Learning to design protein-protein interactions with enhanced generalization

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Authors

BUSHUIEV Anton BUSHUIEV Roman KOUBA Petr FILKIN Anatolii GABRIELOVA Marketa GABRIEL Michal SEDLAR Jiri PLUSKAL4 Tomas DAMBORSKÝ Jiří MAZURENKO Stanislav SIVIC Josef

Year of publication 2024
Type Article in Proceedings
Conference 12th International Conference on Learning Representations 2024
MU Faculty or unit

Faculty of Science

Citation
Web https://openreview.net/forum?id=xcMmebCT7s
Keywords protein-protein interactions; protein design; generalization; self-supervised learning; equivariant 3D representations
Description 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 the PPIRef dataset 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 new, non-leaking splits of standard labeled PPI mutational data and independent case studies optimizing a human antibody against SARS-CoV-2 and increasing the thrombolytic activity of staphylokinase.
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