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Efficient parameter estimation of generalizable coarse-grained protein force fields using contrastive civergence : a maximum likelihood approach

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Várnai, Csilla, Burkoff, Nikolas S. and Wild, David L. (2013) Efficient parameter estimation of generalizable coarse-grained protein force fields using contrastive civergence : a maximum likelihood approach. Journal of Chemical Theory and Computation, Volume 9 (Number 12). pp. 5718-5733. doi:10.1021/ct400628h ISSN 1549-9618.

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Official URL: http://dx.doi.org/10.1021/ct400628h

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Abstract

Maximum Likelihood (ML) optimization schemes are widely used for parameter inference. They maximize the likelihood of some experimentally observed data, with respect to the model parameters iteratively, following the gradient of the logarithm of the likelihood. Here, we employ a ML inference scheme to infer a generalizable, physics-based coarse-grained protein model (which includes Go̅-like biasing terms to stabilize secondary structure elements in room-temperature simulations), using native conformations of a training set of proteins as the observed data. Contrastive divergence, a novel statistical machine learning technique, is used to efficiently approximate the direction of the gradient ascent, which enables the use of a large training set of proteins. Unlike previous work, the generalizability of the protein model allows the folding of peptides and a protein (protein G) which are not part of the training set. We compare the same force field with different van der Waals (vdW) potential forms: a hard cutoff model, and a Lennard-Jones (LJ) potential with vdW parameters inferred or adopted from the CHARMM or AMBER force fields. Simulations of peptides and protein G show that the LJ model with inferred parameters outperforms the hard cutoff potential, which is consistent with previous observations. Simulations using the LJ potential with inferred vdW parameters also outperforms the protein models with adopted vdW parameter values, demonstrating that model parameters generally cannot be used with force fields with different energy functions.

Item Type: Journal Article
Divisions: Faculty of Science, Engineering and Medicine > Science > Chemistry
Faculty of Science, Engineering and Medicine > Research Centres > Warwick Systems Biology Centre
Journal or Publication Title: Journal of Chemical Theory and Computation
Publisher: American Chemical Society
ISSN: 1549-9618
Official Date: 2013
Dates:
DateEvent
2013Published
Volume: Volume 9
Number: Number 12
Page Range: pp. 5718-5733
DOI: 10.1021/ct400628h
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access

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