Maintaining protein localization, structure, and functional interactions via codon usage and coevolution of gene expression: Combining evolutionary bioinformatics with omics-scale data to test hypotheses related to protein function

A major challenge of the omics-era is identifying how a protein functions, both in terms of its specific function and within the context of the various biological processes necessary for the cell's survival. Key elements necessary for a protein to perform its function are efficient and accurate protein localization, protein folding, and interactions with other proteins. Previous work implicated codon usage as a means to modulate protein localization and folding. Using a mechanistic model rooted in population genetics, I examine potential selective differences in codon usage in signal peptides (localization) and protein secondary structures. Although previous work argued signal peptides were under selection for increased translation inefficiency, I find selection is generally consistent with the 5'-regions of non-secreted proteins. I also find that previous work was likely confounded by biases in signal peptide amino acid usage and gene expression. Although the direction of selection on codon usage is mostly consistent between protein secondary structures, the strength of this selection does vary for certain codons. After successful folding and localization of a protein, it must be able to function within the context of other proteins in the cell, often through protein-protein interactions of metabolic pathways. Previous work suggests proteins which are part of the same functional processes within a cell are co-expressed across time and environmental conditions. Using the concept of guilt-by-association, I combine empirical protein abundances (measured via mass spectrometry) with sequence homology based function prediction tools to identify potential functions of proteins of unknown function in \textit{C. thermocellum}. Building upon the concept that functionally-related genes are co-expressed within a species, I demonstrate how phylogenetic comparative methods can be used to detect signals of gene expression coevolution across species while accounting for the shared ancestry of the species in question.