Learning to design protein-protein interactions with enhanced generalization

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Publikace nespadá pod Filozofickou fakultu, ale pod Přírodovědeckou fakultu. Oficiální stránka publikace je na webu muni.cz.
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BUSHUIEV Anton BUSHUIEV Roman KOUBA Petr FILKIN Anatolii GABRIELOVA Marketa GABRIEL Michal SEDLAR Jiri PLUSKAL4 Tomas DAMBORSKÝ Jiří MAZURENKO Stanislav SIVIC Josef

Rok publikování 2024
Druh Článek ve sborníku
Konference 12th International Conference on Learning Representations 2024
Fakulta / Pracoviště MU

Přírodovědecká fakulta

Citace
www https://openreview.net/forum?id=xcMmebCT7s
Klíčová slova protein-protein interactions; protein design; generalization; self-supervised learning; equivariant 3D representations
Popis 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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