Interactome-wide prediction of short, disordered protein interaction motifs in humans (original) (raw)

* Corresponding authors

a Centre for Biological Sciences, University of Southampton, UK
E-mail: r.edwards@southampton.ac.uk
Fax: + 44 23 8059 4459
Tel: + 44 2380 594344

b Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany

c UCD Complex and Adaptive Systems Laboratory & UCD Conway Institute of Biomolecular and Biomedical Sciences, University College Dublin, Ireland

Abstract

Many of the specific functions of intrinsically disordered protein segments are mediated by Short Linear Motifs (SLiMs) interacting with other proteins. Well known examples include SLiMs that interact with 14-3-3, PDZ, SH2, SH3, and WW domains but the true extent and diversity of SLiM-mediated interactions is largely unknown. Here, we attempt to expand our knowledge of human SLiMs by applying _in silico_SLiM prediction to the human interactome. Combining data from seven different interaction databases, we analysed approximately 6000 protein-centred and 1600 domain-centred human interaction datasets of 3+ unrelated proteins that interact with a common partner. Results were placed in context through comparison to randomised datasets of similar size and composition. The search returned thousands of evolutionarily conserved, intrinsically disordered occurrences of hundreds of significantly enriched recurring motifs, including many that have never been previously identified (http://bioware.soton.ac.uk/slimdb/). In addition to True Positive results for at least 25 different known SLiMs, a striking number of “off-target” proteins/domains also returned significantly enriched known motifs. Often, this was due to the non-independence of the datasets, with many proteins sharing interaction partners or contributing interactions to multiple domain datasets. The majority of these motif classes, however, were also found to be significantly enriched in one or more randomised datasets. This highlights the need for care when interpreting motif predictions of this nature but also raises the possibility that SLiM occurrences may be successfully identified independently of interaction data. Although not as compositionally biased as previous studies, patterns matching known SLiMs tended to cluster into a few large groups of similar sequence, while novel predictions tended to be more distinctive and less abundant. Whether this is due to ascertainment bias or a true functional composition bias of SLiMs is not clear and warrants further investigation.

Graphical abstract: Interactome-wide prediction of short, disordered protein interaction motifs in humans

This article is part of the themed collection:Intrinsically Disordered Proteins

You have access to this article

Please wait while we load your content... Something went wrong. Try again?

Supplementary files

Article information

DOI

https://doi.org/10.1039/C1MB05212H

Article type

Paper

Submitted

31 May 2011

Accepted

08 Aug 2011

First published

30 Aug 2011

Download Citation

Mol. BioSyst., 2012,8, 282-295

Permissions