Evidence map›Paper›PMID 41171142›Full record

ArticleNucleic acids research2026

GotEnzymes2: expanding coverage of enzyme kinetics and thermal properties.

Bingxue Lyu, Ke Wu, Yuanyuan Huang, Mihail Anton, Xiongwen Li, Sandra Viknander, Danish Anwer, Yunfeng Yang, Diannan Lu, Eduard Kerkhoven and 4 more

Abstract read
In one paragraph

Article in Nucleic acids research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 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. Article
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

14 authors.

Bingxue LyuInstitute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055,China.
Ke WuInstitute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055,China.
Yuanyuan HuangState Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055,China.
Mihail AntonDepartment of Life Sciences, Chalmers University of Technology, Gothenburg SE-412 96,Sweden.
Xiongwen LiInstitute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055,China.
Sandra ViknanderDepartment of Life Sciences, Chalmers University of Technology, Gothenburg SE-412 96,Sweden.
Danish AnwerDepartment of Life Sciences, Chalmers University of Technology, Gothenburg SE-412 96,Sweden.
Yunfeng YangInstitute of Environment and Ecology, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055,China.
Diannan LuDepartment of Chemical Engineering, Tsinghua University, Beijing 100084,China.
Eduard KerkhovenDepartment of Life Sciences, Chalmers University of Technology, Gothenburg SE-412 96,Sweden.ORCID 0000-0002-3593-5792
Aleksej ZelezniakDepartment of Life Sciences, Chalmers University of Technology, Gothenburg SE-412 96,Sweden.ORCID 0000-0002-3098-9441
Dan GaoInstitute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055,China.
Yu ChenState Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055,China.ORCID 0000-0003-3326-9068
Feiran LiInstitute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055,China.ORCID 0000-0001-9155-5260

Funding

Biotechnology and Biological Sciences Research Council BB/Y000730/1Formas grant 2019-01403National Key R&D Program of China 2024YFA0920300National Natural Science Foundation of China General Project 22478223National Natural Science Foundation of China General Project A2403013Swedish Research Council 2019-05356Swedish Research Council 2023-04254Tsinghua Shenzhen International Graduate School DCE-iBHE-2023-1Tsinghua Shenzhen International Graduate School JC2024004
6 · The paper itself

Abstract

Enzyme kinetics are fundamental for understanding metabolism, yet experimentally measured parameters remain scarce. To address this gap, we introduce GotEnzymes2, a substantially expanded resource covering 10 765 species, 7.3 million enzymes, and 59.6 million unique entries. Compared with the first version, GotEnzymes2 now integrates both catalytic and thermal parameters, enabling unified predictions of kcat, Km,kcat/Km, optimal temperature, and melting temperature. This expansion markedly broadens species and enzyme coverage, creating the most comprehensive database of enzyme kinetic and stability parameters to date. To construct the resource, we systematically benchmarked state-of-the-art models for catalytic and thermal parameter prediction, and incorporated the best-performing strategies to ensure accuracy and generalizability. Altogether, GotEnzymes2 provides the community with a powerful resource for data-driven enzyme discovery, design, and engineering, with broad applications in systems biology, metabolic engineering, and synthetic biology. GotEnzymes2 is publicly accessible at https://metabolicatlas.org/gotenzymes.

Indexed as

Databases, ProteinEnzymesEnzyme StabilityHumansKineticsTemperatureEnzymes

Identifiers

PMID41171142
PMCPMC12807647

What OpenQuestion holds

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LicenceCC BY
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.