Evidence map›Paper›PMID 41319045›Full record

ReviewBriefings in bioinformatics2025

Computational approaches to enzymatic reaction assignment: a review of methods, validations, and future directions.

Luke Kennedy, Mary-Ellen Harper, Miroslava Cuperlovic-Culf

Abstract readReview
In one paragraph

Review in Briefings in bioinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

3 authors.

Luke KennedyDepartment of Biochemistry, Microbiology and Immunology, Faculty of Medicine University of Ottawa, 451 Smyth Road, Ottawa, ON K1H 8M5, Canada.
Mary-Ellen HarperDepartment of Biochemistry, Microbiology and Immunology, Faculty of Medicine University of Ottawa, 451 Smyth Road, Ottawa, ON K1H 8M5, Canada.ORCID 0000-0003-3864-5886
Miroslava Cuperlovic-CulfDepartment of Biochemistry, Microbiology and Immunology, Faculty of Medicine University of Ottawa, 451 Smyth Road, Ottawa, ON K1H 8M5, Canada.

Funding

National Research Council Canada's AIP Challenge ProgramNSERC-CREATE Metabolomics Advanced Training and International Exchange ProgramNSERC Discovery GrantOntario Graduate Scholarship Award
6 · The paper itself

Abstract

Characterizing the proteins and molecules that underpin cellular metabolism is fundamental to advancing our understanding of biological processes. However, the rapidly expanding repertoire of newly identified proteins and metabolites presents significant challenges for experimental characterization and functional analysis. Computational approaches can be used to identify and elucidate catalytic relationships between enzymes and their substrates and provide powerful tools that support biological research and applications in biochemical engineering, and drug discovery. In this review, we describe the problem of reaction assignment for predicting enzymatic reactions leveraging structural, network, and high-throughput experimental data. Also considered are theoretical perspectives motivating the design of computational methods, available resources, and validation techniques. Current and future computational approaches for enzymatic reaction assignment are expected to advance in tandem with technologies for experimental analysis of metabolism, such as metabolomics, and flux-based methods, to expand our understanding of metabolism.

Indexed as

Computational BiologyEnzymesHumansMetabolomicsEnzymesenzyme discoveryenzyme retrievalmetabolic networksmetabolomicsmultiomicsprotein function annotation

Identifiers

PMID41319045
PMCPMC12665039

What OpenQuestion holds

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LicenceCC BY-NC
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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.