LigGrep: a tool for filtering docked poses to improve virtual-screening hit rates

Abstract Structure-based virtual screening (VS) uses computer docking to prioritize candidate small-molecule ligands for subsequent experimental testing. Docking programs evaluate molecular binding in part by predicting the geometry with which a given compound might bind a target receptor (e.g., the...

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Main Authors: Emily J. Ha, Cara T. Lwin, Jacob D. Durrant
Format: Article
Language:English
Published: BMC 2020-11-01
Series:Journal of Cheminformatics
Subjects:
Online Access:http://link.springer.com/article/10.1186/s13321-020-00471-2
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spelling doaj-d7ad762727154e5c9632923de0c2b23e2020-11-25T03:58:35ZengBMCJournal of Cheminformatics1758-29462020-11-0112111210.1186/s13321-020-00471-2LigGrep: a tool for filtering docked poses to improve virtual-screening hit ratesEmily J. Ha0Cara T. Lwin1Jacob D. Durrant2Department of Biological Sciences, Carnegie Mellon UniversityDepartment of Biological Sciences, University of PittsburghDepartment of Biological Sciences, University of PittsburghAbstract Structure-based virtual screening (VS) uses computer docking to prioritize candidate small-molecule ligands for subsequent experimental testing. Docking programs evaluate molecular binding in part by predicting the geometry with which a given compound might bind a target receptor (e.g., the docked “pose” relative to a protein target). Candidate ligands predicted to participate in the same intermolecular interactions typical of known ligands (or ligands that bind related proteins) are arguably more likely to be true binders. Some docking programs allow users to apply constraints during the docking process with the goal of prioritizing these critical interactions. But these programs often have restrictive and/or expensive licenses, and many popular open-source docking programs (e.g., AutoDock Vina) lack this important functionality. We present LigGrep, a free, open-source program that addresses this limitation. As input, LigGrep accepts a protein receptor file, a directory containing many docked-compound files, and a list of user-specified filters describing critical receptor/ligand interactions. LigGrep evaluates each docked pose and outputs the names of the compounds with poses that pass all filters. To demonstrate utility, we show that LigGrep can improve the hit rates of test VS targeting H. sapiens poly(ADPribose) polymerase 1 (HsPARP1), H. sapiens peptidyl-prolyl cis-trans isomerase NIMA-interacting 1 (HsPin1p), and S. cerevisiae hexokinase-2 (ScHxk2p). We hope that LigGrep will be a useful tool for the computational biology community. A copy is available free of charge at http://durrantlab.com/liggrep/ .http://link.springer.com/article/10.1186/s13321-020-00471-2Virtual screeningComputer-aided drug discoveryComputational biologyFilters
collection DOAJ
language English
format Article
sources DOAJ
author Emily J. Ha
Cara T. Lwin
Jacob D. Durrant
spellingShingle Emily J. Ha
Cara T. Lwin
Jacob D. Durrant
LigGrep: a tool for filtering docked poses to improve virtual-screening hit rates
Journal of Cheminformatics
Virtual screening
Computer-aided drug discovery
Computational biology
Filters
author_facet Emily J. Ha
Cara T. Lwin
Jacob D. Durrant
author_sort Emily J. Ha
title LigGrep: a tool for filtering docked poses to improve virtual-screening hit rates
title_short LigGrep: a tool for filtering docked poses to improve virtual-screening hit rates
title_full LigGrep: a tool for filtering docked poses to improve virtual-screening hit rates
title_fullStr LigGrep: a tool for filtering docked poses to improve virtual-screening hit rates
title_full_unstemmed LigGrep: a tool for filtering docked poses to improve virtual-screening hit rates
title_sort liggrep: a tool for filtering docked poses to improve virtual-screening hit rates
publisher BMC
series Journal of Cheminformatics
issn 1758-2946
publishDate 2020-11-01
description Abstract Structure-based virtual screening (VS) uses computer docking to prioritize candidate small-molecule ligands for subsequent experimental testing. Docking programs evaluate molecular binding in part by predicting the geometry with which a given compound might bind a target receptor (e.g., the docked “pose” relative to a protein target). Candidate ligands predicted to participate in the same intermolecular interactions typical of known ligands (or ligands that bind related proteins) are arguably more likely to be true binders. Some docking programs allow users to apply constraints during the docking process with the goal of prioritizing these critical interactions. But these programs often have restrictive and/or expensive licenses, and many popular open-source docking programs (e.g., AutoDock Vina) lack this important functionality. We present LigGrep, a free, open-source program that addresses this limitation. As input, LigGrep accepts a protein receptor file, a directory containing many docked-compound files, and a list of user-specified filters describing critical receptor/ligand interactions. LigGrep evaluates each docked pose and outputs the names of the compounds with poses that pass all filters. To demonstrate utility, we show that LigGrep can improve the hit rates of test VS targeting H. sapiens poly(ADPribose) polymerase 1 (HsPARP1), H. sapiens peptidyl-prolyl cis-trans isomerase NIMA-interacting 1 (HsPin1p), and S. cerevisiae hexokinase-2 (ScHxk2p). We hope that LigGrep will be a useful tool for the computational biology community. A copy is available free of charge at http://durrantlab.com/liggrep/ .
topic Virtual screening
Computer-aided drug discovery
Computational biology
Filters
url http://link.springer.com/article/10.1186/s13321-020-00471-2
work_keys_str_mv AT emilyjha liggrepatoolforfilteringdockedposestoimprovevirtualscreeninghitrates
AT caratlwin liggrepatoolforfilteringdockedposestoimprovevirtualscreeninghitrates
AT jacobddurrant liggrepatoolforfilteringdockedposestoimprovevirtualscreeninghitrates
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