Network evolution: rewiring and signatures of conservation in signaling

• Published in: PLoS computational biology Vol. 8; no. 3; p. e1002411
• Main Authors: Sun, Mark G F, Sikora, Martin, Costanzo, Michael, Boone, Charles, Kim, Philip M
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.03.2012; Public Library of Science (PLoS)
• Subjects: Biology; Comparative analysis; Computational biology; Computer Simulation; Conserved Sequence - genetics; Evolution, Molecular; Fungal Proteins - genetics; Fungi - genetics; Genetics; Homology (Biology); Kinases; Models, Genetic; Peptides; Phosphotransferases; Phylogenetics; Physiological aspects; Protein binding; Proteins; Signal transduction; Signal Transduction - genetics; src Homology Domains - genetics; Studies; Yeast; Canada; Fungi; Signal Transduction; Computer Simulation; Conserved Sequence; Fungal Proteins; Models, Genetic; Evolution, Molecular; src Homology Domains
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1002411

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.