Performance of virtual screening against GPCR homology models: Impact of template selection and treatment of binding site plasticity

Rational drug design for G protein-coupled receptors (GPCRs) is limited by the small number of available atomic resolution structures. We assessed the use of homology modeling to predict the structures of two therapeutically relevant GPCRs and strategies to improve the performance of virtual screeni...

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Autores: Jaiteh, Mariama, Rodríguez Espigares, Ismael, 1990-, Selent, Jana, Carlsson, Jens
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2020
País:España
Institución:Universitat Pompeu Fabra
Repositorio:Repositorio Digital de la UPF
OAI Identifier:oai:repositori.upf.edu:10230/44424
Acceso en línea:http://hdl.handle.net/10230/44424
http://dx.doi.org/10.1371/journal.pcbi.1007680
Access Level:acceso abierto
Palabra clave:Crystal structure
G protein coupled receptors
Serotonin
Simulation and modeling
Molecular docking
Protein structure prediction
Dopamine
Protein structure
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spelling Performance of virtual screening against GPCR homology models: Impact of template selection and treatment of binding site plasticityJaiteh, MariamaRodríguez Espigares, Ismael, 1990-Selent, JanaCarlsson, JensCrystal structureG protein coupled receptorsSerotoninSimulation and modelingMolecular dockingProtein structure predictionDopamineProtein structureRational drug design for G protein-coupled receptors (GPCRs) is limited by the small number of available atomic resolution structures. We assessed the use of homology modeling to predict the structures of two therapeutically relevant GPCRs and strategies to improve the performance of virtual screening against modeled binding sites. Homology models of the D2 dopamine (D2R) and serotonin 5-HT2A receptors (5-HT2AR) were generated based on crystal structures of 16 different GPCRs. Comparison of the homology models to D2R and 5-HT2AR crystal structures showed that accurate predictions could be obtained, but not necessarily using the most closely related template. Assessment of virtual screening performance was based on molecular docking of ligands and decoys. The results demonstrated that several templates and multiple models based on each of these must be evaluated to identify the optimal binding site structure. Models based on aminergic GPCRs showed substantial ligand enrichment and there was a trend toward improved virtual screening performance with increasing binding site accuracy. The best models even yielded ligand enrichment comparable to or better than that of the D2R and 5-HT2AR crystal structures. Methods to consider binding site plasticity were explored to further improve predictions. Molecular docking to ensembles of structures did not outperform the best individual binding site models, but could increase the diversity of hits from virtual screens and be advantageous for GPCR targets with few known ligands. Molecular dynamics refinement resulted in moderate improvements of structural accuracy and the virtual screening performance of snapshots was either comparable to or worse than that of the raw homology models. These results provide guidelines for successful application of structure-based ligand discovery using GPCR homology models.Public Library of Science (PLoS)202020202020info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/10230/44424http://dx.doi.org/10.1371/journal.pcbi.1007680reponame:Repositorio Digital de la UPFinstname:Universitat Pompeu FabraInglésPLoS Comput Biol. 2020; 16(3):e1007680© 2020 Jaiteh et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:repositori.upf.edu:10230/444242026-06-12T07:21:37Z
dc.title.none.fl_str_mv Performance of virtual screening against GPCR homology models: Impact of template selection and treatment of binding site plasticity
title Performance of virtual screening against GPCR homology models: Impact of template selection and treatment of binding site plasticity
spellingShingle Performance of virtual screening against GPCR homology models: Impact of template selection and treatment of binding site plasticity
Jaiteh, Mariama
Crystal structure
G protein coupled receptors
Serotonin
Simulation and modeling
Molecular docking
Protein structure prediction
Dopamine
Protein structure
title_short Performance of virtual screening against GPCR homology models: Impact of template selection and treatment of binding site plasticity
title_full Performance of virtual screening against GPCR homology models: Impact of template selection and treatment of binding site plasticity
title_fullStr Performance of virtual screening against GPCR homology models: Impact of template selection and treatment of binding site plasticity
title_full_unstemmed Performance of virtual screening against GPCR homology models: Impact of template selection and treatment of binding site plasticity
title_sort Performance of virtual screening against GPCR homology models: Impact of template selection and treatment of binding site plasticity
dc.creator.none.fl_str_mv Jaiteh, Mariama
Rodríguez Espigares, Ismael, 1990-
Selent, Jana
Carlsson, Jens
author Jaiteh, Mariama
author_facet Jaiteh, Mariama
Rodríguez Espigares, Ismael, 1990-
Selent, Jana
Carlsson, Jens
author_role author
author2 Rodríguez Espigares, Ismael, 1990-
Selent, Jana
Carlsson, Jens
author2_role author
author
author
dc.subject.none.fl_str_mv Crystal structure
G protein coupled receptors
Serotonin
Simulation and modeling
Molecular docking
Protein structure prediction
Dopamine
Protein structure
topic Crystal structure
G protein coupled receptors
Serotonin
Simulation and modeling
Molecular docking
Protein structure prediction
Dopamine
Protein structure
description Rational drug design for G protein-coupled receptors (GPCRs) is limited by the small number of available atomic resolution structures. We assessed the use of homology modeling to predict the structures of two therapeutically relevant GPCRs and strategies to improve the performance of virtual screening against modeled binding sites. Homology models of the D2 dopamine (D2R) and serotonin 5-HT2A receptors (5-HT2AR) were generated based on crystal structures of 16 different GPCRs. Comparison of the homology models to D2R and 5-HT2AR crystal structures showed that accurate predictions could be obtained, but not necessarily using the most closely related template. Assessment of virtual screening performance was based on molecular docking of ligands and decoys. The results demonstrated that several templates and multiple models based on each of these must be evaluated to identify the optimal binding site structure. Models based on aminergic GPCRs showed substantial ligand enrichment and there was a trend toward improved virtual screening performance with increasing binding site accuracy. The best models even yielded ligand enrichment comparable to or better than that of the D2R and 5-HT2AR crystal structures. Methods to consider binding site plasticity were explored to further improve predictions. Molecular docking to ensembles of structures did not outperform the best individual binding site models, but could increase the diversity of hits from virtual screens and be advantageous for GPCR targets with few known ligands. Molecular dynamics refinement resulted in moderate improvements of structural accuracy and the virtual screening performance of snapshots was either comparable to or worse than that of the raw homology models. These results provide guidelines for successful application of structure-based ligand discovery using GPCR homology models.
publishDate 2020
dc.date.none.fl_str_mv 2020
2020
2020
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10230/44424
http://dx.doi.org/10.1371/journal.pcbi.1007680
url http://hdl.handle.net/10230/44424
http://dx.doi.org/10.1371/journal.pcbi.1007680
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv PLoS Comput Biol. 2020; 16(3):e1007680
dc.rights.none.fl_str_mv http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Public Library of Science (PLoS)
publisher.none.fl_str_mv Public Library of Science (PLoS)
dc.source.none.fl_str_mv reponame:Repositorio Digital de la UPF
instname:Universitat Pompeu Fabra
instname_str Universitat Pompeu Fabra
reponame_str Repositorio Digital de la UPF
collection Repositorio Digital de la UPF
repository.name.fl_str_mv
repository.mail.fl_str_mv
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