From Protein Representations to Functional Hierarchies: Reshaping Representation Geometry for Enzyme Classification

Enzyme Commission (EC) classification assigns protein sequences to hierarchically organized functional classes and is important for biotechnology. Fine-grained EC classification remains challenging, and errors can compromise downstream functional annotation and enzyme discovery. Despite increasingly powerful protein representations, existing methods still insufficiently model the representation geometry of fine-grained EC classes: at the inter-class level, sibling classes can remain poorly separated; at the intra-class level, proteins within the same class can form heterogeneous distributions poorly captured by a single center. To address these challenges, we propose HiPro-EC, a hierarchy-guided multi-prototype framework that reshapes this geometry over frozen protein language model (PLM) representations. It reshapes inter-class structure by increasing sibling-class separation using the EC hierarchy, and models intra-class structure with multiple local prototypes to capture heterogeneous class distributions. Extensive experiments on the CARE benchmark show that HiPro-EC achieves the best performance at the finest EC level across all four official test sets, with consistent gains across multiple PLM backbones. Further analyses show clearer sibling-class separation and larger gains for heterogeneous EC classes, highlighting the value of modeling representation geometry for fine-grained EC classification.