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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
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Official URL: http://dx.doi.org/10.1021/ct400628h
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 | ||||
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| Divisions: | Faculty of Science > Chemistry Faculty of Science > Centre for Systems Biology |
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| Journal or Publication Title: | Journal of Chemical Theory and Computation | ||||
| Publisher: | American Chemical Society | ||||
| ISSN: | 1549-9618 | ||||
| Official Date: | 2013 | ||||
| Dates: |
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| 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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