Models of genetic code structure evolution with variable number of coded labels.

It is assumed that at the early stage of cell evolution its translation machinery was characterized by high noise, i.e. ambiguous assignment of codons to amino acids in the genetic code, which initially encoded only few amino acids.

Some theoretical aspects of reprogramming the standard genetic code.

Reprogramming of the standard genetic code to include non-canonical amino acids (ncAAs) opens new prospects for medicine, industry, and biotechnology. There are several methods of code engineering, which allow us for storing new genetic information in DNA sequences and producing proteins with new properties. Here, we provided a theoretical background for the optimal genetic code expansion, which may find application in the experimental design of the genetic code.

Basic principles of the genetic code extension.

Compounds including non-canonical amino acids (ncAAs) or other artificially designed molecules can find a lot of applications in medicine, industry and biotechnology. They can be produced thanks to the modification or extension of the standard genetic code (SGC). Such peptides or proteins including the ncAAs can be constantly delivered in a stable way by organisms with the customized genetic code.

Optimization of the standard genetic code in terms of two mutation types: Point mutations and frameshifts.

The distinct structure and universality of the standard genetic code (SGC) have fascinated the scientists ever since the first amino acid assignments were discovered. There are several hypotheses trying to explain the origin and evolution of this code. One of them postulates that the SGC evolved to minimize harmful effects of amino acid replacements in proteins, caused by mutations and translational errors.

The influence of different types of translational inaccuracies on the genetic code structure.

Background: The standard genetic code is a recipe for assigning unambiguously 21 labels, i.e. amino acids and stop translation signal, to 64 codons.

Many alternative and theoretical genetic codes are more robust to amino acid replacements than the standard genetic code.

We evaluated the differences between the standard genetic code (SGC) and its known alternative variants in terms of the consequences of amino acids replacements. Furthermore, the properties of all the possible theoretical genetic codes, which differ from the SGC by one, two or three changes in codon assignments were also tested. Although the SGC is closer to the best theoretical codes than to the worst ones due to the minimization of amino acid replacements, from 10% to 27% of the all possible theoretical codes minimize the effect of these replacements better than the SGC.

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.

Correction: Optimization of the standard genetic code according to three codon positions using an evolutionary algorithm.

[This corrects the article DOI: 10.1371/journal.pone.

Optimization of the standard genetic code according to three codon positions using an evolutionary algorithm.

Many biological systems are typically examined from the point of view of adaptation to certain conditions or requirements. One such system is the standard genetic code (SGC), which generally minimizes the cost of amino acid replacements resulting from mutations or mistranslations. However, no full consensus has been reached on the factors that caused the evolution of this feature.

Optimization of amino acid replacement costs by mutational pressure in bacterial genomes.

Mutations are considered a spontaneous and random process, which is important component of evolution because it generates genetic variation. On the other hand, mutations are deleterious leading to non-functional genes and energetically costly repairs. Therefore, one can expect that the mutational pressure is optimized to simultaneously generate genetic diversity and preserve genetic information.

Representations of Search Spaces in the Problem of Mutational Pressure Optimization According to Protein-Coding Sequences.

The proper representation of the search space is the fundamental step in every optimization task, because it has a decisive impact on the quality of potential solutions. In particular, this problem appears when the search spaces are nonstandard and complex, with the large number of candidate solutions that differ from classical forms usually investigated. One of such spaces is the set of continuous-time, homogenous, and stationary Markov processes.

The Impact of Selection at the Amino Acid Level on the Usage of Synonymous Codons.

There are two main forces that affect usage of synonymous codons: directional mutational pressure and selection. The effectiveness of protein translation is usually considered as the main selectional factor. However, biased codon usage can also be a byproduct of a general selection at the amino acid level interacting with nucleotide replacements.

The role of crossover operator in evolutionary-based approach to the problem of genetic code optimization.

One of theories explaining the present structure of canonical genetic code assumes that it was optimized to minimize harmful effects of amino acid replacements resulting from nucleotide substitutions and translational errors. A way to testify this concept is to find the optimal code under given criteria and compare it with the canonical genetic code. Unfortunately, the huge number of possible alternatives makes it impossible to find the optimal code using exhaustive methods in sensible time.