arXiv:2510.01632 · v4
BioBlobs: Unsupervised Discovery of Functional Substructures
摘要
Protein function is often localized to cohesive substructures, such as catalytic triads, binding pockets, and structural motifs, that occupy only a small fraction of a protein's residues. Yet existing predictive pipelines typically aggregate residue-level embeddings and do not explicitly model or infer these functional substructures, leaving the central biological question unanswered: which substructure of a protein is responsible for its function? We introduce BioBlobs, an encoder-agnostic, end-to-end differentiable framework that compresses a protein into a small set of cohesive substructures (blobs) and predicts function from these blobs alone, so that each blob corresponds to a candidate functional region. Across diverse protein function prediction tasks and both sequence-only and structure-aware protein language models, BioBlobs is competitive with strong baselines while operating on only a small fraction of residues. Trained only on protein function labels, BioBlobs recovers expert-curated functional sites in InterPro and experimentally annotated catalytic sites in the Mechanism and Catalytic Site Atlas, demonstrating unsupervised functional substructure discovery and opening a path to large-scale functional site discovery in unannotated proteins. The discovered blobs adapt their spatial scale to the task, ranging from local catalytic sites to entire structural domains. Code is available at https://github.com/OliverLaboratory/BioBlobs.
作者备注：Accepted at the 40th Conference on Neural Information Processing Systems (NeurIPS 2026). 25 pages, 5 figures, 11 tables. Code: https://github.com/OliverLaboratory/BioBlobs