Large-scale in silico mutagenesis experiments reveal optimization of genetic code and codon usage for protein mutational robustness

Abstract

Background How, and the extent to which, evolution acts on DNA and protein sequences to ensure mutational robustness and evolvability is a long-standing open question in the field of molecular evolution. We addressed this issue through the first structurome-scale computational investigation, in which we estimated the change in folding free energy upon all possible single-site mutations introduced in more than 20,000 protein structures, as well as through available experimental stability and fitness data. Results At the amino acid level, we found the protein surface to be more robust against random mutations than the core, this difference being stronger for small proteins. The destabilizing and neutral mutations are more numerous in the core and on the surface, respectively, whereas the stabilizing mutations are about 4% in both regions. At the genetic code level, we observed smallest destabilization for mutations that are due to substitutions of base III in the codon, followed by base I, bases I+III, base II, and other multiple base substitutions. This ranking highly anticorrelates with the codon-anticodon mispairing frequency in the translation process. This suggests that the standard genetic code is optimized to limit the impact of random mutations, but even more so to limit translation errors. At the codon level, both the codon usage and the usage bias appear to optimize mutational robustness and translation accuracy, especially for surface residues. Conclusion Our results highlight the non-universality of mutational robustness and its multiscale dependence on protein features, the structure of the genetic code, and the codon usage. Our analyses and approach are strongly supported by available experimental mutagenesis data.

Causes and Consequences of Purifying Selection on SARS-CoV-2

“…Although second site nucleotide content is considered the key determiner of the chemical property of the encoded amino acid ( Haig and Hurst 1991 ; Freeland and Hurst 1998 ; Gilis et al 2001 ; Schwersensky et al 2020 ), only five of 12 first site versus second site comparisons have higher rates at the first site. The same analysis of the 12 mutational types emphasizes the great disparity in G->U, and to a lesser degree C->U, mutation between 4-fold degenerate sites and codon sites 1 and 2, this despite the fact that some (Leu->Leu) first site C->U mutations are synonymous ( fig.…”

The Mutational Robustness of the Genetic Code and Codon Usage in Environmental Context: A Non-Extremophilic Preference?

“…Here, we focused on the effects propagated by genomic mutations. On this level, the probability that a triplet c i is substituted by c j does not depend on which base is mutated, whereas misread probabilities clearly depend on the position in the event of mistranslation [ 8 , 18 , 100 ], which is an additional, albeit not constant and not hereditary, level of distortion.…”

“…That is an obvious simplification. The expression levels have evolutional importance [ 101 ] related to thermophilic properties [ 102 ] and misfold chance [ 103 ], emphasizing that the genetic code cannot be understood without the other structural aspects of proteins (e.g., change in folding free energy [ 18 , 100 ]). We also note that, compared to other prokaryotes, thermophiles and halophiles possess elevated rates of horizontal gene transfer [ 104 , 105 , 106 ].…”

MutaFrame - an interpretative visualization framework for deleteriousness prediction of missense variants in the human exome

“…As an example, SNPMuSiC is unable to predict a mutation to be deleterious if its molecular effect is to disrupt a protein-protein or protein-ligand interaction necessary for function. However, its contributions perfectly integrate into the MutaFrame framework and provide fundamental information about the variant effects, since stability is well known to be one of the main contributions to folded protein fitness [24]. 3: List of all the features used in the SNPMuSiC model to predict variant deleteriousness.…”