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

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 application of knowledge-based potentials. Various machine learning based protein structural model quality assessment methods are also quite successful. However, performance of traditional physics-based models has not been as effective. Based on our analysis of the fundamental computational limitation behind unsatisfactory performance of physics-based models, we propose a generalized solvation free energy (GSFE) framework, which is intrinsically flexible for multi-scale treatments and is amenable for machine learning implementation. Finally, we implemented a simple example of backbone-based residue level 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.