The architecture of eukaryotic translation

Abstract

Translation in baker's yeast involves the coordinated interaction of 200,000 ribosomes, 3,000,000 tRNAs and between 15,000 and 60,000 mRNAs. It is currently unknown whether this specific constellation of components has particular relevance for the requirements of the yeast proteome, or whether this is simply a frozen accident. Our study uses a computational simulation model of the genome-wide translational apparatus of yeast to explore quantitatively which combinations of mRNAs, ribosomes and tRNAs can produce viable proteomes. Surprisingly, we find that if we only consider total translational activity over time without regard to composition of the proteome, then there are many and widely differing combinations that can generate equivalent synthesis yields. In contrast, translational activity required for generating specific proteomes can only be achieved within a much more constrained parameter space. Furthermore, we find that strongly ribosome limited regimes are optimal for cells in that they are resource efficient and simplify the dynamics of the system.

Figure 1.

The amount of protein produced per second (a) and the number of free ribosomes at steady state (b) as a function of the initiation rate. To improve readability we use log-scale on the horizontal axis. The vertical line indicates the standard parameters with an initiation rate factor of 0.025.

Figure 2.

Comparing the amount of protein expressed as a function of tRNA amount. The solid line indicates the total amount of protein. The thinner line indicates the expression of one of the fastest and one of the slowest ORFs in our simulations.

Figure 3.

The distribution of the translation time excess ratio (see main text for an explanation). It quantifies the impact of traffic jams on the average reading times of codons. A value of 1 means that there is no significant impact of traffic jams on the average reading times of individual codons. Values smaller than 1 are possible because of stochastic fluctuations. We vary the number of ribosomes (left) and the size of the transcriptome (right). Doubling either the number of ribosomes or the number of mRNAs is sufficient to effectively remove the impact of traffic jams.

Figure 4.

The translation rate as a function of the total number of ribosomes. The graph shows both the relative translation rate and the differential/slope (i.e. ).

Figure 5.

The total translation rate as a function of the total amount of mRNA. The plot shows an initiation rate of 10. As a comparison we show the total protein production for 400 000, 200 000 and 100 000 ribosomes at otherwise standard parameters with an initiation rate of 10. The data of these lines corresponds to the relevant points in Figure 1 (left). The arrow indicates the number of translated proteins that are achieved by doubling the mRNA number from the standard parameters. The line labelled ‘Equation (1)’ indicates the capacity translation rate for a given number of mRNAs, as predicted by Equation (1). Going beyond standard parameters, the system operates far below capacity. Note that Equation (1) is no longer valid at very low mRNA numbers because many low copy mRNA molecules will not be available there and hence the transcriptome will be different.