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Structural neighboring property for identifying protein-protein binding sites

BACKGROUND: The protein-protein interaction plays a key role in the control of many biological functions, such as drug design and functional analysis. Determination of binding sites is widely applied in molecular biology research. Therefore, many efficient methods have been developed for identifying...

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Autores principales: Guo, Fei, Li, Shuai Cheng, Wei, Zhexue, Zhu, Daming, Shen, Chao, Wang, Lusheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4565107/
https://www.ncbi.nlm.nih.gov/pubmed/26356630
http://dx.doi.org/10.1186/1752-0509-9-S5-S3
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author Guo, Fei
Li, Shuai Cheng
Wei, Zhexue
Zhu, Daming
Shen, Chao
Wang, Lusheng
author_facet Guo, Fei
Li, Shuai Cheng
Wei, Zhexue
Zhu, Daming
Shen, Chao
Wang, Lusheng
author_sort Guo, Fei
collection PubMed
description BACKGROUND: The protein-protein interaction plays a key role in the control of many biological functions, such as drug design and functional analysis. Determination of binding sites is widely applied in molecular biology research. Therefore, many efficient methods have been developed for identifying binding sites. In this paper, we calculate structural neighboring property through Voronoi diagram. Using 6,438 complexes, we study local biases of structural neighboring property on interface. RESULTS: We propose a novel statistical method to extract interacting residues, and interacting patches can be clustered as predicted interface residues. In addition, structural neighboring property can be adopted to construct a new energy function, for evaluating docking solutions. It includes new statistical property as well as existing energy items. Comparing to existing methods, our approach improves overall F(nat )value by at least 3%. On Benchmark v4.0, our method has average I(rmsd )value of 3.31Å and overall F(nat )value of 63%, which improves upon I(rmsd )of 3.89 Å and F(nat )of 49% for ZRANK, and I(rmsd )of 3.99Å and F(nat )of 46% for ClusPro. On the CAPRI targets, our method has average I(rmsd )value of 3.46 Å and overall F(nat )value of 45%, which improves upon I(rmsd )of 4.18 Å and F(nat )of 40% for ZRANK, and I(rmsd )of 5.12 Å and F(nat )of 32% for ClusPro. CONCLUSIONS: Experiments show that our method achieves better results than some state-of-the-art methods for identifying protein-protein binding sites, with the prediction quality improved in terms of CAPRI evaluation criteria.
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spelling pubmed-45651072015-09-18 Structural neighboring property for identifying protein-protein binding sites Guo, Fei Li, Shuai Cheng Wei, Zhexue Zhu, Daming Shen, Chao Wang, Lusheng BMC Syst Biol Research BACKGROUND: The protein-protein interaction plays a key role in the control of many biological functions, such as drug design and functional analysis. Determination of binding sites is widely applied in molecular biology research. Therefore, many efficient methods have been developed for identifying binding sites. In this paper, we calculate structural neighboring property through Voronoi diagram. Using 6,438 complexes, we study local biases of structural neighboring property on interface. RESULTS: We propose a novel statistical method to extract interacting residues, and interacting patches can be clustered as predicted interface residues. In addition, structural neighboring property can be adopted to construct a new energy function, for evaluating docking solutions. It includes new statistical property as well as existing energy items. Comparing to existing methods, our approach improves overall F(nat )value by at least 3%. On Benchmark v4.0, our method has average I(rmsd )value of 3.31Å and overall F(nat )value of 63%, which improves upon I(rmsd )of 3.89 Å and F(nat )of 49% for ZRANK, and I(rmsd )of 3.99Å and F(nat )of 46% for ClusPro. On the CAPRI targets, our method has average I(rmsd )value of 3.46 Å and overall F(nat )value of 45%, which improves upon I(rmsd )of 4.18 Å and F(nat )of 40% for ZRANK, and I(rmsd )of 5.12 Å and F(nat )of 32% for ClusPro. CONCLUSIONS: Experiments show that our method achieves better results than some state-of-the-art methods for identifying protein-protein binding sites, with the prediction quality improved in terms of CAPRI evaluation criteria. BioMed Central 2015-09-01 /pmc/articles/PMC4565107/ /pubmed/26356630 http://dx.doi.org/10.1186/1752-0509-9-S5-S3 Text en Copyright © 2015 Guo et al. http://creativecommons.org/licenses/by/4.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Guo, Fei
Li, Shuai Cheng
Wei, Zhexue
Zhu, Daming
Shen, Chao
Wang, Lusheng
Structural neighboring property for identifying protein-protein binding sites
title Structural neighboring property for identifying protein-protein binding sites
title_full Structural neighboring property for identifying protein-protein binding sites
title_fullStr Structural neighboring property for identifying protein-protein binding sites
title_full_unstemmed Structural neighboring property for identifying protein-protein binding sites
title_short Structural neighboring property for identifying protein-protein binding sites
title_sort structural neighboring property for identifying protein-protein binding sites
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4565107/
https://www.ncbi.nlm.nih.gov/pubmed/26356630
http://dx.doi.org/10.1186/1752-0509-9-S5-S3
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