REPRINT: Detecting linear motifs in interaction networks

(this is an older post, reprinted so that it'll appear in today's feed: I'll explain why in my next new post)

There's an interesting paper in November's PLoS Biology by Neduva et al., about finding short linear motifs using protein interaction networks.

Many aspects of cell signalling, trafficking, and targeting are governed by interactions between globular protein domains and short peptide segments. These domains often bind multiple peptides that share a common sequence pattern, or “linear motif” (e.g., SH3 binding to PxxP). Many domains are known, though comparatively few linear motifs have been discovered. Their short length (three to eight residues), and the fact that they often reside in disordered regions in proteins makes them difficult to detect through sequence comparison or experiment.

The idea is that for each protein in an interaction network you take its interactors, remove the parts of each that are unlikely to contain linear motifs (like globular domains, coiled coils and signal peptides) and then search the remaining peptide sequences for overrepresented motifs, compared to a control set of 15,000 proteins selected at random from SWISSPROT. The motifs are then ranked according to their p-value, which represents how unlikely the motif is to be so frequently observed in so few proteins.

Three of the previously uncharacterized linear motifs they found in drosophila and yeast were tested in the lab, confirming two of them (doesn't seem like a set big enough to draw any conclusions from, but this is essentially an in-silico paper, after all).

The authors also used the same approach on sets of interacting proteins from the Eukaryotic Linear Motif database and found that often the curated linear motif from ELM was the same as the top ranking motif in their results.

While there isn't anything particularly exciting about the methodology here it's interesting to see protein interaction networks being used for something other than protein classification or hand waving (about network architecture, evolutionary pressures, etc.)

I'm also surprised that nobody has done anything similar up until now. I remember a paper about globular domains being used to predict new protein interactors, but nothing the other way round...