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J Comput Chem. 2017 Jun 5;38(15):1270-1274. doi: 10.1002/jcc.24709. Epub 2017 Apr 16.

A rapid solvent accessible surface area estimator for coarse grained molecular simulations.

Author information

1
Department of Chemistry, University of Michigan, Ann Arbor, MI, 48109.
2
Departments of Biophysics and Chemistry, University of Michigan, Ann Arbor, MI, 48109.

Abstract

The rapid and accurate calculation of solvent accessible surface area (SASA) is extremely useful in the energetic analysis of biomolecules. For example, SASA models can be used to estimate the transfer free energy associated with biophysical processes, and when combined with coarse-grained simulations, can be particularly useful for accounting for solvation effects within the framework of implicit solvent models. In such cases, a fast and accurate, residue-wise SASA predictor is highly desirable. Here, we develop a predictive model that estimates SASAs based on Cα-only protein structures. Through an extensive comparison between this method and a comparable method, POPS-R, we demonstrate that our new method, Protein-C α Solvent Accessibilities or PCASA, shows better performance, especially for unfolded conformations of proteins. We anticipate that this model will be quite useful in the efficient inclusion of SASA-based solvent free energy estimations in coarse-grained protein folding simulations. PCASA is made freely available to the academic community at https://github.com/atfrank/PCASA.

KEYWORDS:

Bayesian linear regression; coarse-grained protein structure; go-model; residue-wise SASA

PMID:
28419507
DOI:
10.1002/jcc.24709
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