The key to design in manufacturing is optimization – hitting the “sweet spot” between competing interests. It’s not always possible to have all the elements of a product be ideal. A laptop computer, for instance, can’t have an extra-large monitor and simultaneously have long battery life and compact design. A muscle car cannot be expected to have the best gas mileage. In the heyday of “faster, better, cheaper” spacecraft, engineers often joked, “pick any two.” In the same way, living cells have to optimize their operations. A couple of recent papers explore how they find that sweet spot.
Efficiency vs accuracy: A paper in PNAS reported, “Genetic code translation displays a linear trade-off between efficiency and accuracy of tRNA selection.”1 Johansson, Zhang and Ehrenberg 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.” At the start of the paper, they acknowledged the competing forces: “Translation of the ancient and universal genetic code into protein on ribosomes requires precise mRNA decoding by aminoacyl-tRNAs (aa-tRNAs) and rapid formation of nascent peptide chains.” A cell needs speed and accuracy. How does it find the sweet spot between them, given that there are limitations of space and time? In the transfer-RNA (tRNA) case, the matching (anticodon) end has to find the right (cognate) codon end within time constraints. The authors state, “Codon reading by aa-tRNAs ultimately relies on the specificity of cognate in relation to noncognate codon–anticodon interactions, but two ribosome-dependent specificity enhancements greatly improve mRNA decoding.”
That’s right; there are two downstream proofreading mechanisms to make sure the matching was done right: “the ribosome enhances the accuracy of codon reading by a twostep mechanism in which initial codon selection by a tRNA is followed by a proofreading step.” Amazing. In short, speed is achieved by the tRNA matching up initially with its cognate, but mechanisms in the ribosome during translation clean up any mistakes. The authors looked for optimization here. They explored the “maximal possible discrimination between a cognate and a noncognate codon–anticodon interaction: the ‘d value’,” and came up with some tentative numbers that will require further elaboration….
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