4.4 Article

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

期刊

JOURNAL OF COMPUTATIONAL CHEMISTRY
卷 38, 期 15, 页码 1270-1274

出版社

WILEY
DOI: 10.1002/jcc.24709

关键词

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

资金

  1. Direct For Mathematical & Physical Scien
  2. Division Of Chemistry [1506273] Funding Source: National Science Foundation

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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 C-only protein structures. Through an extensive comparison between this method and a comparable method, POPS-R, we demonstrate that our new method, Protein-C 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 . (c) 2017 Wiley Periodicals, Inc.

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