Evidence map›Paper›PMID 24130468›Full record

ArticlePLoS computational biology2013

Simple topological features reflect dynamics and modularity in protein interaction networks.

Yuri Pritykin, Mona Singh

Abstract read
In one paragraph

Article in PLoS computational biology, 2013. 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

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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

12 citing papers in PubMed.

  1. Article
  2. Protein conformational switch discerned via network centrality properties.Computational and structural biotechnology journal · 2021
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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

2 authors.

Yuri PritykinDepartment of Computer Science, Princeton University, Princeton, New Jersey, United States of America ; Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, United States of America.
Mona Singh

Funding

Spatial Patterning During DevelopmentP50GM071508 · NIGMS · PRINCETON UNIVERSITY · PI POWELL, SUSAN M · 2004 to 2013
$30.6M
Predicting and analyzing variation in cellular interactomesR01GM076275 · NIGMS · PRINCETON UNIVERSITY · PI SINGH, MONA · 2006 to 2022
$4.0M
NIGMS NIH HHS GM076275NIGMS NIH HHS P50 GM071508NIGMS NIH HHS R01 GM076275
6 · The paper itself

Abstract

The availability of large-scale protein-protein interaction networks for numerous organisms provides an opportunity to comprehensively analyze whether simple properties of proteins are predictive of the roles they play in the functional organization of the cell. We begin by re-examining an influential but controversial characterization of the dynamic modularity of the S. cerevisiae interactome that incorporated gene expression data into network analysis. We analyse the protein-protein interaction networks of five organisms, S. cerevisiae, H. sapiens, D. melanogaster, A. thaliana, and E. coli, and confirm significant and consistent functional and structural differences between hub proteins that are co-expressed with their interacting partners and those that are not, and support the view that the former tend to be intramodular whereas the latter tend to be intermodular. However, we also demonstrate that in each of these organisms, simple topological measures are significantly correlated with the average co-expression of a hub with its partners, independent of any classification, and therefore also reflect protein intra- and inter- modularity. Further, cross-interactomic analysis demonstrates that these simple topological characteristics of hub proteins tend to be conserved across organisms. Overall, we give evidence that purely topological features of static interaction networks reflect aspects of the dynamics and modularity of interactomes as well as previous measures incorporating expression data, and are a powerful means for understanding the dynamic roles of hubs in interactomes.

Indexed as

AnimalsCluster AnalysisComputational BiologyDatabases, ProteinDrosophila melanogasterEscherichia coliHumansProtein Interaction MappingProtein Interaction MapsProteinsSaccharomyces cerevisiaeStatistics, NonparametricProteins

Identifiers

PMID24130468
PMCPMC3794914

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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.