Evidence map›Paper›PMID 19806189›Full record

ArticlePloS one2009

Determining protein complex connectivity using a probabilistic deletion network derived from quantitative proteomics.

Mihaela E Sardiu, Joshua M Gilmore, Michael J Carrozza, Bing Li, Jerry L Workman, Laurence Florens, Michael P Washburn

Abstract read
In one paragraph

Article in PloS one, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed, 1 pooled it
–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

24 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

7 authors.

Mihaela E SardiuStowers Institute for Medical Research, Kansas City, Missouri, United States of America.
Joshua M Gilmore
Michael J Carrozza
Bing Li
Jerry L Workman
Laurence Florens
Michael P Washburn

Funding

Mechanisms of Transcriptional Regulation in ChromatinR37GM047867 · NIGMS · STOWERS INSTITUTE FOR MEDICAL RESEARCH · PI WORKMAN, JERRY L · 2001 to 2010
$3.4M
NIGMS NIH HHS R37 GM047867
6 · The paper itself

Abstract

Protein complexes are key molecular machines executing a variety of essential cellular processes. Despite the availability of genome-wide protein-protein interaction studies, determining the connectivity between proteins within a complex remains a major challenge. Here we demonstrate a method that is able to predict the relationship of proteins within a stable protein complex. We employed a combination of computational approaches and a systematic collection of quantitative proteomics data from wild-type and deletion strain purifications to build a quantitative deletion-interaction network map and subsequently convert the resulting data into an interdependency-interaction model of a complex. We applied this approach to a data set generated from components of the Saccharomyces cerevisiae Rpd3 histone deacetylase complexes, which consists of two distinct small and large complexes that are held together by a module consisting of Rpd3, Sin3 and Ume1. The resulting representation reveals new protein-protein interactions and new submodule relationships, providing novel information for mapping the functional organization of a complex.

Indexed as

Cluster AnalysisGene DeletionGenomeHistone DeacetylasesModels, BiologicalModels, StatisticalProbabilityProtein Interaction MappingProteinsProteomicsRepressor ProteinsSaccharomyces cerevisiaeSaccharomyces cerevisiae ProteinsHistone DeacetylasesProteinsRepressor ProteinsRPD3 protein, S cerevisiaeSaccharomyces cerevisiae ProteinsSIN3 protein, S cerevisiaeUME1 protein, S cerevisiae

Identifiers

PMID19806189
PMCPMC2751824

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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.