Neural network–based approach for improving the evaluation of antibody–antigen docking poses - Meta, Alessandro; Ruocco, Giancarlo; Milanetti, Edoardo (Frontiers in Physics, 2026)
The role of artificial intelligence (AI)–based approaches in computational biology and molecular biophysics has become increasingly central over the past decade; however, many challenges remain unresolved, such as the accurate prediction of protein–protein complexes, the complete solution of which would have a significant impact both on our understanding of cellular mechanisms and on the development of therapeutic and diagnostic strategies. Here, we present a protocol based on multiple minimal neural network (NN)–based approaches, trained on a set of carefully selected physicochemical features, to discriminate docking decoy poses (structurally distant from the experimental complex) from native-like poses (structurally close to the native conformation) within a specific class of biologically relevant protein–protein complexes, namely antibody–antigen systems in which the antigen is a protein. A specific version of the proposed method, trained on a set of antibody–antigen interface descriptors, some of which are derived from graph theory to capture the geometric complexity of intermolecular interactions, was compared with ITScore-PP, the docking score provided by HDOCK. This NN-based approach, demonstrates the ability not only to distinguish native-like poses from decoys, but also, more challengly, to discriminate intermediate poses from native-like ones. Furthermore, it was also able to predict the DockQ score, a widely used metric for assessing docking pose quality, showing a larger absolute Pearson correlation coefficient than ITScore-PP. The ability of our NN-based approach, which relies solely on structural interface features, to identify accurate dockings highlights its potential as a valuable tool for improving the ranking of antibody–antigen docking poses and underscores the importance of sppropriate feature selection in protein-protein interaction modeling.