Evidence map›Paper›PMID 39189002›Full record

ReviewBeilstein journal of organic chemistry2024

Computational toolbox for the analysis of protein-glycan interactions.

Ferran Nieto-Fabregat, Maria Pia Lenza, Angela Marseglia, Cristina Di Carluccio, Antonio Molinaro, Alba Silipo, Roberta Marchetti

Abstract readReview
In one paragraph

Review in Beilstein journal of organic chemistry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. JACS Au · 2025
    Article
  5. Article
  6. A multifunctional anti-O-Antigen human monoclonal antibody protects againstProceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Chemical glycobiology.Beilstein journal of organic chemistry · 2025
    Article
  12. 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

7 authors.

Ferran Nieto-FabregatDepartment of Chemical Sciences, University of Naples Federico II, Via Cinthia 4, 80126, Italy.ORCID https://orcid.org/0000-0001-9847-3030
Maria Pia LenzaDepartment of Chemical Sciences, University of Naples Federico II, Via Cinthia 4, 80126, Italy.ORCID https://orcid.org/0000-0002-9733-5020
Angela MarsegliaDepartment of Chemical Sciences, University of Naples Federico II, Via Cinthia 4, 80126, Italy.ORCID https://orcid.org/0000-0003-1831-6831
Cristina Di CarluccioDepartment of Chemical Sciences, University of Naples Federico II, Via Cinthia 4, 80126, Italy.ORCID https://orcid.org/0000-0001-5895-9829
Antonio MolinaroDepartment of Chemical Sciences, University of Naples Federico II, Via Cinthia 4, 80126, Italy.ORCID https://orcid.org/0000-0002-3456-7369
Alba SilipoDepartment of Chemical Sciences, University of Naples Federico II, Via Cinthia 4, 80126, Italy.ORCID https://orcid.org/0000-0002-5394-6532
Roberta MarchettiDepartment of Chemical Sciences, University of Naples Federico II, Via Cinthia 4, 80126, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Protein-glycan interactions play pivotal roles in numerous biological processes, ranging from cellular recognition to immune response modulation. Understanding the intricate details of these interactions is crucial for deciphering the molecular mechanisms underlying various physiological and pathological conditions. Computational techniques have emerged as powerful tools that can help in drawing, building and visualising complex biomolecules and provide insights into their dynamic behaviour at atomic and molecular levels. This review provides an overview of the main computational tools useful for studying biomolecular systems, particularly glycans, both in free state and in complex with proteins, also with reference to the principles, methodologies, and applications of all-atom molecular dynamics simulations. Herein, we focused on the programs that are generally employed for preparing protein and glycan input files to execute molecular dynamics simulations and analyse the corresponding results. The presented computational toolbox represents a valuable resource for researchers studying protein-glycan interactions and incorporates advanced computational methods for building, visualising and predicting protein/glycan structures, modelling protein-ligand complexes, and analyse MD outcomes. Moreover, selected case studies have been reported to highlight the importance of computational tools in studying protein-glycan systems, revealing the capability of these tools to provide valuable insights into the binding kinetics, energetics, and structural determinants that govern specific molecular interactions.

Indexed as

computational toolsglycan–protein interactionsMDmolecular recognition

Identifiers

PMID39189002
PMCPMC11346309

What OpenQuestion holds

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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.