Genetic code translation displays a linear trade-off between efficiency and accuracy of tRNA selection

Abstract

Rapid and accurate translation of the genetic code into protein is fundamental to life. Yet due to lack of a suitable assay, little is known about the accuracy-determining parameters and their correlation with translational speed. Here, we develop such an assay, based on Mg(2+) concentration changes, to determine maximal accuracy limits for a complete set of single-mismatch codon-anticodon interactions. We found a simple, linear trade-off between efficiency of cognate codon reading and accuracy of tRNA selection. The maximal accuracy was highest for the second codon position and lowest for the third. The results rationalize the existence of proofreading in code reading and have implications for the understanding of tRNA modifications, as well as of translation error-modulating ribosomal mutations and antibiotics. Finally, the results bridge the gap between in vivo and in vitro translation and allow us to calibrate our test tube conditions to represent the environment inside the living cell.

Fig. 1.

Schematic representation of tRNA selection on the ribosome. The end point of initial selection of tRNA in ternary complex is hydrolysis of the EF-Tu-bound GTP. After hydrolysis of GTP, tRNA can be discarded in the proofreading step.

Fig. 2.

The efficiency-accuracy trade-off in initial selection was evaluated for reading all possible single-mismatch codons, compared to the fully matched AAA codon.

Fig. 3.

Efficiency of GTP hydrolysis increases with increasing Mg²⁺ concentration. (A and B) Time evolution of the [³H]GDP level after mixing of with ribosomes programmed with noncognate GAA (green squares) or cognate AAA (black circles) codon with no (A) or 6 mM (B) extra Mg²⁺ addition (see also Fig. S1 for a complete set of mismatch experiments at 2 mM extra Mg²⁺). The initial [³H]GDP increase is determined by the rate of hydrolysis of [³H]GTP in ternary complex. The decrease is due to spontaneous exchange of [³H]GDP for unlabeled GTP on EF-Tu after GTP hydrolysis, and rapid regeneration of [³H]GTP. The slow initial increase of [³H]GDP in noncognate cases (green squares), is faster in high (B) than in low (A) Mg²⁺ concentration, and much slower than in the cognate cases (black line). The noncognate and cognate curves in A and B were obtained in parallel experiments and jointly fitted for precise estimates of the noncognate k_cat/K_m values for GTP hydrolysis. (Insets) [³H]GDP level for the cognate reaction as measured in quench-flow to obtain the cognate k_cat/K_m values for GTP hydrolysis. (C) Efficiency of GTP hydrolysis, k_cat/K_m, for noncognate reading of GAA codon (green squares) and cognate reading of AAA codon (black circles) at varying Mg²⁺ concentration. Data represent weighted averages from at least two experiments ± propagated standard deviation.

Fig. 4.

The rate-accuracy trade-off in initial selection. (A) Rate-accuracy trade-off lines in plots of the cognate k_cat/K_m (AAA codon) versus the accuracy (calculated as the ratio of cognate k_cat/K_m over noncognate k_cat/K_m; Table 1) on all single-mismatch codons. The maximal accuracy, d, is in each misreading case determined by the intercept of the line with the x axis. Data represent weighted averages from at least two experiments ± propagated standard deviation in both dimensions. (B) The inverted noncognate k_cat/K_m versus the inverted cognate k_cat/K_m at different Mg²⁺ concentrations (calculated from data in Table 1). The slopes of the straight lines give the maximal accuracy, d, for all nine single-mismatch codons (Table 2). Symbols as in A. Data represent weighted averages from at least two experiments ± propagated standard deviation in both dimensions. (C) d values as estimated from data and linear fits shown in B.

Fig. 5.

The rate-accuracy trade-off in overall selection. (A) Rate of noncognate f[³H]Met-Lys dipeptide formation for GAA reading at increasing concentration of ternary complex (). The curves represent fitting of the data to a single exponential equation (see SI Text). (B) Mg²⁺ concentration dependence on noncognate dipeptide formation rates at titration of . The curves represent fitting of the data to the Michaelis–Menten equation (see SI Text). Data represent weighted averages from at least two experiments, such as shown in A, ± propagated standard deviation. (C) Rate-accuracy trade-off plots in log–log scale for initial selection (Δ) and overall accuracy (▴) for Lys-tRNA^Lys selection of the AAA in relation to the GAA codon. The initial selection (Δ) is the same as plotted in linear scale in Fig. 4 A and B. Data represent weighted averages from at least two experiments ± propagated standard deviation in both dimensions.