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PyCGTOOL: automated generation of coarse-grained molecular dynamics models from atomistic trajectories.

PyCGTOOL: automated generation of coarse-grained molecular dynamics models from atomistic trajectories.
PyCGTOOL: automated generation of coarse-grained molecular dynamics models from atomistic trajectories.
Development of coarse-grained (CG) molecular dynamics models is often a laborious process which commonly relies upon approximations to similar models, rather than systematic parametrization. PyCGTOOL automates much of the construction of CG models via calculation of both equilibrium values and force constants of internal coordinates directly from atomistic molecular dynamics simulation trajectories. The derivation of bespoke parameters from atomistic simulations improves the quality of the CG model compared to the use of generic parameters derived from other molecules, while automation greatly reduces the time required. The ease of configuration of PyCGTOOL enables the rapid investigation of multiple atom-to-bead mappings and topologies. Although we present PyCGTOOL used in combination with the GROMACS molecular dynamics engine its use of standard trajectory input libraries means that it is in principle compatible with other software. The software is available from the URL https://github.com/jag1g13/pycgtool as the following doi: 10.5281/zenodo.259330.
1549-9596
650-656
Graham, James, Andrew
2fedec7b-d9f6-4299-882a-ab3f792cad06
Essex, Jonathan
1f409cfe-6ba4-42e2-a0ab-a931826314b5
Khalid, Syma
90fbd954-7248-4f47-9525-4d6af9636394
Graham, James, Andrew
2fedec7b-d9f6-4299-882a-ab3f792cad06
Essex, Jonathan
1f409cfe-6ba4-42e2-a0ab-a931826314b5
Khalid, Syma
90fbd954-7248-4f47-9525-4d6af9636394

Graham, James, Andrew, Essex, Jonathan and Khalid, Syma (2017) PyCGTOOL: automated generation of coarse-grained molecular dynamics models from atomistic trajectories. Journal of Chemical Information and Modeling, 57 (4), 650-656. (doi:10.1021/acs.jcim.7b00096).

Record type: Article

Abstract

Development of coarse-grained (CG) molecular dynamics models is often a laborious process which commonly relies upon approximations to similar models, rather than systematic parametrization. PyCGTOOL automates much of the construction of CG models via calculation of both equilibrium values and force constants of internal coordinates directly from atomistic molecular dynamics simulation trajectories. The derivation of bespoke parameters from atomistic simulations improves the quality of the CG model compared to the use of generic parameters derived from other molecules, while automation greatly reduces the time required. The ease of configuration of PyCGTOOL enables the rapid investigation of multiple atom-to-bead mappings and topologies. Although we present PyCGTOOL used in combination with the GROMACS molecular dynamics engine its use of standard trajectory input libraries means that it is in principle compatible with other software. The software is available from the URL https://github.com/jag1g13/pycgtool as the following doi: 10.5281/zenodo.259330.

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s1-ln23660391437600580-1939656818Hwf-248010360IdV898190722366039PDF_HI0001 - Accepted Manuscript
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Accepted/In Press date: 27 March 2017
e-pub ahead of print date: 27 March 2017
Published date: 24 April 2017
Organisations: Chemistry, Electronics & Computer Science, Computational Systems Chemistry

Identifiers

Local EPrints ID: 408522
URI: http://eprints.soton.ac.uk/id/eprint/408522
ISSN: 1549-9596
PURE UUID: 0b014795-d297-4895-884a-cff5cb0678b1
ORCID for James, Andrew Graham: ORCID iD orcid.org/0000-0001-5217-3104
ORCID for Jonathan Essex: ORCID iD orcid.org/0000-0003-2639-2746
ORCID for Syma Khalid: ORCID iD orcid.org/0000-0002-3694-5044

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Date deposited: 23 May 2017 04:01
Last modified: 13 Apr 2021 01:42

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