Evidence map›Paper›PMID 42204163›Full record

ArticleNature communications2026

EnzymeTuning improves enzyme-constrained metabolic modeling and proteome abundance prediction through deep learning.

Xueting Wang, Yongbo Wang, Yingping Zhuang, Guan Wang, Hongzhong Lu

Abstract read
In one paragraph

Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Xueting WangState Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, PR China.ORCID http://orcid.org/0000-0002-0023-7468
Yongbo WangState Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, PR China.
Yingping ZhuangState Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, PR China.
Guan WangState Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, PR China. guanwang@ecust.edu.cn.ORCID http://orcid.org/0000-0003-2341-3054
Hongzhong LuState Key Laboratory of Microbial Metabolism, School of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai, PR China. hongzhonglu@sjtu.edu.cn.ORCID http://orcid.org/0009-0005-8555-1841

Funding

National Natural Science Foundation of China (National Science Foundation of China) 22208211, 22378263National Natural Science Foundation of China (National Science Foundation of China) 22578122Natural Science Foundation of Shanghai (Natural Science Foundation of Shanghai Municipality) 25ZR1402110
6 · The paper itself

Abstract

The accuracy of enzyme kinetic parameters, particularly enzyme turnover numbers (k

Indexed as

Deep LearningEnzymesModels, BiologicalProteomeEscherichia coliKineticsKluyveromycesSaccharomyces cerevisiaeYarrowiaEnzymesProteome

Identifiers

PMID42204163
PMCPMC13478178

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.