Most amino acids are encoded by multiple synonymous codons. For an amino acid, some of its synonymous codons are used much more rarely than others. Analyses of positions of such rare codons in protein sequences revealed that rare codons can impact co-translational protein folding and that positions of some rare codons are evolutionary conserved. Analyses of positions of rare codons in proteins' 3-dimensional structures, which are richer in biochemical information than sequences alone, might further explain the role of rare codons in protein folding. We analyze a protein set recently annotated with codon usage information, considering non-redundant proteins with sufficient structural information. We model the proteins' structures as networks and study potential differences between network positions of amino acids encoded by evolutionary conserved rare, evolutionary non-conserved rare, and commonly used codons. In 84 of the proteins, at least one of the three codon categories occupies significantly more or less network-central positions than the other codon categories. Different protein groups showing different codon centrality trends (i.e., different types of relationships between network positions of the three codon categories) are enriched in different biological functions, implying the existence of a link between codon usage, protein folding, and protein function.
The genetic code is redundant, meaning that most amino acids are encoded by more than one codon. Codons that code for the same amino acid are called synonymous codons. For an amino acid, it is usually the case that some of its synonymous codons encode it in the given genome relatively more commonly than the others @cite_22 @cite_1 . Henceforth, intuitively, when we say common'' codon, we mean a synonymous codon that is used frequently, and when we say rare'' codon, we mean a synonymous codon that is used infrequently. Precise definitions depend on which computational model is used to characterize synonymous codons as common or rare. Several such models exist @cite_24 .
@cite_24 @cite_1 @cite_22
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The unequal utilization of synonymous codons affects numerous cellular processes including translation rates, protein folding and mRNA degradation. In order to understand the biological impact of variable codon usage bias (CUB) between genes and genomes, it is crucial to be able to accurately measure CUB for a given sequence. A large number of metrics have been developed for this purpose, but there is currently no way of systematically testing the accuracy of individual metrics or knowing whether metrics provide consistent results. This lack of standardization can result in false-positive and false-negative findings if underpowered or inaccurate metrics are applied as tools for discovery. Here, we show that the choice of CUB metric impacts both the significance and measured effect sizes in numerous empirical datasets, raising questions about the generality of findings in published research. To bring about standardization, we developed a novel method to create synthetic protein-coding DNA sequences according to different models of codon usage. We use these benchmark sequences to identify the most accurate and robust metrics with regard to sequence length, GC content and amino acid heterogeneity. Finally, we show how our benchmark can aid the development of new metrics by providing feedback on its performance compared to the state of the art. Observed patterns of synonymous codon usage are explained in terms of the joint effects of mutation, selection, and random drift. Examination of the codon usage in 165Escherichia coli genes reveals a consistent trend of increasing bias with increasing gene expression level. Selection on codon usage appears to be unidirectional, so that the pattern seen in lowly expressed genes is best explained in terms of an absence of strong selection. A measure of directional synonymous-codon usage bias, the Codon Adaptation Index, has been developed. In enterobacteria, rates of synonymous substitution are seen to vary greatly among genes, and genes with a high codon bias evolve more slowly. A theoretical study shows that the patterns of extreme codon bias observed for someE. coli (and yeast) genes can be generated by rather small selective differences. The relative plausibilities of various theoretical models for explaining nonrandom codon usage are discussed.