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.

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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
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article

A geometric deep learning framework for genome-wide prediction of enzyme turnover number

Yue Bi, Huan Yee Koh, Guimin Zhang, Lukasz Kurgan et al.
Genome biology
Protein Structure and Dynamics
article

A geometric deep learning framework for genome-wide prediction of enzyme turnover number

Yue Bi, Huan Yee Koh, Guimin Zhang, Lukasz Kurgan, Geoffrey I. Webb, Yumeng Zhang, Xin Cui, Tong Pan, Shantong Hu, Jiangning Song, Xiaoyu Wang
article en

Abstract

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.

Genome biologyVol. 27(1)
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)
Openalex Percentile: Top 59%
Protein Structure and Dynamics
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A geometric deep learning framework for genome-wide prediction of enzyme turnover number — Yue Bi, Huan Yee Koh, et al. · Genome biology (2026) | TGRS Research Map | TGRS