Accurate protein-protein interactions modeling through physics-informed geometric invariant learning

AlphaFold has set a new standard for predicting protein structures from primary sequences; however, it faces challenges with protein complexes across species, engineered proteins, and antigen-antibody interactions, where co-evolutionary signals may be sparse or missing. Herein, we present ProTact, a SE(3)-invariant geometric graph neural network that integrates physics-informed geometric complementarity and trigonometric constraints as inductive biases to enhance protein-protein contact predictions. ProTact is applicable to both experimental and predicted monomer structures and utilizes a modulated key point matching algorithm to approximate accurate docking poses. Experimental evaluations demonstrate that ProTact consistently outperforms state-of-the-art sequence-based and structure-based methods on benchmark datasets, achieving notable relative improvements of 31.63% in average top-10 precision (Precision@10) for CASP 13 and 14 targets and 31.94% for DIPS-Plus datasets on high-quality structures. While performance naturally declines on the more challenging unbound complexes due to large conformational changes, ProTact maintains a competitive edge over baselines. Moreover, when combined with AlphaFold3 as re-scoring functions, ProTact surpasses its default confidence scores, offering over 30.48% improvements in low-MSA contexts. We anticipate that the proposed framework will advance our understanding of protein interactions, functions, and design. ProTact, an SE(3)-invariant geometric GNN, enhances protein-protein contact prediction and docking by integrating physics-informed constraints. It surpasses existing methods in low MSA scenarios and enhances AlphaFold3’s confidence scores.