Generalized Solvation Free Energy, Neural Network Implementation and Application in Structural Model Assessment, Refinement and Design
Prof. Pu Tian
College of Life Sciences, Jilin University

Traditionally free energy of biomolecular systems is divided into gas phase contribution and solvation free energy, which is further split into two independent contributions (polar and non-polar). Such formulation increases variance of calculation on the one hand, and has no direct experimental validation on the other hand. The generalized solvation free energy (GSFE) is proposed to overcome these limitations. In GSFE, each comprising unit is both a solute and part of the solvent for its neighboring unit. Consequently, solvent is generally heterogeneous and specific for each unit. The definition of comprising unit is inherently flexible and multi-scale (e.g. atoms, atomic groups, amino acids and/or their clusters etc.). GSFE is first implemented for proteins at residue level with neural network and utilized for assessment of protein structural models, structural refinement and design. Implementation of GSFE at atomistic level is undergoing.

About the Speaker


2019-11-28 2:00 PM
Room: A303 Meeting Room
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