title_sort exploiting residue-level and profile-level interface propensities for usage in binding sites prediction of proteins
publisher BMC
series BMC Bioinformatics
issn 1471-2105
publishDate 2007-05-01
description <p>Abstract</p> <p>Background</p> <p>Recognition of binding sites in proteins is a direct computational approach to the characterization of proteins in terms of biological and biochemical function. Residue preferences have been widely used in many studies but the results are often not satisfactory. Although different amino acid compositions among the interaction sites of different complexes have been observed, such differences have not been integrated into the prediction process. Furthermore, the evolution information has not been exploited to achieve a more powerful propensity.</p> <p>Result</p> <p>In this study, the residue interface propensities of four kinds of complexes (homo-permanent complexes, homo-transient complexes, hetero-permanent complexes and hetero-transient complexes) are investigated. These propensities, combined with sequence profiles and accessible surface areas, are inputted to the support vector machine for the prediction of protein binding sites. Such propensities are further improved by taking evolutional information into consideration, which results in a class of novel propensities at the profile level, i.e. the binary profiles interface propensities. Experiment is performed on the 1139 non-redundant protein chains. Although different residue interface propensities among different complexes are observed, the improvement of the classifier with residue interface propensities can be negligible in comparison with that without propensities. The binary profile interface propensities can significantly improve the performance of binding sites prediction by about ten percent in term of both precision and recall.</p> <p>Conclusion</p> <p>Although there are minor differences among the four kinds of complexes, the residue interface propensities cannot provide efficient discrimination for the complicated interfaces of proteins. The binary profile interface propensities can significantly improve the performance of binding sites prediction of protein, which indicates that the propensities at the profile level are more accurate than those at the residue level.</p>
url http://www.biomedcentral.com/1471-2105/8/147
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AT wangxiaolong exploitingresiduelevelandprofilelevelinterfacepropensitiesforusageinbindingsitespredictionofproteins
AT dongqiwen exploitingresiduelevelandprofilelevelinterfacepropensitiesforusageinbindingsitespredictionofproteins
AT guanyi exploitingresiduelevelandprofilelevelinterfacepropensitiesforusageinbindingsitespredictionofproteins
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