Evidence map›Paper›PMID 33174598›Full record

ArticleNucleic acids research2021

LectomeXplore, an update of UniLectin for the discovery of carbohydrate-binding proteins based on a new lectin classification.

François Bonnardel, Julien Mariethoz, Serge Pérez, Anne Imberty, Frédérique Lisacek

Open access · goldAbstract read
In one paragraph

Article in Nucleic acids research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed, 1 pooled it
2.3field-weighted citation impact, top 11% of its field
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

28 citing papers in PubMed, 1 synthesis or guideline pooled it, 57 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Predictions from deep learning propose substantial protein-carbohydrate interplay.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  4. Article
  5. Review
  6. Article
  7. Article
  8. Microbial Lectin-Targeting Mucus: A Glycobioinformatic Approach.Methods in molecular biology (Clifton, N.J.) · 2025
    Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Review
  14. A Boltzmann model predicts glycan structures from lectin binding.bioRxiv : the preprint server for biology · 2024
    Article
  15. Article
  16. Article
  17. Article
  18. Elucidating the glycan-binding specificity and structure ofBeilstein journal of organic chemistry · 2024
    Article
  19. Article
  20. 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

5 authors at 2 institutions in 2 countries.

François BonnardelUniv. Grenoble Alpes, CNRS, CERMAV, 38000 Grenoble, France.
Julien MariethozProteome Informatics Group, SIB Swiss Institute of Bioinformatics, CH-1227 Geneva, Switzerland.
Serge PérezUniv. Grenoble Alpes, CNRS, CERMAV, 38000 Grenoble, France.
Anne ImbertyUniv. Grenoble Alpes, CNRS, CERMAV, 38000 Grenoble, France.
Frédérique LisacekProteome Informatics Group, SIB Swiss Institute of Bioinformatics, CH-1227 Geneva, Switzerland.
University of Geneva · CHCentre National de la Recherche Scientifique · FR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Lectins are non-covalent glycan-binding proteins mediating cellular interactions but their annotation in newly sequenced organisms is lacking. The limited size of functional domains and the low level of sequence similarity challenge usual bioinformatics tools. The identification of lectin domains in proteomes requires the manual curation of sequence alignments based on structural folds. A new lectin classification is proposed. It is built on three levels: (i) 35 lectin domain folds, (ii) 109 classes of lectins sharing at least 20% sequence similarity and (iii) 350 families of lectins sharing at least 70% sequence similarity. This information is compiled in the UniLectin platform that includes the previously described UniLectin3D database of curated lectin 3D structures. Since its first release, UniLectin3D has been updated with 485 additional 3D structures. The database is now complemented by two additional modules: PropLec containing predicted β-propeller lectins and LectomeXplore including predicted lectins from sequences of the NBCI-nr and UniProt for every curated lectin class. UniLectin is accessible at https://www.unilectin.eu/.

Indexed as

Databases, ProteinGenomeAmino Acid SequenceAnimalsAnthozoaComputational BiologyHumansInternetLectinsProtein Conformation, alpha-HelicalProtein Conformation, beta-StrandProtein Interaction Domains and MotifsProteomeReceptors, Cell SurfaceSequence AlignmentSequence Homology, Amino AcidLectinsProteomeReceptors, Cell Surfacesaccharide-binding proteins

Identifiers

PMID33174598
PMCPMC7778903
OpenAlexW3099775347

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.