Evidence map›Paper›PMID 30420693›Full record

ArticleNature chemical biology2018

Functional and informatics analysis enables glycosyltransferase activity prediction.

Min Yang, Charlie Fehl, Karen V Lees, Eng-Kiat Lim, Wendy A Offen, Gideon J Davies, Dianna J Bowles, Matthew G Davidson, Stephen J Roberts, Benjamin G Davis

Abstract read
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In one paragraph

Article in Nature chemical biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers.

0numbers the graph read from it
0cells of the map it votes in
58citing 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

58 citing papers in PubMed.

  1. Predicting Functional Diversity Within Enzyme Families: A Practical Perspective.Chembiochem : a European journal of chemical biology · 2026
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  10. Unravelling the Functional Diversity of Type III Polyketide Synthases in Fungi.Angewandte Chemie (International ed. in English) · 2025
    Article
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  12. Article
  13. Review
  14. Article
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors.

Min Yang *Chemistry Research Laboratory, Oxford University, Oxford, UK.
Charlie Fehl *Chemistry Research Laboratory, Oxford University, Oxford, UK.
Karen V LeesDepartment of Engineering Science, University of Oxford, Oxford, UK.
Eng-Kiat LimCenter for Novel Agricultural Products, Department of Biology, University of York, York, UK.
Wendy A OffenYork Structural Biology Laboratory, Department of Chemistry, University of York, York, UK.ORCID http://orcid.org/0000-0002-2758-4531
Gideon J DaviesYork Structural Biology Laboratory, Department of Chemistry, University of York, York, UK.ORCID http://orcid.org/0000-0002-7343-776X
Dianna J BowlesCenter for Novel Agricultural Products, Department of Biology, University of York, York, UK.
Matthew G DavidsonCentre for Sustainable Chemical Technologies, Department of Chemistry, University of Bath, Bath, UK.ORCID http://orcid.org/0000-0002-1737-5189
Stephen J RobertsDepartment of Engineering Science, University of Oxford, Oxford, UK.
Benjamin G DavisChemistry Research Laboratory, Oxford University, Oxford, UK. ben.davis@chem.ox.ac.uk.ORCID http://orcid.org/0000-0002-5056-407X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The elucidation and prediction of how changes in a protein result in altered activities and selectivities remain a major challenge in chemistry. Two hurdles have prevented accurate family-wide models: obtaining (i) diverse datasets and (ii) suitable parameter frameworks that encapsulate activities in large sets. Here, we show that a relatively small but broad activity dataset is sufficient to train algorithms for functional prediction over the entire glycosyltransferase superfamily 1 (GT1) of the plant Arabidopsis thaliana. Whereas sequence analysis alone failed for GT1 substrate utilization patterns, our chemical-bioinformatic model, GT-Predict, succeeded by coupling physicochemical features with isozyme-recognition patterns over the family. GT-Predict identified GT1 biocatalysts for novel substrates and enabled functional annotation of uncharacterized GT1s. Finally, analyses of GT-Predict decision pathways revealed structural modulators of substrate recognition, thus providing information on mechanisms. This multifaceted approach to enzyme prediction may guide the streamlined utilization (and design) of biocatalysts and the discovery of other family-wide protein functions.

Indexed as

Structure-Activity RelationshipAlgorithmsArabidopsis ProteinsCatalytic DomainComputational BiologyGlucosyltransferasesGlycosyltransferasesMutagenesis, Site-DirectedNovobiocinPhylogenyResveratrolArabidopsis ProteinsGlucosyltransferasesGlycosyltransferasesNovobiocinResveratrolUGT72B1 protein, Arabidopsis

Identifiers

PMID30420693

What OpenQuestion holds

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Read underepoch 390

Registered trials

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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.