Evidence map›Paper›PMID 41188073›Full record

ArticleNucleic acids research2026

Open Enzyme Database: a community-wide repository for sharing enzyme data.

Le Yuan, David M Bianchi, Katherine Arneson, Bingji Guo, Sara Lambert, Chris Stephens, Yash H Wasnik, Christopher Pond, Matthew J Berry, Huimin Zhao

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. Review
  2. Artificial Intelligence Powers Protein Functional Annotation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Review
  3. Protein engineering: status report.Protein engineering, design & selection : PEDS · 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

10 authors.

Le YuanDepartment of Chemical and Biomolecular Engineering, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States.ORCID 0000-0003-3317-9011
David M BianchiNSF Molecule Maker Lab Institute, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States.
Katherine ArnesonNSF Molecule Maker Lab Institute, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States.
Bingji GuoNSF Molecule Maker Lab Institute, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States.
Sara LambertNSF Molecule Maker Lab Institute, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States.
Chris StephensNSF Molecule Maker Lab Institute, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States.
Yash H WasnikNSF Molecule Maker Lab Institute, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States.
Christopher PondNSF Molecule Maker Lab Institute, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States.
Matthew J BerryNSF Molecule Maker Lab Institute, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States.
Huimin ZhaoDepartment of Chemical and Biomolecular Engineering, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States.ORCID 0000-0002-9069-6739

Funding

National Science Foundation 2019897National Science Foundation 2505932National Science Foundation DBI-2400058National Science Foundation OISE-2435374
6 · The paper itself

Abstract

Enzymes are the molecular machines of life and play an indispensable role in numerous biotechnological and biomedical applications. Despite the availability of a few enzyme databases, their utility is often hindered by limitations such as infrequent updates, lack of standardization, and inadequate support for machine learning. In this study, we developed the Open Enzyme Database (OED), a community-wide repository and web-based infrastructure designed to facilitate the sharing and exploration of enzyme data. As a user-friendly platform, the first release of the OED provides extensively curated information on enzyme kinetic parameters, structural data, enzymatic reactions, assay conditions, and functional annotations. It also features cutting-edge artificial intelligence (AI) tools for enzyme property prediction, along with cheminformatics-powered algorithms for the identification of promising enzymes. This database should greatly accelerate research and applications in areas such as biocatalysis, systems biology, synthetic biology, and enzyme engineering, as well as AI. The resource is freely available at https://openenzymedb.platform.moleculemaker.org/.

Indexed as

Databases, ProteinEnzymesAlgorithmsInformation DisseminationInternetKineticsSoftwareEnzymes

Identifiers

PMID41188073
PMCPMC12807643

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.