Evidence map›Paper›PMID 41370356›Full record

ArticlePLoS computational biology2025

Computational characterization of the xanthan gum glycosyltransferase GumK.

Davide Luciano, F Emil Thomasen, Kresten Lindorff-Larsen, Gaston Courtade

Abstract read
In one paragraph

Article in PLoS computational biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

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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

1 citing paper in PubMed.

  1. 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

4 authors.

Davide LucianoDepartment of Biotechnology and Food Science, NTNU Norwegian University of Science and Technology, Trondheim, Norway.
F Emil ThomasenLinderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen N, Denmark.
Kresten Lindorff-LarsenLinderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen N, Denmark.
Gaston CourtadeDepartment of Biotechnology and Food Science, NTNU Norwegian University of Science and Technology, Trondheim, Norway.ORCID 0000-0002-1644-3223

Funding

Novo Nordisk Foundation
6 · The paper itself

Abstract

The activity of GT-B glycosyltransferases (GTs) depends on their conformational flexibility and high substrate specificity, but the molecular basis of these features is still not well defined. The GT70 family contains a single well-characterized enzyme, GumK, a glucuronosyltransferase from Xanthomonas campestris required for xanthan gum biosynthesis. Here, we applied multiscale molecular simulations and sequence analysis to probe GumK dynamics and substrate specificity. We show that GumK undergoes twisting and bending motions constrained by interdomain contacts and modulated by membrane anchoring. Acceptor-substrate binding within an amphiphilic clamp promotes opening, whereas donor-substrate binding stabilizes closure, defining a substrate-dependent catalytic cycle. Specificity for UDP-glucuronate is mediated by a conserved electrostatic environment centered on Lys307 and a hydrophobic triad that orients the sugar moiety. On the acceptor side, the binding site selectively accommodates polyisoprenyl carriers up to three isoprene units in length and wraps around the substrate, constraining the trisaccharide moiety in a catalytically competent conformation. Comparative analysis highlights GumK-specific motifs that distinguish it from homologous GTs. This work provides mechanistic insight into the GT70 family and the dynamic behavior of GT-B enzymes, establishing principles for the rational engineering of GumK to modify the monosaccharide composition of xanthan gum.

Indexed as

Bacterial ProteinsGlycosyltransferasesPolysaccharides, BacterialBinding SitesComputational BiologyMolecular Dynamics SimulationProtein ConformationSubstrate SpecificityXanthomonas campestrisBacterial ProteinsGlycosyltransferasesPolysaccharides, Bacterialxanthan gum

Identifiers

PMID41370356
PMCPMC12768416

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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.