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Abstract
An important challenge of system biology is to understand how proteins interact with each other to dynamically orchestrate biological functions. This thesis focused on understanding the dynamics of protein interaction networks in the yeast Saccharomyces cerevisiae; this model organism has the most comprehensive set of genomics, proteomics, and protein-protein interaction datasets publically available for bioinformatic and systems biology analyses. I investigated several different aspects of dynamics in protein interaction networks. Firstly, I asked whether protein complexes are made of core, module and attachment proteins. Analysis suggested core proteins were most likely to be mediated by stable domain-domain interactions, followed by module and attachment proteins. Furthermore, we proposed that some protein complexes are likely to be tightly regulated, by only expressing core proteins ‘just-in-time’ to activate the complex when it is needed.
Secondly, we asked whether high-throughput protein-protein interaction data could be used to provide clues on the architecture of protein complexes. Pairwise interaction data was shown to help in defining complex membership, while cores and modules of protein complexes could help determine the spatial proximity of proteins. Predicted domain-domain interactions could explain some interactions within protein complexes, but false positives complicated the analysis.
Thirdly, I showed that post-translational modifications involved in protein-protein interactions are likely to be on the surface of proteins, while artifactual modifications are not preferentially found in coils and helices. Parts of protein structures that mediate transient interactions tend to be intrinsically disordered, and can contain interaction motifs and post-translational modifications that could be recognized and bound by domains.
Fourthly, using peptide mass fingerprinting data, I have found 83 arginine and lysine methylation sites in 66 proteins. Evidence from this dataset suggests lysine methylation could block the action of ubiquitin ligase. Fifthly, we asked the question whether overexpression of certain proteins could affect the dynamics of interaction. Proteins deleterious upon overexpression tend to be low in abundance, high in intrinsic disorder, and have a high number of interaction partners. Finally, the investigations above are discussed to show how the sequence-based effects, the abundance-based effects, and conditional binding effects influence the dynamics of protein interaction networks.