LOCUS: Label-Gated Region Proposals for Protein Function Prediction

Where function resides in a protein is tightly coupled to what that function is. Recent predictors pair protein language model representations with structural encoders and reduce them to a single protein-level vector. The design is effective but leaves no way to express how one region bears on one label. Catalytic residues and binding pockets are sharply localized, while many biological process and cellular component terms have no compact structural correlate at all. Routing every label through one pooled vector and routing every label through a site bottleneck therefore fail for opposite reasons. We introduce Locus, a framework that gives localized evidence its own pathway and keeps the global one. A class-agnostic region proposal network inspired by object detection emits multi-scale soft 3D regions over the residue graph, supervised only by residue-level annotations of active sites, binding sites and motifs. Graph non-maximum suppression selects among the proposals, and each survivor is pooled into a region-of-interest token. A classifier adds this regional evidence to global sequence and structure features as label-specific gated residuals. Every label then decides how much local evidence to admit, and no functional label enters the model as an input. Since the fusion is additive and decomposes exactly, the model that uses the proposal pathway can also measure its own contribution. On the standard Gene Ontology and enzyme classification benchmarks Locus exceeds published sequence- and structure-based predictors under the official evaluator. The regional pathway contributes in a strongly label-dependent way: it is worth a margin clear of zero on all three Gene Ontology branches and limited contributions on enzyme classification.