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

Idrees S, Pérez-Bercoff Å & Edwards RJ. (2018) SLiMEnrich: computational assessment of protein–protein interaction data as a source of domain-motif interactions. PeerJ 6:e5858 https://doi.org/10.7717/peerj.5858

Abstract

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 (SLiMEnrich) 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. SLiMEnrich 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. SLiMEnrich analysis supports Y2H data as a source of DMIs and highlights the high false positive rates associated with naïve DMI prediction. SLiMEnrich 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/.

The SLiMEnrich Shiny App is now live

Sobia’s first Shiny App is now up and running for final pre-publication testing on our new EdwardsLab RShiny server. Please feel free to try it out. Any comments and suggestions can be posted here, by email, or via the GitHub issues page.

The SLiMEnrich App allows users to predict Domain-Motif Interactions (DMIs) from Protein-Protein Interaction data (PPI) and to estimate the background distribution of expected DMI by chance through a randomisation approach. The walkthrough has more details.

SLiMEnrich can be used to:

Estimate the enrichment of SLiM-mediated DMI in a given PPI dataset.

Generate predictions for DMI-mediated from a PPI dataset. Predictions are based on known SLiM-Domain interactions.

Estimate the False Positive Rate of those DMI predictions.

With a bit of imagination, SLiMEnrich can be adapted to generate and assess predictions for other kinds of interactions. See docs for details.

SLiMEnrich is available via the SLiMEnrich RShiny webserver and can be downloaded for local running from the SLiMEnrich GitHub repo.