Abstract
The analysis of network evolution has been hampered by limited availability of protein interaction data for different organisms. In this study, we investigate evolutionary mechanisms in Src Homology 3 (SH3) domain and kinase interaction networks using high-resolution specificity profiles. We constructed and examined networks for 23 fungal species ranging from Saccharomyces cerevisiae to Schizosaccharomyces pombe. We quantify rates of different rewiring mechanisms and show that interaction change through binding site evolution is faster than through gene gain or loss. We found that SH3 interactions evolve swiftly, at rates similar to those found in phosphoregulation evolution. Importantly, we show that interaction changes are sufficiently rapid to exhibit saturation phenomena at the observed timescales. Finally, focusing on the SH3 interaction network, we observe extensive clustering of binding sites on target proteins by SH3 domains and a strong correlation between the number of domains that bind a target protein (target in-degree) and interaction conservation. The relationship between in-degree and interaction conservation is driven by two different effects, namely the number of clusters that correspond to interaction interfaces and the number of domains that bind to each cluster leads to sequence specific conservation, which in turn results in interaction conservation. In summary, we uncover several network evolution mechanisms likely to generalize across peptide recognition modules.
Highlights
Peptide recognition modules (PRMs) and kinase domains bind short linear peptide motifs on their protein binding partners and are integral members of many signaling pathways [1,2,3]
We attempt to overcome this limitation by computationally constructing protein interaction networks for 23 relatively tightly spaced yeast species
Our analysis reveals that interaction changes are very fast – fast enough that the number of changes saturates, i.e., the actual rate of change has been strongly underestimated in previous studies
Summary
Peptide recognition modules (PRMs) and kinase domains bind short linear peptide motifs on their protein binding partners and are integral members of many signaling pathways [1,2,3]. A global comparative analysis on network rewiring from existing experimental datasets has suggested that regulatory networks are among the fastest evolving biological networks [21] These comparative studies are hampered by two problems: The analyzed networks are often incomplete and the species examined are highly diverged. An obvious problem is that interactions in species similar to the model organisms (such as yeast or worm) are usually inferred by means of orthology mapping, which prohibits any kind of evolutionary analysis based on them [22,23]. Unlike these mapping methods, predicting interactions via PWMs enables
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