A rapid solvent accessible surface area estimator for coarse grained molecular simulations. Issue 15 (16th April 2017)

• Record Type: Journal Article
• Authors: Wei, Shuai; Brooks, Charles L.; Frank, Aaron T.
• Other Names: Hirst Jonathan guestEditor.; Im Wonpil guestEditor.; Shea Joan‐Emma guestEditor.
• Is Part Of: Journal of computational chemistry. Volume 38:Issue 15(2017)
• Page Start: 1270
• Page End: 1274
• Publication Date: 2017-04-16
• Subjects: residue‐wise SASA -- go‐model -- coarse‐grained protein structure -- Bayesian linear regression; Chemistry -- Data processing -- Periodicals; 542.85
• DOI: 10.1002/jcc.24709
• Languages: English
• ISSNs: 0192-8651

Abstract

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 C α ‐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 athttps://github.com/atfrank/PCASA . © 2017 Wiley Periodicals, Inc. Abstract : A fast and straightforward method is developed that predicts residue‐wise solvent accessible surface areas (SASAs) from C α coordinates of protein structures. The method, P rotein‐C α S olventA ccessibilities or PCASA, should find utility as a tool for energetic and structural analysis of coarse‐grained protein models. To ensure that the method was not biased toward native‐like (folded) structures, PCASA is trained on a large dataset containing both folded and unfolded protein conformations. The resulting model is found to accurately recapitulate all‐atom reference SASAs.