Enhanced-Sampling Simulations for the Estimation of Ligand Binding Kinetics: Current Status and Perspective

The dissociation rate (k (off)) associated with ligand unbinding events from proteins is a parameter of fundamental importance in drug design. Here we review recent major advancements in molecular simulation methodologies for the prediction of k (off). Next, we discuss the impact of the potential en...

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Autores principales: Ahmad, Katya, Rizzi, Andrea, Capelli, Riccardo, Mandelli, Davide, Lyu, Wenping, Carloni, Paolo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Molecular Biosciences
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9216551/
https://www.ncbi.nlm.nih.gov/pubmed/35755817
http://dx.doi.org/10.3389/fmolb.2022.899805
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author Ahmad, Katya
Rizzi, Andrea
Capelli, Riccardo
Mandelli, Davide
Lyu, Wenping
Carloni, Paolo
author_facet Ahmad, Katya
Rizzi, Andrea
Capelli, Riccardo
Mandelli, Davide
Lyu, Wenping
Carloni, Paolo
author_sort Ahmad, Katya
collection PubMed
description The dissociation rate (k (off)) associated with ligand unbinding events from proteins is a parameter of fundamental importance in drug design. Here we review recent major advancements in molecular simulation methodologies for the prediction of k (off). Next, we discuss the impact of the potential energy function models on the accuracy of calculated k (off) values. Finally, we provide a perspective from high-performance computing and machine learning which might help improve such predictions.
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spelling pubmed-92165512022-06-23 Enhanced-Sampling Simulations for the Estimation of Ligand Binding Kinetics: Current Status and Perspective Ahmad, Katya Rizzi, Andrea Capelli, Riccardo Mandelli, Davide Lyu, Wenping Carloni, Paolo Front Mol Biosci Molecular Biosciences The dissociation rate (k (off)) associated with ligand unbinding events from proteins is a parameter of fundamental importance in drug design. Here we review recent major advancements in molecular simulation methodologies for the prediction of k (off). Next, we discuss the impact of the potential energy function models on the accuracy of calculated k (off) values. Finally, we provide a perspective from high-performance computing and machine learning which might help improve such predictions. Frontiers Media S.A. 2022-06-08 /pmc/articles/PMC9216551/ /pubmed/35755817 http://dx.doi.org/10.3389/fmolb.2022.899805 Text en Copyright © 2022 Ahmad, Rizzi, Capelli, Mandelli, Lyu and Carloni. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Molecular Biosciences
Ahmad, Katya
Rizzi, Andrea
Capelli, Riccardo
Mandelli, Davide
Lyu, Wenping
Carloni, Paolo
Enhanced-Sampling Simulations for the Estimation of Ligand Binding Kinetics: Current Status and Perspective
title Enhanced-Sampling Simulations for the Estimation of Ligand Binding Kinetics: Current Status and Perspective
title_full Enhanced-Sampling Simulations for the Estimation of Ligand Binding Kinetics: Current Status and Perspective
title_fullStr Enhanced-Sampling Simulations for the Estimation of Ligand Binding Kinetics: Current Status and Perspective
title_full_unstemmed Enhanced-Sampling Simulations for the Estimation of Ligand Binding Kinetics: Current Status and Perspective
title_short Enhanced-Sampling Simulations for the Estimation of Ligand Binding Kinetics: Current Status and Perspective
title_sort enhanced-sampling simulations for the estimation of ligand binding kinetics: current status and perspective
topic Molecular Biosciences
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9216551/
https://www.ncbi.nlm.nih.gov/pubmed/35755817
http://dx.doi.org/10.3389/fmolb.2022.899805
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