Kinetics of the interactions between yeast elongation factors 1A and 1Bα, guanine nucleotides, and aminoacyl-tRNA.

The interactions of elongation factor 1A (eEF1A) from Saccharomyces cerevisiae with elongation factor 1Bα (eEF1Bα), guanine nucleotides, and aminoacyl-tRNA were studied kinetically by fluorescence stopped-flow. eEF1A has similar affinities for GDP and GTP, 0.4 and 1.1 μM, respectively. Dissociation of nucleotides from eEF1A in the absence of the guanine nucleotide exchange factor is slow (about 0.1 s(−1)) and is accelerated by eEF1Bα by 320-fold and 250-fold for GDP and GTP, respectively. The rate constant of eEF1Bα binding to eEF1A (10(7)–10(8) M(−1) s(−1)) is independent of guanine nucleotides. At the concentrations of nucleotides and factors prevailing in the cell, the overall exchange rate is expected to be in the range of 6 s(−1), which is compatible with the rate of protein synthesis in the cell. eEF1A·GTP binds Phe-tRNA(Phe) with a K(d) of 3 nM, whereas eEF1A·GDP shows no significant binding, indicating that eEF1A has similar tRNA binding properties as its prokaryotic homolog, EF-Tu

Colicin E3 cleavage of 16S rRNA impairs decoding and accelerates tRNA translocation on Escherichia coli ribosomes

The cytotoxin colicin E3 targets the 30S subunit of bacterial ribosomes and specifically cleaves 16S rRNA at the decoding centre, thereby inhibiting translation. Although the cleavage site is well known, it is not clear which step of translation is inhibited. We studied the effects of colicin E3 cleavage on ribosome functions by analysing individual steps of protein synthesis. We find that the cleavage affects predominantly the elongation step. The inhibitory effect of colicin E3 cleavage originates from the accumulation of sequential impaired decoding events, each of which results in low occupancy of the A site and, consequently, decreasing yield of elongating peptide. The accumulation leads to an almost complete halt of translation after reading of a few codons. The cleavage of 16S rRNA does not impair monitoring of codon–anticodon complexes or GTPase activation during elongation-factor Tu-dependent binding of aminoacyl-tRNA, but decreases the stability of the codon–recognition complex and slows down aminoacyl-tRNA accommodation in the A site. The tRNA–mRNA translocation is faster on colicin E3-cleaved than on intact ribosomes and is less sensitive to inhibition by the antibiotic viomycin

Charting the dynamics of translation

Codon usage bias (CUB) is the well-known phenomenon that the frequency of synonymous codons is unequal. This is presumably the result of adaptive pressures favouring some codons over others. The underlying reason for this pressure is unknown, although a large number of possible driver mechanisms have been proposed. According to one hypothesis, the decoding time could be such a driver. A tacit assumption of this hypothesis is that faster codons lead to a higher translation rate which in turn is more resource efficient. While it is generally assumed that there is such a link, there are no rigorous studies to establish under which conditions the link between translation speed and rate actually exists. Using a computational simulation model and explicitly calculated codon decoding times, this contribution maps the entire range of dynamical regimes of translation. These simulations make it possible to understand precisely under which conditions translation speed and rate are linked

Unique Cost Dynamics Elucidate the Role of Frameshifting Errors in Promoting Translational Robustness

There is now considerable evidence supporting the view that codon usage is frequently under selection for translational accuracy. There are, however, multiple forms of inaccuracy (missense, premature termination, and frameshifting errors) and pinpointing a particular error process behind apparently adaptive mRNA anatomy is rarely straightforward. Understanding differences in the fitness costs associated with different types of translational error can help us devise critical tests that can implicate one error process to the exclusion of others. To this end, we present a model that captures distinct features of frameshifting cost and apply this to 641 prokaryotic genomes. We demonstrate that, although it is commonly assumed that the ribosome encounters an off-frame stop codon soon after the frameshift and costs of mis-elongation are therefore limited, genomes with high GC content typically incur much larger per-error costs. We go on to derive the prediction, unique to frameshifting errors, that differences in translational robustness between the 5′ and 3′ ends of genes should be less pronounced in genomes with higher GC content. This prediction we show to be correct. Surprisingly, this does not mean that GC-rich organisms necessarily carry a greater fitness burden as a consequence of accidental frameshifting. Indeed, increased per-error costs are often more than counterbalanced by lower predicted error rates owing to more diverse anticodon repertoires in GC-rich genomes. We therefore propose that selection on tRNA repertoires may operate to reduce frameshifting errors