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Mapping the 3D structures of small molecule binding sites
BACKGROUND: Analysis of the 3D structures of protein–ligand binding sites can provide valuable insights for drug discovery. Binding site comparison (BSC) studies can be employed to elucidate the function of orphan proteins or to predict the potential for polypharmacology. Many previous binding site...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5395517/ http://dx.doi.org/10.1186/s13321-016-0180-0 |
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author | Meyers, Joshua Brown, Nathan Blagg, Julian |
author_facet | Meyers, Joshua Brown, Nathan Blagg, Julian |
author_sort | Meyers, Joshua |
collection | PubMed |
description | BACKGROUND: Analysis of the 3D structures of protein–ligand binding sites can provide valuable insights for drug discovery. Binding site comparison (BSC) studies can be employed to elucidate the function of orphan proteins or to predict the potential for polypharmacology. Many previous binding site analyses only consider binding sites surrounding an experimentally observed bound ligand. RESULTS: To encompass potential protein–ligand binding sites that do not have ligands known to bind, we have incorporated fpocket cavity detection software and assessed the impact of this inclusion on BSC performance. Using fpocket, we generated a database of ligand-independent potential binding sites and applied the BSC tool, SiteHopper, to analyze similarity relationships between protein binding sites. We developed a method for clustering potential binding sites using a curated dataset of structures for six therapeutically relevant proteins from diverse protein classes in the protein data bank. Two clustering methods were explored; hierarchical clustering and a density-based method adept at excluding noise and outliers from a dataset. We introduce circular plots to visualize binding site structure space. From the datasets analyzed in this study, we highlight a structural relationship between binding sites of cationic trypsin and prothrombin, protein targets known to bind structurally similar small molecules, exemplifying the potential utility of objectively and holistically mapping binding site space from the structural proteome. CONCLUSIONS: We present a workflow for the objective mapping of potential protein–ligand binding sites derived from the currently available structural proteome. We show that ligand-independent binding site detection tools can be introduced without excessive penalty on BSC performance. Clustering combined with intuitive visualization tools can be applied to map relationships between the 3D structures of protein binding sites. [Figure: see text] ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13321-016-0180-0) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5395517 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-53955172017-05-05 Mapping the 3D structures of small molecule binding sites Meyers, Joshua Brown, Nathan Blagg, Julian J Cheminform Research Article BACKGROUND: Analysis of the 3D structures of protein–ligand binding sites can provide valuable insights for drug discovery. Binding site comparison (BSC) studies can be employed to elucidate the function of orphan proteins or to predict the potential for polypharmacology. Many previous binding site analyses only consider binding sites surrounding an experimentally observed bound ligand. RESULTS: To encompass potential protein–ligand binding sites that do not have ligands known to bind, we have incorporated fpocket cavity detection software and assessed the impact of this inclusion on BSC performance. Using fpocket, we generated a database of ligand-independent potential binding sites and applied the BSC tool, SiteHopper, to analyze similarity relationships between protein binding sites. We developed a method for clustering potential binding sites using a curated dataset of structures for six therapeutically relevant proteins from diverse protein classes in the protein data bank. Two clustering methods were explored; hierarchical clustering and a density-based method adept at excluding noise and outliers from a dataset. We introduce circular plots to visualize binding site structure space. From the datasets analyzed in this study, we highlight a structural relationship between binding sites of cationic trypsin and prothrombin, protein targets known to bind structurally similar small molecules, exemplifying the potential utility of objectively and holistically mapping binding site space from the structural proteome. CONCLUSIONS: We present a workflow for the objective mapping of potential protein–ligand binding sites derived from the currently available structural proteome. We show that ligand-independent binding site detection tools can be introduced without excessive penalty on BSC performance. Clustering combined with intuitive visualization tools can be applied to map relationships between the 3D structures of protein binding sites. [Figure: see text] ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13321-016-0180-0) contains supplementary material, which is available to authorized users. Springer International Publishing 2016-12-06 /pmc/articles/PMC5395517/ http://dx.doi.org/10.1186/s13321-016-0180-0 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 Meyers, Joshua Brown, Nathan Blagg, Julian Mapping the 3D structures of small molecule binding sites |
title | Mapping the 3D structures of small molecule binding sites |
title_full | Mapping the 3D structures of small molecule binding sites |
title_fullStr | Mapping the 3D structures of small molecule binding sites |
title_full_unstemmed | Mapping the 3D structures of small molecule binding sites |
title_short | Mapping the 3D structures of small molecule binding sites |
title_sort | mapping the 3d structures of small molecule binding sites |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5395517/ http://dx.doi.org/10.1186/s13321-016-0180-0 |
work_keys_str_mv | AT meyersjoshua mappingthe3dstructuresofsmallmoleculebindingsites AT brownnathan mappingthe3dstructuresofsmallmoleculebindingsites AT blaggjulian mappingthe3dstructuresofsmallmoleculebindingsites |