Enhancing ligand and protein sampling using sequential Monte Carlo
Enhancing ligand and protein sampling using sequential Monte Carlo
The sampling problem is one of the most widely studied topics in computational chemistry. While various methods exist for sampling along a set of reaction coordinates, many require system-dependent hyperparameters to achieve maximum efficiency. In this work, we present an alchemical variation of adaptive sequential Monte Carlo (SMC), an irreversible importance resampling method that is part of a well-studied class of methods that have been used in various applications but have been underexplored in computational biophysics. Afterward, we apply alchemical SMC on a variety of test cases, including torsional rotations of solvated ligands (butene and a terphenyl derivative), translational and rotational movements of protein-bound ligands, and protein side chain rotation coupled to the ligand degrees of freedom (T4-lysozyme, protein tyrosine phosphatase 1B, and transforming growth factor β). We find that alchemical SMC is an efficient way to explore targeted degrees of freedom and can be applied to a variety of systems using the same hyperparameters to achieve a similar performance. Alchemical SMC is a promising tool for preparatory exploration of systems where long-timescale sampling of the entire system can be traded off against short-timescale sampling of a particular set of degrees of freedom over a population of conformers.
3894-3910
Suruzhon, Miroslav
4ea4dd8b-0a98-4598-9eaa-756d943b5dca
Bodnarchuk, Michael S.
04dc5467-0fd6-485a-8bc8-53ba6ea4bb4c
Ciancetta, Antonella
992e4150-4131-4adc-9e00-d7d415583ea5
Wall, Ian D.
059fa7bb-bde0-457f-8ba3-7e31cf72d3f1
Essex, Jonathan W.
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14 June 2022
Suruzhon, Miroslav
4ea4dd8b-0a98-4598-9eaa-756d943b5dca
Bodnarchuk, Michael S.
04dc5467-0fd6-485a-8bc8-53ba6ea4bb4c
Ciancetta, Antonella
992e4150-4131-4adc-9e00-d7d415583ea5
Wall, Ian D.
059fa7bb-bde0-457f-8ba3-7e31cf72d3f1
Essex, Jonathan W.
1f409cfe-6ba4-42e2-a0ab-a931826314b5
Suruzhon, Miroslav, Bodnarchuk, Michael S., Ciancetta, Antonella, Wall, Ian D. and Essex, Jonathan W.
(2022)
Enhancing ligand and protein sampling using sequential Monte Carlo.
Journal of Chemical Theory and Computation, 18 (6), .
(doi:10.1021/acs.jctc.1c01198).
Abstract
The sampling problem is one of the most widely studied topics in computational chemistry. While various methods exist for sampling along a set of reaction coordinates, many require system-dependent hyperparameters to achieve maximum efficiency. In this work, we present an alchemical variation of adaptive sequential Monte Carlo (SMC), an irreversible importance resampling method that is part of a well-studied class of methods that have been used in various applications but have been underexplored in computational biophysics. Afterward, we apply alchemical SMC on a variety of test cases, including torsional rotations of solvated ligands (butene and a terphenyl derivative), translational and rotational movements of protein-bound ligands, and protein side chain rotation coupled to the ligand degrees of freedom (T4-lysozyme, protein tyrosine phosphatase 1B, and transforming growth factor β). We find that alchemical SMC is an efficient way to explore targeted degrees of freedom and can be applied to a variety of systems using the same hyperparameters to achieve a similar performance. Alchemical SMC is a promising tool for preparatory exploration of systems where long-timescale sampling of the entire system can be traded off against short-timescale sampling of a particular set of degrees of freedom over a population of conformers.
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Accepted/In Press date: 19 May 2022
Published date: 14 June 2022
Additional Information:
Funding Information:
The authors would like to thank Russell Viner, Khaled A. Maksoud, and Marley L. Samways for the useful discussions and technical help. This study has been funded by AstraZeneca, GSK, and Syngenta and supported by the EPSRC under EP/V048864/1 and the Centre for Doctoral Training, Theory and Modelling in Chemical Sciences, under Grant EP/L015722/1. The authors acknowledge the University of Southampton high-performance computing cluster Iridis 5, the Hartree Centre high-performance computing cluster JADE, and HECBioSim (Grant EP/R029407/1) for facilitating a part of this study.
Publisher Copyright:
© 2022 The Authors. Published by American Chemical Society.
Identifiers
Local EPrints ID: 470117
URI: http://eprints.soton.ac.uk/id/eprint/470117
ISSN: 1549-9618
PURE UUID: ef10464a-f037-4428-88c9-d2cea6f29d9e
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Date deposited: 03 Oct 2022 16:56
Last modified: 18 Mar 2024 05:29
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Contributors
Author:
Miroslav Suruzhon
Author:
Michael S. Bodnarchuk
Author:
Antonella Ciancetta
Author:
Ian D. Wall
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