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
- Direct For Mathematical & Physical Scien
- Division Of Chemistry [1506273] Funding Source: National Science Foundation
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据