On fine-tuning Boltz-2 for protein-protein affinity prediction
This study addresses the underperformance of structure-based models relative to sequence-based methods in protein–protein interaction (PPI) binding affinity regression. To bridge this gap, we adapt the advanced structural model Boltz-2 for PPI affinity prediction and propose Boltz-2-PPI—a multimodal framework that jointly leverages Boltz-2-derived 3D structural representations and sequence embeddings (e.g., from ESM-2), integrated via transfer learning and feature fusion to achieve cross-modal complementarity. Experiments on TCR3d and PPB-affinity benchmarks show that while pure structural models remain inferior to sequence-based baselines—even with high-resolution structures—their performance improves substantially upon multimodal fusion (ΔRMSE ≤ 0.8 kcal/mol). These results empirically validate the orthogonality and synergy between structural and sequential signals, reveal critical limitations of current structural representations in affinity modeling, and establish a reproducible multimodal paradigm for PPI affinity prediction.