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Software for the automated generation of coarse-grained molecular dynamics models

Software for the automated generation of coarse-grained molecular dynamics models
Software for the automated generation of coarse-grained molecular dynamics models
This work presents a software tool for the automated parametrisation of coarsegrained (CG) molecular dynamics (MD) models and the environment that led to its necessity. It is available at https://github.com/jag1g13/pycgtool and had been registered as doi:10.5281/zenodo.569555.

Numerical simulations are used in chemistry to span the necessary range in scale from atoms to biological systems such that we might understand their behaviour. However, the computational demand to simulate such systems at the atomic level is increasing as we continue to study larger systems over longer timescales. Significant efficiency gains are seen when employing CG MD over traditional all-atom (AA) MD, at the cost of some accuracy.

Sugars are a component of many biomolecule classes such as carbohydrates, glycoproteins and lipopolysaccharides, but are relatively neglected in molecular dynamics simulations. In recent years there has been a trend towards CG models in the field of biomolecular simulation, allowing larger systems to be simulated for longer, but there are currently no satisfactory CG representations of sugars.

The first strand of research described here was to develop a CG model of sugars as part of the existing ELBA forcefield, using a high quality electrostatic model incorporating dipoles as well as point charges. This can be seen in sections 2 and 3.

However, creating an adequate model required a representation of sugars which did not provide benefits over existing AA or united-atom (UA) models. Because of this, the research was taken in a different direction: to address the difficulty of developing CG models, by automation of significant parts of the process. The new aim was to develop a software tool which would allow researchers to more quickly and simply develop their own models, both within the ELBA forcefield, and within the popular MARTINI forcefield.

In order to address this, software was written to automate parts of the process of creating a new coarse-grained molecular dynamics model. The software makes use of a modification to the Boltzmann Inversion used by other software for forcefield parametrisation, which means that the calculated parameters are consistent with established forcefields such as ELBA and MARTINI. Though there are other tools fulfilling a similar purpose, they do not make use of this modification, and thus their output is not consistent with these models. Focus was given to ensuring that best practices in research software engineering (RSE, a term only now becoming established) were followed, ensuring that the software is reliable, usable and extensible.

The software was used in the parametrisation of the ELBA sugar model, and has been used by others for parametrisation of models within the MARTINI forcefield.
University of Southampton
Graham, James, Andrew
2fedec7b-d9f6-4299-882a-ab3f792cad06
Graham, James, Andrew
2fedec7b-d9f6-4299-882a-ab3f792cad06
Khalid, Syma
90fbd954-7248-4f47-9525-4d6af9636394

Graham, James, Andrew (2019) Software for the automated generation of coarse-grained molecular dynamics models. University of Southampton, Doctoral Thesis, 83pp.

Record type: Thesis (Doctoral)

Abstract

This work presents a software tool for the automated parametrisation of coarsegrained (CG) molecular dynamics (MD) models and the environment that led to its necessity. It is available at https://github.com/jag1g13/pycgtool and had been registered as doi:10.5281/zenodo.569555.

Numerical simulations are used in chemistry to span the necessary range in scale from atoms to biological systems such that we might understand their behaviour. However, the computational demand to simulate such systems at the atomic level is increasing as we continue to study larger systems over longer timescales. Significant efficiency gains are seen when employing CG MD over traditional all-atom (AA) MD, at the cost of some accuracy.

Sugars are a component of many biomolecule classes such as carbohydrates, glycoproteins and lipopolysaccharides, but are relatively neglected in molecular dynamics simulations. In recent years there has been a trend towards CG models in the field of biomolecular simulation, allowing larger systems to be simulated for longer, but there are currently no satisfactory CG representations of sugars.

The first strand of research described here was to develop a CG model of sugars as part of the existing ELBA forcefield, using a high quality electrostatic model incorporating dipoles as well as point charges. This can be seen in sections 2 and 3.

However, creating an adequate model required a representation of sugars which did not provide benefits over existing AA or united-atom (UA) models. Because of this, the research was taken in a different direction: to address the difficulty of developing CG models, by automation of significant parts of the process. The new aim was to develop a software tool which would allow researchers to more quickly and simply develop their own models, both within the ELBA forcefield, and within the popular MARTINI forcefield.

In order to address this, software was written to automate parts of the process of creating a new coarse-grained molecular dynamics model. The software makes use of a modification to the Boltzmann Inversion used by other software for forcefield parametrisation, which means that the calculated parameters are consistent with established forcefields such as ELBA and MARTINI. Though there are other tools fulfilling a similar purpose, they do not make use of this modification, and thus their output is not consistent with these models. Focus was given to ensuring that best practices in research software engineering (RSE, a term only now becoming established) were followed, ensuring that the software is reliable, usable and extensible.

The software was used in the parametrisation of the ELBA sugar model, and has been used by others for parametrisation of models within the MARTINI forcefield.

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More information

Published date: 25 April 2019

Identifiers

Local EPrints ID: 437719
URI: http://eprints.soton.ac.uk/id/eprint/437719
PURE UUID: 50e6792c-23d6-45aa-a65b-9d82dec409d9
ORCID for James, Andrew Graham: ORCID iD orcid.org/0000-0001-5217-3104
ORCID for Syma Khalid: ORCID iD orcid.org/0000-0002-3694-5044

Catalogue record

Date deposited: 12 Feb 2020 17:35
Last modified: 13 Feb 2020 01:36

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