Predicting Ligand Binding Sites on Protein Surfaces by 3-Dimensional Probability Density Distributions of Interacting Atoms.
Predicting ligand binding sites (LBSs) on protein structures, which are obtained either from experimental or computational methods, is a useful first step in functional annotation or structure-based drug design for the protein structures. In this work, the structure-based machine learning algorithm...
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doaj-9a41de82ba2b442896be0ec0b9d0b8212020-11-25T00:07:59ZengPublic Library of Science (PLoS)PLoS ONE1932-62032016-01-01118e016031510.1371/journal.pone.0160315Predicting Ligand Binding Sites on Protein Surfaces by 3-Dimensional Probability Density Distributions of Interacting Atoms.Jhih-Wei JianPavadai ElumalaiThejkiran PittiChih Yuan WuKeng-Chang TsaiJeng-Yih ChangHung-Pin PengAn-Suei YangPredicting ligand binding sites (LBSs) on protein structures, which are obtained either from experimental or computational methods, is a useful first step in functional annotation or structure-based drug design for the protein structures. In this work, the structure-based machine learning algorithm ISMBLab-LIG was developed to predict LBSs on protein surfaces with input attributes derived from the three-dimensional probability density maps of interacting atoms, which were reconstructed on the query protein surfaces and were relatively insensitive to local conformational variations of the tentative ligand binding sites. The prediction accuracy of the ISMBLab-LIG predictors is comparable to that of the best LBS predictors benchmarked on several well-established testing datasets. More importantly, the ISMBLab-LIG algorithm has substantial tolerance to the prediction uncertainties of computationally derived protein structure models. As such, the method is particularly useful for predicting LBSs not only on experimental protein structures without known LBS templates in the database but also on computationally predicted model protein structures with structural uncertainties in the tentative ligand binding sites.http://europepmc.org/articles/PMC4981321?pdf=render |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jhih-Wei Jian Pavadai Elumalai Thejkiran Pitti Chih Yuan Wu Keng-Chang Tsai Jeng-Yih Chang Hung-Pin Peng An-Suei Yang |
spellingShingle |
Jhih-Wei Jian Pavadai Elumalai Thejkiran Pitti Chih Yuan Wu Keng-Chang Tsai Jeng-Yih Chang Hung-Pin Peng An-Suei Yang Predicting Ligand Binding Sites on Protein Surfaces by 3-Dimensional Probability Density Distributions of Interacting Atoms. PLoS ONE |
author_facet |
Jhih-Wei Jian Pavadai Elumalai Thejkiran Pitti Chih Yuan Wu Keng-Chang Tsai Jeng-Yih Chang Hung-Pin Peng An-Suei Yang |
author_sort |
Jhih-Wei Jian |
title |
Predicting Ligand Binding Sites on Protein Surfaces by 3-Dimensional Probability Density Distributions of Interacting Atoms. |
title_short |
Predicting Ligand Binding Sites on Protein Surfaces by 3-Dimensional Probability Density Distributions of Interacting Atoms. |
title_full |
Predicting Ligand Binding Sites on Protein Surfaces by 3-Dimensional Probability Density Distributions of Interacting Atoms. |
title_fullStr |
Predicting Ligand Binding Sites on Protein Surfaces by 3-Dimensional Probability Density Distributions of Interacting Atoms. |
title_full_unstemmed |
Predicting Ligand Binding Sites on Protein Surfaces by 3-Dimensional Probability Density Distributions of Interacting Atoms. |
title_sort |
predicting ligand binding sites on protein surfaces by 3-dimensional probability density distributions of interacting atoms. |
publisher |
Public Library of Science (PLoS) |
series |
PLoS ONE |
issn |
1932-6203 |
publishDate |
2016-01-01 |
description |
Predicting ligand binding sites (LBSs) on protein structures, which are obtained either from experimental or computational methods, is a useful first step in functional annotation or structure-based drug design for the protein structures. In this work, the structure-based machine learning algorithm ISMBLab-LIG was developed to predict LBSs on protein surfaces with input attributes derived from the three-dimensional probability density maps of interacting atoms, which were reconstructed on the query protein surfaces and were relatively insensitive to local conformational variations of the tentative ligand binding sites. The prediction accuracy of the ISMBLab-LIG predictors is comparable to that of the best LBS predictors benchmarked on several well-established testing datasets. More importantly, the ISMBLab-LIG algorithm has substantial tolerance to the prediction uncertainties of computationally derived protein structure models. As such, the method is particularly useful for predicting LBSs not only on experimental protein structures without known LBS templates in the database but also on computationally predicted model protein structures with structural uncertainties in the tentative ligand binding sites. |
url |
http://europepmc.org/articles/PMC4981321?pdf=render |
work_keys_str_mv |
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