Evidence map›Paper›PMID 12368246›Full record

ArticleGenome research2002

Inferring domain-domain interactions from protein-protein interactions.

Minghua Deng, Shipra Mehta, Fengzhu Sun, Ting Chen

Open access · bronzeAbstract readComparative StudyValidation Study
In one paragraph

Article in Genome research, 2002. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 125 papers.

0numbers the graph read from it
0cells of the map it votes in
125citing 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

125 citing papers in PubMed, 448 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Mutual annotation-based prediction of protein domain functions with Domain2GO.Protein science : a publication of the Protein Society · 2024
    Article
  5. Review
  6. Review
  7. Article
  8. Article
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  11. Prediction of Protein-Protein Interactions Based on Domain.Computational and mathematical methods in medicine · 2019
    Article
  12. Article
  13. Article
  14. Review
  15. Article
  16. Inference of protein-protein interaction networks from multiple heterogeneous data.EURASIP journal on bioinformatics & systems biology · 2016
    Article
  17. Article
  18. Article
  19. Article
  20. Article

65 more citing papers are in PubMed but not listed here.

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 1 institution in 1 country.

Minghua DengProgram in Molecular and Computational Biology, Department of Biological Sciences, University of Southern California, Los Angeles, California 90089, USA.
Shipra Mehta
Fengzhu Sun
Ting Chen
University of Southern California · US

Funding

STATISTICAL STUDIES OF MTDNA INVOLVEMENT IN DISEASESR29DK053392 · NIDDK · UNIVERSITY OF SOUTHERN CALIFORNIA · PI SUN, FENGZHU · 1998 to 2002
$348k
NIDDK NIH HHS DK53392
6 · The paper itself

Abstract

The interaction between proteins is one of the most important features of protein functions. Behind protein-protein interactions there are protein domains interacting physically with one another to perform the necessary functions. Therefore, understanding protein interactions at the domain level gives a global view of the protein interaction network, and possibly of protein functions. Two research groups used yeast two-hybrid assays to generate 5719 interactions between proteins of the yeast Saccharomyces cerevisiae. This allows us to study the large-scale conserved patterns of interactions between protein domains. Using evolutionarily conserved domains defined in a protein-domain database called PFAM (http://PFAM.wustl.edu), we apply a Maximum Likelihood Estimation method to infer interacting domains that are consistent with the observed protein-protein interactions. We estimate the probabilities of interactions between every pair of domains and measure the accuracies of our predictions at the protein level. Using the inferred domain-domain interactions, we predict interactions between proteins. Our predicted protein-protein interactions have a significant overlap with the protein-protein interactions (MIPS: http://mips.gfs.de) obtained by methods other than the two-hybrid assays. The mean correlation coefficient of the gene expression profiles for our predicted interaction pairs is significantly higher than that for random pairs. Our method has shown robustness in analyzing incomplete data sets and dealing with various experimental errors. We found several novel protein-protein interactions such as RPS0A interacting with APG17 and TAF40 interacting with SPT3, which are consistent with the functions of the proteins.

Indexed as

Protein Interaction MappingProtein Structure, TertiaryComputational BiologyGene Expression ProfilingGene Expression Regulation, FungalGenes, FungalLikelihood FunctionsSaccharomyces cerevisiae ProteinsSaccharomyces cerevisiae Proteins

Identifiers

PMID12368246
PMCPMC187530
OpenAlexW2045131140

What OpenQuestion holds

Textmetadata
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.