Biochemical networks in single cells can display large fluctuations in molecule numbers, making mesoscopic approaches necessary for correct system descriptions. We present a general method that allows rapid characterization of the stochastic properties of intracellular networks. The starting point is a macroscopic description that identifies the system's elementary reactions in terms of rate laws and stoichiometries. From this formulation follows directly the stationary solution of the linear noise approximation (LNA) of the Master equation for all the components in the network. The method complements bifurcation studies of the system's parameter dependence by providing estimates of sizes, correlations, and time scales of stochastic fluctuations. We describe how the LNA can give precise system descriptions also near macroscopic instabilities by suitable variable changes and elimination of fast variables.

Proteins are made from 19 aa and, curiously, one N -alkylamino acid (“imino acid”), proline (Pro). Pro is thought to be incorporated by the translation apparatus at the same rate as the 19 aa, even though the alkyl group in Pro resides directly on the nitrogen nucleophile involved in peptide bond formation. Here, by combining quench-flow kinetics and charging of tRNAs with cognate and noncognate amino acids, we find that Pro incorporates in translation significantly more slowly than Phe or Ala and that other N -alkylamino acids incorporate much more slowly. Our results show that the slowest step in incorporation of N -alkylamino acids is accommodation/peptidyl transfer after GTP hydrolysis on EF-Tu. The relative incorporation rates correlate with expectations from organic chemistry, suggesting that amino acid sterics and basicities affect translation rates at the peptidyl transfer step. Cognate isoacceptor tRNAs speed Pro incorporation to rates compatible with in vivo, although still 3–6 times slower than Phe incorporation from Phe-tRNA ^Phe depending on the Pro codon. Results suggest that Pro is the only N -alkylamino acid in the genetic code because it has a privileged cyclic structure that is more reactive than other N -alkylamino acids. Our data on the variation of the rate of incorporation of Pro from native Pro-tRNA ^Pro isoacceptors at 4 different Pro codons help explain codon bias not accounted for by the “tRNA abundance” hypothesis.

We modeled how the charged levels of different transfer RNAs (tRNAs) that carry the same amino acid (isoacceptors) respond when this amino acid becomes growth-limiting. The charged levels will approach zero for some isoacceptors (such as \batchmode \documentclass[fleqn,10pt,legalpaper]{article} \usepackage{amssymb} \usepackage{amsfonts} \usepackage{amsmath} \pagestyle{empty} \begin{document} \(\mathrm{tRNA}_{2}^{\mathrm{Leu}}\) \end{document} ) and remain high for others (such as \batchmode \documentclass[fleqn,10pt,legalpaper]{article} \usepackage{amssymb} \usepackage{amsfonts} \usepackage{amsmath} \pagestyle{empty} \begin{document} \(\mathrm{tRNA}_{4}^{\mathrm{Leu}}\) \end{document} ), as determined by the concentrations of isoacceptors and how often their codons occur in protein synthesis. The theory accounts for (synonymous) codons for the same amino acid that are used in ribosome-mediated transcriptional attenuation, the choices of synonymous codons in trans-translating transfermessenger RNA, and the overrepresentation of rare codons in messenger RNAs for amino acid biosynthetic enzymes.

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.