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Predicting binding sites of hydrolase-inhibitor complexes by combining several methods

BACKGROUND: Protein-protein interactions play a critical role in protein function. Completion of many genomes is being followed rapidly by major efforts to identify interacting protein pairs experimentally in order to decipher the networks of interacting, coordinated-in-action proteins. Identificati...

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Autores principales: Sen, Taner Z, Kloczkowski, Andrzej, Jernigan, Robert L, Yan, Changhui, Honavar, Vasant, Ho, Kai-Ming, Wang, Cai-Zhuang, Ihm, Yungok, Cao, Haibo, Gu, Xun, Dobbs, Drena
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2004
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC544855/
https://www.ncbi.nlm.nih.gov/pubmed/15606919
http://dx.doi.org/10.1186/1471-2105-5-205
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author Sen, Taner Z
Kloczkowski, Andrzej
Jernigan, Robert L
Yan, Changhui
Honavar, Vasant
Ho, Kai-Ming
Wang, Cai-Zhuang
Ihm, Yungok
Cao, Haibo
Gu, Xun
Dobbs, Drena
author_facet Sen, Taner Z
Kloczkowski, Andrzej
Jernigan, Robert L
Yan, Changhui
Honavar, Vasant
Ho, Kai-Ming
Wang, Cai-Zhuang
Ihm, Yungok
Cao, Haibo
Gu, Xun
Dobbs, Drena
author_sort Sen, Taner Z
collection PubMed
description BACKGROUND: Protein-protein interactions play a critical role in protein function. Completion of many genomes is being followed rapidly by major efforts to identify interacting protein pairs experimentally in order to decipher the networks of interacting, coordinated-in-action proteins. Identification of protein-protein interaction sites and detection of specific amino acids that contribute to the specificity and the strength of protein interactions is an important problem with broad applications ranging from rational drug design to the analysis of metabolic and signal transduction networks. RESULTS: In order to increase the power of predictive methods for protein-protein interaction sites, we have developed a consensus methodology for combining four different methods. These approaches include: data mining using Support Vector Machines, threading through protein structures, prediction of conserved residues on the protein surface by analysis of phylogenetic trees, and the Conservatism of Conservatism method of Mirny and Shakhnovich. Results obtained on a dataset of hydrolase-inhibitor complexes demonstrate that the combination of all four methods yield improved predictions over the individual methods. CONCLUSIONS: We developed a consensus method for predicting protein-protein interface residues by combining sequence and structure-based methods. The success of our consensus approach suggests that similar methodologies can be developed to improve prediction accuracies for other bioinformatic problems.
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spelling pubmed-5448552005-01-21 Predicting binding sites of hydrolase-inhibitor complexes by combining several methods Sen, Taner Z Kloczkowski, Andrzej Jernigan, Robert L Yan, Changhui Honavar, Vasant Ho, Kai-Ming Wang, Cai-Zhuang Ihm, Yungok Cao, Haibo Gu, Xun Dobbs, Drena BMC Bioinformatics Methodology Article BACKGROUND: Protein-protein interactions play a critical role in protein function. Completion of many genomes is being followed rapidly by major efforts to identify interacting protein pairs experimentally in order to decipher the networks of interacting, coordinated-in-action proteins. Identification of protein-protein interaction sites and detection of specific amino acids that contribute to the specificity and the strength of protein interactions is an important problem with broad applications ranging from rational drug design to the analysis of metabolic and signal transduction networks. RESULTS: In order to increase the power of predictive methods for protein-protein interaction sites, we have developed a consensus methodology for combining four different methods. These approaches include: data mining using Support Vector Machines, threading through protein structures, prediction of conserved residues on the protein surface by analysis of phylogenetic trees, and the Conservatism of Conservatism method of Mirny and Shakhnovich. Results obtained on a dataset of hydrolase-inhibitor complexes demonstrate that the combination of all four methods yield improved predictions over the individual methods. CONCLUSIONS: We developed a consensus method for predicting protein-protein interface residues by combining sequence and structure-based methods. The success of our consensus approach suggests that similar methodologies can be developed to improve prediction accuracies for other bioinformatic problems. BioMed Central 2004-12-17 /pmc/articles/PMC544855/ /pubmed/15606919 http://dx.doi.org/10.1186/1471-2105-5-205 Text en Copyright © 2004 Sen et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Methodology Article
Sen, Taner Z
Kloczkowski, Andrzej
Jernigan, Robert L
Yan, Changhui
Honavar, Vasant
Ho, Kai-Ming
Wang, Cai-Zhuang
Ihm, Yungok
Cao, Haibo
Gu, Xun
Dobbs, Drena
Predicting binding sites of hydrolase-inhibitor complexes by combining several methods
title Predicting binding sites of hydrolase-inhibitor complexes by combining several methods
title_full Predicting binding sites of hydrolase-inhibitor complexes by combining several methods
title_fullStr Predicting binding sites of hydrolase-inhibitor complexes by combining several methods
title_full_unstemmed Predicting binding sites of hydrolase-inhibitor complexes by combining several methods
title_short Predicting binding sites of hydrolase-inhibitor complexes by combining several methods
title_sort predicting binding sites of hydrolase-inhibitor complexes by combining several methods
topic Methodology Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC544855/
https://www.ncbi.nlm.nih.gov/pubmed/15606919
http://dx.doi.org/10.1186/1471-2105-5-205
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