A simple neural network implementation of generalized solvation free energy for assessment of protein structural models

Abstract: 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 traditionalphysics-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 (bothknowledge-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 latestknowledge-based'' atomic potentials in distinguishing native structures from decoys.