Discovering mutations enhancing protein-protein interactions (PPIs) is critical for advancing biomedical research and developing improved therapeutics. While machine learning approaches have substantially advanced the field, they often struggle to generalize beyond training data in practical scenarios. The contributions of this work are three-fold. First, we construct PPIRef, the largest and non-redundant dataset of 3D protein-protein interactions, enabling effective large-scale learning. Second, we leverage PPIRef to pre-train PPIformer, a new SE(3)-equivariant model, generalizing across diverse protein-binder variants. We fine-tune PPIformer to predict effects of mutations on protein-protein interactions via a thermodynamically motivated adjustment of the pre-training loss function. Finally, we demonstrate the enhanced generalization of our new PPIformer approach by outperforming other state-of-the-art methods on the new non-leaking splits of the standard labeled PPI mutational data and independent case studies optimizing a human antibody against SARS-CoV-2 and increasing staphylokinase thrombolytic activity.
Learning to design protein-protein interactions with enhanced generalization
Anton Bushuiev Roman Bushuiev Petr Kouba Anatolii Filkin Marketa Gabrielova Michal Gabriel Jiri Sedlar Tomas Pluskal Jiri Damborsky Stanislav Mazurenko Josef Sivic