DoReMi: Context-Based Prioritization of Linear Motif Matches

PeerJ. 2014 Mar 20;2:e315. doi: 10.7717/peerj.315. eCollection 2014.

Abstract

Many protein domains bind to short peptide sequences, called linear motifs. Data on their sequence specificities is sparse, which is why biologists usually resort to basic pattern searches to identify new putative binding sites for experimental follow-up. Most motifs have poor specificity and prioritization of the matches is thus crucial when scanning a full proteome with a pattern. Here we present a generic method to prioritize motif occurrence predictions by using cellular contextual information. We take 2 parameters as input: the motif occurrences and one or more of the interacting domains. The potential hits are ranked based on how strongly the context network associates them with a protein containing one of the specified domains, which leads to an increased predictive performance. The method is available through a web interface at doremi.jensenlab.org, which allows for an easy application of the method. We show that this approach leads to improved predictions of binding partners for PDZ domains and the SUMO binding domain. This is consistent with the earlier observation that coupling sequence motifs with network information improves kinase-specific substrate predictions.

Keywords: Linear motifs; Prediction method; Protein interaction network; Web server.

Grant support

This work was in part funded by the Novo Nordisk Foundation Center for Protein Research. NJH is funded by Science Foundation Ireland (Grant 08/IN.1/B1864). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.