Multi-objective genetic algorithms in the study of the genetic code’s adaptability

Localized Context-Dependent Effects of the "Ambush" Hypothesis: More Off-Frame Stop Codons Downstream of Shifty Codons

The ambush hypothesis speculates that off-frame stop codons increase translational efficiency after ribosomal frameshifts by stopping early frameshifted translation. Some evidences fit this hypothesis: (1) synonymous codon usages increase with their potential contribution to off-frame stops; (2) the genetic code assigns frequent amino acids to codon families contributing to off-frame stops; (3) positive biases for off-frame stops (AT rich) occur despite adverse nucleotide (GC) biases; and (4) mitochondrial off-frame stop codon densities increase with ribosomal structural instability, potential proxy of frameshift frequencies. In this study, analyses of vertebrate mitogenes and tRNA synthetase genes from all superkingdoms and viruses test a new prediction of the ambush hypothesis: sequences immediately downstream of frameshift-inducing homopolymer codons (AAA, CCC, GGG, and TTT) are off-frame stop rich. Codons immediately downstream of homopolymer codons form more than average off-frame stops, biases are stronger than for corresponding upstream distances and for any other group of synonymous codons. Sequences downstream of that high-density region are off-frame stop depleted. This decrease suggests that off-frame stops, combined with suppressor tRNAs regulate translation of overlapping coding sequences. Results show the predictive power of the ambush hypothesis, from macroevolutionary (genetic code structure) to detailed gene sequence anatomy.

The optimality of the standard genetic code assessed by an eight-objective evolutionary algorithm

BACKGROUND

The standard genetic code (SGC) is a unique set of rules which assign amino acids to codons. Similar amino acids tend to have similar codons indicating that the code evolved to minimize the costs of amino acid replacements in proteins, caused by mutations or translational errors. However, if such optimization in fact occurred, many different properties of amino acids must have been taken into account during the code evolution. Therefore, this problem can be reformulated as a multi-objective optimization task, in which the selection constraints are represented by measures based on various amino acid properties.

RESULTS

To study the optimality of the SGC we applied a multi-objective evolutionary algorithm and we used the representatives of eight clusters, which grouped over 500 indices describing various physicochemical properties of amino acids. Thanks to that we avoided an arbitrary choice of amino acid features as optimization criteria. As a consequence, we were able to conduct a more general study on the properties of the SGC than the ones presented so far in other papers on this topic. We considered two models of the genetic code, one preserving the characteristic codon blocks structure of the SGC and the other without this restriction. The results revealed that the SGC could be significantly improved in terms of error minimization, hereby it is not fully optimized. Its structure differs significantly from the structure of the codes optimized to minimize the costs of amino acid replacements. On the other hand, using newly defined quality measures that placed the SGC in the global space of theoretical genetic codes, we showed that the SGC is definitely closer to the codes that minimize the costs of amino acids replacements than those maximizing them.

CONCLUSIONS

The standard genetic code represents most likely only partially optimized systems, which emerged under the influence of many different factors. Our findings can be useful to researchers involved in modifying the genetic code of the living organisms and designing artificial ones.