Simulated evolution applied to study the genetic code optimality using a model of codon reassignments

<p>Abstract</p> <p>Background</p> <p>As the canonical code is not universal, different theories about its origin and organization have appeared. The optimization or level of adaptation of the canonical genetic code was measured taking into account the harmful consequences resulting from point mutations leading to the replacement of one amino acid for another. There are two basic theories to measure the level of optimization: the statistical approach, which compares the canonical genetic code with many randomly generated alternative ones, and the engineering approach, which compares the canonical code with the best possible alternative.</p> <p>Results</p> <p>Here we used a genetic algorithm to search for better adapted hypothetical codes and as a method to guess the difficulty in finding such alternative codes, allowing to clearly situate the canonical code in the fitness landscape. This novel proposal of the use of evolutionary computing provides a new perspective in the open debate between the use of the statistical approach, which postulates that the genetic code conserves amino acid properties far better than expected from a random code, and the engineering approach, which tends to indicate that the canonical genetic code is still far from optimal. We used two models of hypothetical codes: one that reflects the known examples of codon reassignment and the model most used in the two approaches which reflects the current genetic code translation table. Although the standard code is far from a possible optimum considering both models, when the more realistic model of the codon reassignments was used, the evolutionary algorithm had more difficulty to overcome the efficiency of the canonical genetic code.</p> <p>Conclusions</p> <p>Simulated evolution clearly reveals that the canonical genetic code is far from optimal regarding its optimization. Nevertheless, the efficiency of the canonical code increases when mistranslations are taken into account with the two models, as indicated by the fact that the best possible codes show the patterns of the standard genetic code. Our results are in accordance with the postulates of the engineering approach and indicate that the main arguments of the statistical approach are not enough to its assertion of the extreme efficiency of the canonical genetic code.</p

Enabling evolution with expanded genetic codes

The natural genetic code is largely shared amongst all terrestrial life, though variations do exist that provide evidence of genetic code evolution. The field of synthetic biology has developed methods to augment and modify the genetic code, by reassigning codons and genetically incorporating noncanonical amino acids (NCAAs). Most commonly, amino-acyl tRNA synthetases and their cognate tRNAs are engineered to recognize (NCAAs), the tRNA charged with an NCAA is then used during translation to decode the amber stop codon. Using this method, over 200 unique NCAAs have been added to the genetic code, allowing for the site-specific incorporation of useful chemistries, including covalent bond formation and fluorescence. NCAAs have been implemented in biological research and protein engineering. The second chapter of this dissertation explores the biophysical parameters that allow efficient crosslinking with a NCAA. We investigated several environmental factors which could impact crosslinking between two interacting proteins. We define a set of parameters that permit efficient crosslinking. Noncanonical amino acids are a potentially powerful tool in understanding genetic code evolution. Evolutionary experiments using NCAAs have already been used to explore proposed theories on codon evolution, including codon capture and ambiguous intermediate theories. Though several studies have explored the implications of NCAAs in evolution, significant obstacles have prevented the long-term evolution of wild-type organisms with expanded genetic codes. In chapter three of this dissertation we demonstrate that a single essential enzyme can be engineered to be structurally dependent on a genetically encoded NCAA. This confers a functional addiction to the NCAA and the addiction can be transferred to new bacterial hosts using a DNA plasmid. Chapter four expands on this idea, attempting to use the chemistries bestowed by the NCAA for active site chemistry. Finally, in chapter five we demonstrate that a protein-conferred cellular NCAA addiction is sufficient for the long-term evolution of bacteria with a recoded amber codon. We investigated the genetic and phenotypic impact of evolution with an expanded genetic code.Biochemistr

Assessing the robustness of genetic codes and genomes

Deux approches principales existent pour évaluer la robustesse des codes génétiques et des séquences de codage. L'approche statistique est basée sur des estimations empiriques de probabilité calculées à partir d'échantillons aléatoires de permutations représentant les affectations d'acides aminés aux codons, alors que l'approche basée sur l'optimisation repose sur le pourcentage d’optimisation, généralement calculé en utilisant des métaheuristiques. Nous proposons une méthode basée sur les deux premiers moments de la distribution des valeurs de robustesse pour tous les codes génétiques possibles. En se basant sur une instance polynomiale du Problème d'Affectation Quadratique, nous proposons un algorithme vorace exact pour trouver la valeur minimale de la robustesse génomique. Pour réduire le nombre d'opérations de calcul des scores et de la borne supérieure de Cantelli, nous avons développé des méthodes basées sur la structure de voisinage du code génétique et sur la comparaison par paires des codes génétiques, entre autres. Pour calculer la robustesse des codes génétiques naturels et des génomes procaryotes, nous avons choisi 23 codes génétiques naturels, 235 propriétés d'acides aminés, ainsi que 324 procaryotes thermophiles et 418 procaryotes non thermophiles. Parmi nos résultats, nous avons constaté que bien que le code génétique standard soit plus robuste que la plupart des codes génétiques, certains codes génétiques mitochondriaux et nucléaires sont plus robustes que le code standard aux troisièmes et premières positions des codons, respectivement. Nous avons observé que l'utilisation des codons synonymes tend à être fortement optimisée pour amortir l'impact des changements d'une seule base, principalement chez les procaryotes thermophiles.There are two main approaches to assess the robustness of genetic codes and coding sequences. The statistical approach is based on empirical estimates of probabilities computed from random samples of permutations representing assignments of amino acids to codons, whereas, the optimization-based approach relies on the optimization percentage frequently computed by using metaheuristics. We propose a method based on the first two moments of the distribution of robustness values for all possible genetic codes. Based on a polynomially solvable instance of the Quadratic Assignment Problem, we propose also an exact greedy algorithm to find the minimum value of the genome robustness. To reduce the number of operations for computing the scores and Cantelli’s upper bound, we developed methods based on the genetic code neighborhood structure and pairwise comparisons between genetic codes, among others. For assessing the robustness of natural genetic codes and genomes, we have chosen 23 natural genetic codes, 235 amino acid properties, as well as 324 thermophilic and 418 non-thermophilic prokaryotes. Among our results, we found that although the standard genetic code is more robust than most genetic codes, some mitochondrial and nuclear genetic codes are more robust than the standard code at the third and first codon positions, respectively. We also observed that the synonymous codon usage tends to be highly optimized to buffer the impact of single-base changes, mainly, in thermophilic prokaryotes