On fine-tuning Boltz-2 for protein-protein affinity prediction
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912753217699840 |
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| author | King, James Cornwall, Lewis Nica, Andrei Cristian Day, James Sim, Aaron Dalchau, Neil Wollman, Lilly Meyers, Joshua |
| author_facet | King, James Cornwall, Lewis Nica, Andrei Cristian Day, James Sim, Aaron Dalchau, Neil Wollman, Lilly Meyers, Joshua |
| contents | Accurate prediction of protein-protein binding affinity is vital for understanding molecular interactions and designing therapeutics. We adapt Boltz-2, a state-of-the-art structure-based protein-ligand affinity predictor, for protein-protein affinity regression and evaluate it on two datasets, TCR3d and PPB-affinity. Despite high structural accuracy, Boltz-2-PPI underperforms relative to sequence-based alternatives in both small- and larger-scale data regimes. Combining embeddings from Boltz-2-PPI with sequence-based embeddings yields complementary improvements, particularly for weaker sequence models, suggesting different signals are learned by sequence- and structure-based models. Our results echo known biases associated with training with structural data and suggest that current structure-based representations are not primed for performant affinity prediction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_06592 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | On fine-tuning Boltz-2 for protein-protein affinity prediction King, James Cornwall, Lewis Nica, Andrei Cristian Day, James Sim, Aaron Dalchau, Neil Wollman, Lilly Meyers, Joshua Machine Learning Biomolecules Accurate prediction of protein-protein binding affinity is vital for understanding molecular interactions and designing therapeutics. We adapt Boltz-2, a state-of-the-art structure-based protein-ligand affinity predictor, for protein-protein affinity regression and evaluate it on two datasets, TCR3d and PPB-affinity. Despite high structural accuracy, Boltz-2-PPI underperforms relative to sequence-based alternatives in both small- and larger-scale data regimes. Combining embeddings from Boltz-2-PPI with sequence-based embeddings yields complementary improvements, particularly for weaker sequence models, suggesting different signals are learned by sequence- and structure-based models. Our results echo known biases associated with training with structural data and suggest that current structure-based representations are not primed for performant affinity prediction. |
| title | On fine-tuning Boltz-2 for protein-protein affinity prediction |
| topic | Machine Learning Biomolecules |
| url | https://arxiv.org/abs/2512.06592 |