The protein translation machinery is expressed for maximal efficiency in Escherichia coli

Abstract

Protein synthesis is the most expensive process in fast-growing bacteria. Experimentally observed growth rate dependencies of the translation machinery form the basis of powerful phenomenological growth laws; however, a quantitative theory on the basis of biochemical and biophysical constraints is lacking. Here, we show that the growth rate-dependence of the concentrations of ribosomes, tRNAs, mRNA, and elongation factors observed in Escherichia coli can be predicted accurately from a minimization of cellular costs in a mechanistic model of protein translation. The model is constrained only by the physicochemical properties of the molecules and has no adjustable parameters. The costs of individual components (made of protein and RNA parts) can be approximated through molecular masses, which correlate strongly with alternative cost measures such as the molecules’ carbon content or the requirement of energy or enzymes for their biosynthesis. Analogous cost minimization approaches may facilitate similar quantitative insights also for other cellular subsystems.

First-principles model of optimal translation factors stoichiometry

“…A potential explanation is that the corresponding reactions may not be binding or diffusion-limited, which would lead to a non-negligible fraction of tlFs sequestered at the catalytic step and thereby require higher total concentrations. Indeed, recent detailed modeling of the EF-Ts (Hu et al, 2020) cycle estimated only a small fraction (6 to 48%) of its abundance was in the free form in the cell, consistent with the large deviation we observe for this factor from our diffusion only prediction. Our optimization model can also be solved analytically in the non-binding-limited regime (Table 2), with the finite catalytic rate leading to an additional contribution of the form ∝ * ∕ .…”

“…Previous models have focused on expression optimization for the full translation sector, ribosomes (Scott et al, 2010(Scott et al, , 2014Belliveau et al, 2021), and the abundant elongation factors EF-Tu (Ehrenberg and Kurland, 1984;Klumpp et al, 2013). In a recent study, Hu and colleagues considered additional RNA components and EF-Ts in their optimization procedure (Hu et al, 2020). In line with the conclusions of these previous studies, our results demonstrate that multiple components of the translation machinery, regardless of their observed expression level, are simultaneously co-limiting for cell growth.…”

“…Remarkably, estimates of this ratio in the cell suggest that this is barely the case (between 6-7 tRNAs/ribosome at fast growth (Dong et al, 1996)). Although our model treats the total tRNA abundance as a measured parameter and omits its selective pressure (see (Hu et al, 2020) which includes RNA mass in their optimization procedure), the abundance of three core components of the tRNA cycle appear to be at the special point where the transition line plateau, that is set by total tRNA abundance, just crosses the EF-Tu-only optimum (blue line in Figure 3B). At this point, all three components are co-limiting.…”

“…Early studies predicted expression of the highly expressed elongation factor Tu (EF-Tu) relative to the ribosome (Klumpp et al, 2013;Ehrenberg and Kurland, 1984) by maximizing translational flux per unit proteome. More recently, expression of several other components involved in the elongation step (ribosomes, tRNA, mRNA, EF-Tu, and EF-Ts) was predicted by minimizing the total mass of the components at a fixed translational flux (Hu et al, 2020). The selective pressure on expression levels remains to be determined for most members of the translation machinery, including initiation and termination factors that are much more lowly expressed and often assumed to be non-limiting.…”

“…Here we sought to derive an intuitive model to understand the quantitative abundance hierarchy (Figure 1B) among the core translation factors (tlFs), which have well-characterized functions (Table 1, schematic in Figure 1A). Our goal is not to exhaustively model the heterogeneous movement of ribosomes on the transcriptome (Shaw et al, 2003;Reuveni et al, 2011;Subramaniam et al, 2014;Dykeman, 2020) or to include as many details of the underlying molecular steps as possible (Hu et al, 2020;Vieira et al, 2016). Instead, we coarse-grained global translation into a cycle that consists of sequential steps with interconnected fluxes that depend on core tlFs concentrations.…”