Surf2Spot: a surface-informed geometry-aware model for predicting partner-independent binder and nanobody design hotspots.

Protein-protein interactions (PPIs) and nanobody-antigen interactions (NAIs) play essential roles in cellular function, yet potential hotspot prediction remains challenging. We present Surf2Spot, a deep learning framework that integrates sequence embeddings, structural features, and protein surface properties to predicts surface regions with a high propensity to contain PPI hotspot residues. Unlike complex-prediction approaches that require predefined binding partners, Surf2Spot identifies interaction-prone regions directly from target sequences or predicted monomeric structures, enabling partner-independent hotspot prediction. By jointly modeling structural and physicochemical determinants, Surf2Spot achieves strong hotspot prediction performance on curated PPI and NAI datasets, outperforming existing methods in terms of F1-scores and the area under the precision-recall curve (AUPRC). Case studies on NbPDS1 and VdPDA1 demonstrate that Surf2Spot can identify putative hotspot residues within functional domains that are enriched in experimentally validated binder designs. For the tested targets, Surf2Spot-guided designs (with RFdiffusion and BindCraft) yielded a four-fold increase in successful design throughput and enhanced binding affinities compared to baseline strategies in a target-dependent manner. These results establish Surf2Spot as a powerful tool for hotspot discovery and rational protein engineering.