A geometric deep learning framework for genome-wide prediction of enzyme turnover number
By bridging the gap between sequence, structure, and function, KcatNet establishes a robust foundation for advancing understanding of molecular-level mechanisms and accelerating enzyme engineering efforts.
Authors
- Yue Bi (ORCID: https://orcid.org/0009-0001-6220-7475)
- Huan Yee Koh (ORCID: https://orcid.org/0000-0002-0488-2616)
- Guimin Zhang (ORCID: https://orcid.org/0000-0001-6674-5820)
- Lukasz Kurgan
- Geoffrey I. Webb
- Yumeng Zhang
- Xin Cui
- Tong Pan
- Shantong Hu
- Jiangning Song
- Xiaoyu Wang
Institutions
- The University of Melbourne (AU)
- Virginia Commonwealth University (US)
- Discovery Institute (US)
- Australian Regenerative Medicine Institute (AU)
- Monash University (AU)
- Beijing University of Chemical Technology (CN)
Publication Details
- Journal
- Genome biology
- Published
- 2026-02-11
- DOI
- https://doi.org/10.1186/s13059-026-03986-3
- Primary Topic
- Protein Structure and Dynamics
- Type
- article
- Field-Weighted Citation Impact
- 0.00