Learning to design protein-protein interactions with enhanced generalization

0585897 - ÚOCHB 2025 RIV AT eng C - Conference Paper (international conference)
Bushuiev, A. - Bushuiev, Roman - Kouba, P. - Filkin, A. - Gabrielová, M. - Gabriel, M. - Sedlář, J. - Pluskal, Tomáš - Damborský, J. - Mazurenko, S. - Šivic, J.
Learning to design protein-protein interactions with enhanced generalization.
ICLR 2024. The Twelfth International Conference on Learning Representations. Vienna: ICLR, 2024.
[ICLR 2024. International Conference on Learning Representations /12./. Vienna (AT), 07.05.2024-11.05.2024]
R&D Projects: GA MŠMT(CZ) LM2023055; GA ČR(CZ) GM21-11563M
EU Projects: European Commission(XE) 891397 - KavaTarget
Research Infrastructure: e-INFRA CZ II - 90254; RECETOX RI II - 90269
Institutional support: RVO:61388963
Keywords : protein-protein interactions * protein design * generalization * self-supervised learning * equivariant 3D representations
OECD category: Other biological topics
https://openreview.net/forum?id=xcMmebCT7s

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.
Permanent Link: https://hdl.handle.net/11104/0353542