Artificial Neural Network to Predict Structure-based Protein-protein Free Energy of Binding from Rosetta-calculated Properties

The prediction of the free energy (ΔG) of binding for protein-protein complexes is of general scientific interest that allows a variety of applications in the fields of molecular and chemical biology, material sciences, and biotechnology. Despite its centrality in understanding protein association phenomena and protein engineering, the ΔG of binding is a daunting quantity to be obtained theoretically. In this work, we devise a novel Artificial Neural Network model to predict the ΔG of binding for a given three-dimensional structure of a protein-protein complex with Rosetta-calculated properties. Our model presents a root-mean-square error of 1.667 kcal/mol outperforming available state-of-art tools. Validation of the model for a variety of protein-protein complexes is showcased.