Model of Genetic Code Structure Evolution under Various Types of Codon Reading

Abstract

The standard genetic code (SGC) is a set of rules according to which 64 codons are assigned to 20 canonical amino acids and stop coding signal. As a consequence, the SGC is redundant because there is a greater number of codons than the number of encoded labels. This redundancy implies the existence of codons that encode the same genetic information. The size and organization of such synonymous codon blocks are important characteristics of the SGC structure whose evolution is still unclear. Therefore, we studied possible evolutionary mechanisms of the codon block structure. We conducted computer simulations assuming that coding systems at early stages of the SGC evolution were sets of ambiguous codon assignments with high entropy. We included three types of reading systems characterized by different inaccuracy and pattern of codon recognition. In contrast to the previous study, we allowed for evolution of the reading systems and their competition. The simulations performed under minimization of translational errors and reduction of coding ambiguity produced the coding system resistant to these errors. The reading system similar to that present in the SGC dominated the others very quickly. The survived system was also characterized by low entropy and possessed properties similar to that in the SGC. Our simulation show that the unambiguous SGC could emerged from a code with a lower level of ambiguity and the number of tRNAs increased during the evolution.

Keywords: amino acid; codon; evolution; genetic code.

Figure 1

Changes in the best approximation of the fitness function F (y-axis) with the number of generations (x-axis) based on the GAM model and 10 simulation runs with different initial seeds. The y-axis is shown in a logarithmic scale.

Figure 2

Changes in the average entropy values (y-axis) calculated from distributions of codon assignments $H_c\left(\mathcal{P}\right)$ (A), and reading system types $H_r\left(\mathcal{P}\right)$ (B), with the number of generations (x-axis).

Figure 3

The expected value of the total number of encoded labels using various reading types $M_1$, $M_2$, and $M_3$ (y-axis) with the number of generations (x-axis). Notice that the expected number of labels read by the $M_1$ system started dominated among all considered types of reading very quickly.

Figure 4

The heatmap of different types of reading systems (y-axis) at the end of the simulations. This is a graphical representation of a matrix in which each genetic label (x-axis) ascribes a probability that is read by a given reading type. Please compare with Figure 5 at the beginning of the simulations.

Figure 5

The heatmap of different types of reading systems (y-axis) at the beginning of simulation. This is a graphical representation of a matrix in which each genetic label ascribes a probability that is read by a given reading type.

Figure 6

The heatmap of genetic code encoding 21 labels by 64 codons at the end of simulation. This is a graphical representation of the matrix $\mathcal{P}=\left(p_{cl}\right)$, in which each element $p_{cl}$ ascribes a probability that a codon c in a row encodes a label l in a column. Please compare with Figure 7 at the beginning of the simulations.

Figure 7

The heatmap of genetic code encoding 21 labels by 64 codons at the beginning of the simulation. This is a graphical representation of the matrix $\mathcal{P}=\left(p_{cl}\right)$, in which each element $p_{cl}$ ascribes a probability that a codon c in a row encodes a label l in a column.

Figure 8

The distribution of the size of codon groups encoding a given label in the genetic code at the end of the simulation.