ProtSurf: Learning Transferable Protein Surface Representations from Large-Scale Protein Interfaces

Protein sequence and structure have emerged as powerful reusable modalities for protein representation learning. In contrast, protein surfaces—which mediate protein-protein interactions—remain underexplored as transferable representations. We introduce ProtSurf, a hierarchical self-supervised framework that formulates protein surface learning as a scalable representation learning problem and learns residue-aligned surface representations from large-scale protein interfaces. ProtSurf models protein surfaces at local and global scales and investigates how protein surface representations benefit from increasing model capacity and pretraining data diversity. We pretrain ProtSurf on approximately 200,000 experimentally determined RCSB protein structures and 1.6 million domain–domain interfaces derived from AlphaFold-predicted structures. We evaluate ProtSurf residue embeddings for binding affinity prediction and achieve strong performance across multiple benchmarks. Beyond affinity prediction, ProtSurf provides, to our knowledge, the first transferable residue-level surface representation learned at scale, establishing protein surfaces as a representation modality alongside sequence and structure.