Positive and Negative Cognate Amino Acid Bias Affects Compositions of Aminoacyl-tRNA Synthetases and Reflects Functional Constraints on Protein Structure

By comparing phylogenetically related tRNA synthetases (enzymes that specifically aminoacylate tRNAs), a controlled natural experiment can reveal synthetases’ coevolution with their cognate amino acid substrate. Analyses of metabolic cost minimization confirm the existence of cognate avoidance in tRNA synthetase compositions. It is found that cognate avoidance increases and decreases, respectively, with proteomic amino acid usage in Escherichia coli and Bacillus subtilis. In E. coli, cognate avoidance did not decrease with cognate abundance in the colon, but decrease with tRNA synthetase editing sites and cognate impact on protein structure, revealing that hydrophobic interactions and beta-sheet formation constrain the folding of tRNA synthetase classes I and II, respectively. Analyses of cognate bias yield information on how proteins function in E. coli, because function constrains cognate bias. In B. subtilis, cognate avoidance occurred for rare and abundant amino acids in the soil, positive bias existed for cognates with intermediate abundances. Presumably, life history strategies (endosymbiont versus free living) and environmental compositions modulate cost minimization of amino acid usages. Avoidance of ‘expensive’ residues in tRNA synthetases is inversely proportional to cognate avoidance and protein size in E. coli, but not B. subtilis. In relation to cost minimization, E. coli’s predictable environment perhaps enabled to reach evolutionary (balancing) equilibrium between various factors affecting protein synthesis costs, where decreasing costs through one factor requires increasing other costs. Phylogenetically controlled comparisons can detect more statistically significant biases than similar analyses using randomly selected control proteins, stressing the power of carefully designed natural experiments. This work confirms the importance of biosynthetic cost minimization, and challenges neutralistic approaches of biomolecular evolution.