Work

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

Year: 2025

Type: article

Abstract: GO-HKP is a Gene Ontology hierarchy-driven framework for predicting enzyme turnover numbers ( k cat ) with improved coverage, generalizability, and interpretability. It integrates curated UniProt data, ontology-based k cat propagation, and sequence-driven GO annotation (DeepGO-SE) to infer k 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. • Ontology-driven framework integrates GO hierarchy for enzyme k cat prediction. • Curated UniProt-GO dataset enables robust and scalable kinetic annotation. • Dual strategy mapping combines empirical GO annotation and DeepGO-SE inference. • Superior coverage achieved across four diverse genome-scale metabolic models (GEMs).

Source: Software Impacts

DOI: 10.1016/j.simpa.2025.100803

Authors: Qinghan Meng, Zhitao Mao, Hao Chen, Yuanyuan Huang, Hongwu Ma

Institutions: Tianjin University of Science and Technology, Chinese Academy of Sciences, Tianjin Institute of Industrial Biotechnology, State Key Laboratory of Biocatalysis and Enzyme Engineering, Hubei University

Language: English

Topic: Microbial Metabolic Engineering and Bioproduction

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: gold

Estimated APC paid: $600

Funders: National Natural Science Foundation of China, State Key Laboratory of Biocatalysis and Enzyme Engineering