Solvation is a fundamental process of interactions between solute molecules and solvent or ions in the aqueous environment. Accurate models of solvation are essential prerequisites for the quantitative description and analysis of important biological processes involving the folding, encounter, recognition, and binding of biomolecular assemblies. Solvation models can be roughly divided into two classes: explicit ones that treat the solvent in molecular or atomic detail and implicit solvent models that treat the solvent as a dielectric continuum. Because of their efficiency, implicit solvent models have become very popular for a variety of biological applications, including rational drug design, estimations of folding energies, binding affinities, pKa values, and the analysis of structure, mutation, and many other thermodynamic and kinetic quantities. However, ad hoc assumptions about solvent-solute interfaces are currently used in most implicit solvent models, impeding their reliability, accuracy and efficiency. The proposed project addresses this problem by developing a differential geometry-based multiscale framework. Upon energy minimization, our framework generates the interface between the continuum solvent and the discrete atomistic solute. In particular, variation of the full free energy functional gives rise to selfconsistently coupled geometric and Poisson-Boltzmann equations. The resulting equations will be solved with advanced algorithms. Extensive validations and applications are designed to ensure that the proposed multiscale paradigm yields accurate solvation properties. The importance of implicit solvent models is supported by the thousands of applications in the literature. The proposed research addresses serious limitations in existing models arising from ad hoc assumptions of the solvent-solute interface by the introduction of a new mathematical framework to construct physical interfaces. In total, this proposal offers an innovative approach to an important area in biomolecular modeling .

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Research Project (R01)
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Special Emphasis Panel (ZGM1-CBCB-5 (BM))
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Preusch, Peter
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Michigan State University
Biostatistics & Other Math Sci
Schools of Arts and Sciences
East Lansing
United States
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Xia, Kelin; Zhao, Zhixiong; Wei, Guo-Wei (2015) Multiresolution persistent homology for excessively large biomolecular datasets. J Chem Phys 143:134103
Xia, Kelin; Opron, Kristopher; Wei, Guo-Wei (2015) Multiscale Gaussian network model (mGNM) and multiscale anisotropic network model (mANM). J Chem Phys 143:204106

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