The Key Parameters that Govern Translation Efficiency

Abstract

Translation of mRNA into protein is a fundamental yet complex biological process with multiple factors that can potentially affect its efficiency. Here, we study a stochastic model describing the traffic flow of ribosomes along the mRNA (namely, the inhomogeneous -TASEP), and identify the key parameters that govern the overall rate of protein synthesis, sensitivity to initiation rate changes, and efficiency of ribosome usage. By analyzing a continuum limit of the model, we obtain closed-form expressions for stationary currents and ribosomal densities, which agree well with Monte Carlo simulations. Furthermore, we completely characterize the phase transitions in the system, and by applying our theoretical results, we formulate design principles that detail how to tune the key parameters we identified to optimize translation efficiency. Using ribosome profiling data from S. cerevisiae, we shows that its translation system is generally consistent with these principles. Our theoretical results have implications for evolutionary biology, as well as synthetic biology.

Translation regulation of Japanese encephalitis virus revealed by ribosome profiling

“…Translational activity of the uORF was validated using reporter constructs (Fig.4B). While the functional significance of the putative peptide remains unknown, a close overlap of uORF stop codon (100 th nt) with main ORF's start codon (96 th nt, Fig.4C) could directly impact ribosome loading and usage rates-key parameters in regulating protein synthesis [50]. Although a canonical structure of 5 UTR is shared amongst flaviviruses [51], this strategy of translation initiation might suggest the enhanced translational efficiency of JEV and ZIKV [13] over DENV [9].…”

What determines eukaryotic translation elongation: recent molecular and quantitative analyses of protein synthesis

“…Such models have been extensively used in the interpretation and analysis of ribo-seq data, especially to infer initiation and elongation rates [7,11,56,[119][120][121][122][123] and to quantify ribosome queueing [68]. In addition, the study of these theoretical models has shed light on the main features of the codon sequence determining protein synthesis rate [10]. They have been applied to ribo-seq data mainly in yeast (S. Cerevisiae), more rarely in mammals [56,123].…”

“…The simplest approximation assumes that the density of ribosomes is sufficiently low that ribosome collisions can be neglected, significantly reducing the complexity of the model. The approximation is often used in the interpretation of ribo-seq data, since there is evidence that translation mostly happens in a low-density regime, for example in wild-type yeast [ 10 ], though it is still debated how important collisions are in different systems under different conditions [ 69 , 71 , 132 ]. When it is valid, the density of reads at each codon can be approximated by the flux (gene specific, equivalent to the protein synthesis rate per mRNA) divided by the elongation rate (specific to the codon being translated and to the codon context) (for a detailed explanation, see the supplementary material of [ 119 ]).…”

“…In combination with theoretical modelling and simulations, ribo-seq data have shown that initiation is the major rate-limiting step of translation in yeast, under normal conditions [ 10 , 11 ]. Under cellular stress or disease, global translation is repressed by shutting down cap-dependent initiation, while selective translation is achieved by cap-independent initiation [ 12 ].…”

The fitness landscapes of translation

“…In line with the experimental observations [7], we assume that the effect of the synonymous mutations on organismal fitness is mediated by the efficiency of protein translation. The process of translation can be described by stochastic particle models of exclusion type, which have been used in the field for more than 50 years [10,11,12,13,14,15]. These models treat ribosomes as particles moving unidirectionally along a one-dimensional lattice of sites representing the codons.…”