Evidence map›Paper›PMID 16872538›Full record

ArticleBMC bioinformatics2006

PIPE: a protein-protein interaction prediction engine based on the re-occurring short polypeptide sequences between known interacting protein pairs.

Sylvain Pitre, Frank Dehne, Albert Chan, Jim Cheetham, Alex Duong, Andrew Emili, Marinella Gebbia, Jack Greenblatt, Mathew Jessulat, Nevan Krogan and 2 more

Open access · goldAbstract read
In one paragraph

Article in BMC bioinformatics, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 68 papers.

0numbers the graph read from it
0cells of the map it votes in
68citing papers in PubMed
3.8field-weighted citation impact, top 6% 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

68 citing papers in PubMed, 217 citations in OpenAlex.

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8 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

12 authors at 3 institutions in 2 countries.

Sylvain PitreSchool of Computer Science, Carleton University, Ottawa, Canada. sylverbullit@hotmail.com
Frank Dehne
Albert Chan
Jim Cheetham
Alex Duong
Andrew Emili
Marinella Gebbia
Jack Greenblatt
Mathew Jessulat
Nevan Krogan
Xuemei Luo
Ashkan Golshani
Carleton University · CAUniversity of Toronto · CAFayetteville State University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIdentification of protein interaction networks has received considerable attention in the post-genomic era. The currently available biochemical approaches used to detect protein-protein interactions are all time and labour intensive. Consequently there is a growing need for the development of computational tools that are capable of effectively identifying such interactions.

resultsHere we explain the development and implementation of a novel Protein-Protein Interaction Prediction Engine termed PIPE. This tool is capable of predicting protein-protein interactions for any target pair of the yeast Saccharomyces cerevisiae proteins from their primary structure and without the need for any additional information or predictions about the proteins. PIPE showed a sensitivity of 61% for detecting any yeast protein interaction with 89% specificity and an overall accuracy of 75%. This rate of success is comparable to those associated with the most commonly used biochemical techniques. Using PIPE, we identified a novel interaction between YGL227W (vid30) and YMR135C (gid8) yeast proteins. This lead us to the identification of a novel yeast complex that here we term vid30 complex (vid30c). The observed interaction was confirmed by tandem affinity purification (TAP tag), verifying the ability of PIPE to predict novel protein-protein interactions. We then used PIPE analysis to investigate the internal architecture of vid30c. It appeared from PIPE analysis that vid30c may consist of a core and a secondary component. Generation of yeast gene deletion strains combined with TAP tagging analysis indicated that the deletion of a member of the core component interfered with the formation of vid30c, however, deletion of a member of the secondary component had little effect (if any) on the formation of vid30c. Also, PIPE can be used to analyse yeast proteins for which TAP tagging fails, thereby allowing us to predict protein interactions that are not included in genome-wide yeast TAP tagging projects.

conclusionPIPE analysis can predict yeast protein-protein interactions. Also, PIPE analysis can be used to study the internal architecture of yeast protein complexes. The data also suggests that a finite set of short polypeptide signals seem to be responsible for the majority of the yeast protein-protein interactions.

Indexed as

AlgorithmsBase SequenceComputational BiologyMolecular Sequence DataPeptidesProtein Interaction MappingProteinsProteomicsSaccharomyces cerevisiaeSaccharomyces cerevisiae ProteinsSensitivity and SpecificitySoftwareTwo-Hybrid System TechniquesPeptidesProteinsSaccharomyces cerevisiae Proteins

Identifiers

PMID16872538
PMCPMC1557541
OpenAlexW2161701113

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.