Can molecular dynamics simulations improve the structural accuracy and virtual screening performance of GPCR models?

The determination of G protein-coupled receptor (GPCR) structures at atomic resolution has improved understanding of cellular signaling and will accelerate the development of new drug candidates. However, experimental structures still remain unavailable for a majority of the GPCR family. GPCR struct...

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Autores: Kapla, Jon, Rodríguez Espigares, Ismael, 1990-, Ballante, Flavio, Selent, Jana, Carlsson, Jens
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2021
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:10230/47782
Acceso en línea:http://hdl.handle.net/10230/47782
http://dx.doi.org/10.1371/journal.pcbi.1008936
Access Level:acceso abierto
Palabra clave:Crystal structure
G protein coupled receptors
Simulation and modeling
Protein structure prediction
Biochemical simulations
Protein structure
Molecular dynamics
Protein structure determination
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spelling Can molecular dynamics simulations improve the structural accuracy and virtual screening performance of GPCR models?Kapla, JonRodríguez Espigares, Ismael, 1990-Ballante, FlavioSelent, JanaCarlsson, JensCrystal structureG protein coupled receptorsSimulation and modelingProtein structure predictionBiochemical simulationsProtein structureMolecular dynamicsProtein structure determinationThe determination of G protein-coupled receptor (GPCR) structures at atomic resolution has improved understanding of cellular signaling and will accelerate the development of new drug candidates. However, experimental structures still remain unavailable for a majority of the GPCR family. GPCR structures and their interactions with ligands can also be modelled computationally, but such predictions have limited accuracy. In this work, we explored if molecular dynamics (MD) simulations could be used to refine the accuracy of in silico models of receptor-ligand complexes that were submitted to a community-wide assessment of GPCR structure prediction (GPCR Dock). Two simulation protocols were used to refine 30 models of the D3 dopamine receptor (D3R) in complex with an antagonist. Close to 60 μs of simulation time was generated and the resulting MD refined models were compared to a D3R crystal structure. In the MD simulations, the transmembrane helix region of the models generally drifted further away from the crystal structure conformation. However, MD refinement was able to improve the accuracy of the ligand binding mode and the second extracellular loop region. The best refinement protocol improved agreement with the experimentally observed ligand binding mode for a majority of the models. Receptor structures with improved virtual screening performance, which was assessed by molecular docking of ligands and decoys, could also be identified among the MD refined models. Application of weak restraints to the transmembrane helixes in the MD simulations further improved predictions of the ligand binding mode and second extracellular loop. These results provide guidelines for application of MD refinement in prediction of GPCR-ligand complexes and directions for further method development.Public Library of Science (PLoS)202120212021info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/10230/47782http://dx.doi.org/10.1371/journal.pcbi.1008936reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésPLoS Comput Biol. 2021;17(5):e1008936© 2021 Kapla et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, 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:recercat.cat:10230/477822026-05-29T05:05:01Z
dc.title.none.fl_str_mv Can molecular dynamics simulations improve the structural accuracy and virtual screening performance of GPCR models?
title Can molecular dynamics simulations improve the structural accuracy and virtual screening performance of GPCR models?
spellingShingle Can molecular dynamics simulations improve the structural accuracy and virtual screening performance of GPCR models?
Kapla, Jon
Crystal structure
G protein coupled receptors
Simulation and modeling
Protein structure prediction
Biochemical simulations
Protein structure
Molecular dynamics
Protein structure determination
title_short Can molecular dynamics simulations improve the structural accuracy and virtual screening performance of GPCR models?
title_full Can molecular dynamics simulations improve the structural accuracy and virtual screening performance of GPCR models?
title_fullStr Can molecular dynamics simulations improve the structural accuracy and virtual screening performance of GPCR models?
title_full_unstemmed Can molecular dynamics simulations improve the structural accuracy and virtual screening performance of GPCR models?
title_sort Can molecular dynamics simulations improve the structural accuracy and virtual screening performance of GPCR models?
dc.creator.none.fl_str_mv Kapla, Jon
Rodríguez Espigares, Ismael, 1990-
Ballante, Flavio
Selent, Jana
Carlsson, Jens
author Kapla, Jon
author_facet Kapla, Jon
Rodríguez Espigares, Ismael, 1990-
Ballante, Flavio
Selent, Jana
Carlsson, Jens
author_role author
author2 Rodríguez Espigares, Ismael, 1990-
Ballante, Flavio
Selent, Jana
Carlsson, Jens
author2_role author
author
author
author
dc.subject.none.fl_str_mv Crystal structure
G protein coupled receptors
Simulation and modeling
Protein structure prediction
Biochemical simulations
Protein structure
Molecular dynamics
Protein structure determination
topic Crystal structure
G protein coupled receptors
Simulation and modeling
Protein structure prediction
Biochemical simulations
Protein structure
Molecular dynamics
Protein structure determination
description The determination of G protein-coupled receptor (GPCR) structures at atomic resolution has improved understanding of cellular signaling and will accelerate the development of new drug candidates. However, experimental structures still remain unavailable for a majority of the GPCR family. GPCR structures and their interactions with ligands can also be modelled computationally, but such predictions have limited accuracy. In this work, we explored if molecular dynamics (MD) simulations could be used to refine the accuracy of in silico models of receptor-ligand complexes that were submitted to a community-wide assessment of GPCR structure prediction (GPCR Dock). Two simulation protocols were used to refine 30 models of the D3 dopamine receptor (D3R) in complex with an antagonist. Close to 60 μs of simulation time was generated and the resulting MD refined models were compared to a D3R crystal structure. In the MD simulations, the transmembrane helix region of the models generally drifted further away from the crystal structure conformation. However, MD refinement was able to improve the accuracy of the ligand binding mode and the second extracellular loop region. The best refinement protocol improved agreement with the experimentally observed ligand binding mode for a majority of the models. Receptor structures with improved virtual screening performance, which was assessed by molecular docking of ligands and decoys, could also be identified among the MD refined models. Application of weak restraints to the transmembrane helixes in the MD simulations further improved predictions of the ligand binding mode and second extracellular loop. These results provide guidelines for application of MD refinement in prediction of GPCR-ligand complexes and directions for further method development.
publishDate 2021
dc.date.none.fl_str_mv 2021
2021
2021
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/10230/47782
http://dx.doi.org/10.1371/journal.pcbi.1008936
url http://hdl.handle.net/10230/47782
http://dx.doi.org/10.1371/journal.pcbi.1008936
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv PLoS Comput Biol. 2021;17(5):e1008936
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:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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