SLiM-Enrich: computational assessment of protein–protein interaction data as a source of domain-motif interactions

Many important cellular processes involve protein–protein interactions (PPIs) mediated by a Short Linear Motif (SLiM) in one protein interacting with a globular domain in another. Despite their significance, these domain-motif interactions (DMIs) are typically low affinity, which makes them challeng...

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Vydáno v:PeerJ (San Francisco, CA) Ročník 6; s. e5858
Hlavní autoři: Idrees, Sobia, Pérez-Bercoff, Åsa, Edwards, Richard J.
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States PeerJ. Ltd 31.10.2018
PeerJ Inc
Témata:
Bioinformatics
Computational Biology
Domain-motif interactions
Eukaryotes
Molecular Biology
Protein disorder
Protein-protein interactions
Shiny app
Short linear motifs
Yeast two-hybrid
Short linear motifs
Protein–protein interactions
Protein disorder
Domain-motif interactions
Yeast two-hybrid
Shiny app
ISSN:2167-8359, 2167-8359
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Shrnutí:Many important cellular processes involve protein–protein interactions (PPIs) mediated by a Short Linear Motif (SLiM) in one protein interacting with a globular domain in another. Despite their significance, these domain-motif interactions (DMIs) are typically low affinity, which makes them challenging to identify by classical experimental approaches, such as affinity pulldown mass spectrometry (AP-MS) and yeast two-hybrid (Y2H). DMIs are generally underrepresented in PPI networks as a result. A number of computational methods now exist to predict SLiMs and/or DMIs from experimental interaction data but it is yet to be established how effective different PPI detection methods are for capturing these low affinity SLiM-mediated interactions. Here, we introduce a new computational pipeline (SLiM-Enrich) to assess how well a given source of PPI data captures DMIs and thus, by inference, how useful that data should be for SLiM discovery. SLiM-Enrich interrogates a PPI network for pairs of interacting proteins in which the first protein is known or predicted to interact with the second protein via a DMI. Permutation tests compare the number of known/predicted DMIs to the expected distribution if the two sets of proteins are randomly associated. This provides an estimate of DMI enrichment within the data and the false positive rate for individual DMIs. As a case study, we detect significant DMI enrichment in a high-throughput Y2H human PPI study. SLiM-Enrich analysis supports Y2H data as a source of DMIs and highlights the high false positive rates associated with naïve DMI prediction. SLiM-Enrich is available as an R Shiny app. The code is open source and available via a GNU GPL v3 license at: https://github.com/slimsuite/SLiMEnrich . A web server is available at: http://shiny.slimsuite.unsw.edu.au/SLiMEnrich/ .
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ISSN:2167-8359
2167-8359
DOI:10.7717/peerj.5858