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Protein ligand-specific binding residue predictions by an ensemble classifier

BACKGROUND: Prediction of ligand binding sites is important to elucidate protein functions and is helpful for drug design. Although much progress has been made, many challenges still need to be addressed. Prediction methods need to be carefully developed to account for chemical and structural differ...

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Autores principales: Hu, Xiuzhen, Wang, Kai, Dong, Qiwen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5114821/
https://www.ncbi.nlm.nih.gov/pubmed/27855637
http://dx.doi.org/10.1186/s12859-016-1348-3
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author Hu, Xiuzhen
Wang, Kai
Dong, Qiwen
author_facet Hu, Xiuzhen
Wang, Kai
Dong, Qiwen
author_sort Hu, Xiuzhen
collection PubMed
description BACKGROUND: Prediction of ligand binding sites is important to elucidate protein functions and is helpful for drug design. Although much progress has been made, many challenges still need to be addressed. Prediction methods need to be carefully developed to account for chemical and structural differences between ligands. RESULTS: In this study, we present ligand-specific methods to predict the binding sites of protein-ligand interactions. First, a sequence-based method is proposed that only extracts features from protein sequence information, including evolutionary conservation scores and predicted structure properties. An improved AdaBoost algorithm is applied to address the serious imbalance problem between the binding and non-binding residues. Then, a combined method is proposed that combines the current template-free method and four other well-established template-based methods. The above two methods predict the ligand binding sites along the sequences using a ligand-specific strategy that contains metal ions, acid radical ions, nucleotides and ferroheme. Testing on a well-established dataset showed that the proposed sequence-based method outperformed the profile-based method by 4–19% in terms of the Matthews correlation coefficient on different ligands. The combined method outperformed each of the individual methods, with an improvement in the average Matthews correlation coefficients of 5.55% over all ligands. The results also show that the ligand-specific methods significantly outperform the general-purpose methods, which confirms the necessity of developing elaborate ligand-specific methods for ligand binding site prediction. CONCLUSIONS: Two efficient ligand-specific binding site predictors are presented. The standalone package is freely available for academic usage at http://dase.ecnu.edu.cn/qwdong/TargetCom/TargetCom_standalone.tar.gz or request upon the corresponding author. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-016-1348-3) contains supplementary material, which is available to authorized users.
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spelling pubmed-51148212016-11-25 Protein ligand-specific binding residue predictions by an ensemble classifier Hu, Xiuzhen Wang, Kai Dong, Qiwen BMC Bioinformatics Research Article BACKGROUND: Prediction of ligand binding sites is important to elucidate protein functions and is helpful for drug design. Although much progress has been made, many challenges still need to be addressed. Prediction methods need to be carefully developed to account for chemical and structural differences between ligands. RESULTS: In this study, we present ligand-specific methods to predict the binding sites of protein-ligand interactions. First, a sequence-based method is proposed that only extracts features from protein sequence information, including evolutionary conservation scores and predicted structure properties. An improved AdaBoost algorithm is applied to address the serious imbalance problem between the binding and non-binding residues. Then, a combined method is proposed that combines the current template-free method and four other well-established template-based methods. The above two methods predict the ligand binding sites along the sequences using a ligand-specific strategy that contains metal ions, acid radical ions, nucleotides and ferroheme. Testing on a well-established dataset showed that the proposed sequence-based method outperformed the profile-based method by 4–19% in terms of the Matthews correlation coefficient on different ligands. The combined method outperformed each of the individual methods, with an improvement in the average Matthews correlation coefficients of 5.55% over all ligands. The results also show that the ligand-specific methods significantly outperform the general-purpose methods, which confirms the necessity of developing elaborate ligand-specific methods for ligand binding site prediction. CONCLUSIONS: Two efficient ligand-specific binding site predictors are presented. The standalone package is freely available for academic usage at http://dase.ecnu.edu.cn/qwdong/TargetCom/TargetCom_standalone.tar.gz or request upon the corresponding author. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-016-1348-3) contains supplementary material, which is available to authorized users. BioMed Central 2016-11-17 /pmc/articles/PMC5114821/ /pubmed/27855637 http://dx.doi.org/10.1186/s12859-016-1348-3 Text en © The Author(s). 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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 Article
Hu, Xiuzhen
Wang, Kai
Dong, Qiwen
Protein ligand-specific binding residue predictions by an ensemble classifier
title Protein ligand-specific binding residue predictions by an ensemble classifier
title_full Protein ligand-specific binding residue predictions by an ensemble classifier
title_fullStr Protein ligand-specific binding residue predictions by an ensemble classifier
title_full_unstemmed Protein ligand-specific binding residue predictions by an ensemble classifier
title_short Protein ligand-specific binding residue predictions by an ensemble classifier
title_sort protein ligand-specific binding residue predictions by an ensemble classifier
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5114821/
https://www.ncbi.nlm.nih.gov/pubmed/27855637
http://dx.doi.org/10.1186/s12859-016-1348-3
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