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.
650-656
Graham, James
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Essex, Jonathan
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Khalid, Syma
90fbd954-7248-4f47-9525-4d6af9636394
24 April 2017
Graham, James
ba426094-6e0d-4ecf-a1e5-4a4e0a4f62fd
Essex, Jonathan
1f409cfe-6ba4-42e2-a0ab-a931826314b5
Khalid, Syma
90fbd954-7248-4f47-9525-4d6af9636394
Graham, James, 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), .
(doi:10.1021/acs.jcim.7b00096).
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.
Text
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- 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
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Date deposited: 23 May 2017 04:01
Last modified: 16 Mar 2024 05:17
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Author:
James Graham
Author:
Syma Khalid
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