  Rapid and accurate assessment of protein structural models is essential for
protein structure prediction and design. Great progress has been made in this
regard, especially by recent development of ``knowledge-based'' potentials.
Various machine learning based protein structural model quality assessment was
also quite successful. However, performance of traditional ``physics-based''
potentials have not been as effective. Based on analysis of computational
limitations of present solvation free energy formulation, which partially
underlies unsatisfactory performance of ``physics-based'' potentials, we
proposed a generalized sovation free energy (GSFE) framework. GSFE is
intrinsically flexible for multi-scale treatments and is amenable for machine
learning implementation. In this framework, each physical comprising unit of a
complex molecular system has its own specific solvent environment. One
distinctive feature of GSFE is that high order correlations within selected
solvent environment might be captured through machine learning, in contrast to
present empirical potentials (both ``knowledge-based'' and ``physics-based'')
that are mainly based on pairwise interactions. Finally, we implemented a
simple example of backbone and side-chain orientation based residue level
protein GSFE with neural network, which was found to have competitive
performance when compared with highly complex latest ``knowledge-based'' atomic
potentials in distinguishing native structures from decoys.

Computational Physics;Biological Physics