Evidence map›Paper›PMID 18590549›Full record

ArticleBMC genomics2008

The topology of the bacterial co-conserved protein network and its implications for predicting protein function.

Anis Karimpour-Fard, Sonia M Leach, Lawrence E Hunter, Ryan T Gill

Open access · goldAbstract read
In one paragraph

Article in BMC genomics, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
1.2field-weighted citation impact, top 21% 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

8 citing papers in PubMed, 18 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. 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 at 2 institutions in 2 countries.

Anis Karimpour-FardCenter for Computational Pharmacology, University of Colorado School of Medicine, Aurora, Colorado 80045, USA. anis.karimpour-fard@uchsc.edu
Sonia M Leach
Lawrence E Hunter
Ryan T Gill
University of Colorado Denver · USUniversity of Colorado Boulder · US

Funding

Computational Bioscience Program Training GrantT15LM009451 · NLM · UNIVERSITY OF COLORADO DENVER · PI Katherina Kechris-Mays, Arjun Krishnan · 2007 to 2026
$11.7M
Technology Development for a MolBio Knowledge-BaseR01LM008111 · NLM · UNIVERSITY OF COLORADO DENVER · PI HUNTER, LAWRENCE E · 2004 to 2021
$9.7M
Bio Text NLPR01LM009254 · NLM · UNIVERSITY OF COLORADO DENVER · PI HUNTER, LAWRENCE E · 2006 to 2018
$5.9M
Ontologies and Biomedical Language ProcessingR01GM083649 · NIGMS · UNIVERSITY OF COLORADO DENVER · PI HUNTER, LAWRENCE E · 2007 to 2010
$2.5M
BioMolecular Networks in Antibiotic Resistance EvolutionK25AI064338 · NIAID · UNIVERSITY OF COLORADO AT BOULDER · PI GILL, RYAN T · 2005 to 2008
$564k
NIAID NIH HHS K25 AI064338NIGMS NIH HHS R01 GM083649NIGMS NIH HHS R01-GM083649NLM NIH HHS 5R01-LM009254NLM NIH HHS R01 LM008111NLM NIH HHS R01-LM-008111NLM NIH HHS R01 LM009254NLM NIH HHS T15 LM009451
6 · The paper itself

Abstract

backgroundProtein-protein interactions networks are most often generated from physical protein-protein interaction data. Co-conservation, also known as phylogenetic profiles, is an alternative source of information for generating protein interaction networks. Co-conservation methods generate interaction networks among proteins that are gained or lost together through evolution. Co-conservation is a particularly useful technique in the compact bacteria genomes. Prior studies in yeast suggest that the topology of protein-protein interaction networks generated from physical interaction assays can offer important insight into protein function. Here, we hypothesize that in bacteria, the topology of protein interaction networks derived via co-conservation information could similarly improve methods for predicting protein function. Since the topology of bacteria co-conservation protein-protein interaction networks has not previously been studied in depth, we first perform such an analysis for co-conservation networks in E. coli K12. Next, we demonstrate one way in which network connectivity measures and global and local function distribution can be exploited to predict protein function for previously uncharacterized proteins.

resultsOur results showed, like most biological networks, our bacteria co-conserved protein-protein interaction networks had scale-free topologies. Our results indicated that some properties of the physical yeast interaction network hold in our bacteria co-conservation networks, such as high connectivity for essential proteins. However, the high connectivity among protein complexes in the yeast physical network was not seen in the co-conservation network which uses all bacteria as the reference set. We found that the distribution of node connectivity varied by functional category and could be informative for function prediction. By integrating of functional information from different annotation sources and using the network topology, we were able to infer function for uncharacterized proteins.

conclusionInteractions networks based on co-conservation can contain information distinct from networks based on physical or other interaction types. Our study has shown co-conservation based networks to exhibit a scale free topology, as expected for biological networks. We also revealed ways that connectivity in our networks can be informative for the functional characterization of proteins.

Indexed as

AlgorithmsCluster AnalysisEscherichia coli K12Genome, BacterialMetabolic Networks and PathwaysPhylogenyPredictive Value of TestsProtein Interaction MappingReference ValuesReproducibility of Results

Identifiers

PMID18590549
PMCPMC2488357
OpenAlexW2166754203

What OpenQuestion holds

Textmetadata
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.