Evidence map›Paper›PMID 38334822›Full record

ArticleArchives of microbiology2024

Prediction of motif-mediated viral mimicry through the integration of host-pathogen interactions.

Sobia Idrees, Keshav Raj Paudel, Philip M Hansbro

Abstract read
In one paragraph

Article in Archives of microbiology, 2024. 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. Predicting Motif-Mediated Interactions Based on Viral Genomic Composition.International journal of molecular sciences · 2025
    Article
  2. Review
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.

Sobia IdreesSchool of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW, Australia. sobia.idrees@uts.edu.au.
Keshav Raj PaudelCentre for Inflammation, School of Life Sciences, Faculty of Science, Centenary Institute and the University of Technology Sydney, Sydney, NSW, Australia.
Philip M HansbroCentre for Inflammation, School of Life Sciences, Faculty of Science, Centenary Institute and the University of Technology Sydney, Sydney, NSW, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

One of the mechanisms viruses use in hijacking host cellular machinery is mimicking Short Linear Motifs (SLiMs) in host proteins to maintain their life cycle inside host cells. In the face of the escalating volume of virus-host protein-protein interactions (vhPPIs) documented in databases; the accurate prediction of molecular mimicry remains a formidable challenge due to the inherent degeneracy of SLiMs. Consequently, there is a pressing need for computational methodologies to predict new instances of viral mimicry. Our present study introduces a DMI-de-novo pipeline, revealing that vhPPIs catalogued in the VirHostNet3.0 database effectively capture domain-motif interactions (DMIs). Notably, both affinity purification coupled mass spectrometry and yeast two-hybrid assays emerged as good approaches for delineating DMIs. Furthermore, we have identified new vhPPIs mediated by SLiMs across different viruses. Importantly, the de-novo prediction strategy facilitated the recognition of several potential mimicry candidates implicated in the subversion of host cellular proteins. The insights gleaned from this research not only enhance our comprehension of the mechanisms by which viruses co-opt host cellular machinery but also pave the way for the development of novel therapeutic interventions.

Indexed as

ProteinsVirusesAmino Acid MotifsHost-Pathogen InteractionsProteinsBioinformaticsShort linear motifsViral mimicryVirus-host interactions

Identifiers

PMID38334822
PMCPMC10858152

What OpenQuestion holds

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LicenceCC BY
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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.