Adaptive simulations, towards interactive protein-ligand modeling

Abstract Modeling the dynamic nature of protein-ligand binding with atomistic simulations is one of the main challenges in computational biophysics, with important implications in the drug design process. Although in the past few years hardware and software advances have significantly revamped the u...

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Autores principales:	Daniel Lecina, Joan F. Gilabert, Victor Guallar
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/c0134478081246958b225c6b67e92df0
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spelling	oai:doaj.org-article:c0134478081246958b225c6b67e92df02021-12-02T16:06:49ZAdaptive simulations, towards interactive protein-ligand modeling10.1038/s41598-017-08445-52045-2322https://doaj.org/article/c0134478081246958b225c6b67e92df02017-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-08445-5https://doaj.org/toc/2045-2322Abstract Modeling the dynamic nature of protein-ligand binding with atomistic simulations is one of the main challenges in computational biophysics, with important implications in the drug design process. Although in the past few years hardware and software advances have significantly revamped the use of molecular simulations, we still lack a fast and accurate ab initio description of the binding mechanism in complex systems, available only for up-to-date techniques and requiring several hours or days of heavy computation. Such delay is one of the main limiting factors for a larger penetration of protein dynamics modeling in the pharmaceutical industry. Here we present a game-changing technology, opening up the way for fast reliable simulations of protein dynamics by combining an adaptive reinforcement learning procedure with Monte Carlo sampling in the frame of modern multi-core computational resources. We show remarkable performance in mapping the protein-ligand energy landscape, being able to reproduce the full binding mechanism in less than half an hour, or the active site induced fit in less than 5 minutes. We exemplify our method by studying diverse complex targets, including nuclear hormone receptors and GPCRs, demonstrating the potential of using the new adaptive technique in screening and lead optimization studies.Daniel LecinaJoan F. GilabertVictor GuallarNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-11 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Daniel Lecina Joan F. Gilabert Victor Guallar Adaptive simulations, towards interactive protein-ligand modeling
description	Abstract Modeling the dynamic nature of protein-ligand binding with atomistic simulations is one of the main challenges in computational biophysics, with important implications in the drug design process. Although in the past few years hardware and software advances have significantly revamped the use of molecular simulations, we still lack a fast and accurate ab initio description of the binding mechanism in complex systems, available only for up-to-date techniques and requiring several hours or days of heavy computation. Such delay is one of the main limiting factors for a larger penetration of protein dynamics modeling in the pharmaceutical industry. Here we present a game-changing technology, opening up the way for fast reliable simulations of protein dynamics by combining an adaptive reinforcement learning procedure with Monte Carlo sampling in the frame of modern multi-core computational resources. We show remarkable performance in mapping the protein-ligand energy landscape, being able to reproduce the full binding mechanism in less than half an hour, or the active site induced fit in less than 5 minutes. We exemplify our method by studying diverse complex targets, including nuclear hormone receptors and GPCRs, demonstrating the potential of using the new adaptive technique in screening and lead optimization studies.
format	article
author	Daniel Lecina Joan F. Gilabert Victor Guallar
author_facet	Daniel Lecina Joan F. Gilabert Victor Guallar
author_sort	Daniel Lecina
title	Adaptive simulations, towards interactive protein-ligand modeling
title_short	Adaptive simulations, towards interactive protein-ligand modeling
title_full	Adaptive simulations, towards interactive protein-ligand modeling
title_fullStr	Adaptive simulations, towards interactive protein-ligand modeling
title_full_unstemmed	Adaptive simulations, towards interactive protein-ligand modeling
title_sort	adaptive simulations, towards interactive protein-ligand modeling
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/c0134478081246958b225c6b67e92df0
work_keys_str_mv	AT daniellecina adaptivesimulationstowardsinteractiveproteinligandmodeling AT joanfgilabert adaptivesimulationstowardsinteractiveproteinligandmodeling AT victorguallar adaptivesimulationstowardsinteractiveproteinligandmodeling
_version_	1718384867501146112

Adaptive simulations, towards interactive protein-ligand modeling

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