GO-HKP: A Gene Ontology hierarchy-driven framework for enzyme kcat prediction

Abstract

GO-HKP is a Gene Ontology hierarchy-driven framework for predicting enzyme turnover numbers ($k_{\mathrm{cat}}$) with improved coverage, generalizability, and interpretability. It integrates curated UniProt data, ontology-based $k_{\mathrm{cat}}$ propagation, and sequence-driven GO annotation (DeepGO-SE) to infer $k_{\mathrm{cat}}$ for both annotated and novel enzymes. Benchmarking across four genome-scale metabolic models demonstrated substantial improvements in reaction coverage — by 56.67%, 25.1%, 16.0%, and 14.5% — compared with existing methods, highlighting its strong gap-filling capability. GO-HKP offers a biologically grounded, scalable, and transparent approach, supporting applications in metabolic engineering, drug discovery, and systems biology. The framework and Python package are available via GitHub for broad usability and reproducibility.