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A novel protein descriptor for the prediction of drug binding sites

BACKGROUND: Binding sites are the pockets of proteins that can bind drugs; the discovery of these pockets is a critical step in drug design. With the help of computers, protein pockets prediction can save manpower and financial resources. RESULTS: In this paper, a novel protein descriptor for the pr...

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Autores principales: Jiang, Mingjian, Li, Zhen, Bian, Yujie, Wei, Zhiqiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6749706/
https://www.ncbi.nlm.nih.gov/pubmed/31533611
http://dx.doi.org/10.1186/s12859-019-3058-0
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author Jiang, Mingjian
Li, Zhen
Bian, Yujie
Wei, Zhiqiang
author_facet Jiang, Mingjian
Li, Zhen
Bian, Yujie
Wei, Zhiqiang
author_sort Jiang, Mingjian
collection PubMed
description BACKGROUND: Binding sites are the pockets of proteins that can bind drugs; the discovery of these pockets is a critical step in drug design. With the help of computers, protein pockets prediction can save manpower and financial resources. RESULTS: In this paper, a novel protein descriptor for the prediction of binding sites is proposed. Information on non-bonded interactions in the three-dimensional structure of a protein is captured by a combination of geometry-based and energy-based methods. Moreover, due to the rapid development of deep learning, all binding features are extracted to generate three-dimensional grids that are fed into a convolution neural network. Two datasets were introduced into the experiment. The sc-PDB dataset was used for descriptor extraction and binding site prediction, and the PDBbind dataset was used only for testing and verification of the generalization of the method. The comparison with previous methods shows that the proposed descriptor is effective in predicting the binding sites. CONCLUSIONS: A new protein descriptor is proposed for the prediction of the drug binding sites of proteins. This method combines the three-dimensional structure of a protein and non-bonded interactions with small molecules to involve important factors influencing the formation of binding site. Analysis of the experiments indicates that the descriptor is robust for site prediction. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12859-019-3058-0) contains supplementary material, which is available to authorized users.
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spelling pubmed-67497062019-09-23 A novel protein descriptor for the prediction of drug binding sites Jiang, Mingjian Li, Zhen Bian, Yujie Wei, Zhiqiang BMC Bioinformatics Methodology Article BACKGROUND: Binding sites are the pockets of proteins that can bind drugs; the discovery of these pockets is a critical step in drug design. With the help of computers, protein pockets prediction can save manpower and financial resources. RESULTS: In this paper, a novel protein descriptor for the prediction of binding sites is proposed. Information on non-bonded interactions in the three-dimensional structure of a protein is captured by a combination of geometry-based and energy-based methods. Moreover, due to the rapid development of deep learning, all binding features are extracted to generate three-dimensional grids that are fed into a convolution neural network. Two datasets were introduced into the experiment. The sc-PDB dataset was used for descriptor extraction and binding site prediction, and the PDBbind dataset was used only for testing and verification of the generalization of the method. The comparison with previous methods shows that the proposed descriptor is effective in predicting the binding sites. CONCLUSIONS: A new protein descriptor is proposed for the prediction of the drug binding sites of proteins. This method combines the three-dimensional structure of a protein and non-bonded interactions with small molecules to involve important factors influencing the formation of binding site. Analysis of the experiments indicates that the descriptor is robust for site prediction. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12859-019-3058-0) contains supplementary material, which is available to authorized users. BioMed Central 2019-09-18 /pmc/articles/PMC6749706/ /pubmed/31533611 http://dx.doi.org/10.1186/s12859-019-3058-0 Text en © The Author(s) 2019 Open Access This 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 Methodology Article
Jiang, Mingjian
Li, Zhen
Bian, Yujie
Wei, Zhiqiang
A novel protein descriptor for the prediction of drug binding sites
title A novel protein descriptor for the prediction of drug binding sites
title_full A novel protein descriptor for the prediction of drug binding sites
title_fullStr A novel protein descriptor for the prediction of drug binding sites
title_full_unstemmed A novel protein descriptor for the prediction of drug binding sites
title_short A novel protein descriptor for the prediction of drug binding sites
title_sort novel protein descriptor for the prediction of drug binding sites
topic Methodology Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6749706/
https://www.ncbi.nlm.nih.gov/pubmed/31533611
http://dx.doi.org/10.1186/s12859-019-3058-0
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