An artificial neural network model to predict structure-based protein-protein free energy of binding from Rosetta-calculated properties.
Matheus Vitor Ferreira FerrazJosé C S NetoRoberto Dias Lins NetoErico S TeixeiraPublished in: Physical chemistry chemical physics : PCCP (2023)
The prediction of the free energy (Δ G ) of binding for protein-protein complexes is of general scientific interest as it has a variety of applications in the fields of molecular and chemical biology, materials science, and biotechnology. Despite its centrality in understanding protein association phenomena and protein engineering, the Δ G of binding is a daunting quantity to obtain theoretically. In this work, we devise a novel Artificial Neural Network (ANN) model to predict the Δ G of binding for a given three-dimensional structure of a protein-protein complex with Rosetta-calculated properties. Our model was tested using two data sets, and it presented a root-mean-square error ranging from 1.67 kcal mol -1 to 2.45 kcal mol -1 , showing a better performance compared to the available state-of-the-art tools. Validation of the model for a variety of protein-protein complexes is showcased.