The University of Southampton
University of Southampton Institutional Repository

ParaMol: A package for parametrization of molecular mechanics force fields

ParaMol: A package for parametrization of molecular mechanics force fields
ParaMol: A package for parametrization of molecular mechanics force fields
The ensemble of structures generated by molecular mechanics (MM) simulations is determined by the functional form of the force field employed and its parameterization. For a given functional form, the quality of the parameterization is crucial and will determine how accurately we can compute observable properties from simulations. While accurate force field parameterizations are available for biomolecules, such as proteins or DNA, the parameterization of new molecules, such as drug candidates, is particularly challenging as these may involve functional groups and interactions for which accurate parameters may not be available. Here, in an effort to address this problem, we present ParaMol, a Python package that has a special focus on the parameterization of bonded and nonbonded terms of druglike molecules by fitting to ab initio data. We demonstrate the software by deriving bonded terms’ parameters of three widely known drug molecules, viz. aspirin, caffeine, and a norfloxacin analogue, for which we show that, within the constraints of the functional form, the methodologies implemented in ParaMol are able to derive near-ideal parameters. Additionally, we illustrate the best practices to follow when employing specific parameterization routes. We also determine the sensitivity of different fitting data sets, such as relaxed dihedral scans and configurational ensembles, to the parameterization procedure, and discuss the features of the various weighting methods available to weight configurations. Owing to ParaMol’s capabilities, we propose that this software can be introduced as a routine step in the protocol normally employed to parameterize druglike molecules for MM simulations.
1549-9596
Morado, Joao
f83f0c26-bbe3-420c-9999-e22ab439c9c6
Mortenson, Paul N.
765f1d79-fcd6-4104-b033-b534d8d31f65
Verdonk, Marcel L.
85965663-3c55-4c53-9f98-ff9d707a8056
Ward, Richard A.
b984efd8-16fc-4e47-992c-5ee5c426fc50
Essex, Jonathan W.
1f409cfe-6ba4-42e2-a0ab-a931826314b5
Skylaris, Chris-Kriton
8f593d13-3ace-4558-ba08-04e48211af61
Morado, Joao
f83f0c26-bbe3-420c-9999-e22ab439c9c6
Mortenson, Paul N.
765f1d79-fcd6-4104-b033-b534d8d31f65
Verdonk, Marcel L.
85965663-3c55-4c53-9f98-ff9d707a8056
Ward, Richard A.
b984efd8-16fc-4e47-992c-5ee5c426fc50
Essex, Jonathan W.
1f409cfe-6ba4-42e2-a0ab-a931826314b5
Skylaris, Chris-Kriton
8f593d13-3ace-4558-ba08-04e48211af61

Morado, Joao, Mortenson, Paul N., Verdonk, Marcel L., Ward, Richard A., Essex, Jonathan W. and Skylaris, Chris-Kriton (2022) ParaMol: A package for parametrization of molecular mechanics force fields. Journal of Chemical Information and Modeling. (doi:10.1021/acs.jcim.0c01444).

Record type: Article

Abstract

The ensemble of structures generated by molecular mechanics (MM) simulations is determined by the functional form of the force field employed and its parameterization. For a given functional form, the quality of the parameterization is crucial and will determine how accurately we can compute observable properties from simulations. While accurate force field parameterizations are available for biomolecules, such as proteins or DNA, the parameterization of new molecules, such as drug candidates, is particularly challenging as these may involve functional groups and interactions for which accurate parameters may not be available. Here, in an effort to address this problem, we present ParaMol, a Python package that has a special focus on the parameterization of bonded and nonbonded terms of druglike molecules by fitting to ab initio data. We demonstrate the software by deriving bonded terms’ parameters of three widely known drug molecules, viz. aspirin, caffeine, and a norfloxacin analogue, for which we show that, within the constraints of the functional form, the methodologies implemented in ParaMol are able to derive near-ideal parameters. Additionally, we illustrate the best practices to follow when employing specific parameterization routes. We also determine the sensitivity of different fitting data sets, such as relaxed dihedral scans and configurational ensembles, to the parameterization procedure, and discuss the features of the various weighting methods available to weight configurations. Owing to ParaMol’s capabilities, we propose that this software can be introduced as a routine step in the protocol normally employed to parameterize druglike molecules for MM simulations.

Text
ParaMol_paper - Accepted Manuscript
Restricted to Repository staff only until 21 March 2022.
Request a copy
Text
ParaMol_paper_SI
Restricted to Repository staff only
Request a copy

More information

Accepted/In Press date: 10 March 2021
e-pub ahead of print date: 21 March 2022

Identifiers

Local EPrints ID: 448304
URI: http://eprints.soton.ac.uk/id/eprint/448304
ISSN: 1549-9596
PURE UUID: 5b3d947c-f75e-43d6-bc8e-88c1beeac0de
ORCID for Jonathan W. Essex: ORCID iD orcid.org/0000-0003-2639-2746
ORCID for Chris-Kriton Skylaris: ORCID iD orcid.org/0000-0003-0258-3433

Catalogue record

Date deposited: 19 Apr 2021 16:33
Last modified: 20 Apr 2021 01:41

Export record

Altmetrics

Contributors

Author: Joao Morado
Author: Paul N. Mortenson
Author: Marcel L. Verdonk
Author: Richard A. Ward

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×