Evidence map›Paper›PMID 41673757›Full record

ArticleGenome biology2026

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

Tong Pan, Xin Cui, Huan Yee Koh, Yue Bi, Xiaoyu Wang, Yumeng Zhang, Shantong Hu, Geoffrey I Webb, Lukasz Kurgan, Guimin Zhang and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Genome biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Advances in Machine Learning Models for Predicting Enzyme Kinetic Parameters.Journal of chemical information and modeling · 2026
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Tong PanBiomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC, 3800, Australia.
Xin CuiState Key Laboratory of Green Biomanufacturing, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing, 100029, China.
Huan Yee KohDepartment of Data Science and Artificial Intelligence, Monash University, VIC, 3800, Australia.
Yue BiBiomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC, 3800, Australia.
Xiaoyu WangBiomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC, 3800, Australia.
Yumeng ZhangBiomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC, 3800, Australia.
Shantong HuState Key Laboratory of Green Biomanufacturing, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing, 100029, China.
Geoffrey I WebbDepartment of Data Science and Artificial Intelligence, Monash University, VIC, 3800, Australia.
Lukasz KurganDepartment of Computer Science, Virginia Commonwealth University, Richmond, 23284, USA. lkurgan@vcu.edu.
Guimin ZhangState Key Laboratory of Green Biomanufacturing, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing, 100029, China. 2021500017@buct.edu.cn.
Jiangning SongBiomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC, 3800, Australia. jiangning.song@monash.edu.

Funding

National Health and Medical Research Council of Australia APP1127948, APP1144652, APP2036864
6 · The paper itself

Abstract

backgroundEnzyme turnover numbers (

resultsTo address this, we present KcatNet, a geometric deep learning model designed for high-throughput prediction of

conclusionBy 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.

Indexed as

Deep LearningEnzymesGraph Neural NetworksAmino Acid SequenceAnimalsCatalysisCatalytic DomainDrosophila melanogasterGenome-Wide Association StudyHumansEnzymesCatalytic efficiencyDeep learningEnzyme engineeringEnzyme kineticsMetabolic modelingProtein language modelTurnover number

Identifiers

PMID41673757
PMCPMC12998014

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.