Wednesday, 31 October 2018

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


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: A web server is available at:

Ziying Zhang (Visiting Masters student)

Ziying Zhang is a visiting Masters student from Wageningen University in the Netherlands. She got a bachelor degree with the background of bioscience at Hainan University, China in 2016. She started her MSc in bioinformatics at Wageningen University in February 2017. The project for Ziying’s Masters thesis focused on developing a downstream analysis toolbox for microbial diversity analysis, executing SPARQL to query RDF datasets of 16s rRNA before launching an analysis to simplify the pre-processing procedures.

Ziying started an internship at the Edwards Lab at the end of October 2018, working on genome size prediction from kmer profiles. Her interests include genomics, genetics, bioinformatics, programming and data management.