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

Sobia Idrees,Åsa Pérez-Bercoff,Richard J Edwards

doi: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 (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/.

Highlights

Proteins interact with their partners through two main classes of functional modules: globular domains and Short Linear Motifs (SLiMs) (Bhattacharyya et al, 2006)
SLiMEnrich analysis revealed the case study Y2H data to be enriched for domain-motif interactions (DMIs) under all DMI prediction strategies (Fig. 5, Table 1)
There are many data- and method-specific factors that will determine whether protein– protein interaction (PPI) data are useful for short linear motif (SLiM) prediction

Summary

Introduction

Proteins interact with their partners through two main classes of functional modules: globular domains and Short Linear Motifs (SLiMs) (Bhattacharyya et al, 2006). SLiMs are short protein regions (typically 3–10 amino acids long) with a small number of key residues that mediate domain-motif interactions (DMIs) with the globular domain of a protein–protein interaction (PPI) partner (Davey et al, 2012). These DMIs underpin critical cellular functions, including cell cycle regulation, cell compartment targeting, post-translational modification, protein degradation, and signal transduction. This was used as evidence that many more SLiMs and DMI are yet to be discovered, and raises concerns that these methods are depleted for DMIs

Methods

Results

Discussion

Conclusion