The regularities of the changes of amino acid physico-chemical properties within the genetic code

Summary All the codons of the genetic code can be arranged into the closed one-step mutation ring, containing three periods of the same sequence of mutations (2,3,3,3,1,3,3,3,1,3,3,3,1,3,3,3,2,3,3,3). The codons of Gly play a role of the connecting element between the end of the third, and the beginning of the first period of the genetic code. The reactivity of amino acids, expressed by the reaction rates of aminolysis reaction of N-hydroxysuccinimide esters of protected amino acids with p-anisidine, changes periodically with the respect to the mutation periods of the genetic code. Chou-Fasman P as well as P conformational parameters of amino acids, and also the compositional frequencies of amino acids in proteins, demonstrate the pseudosymmetry pattern with respect to the center of one-step mutation ring, which is situated between Thr ACY and ACR codons.     

A hardware interpretation of the evolution of the genetic code

A quantitative rationale for the evolution of the genetic code is developed considering the principle of minimal hardware. This principle defines an optimal code as one that minimizes for a given amount of information encoded, the product of the number of physical devices used by the average complexity of each device. By identifying the number of different amino acids, number of nucleotide positions per codon and number of base types that can occupy each such position with, respectively, the amount of information, number of devices and the complexity, we show that optimal codes occur for 3, 7 and 20 amino acids with codons having a single, two and three base positions per codon, respectively. The advantage of a code of exactly 4 symbols is deduced, as well as a plausible evolutionary pathway from a code of doublets to triplets. The present day code of 20 amino acids encoded by 64 codons is shown to be the most optimal in an absolute sense. Using a tetraplet code further evolution to a code in which there would be 55 amino acids is in principle possible, but such a code would deviate slightly more than the present day code from the minimal hardware configuration. The change from a triplet code to a tetraplet code would occur at about 32 amino acids. Our conclusions are independent of, but consistent with, the observed physico-chemical properties of the amino acids and codon structures. These correlations could have evolved within the constrains imposed by the minimal hardware principle.     

Natural history and experimental evolution of the genetic code

The standard genetic code is a set of rules that relates the 20 canonical amino acids in proteins to groups of three bases in the mRNA. It evolved from a more primitive form and the attempts to reconstruct its natural history are based on its present-day features. Genetic code engineering as a new research field was developed independently in a few laboratories during the last 15 years. The main intention is to re-program protein synthesis by expanding the coding capacities of the genetic code via re-assignment of specific codons to un-natural amino acids. This article focuses on the question as to which extent hypothetical scenarios that led to codon re-assignments during the evolution of the genetic code are relevant for its further evolution in the laboratory. Current attempts to engineer the genetic code are reviewed with reference to theoretical works on its natural history. Integration of the theoretical considerations into experimental concepts will bring us closer to designer cells with target-engineered genetic codes that should open not only tremendous possibilities for the biotechnology of the twenty-first century but will also provide a basis for the design of novel life forms.     

Stability of the genetic code and optimal parameters of amino acids

The standard genetic code is known to be much more efficient in minimizing adverse effects of misreading errors and one-point mutations in comparison with a random code having the same structure, i.e. the same number of codons coding for each particular amino acid. We study the inverse problem, how the code structure affects the optimal physico-chemical parameters of amino acids ensuring the highest stability of the genetic code. It is shown that the choice of two or more amino acids with given properties determines unambiguously all the others. In this sense the code structure determines strictly the optimal parameters of amino acids or the corresponding scales may be derived directly from the genetic code. In the code with the structure of the standard genetic code the resulting values for hydrophobicity obtained in the scheme “leave one out” and in the scheme with fixed maximum and minimum parameters correlate significantly with the natural scale. The comparison of the optimal and natural parameters allows assessing relative impact of physico-chemical and error-minimization factors during evolution of the genetic code. As the resulting optimal scale depends on the choice of amino acids with given parameters, the technique can also be applied to testing various scenarios of the code evolution with increasing number of codified amino acids. Our results indicate the co-evolution of the genetic code and physico-chemical properties of recruited amino acids.     

Differential effect of amino acid residues on the stability of double helices formed from polyribonucleotides and its possible relation to the evolution of the genetic code

Summary The interaction of amino acid residues with polyribonucleotides was characterized by measurements of melting temperatures (t_m) for poly(A) poly(U) and poly(I)poly(C) as functions of the concentrations of various amino acid amides. The amides of hydrophilic amino acids lead to a continuous increase of tm with increasing concentration, whereas amides of hydrophobic amino acids induce a decrease of t_m at low concentrations (1 mM) followed by an increase at higher concentrations. Analysis of the data by a simple site model provides the affinity of each ligand for the double helix relative to that for the single strands. This parameter decreases in the order Ala>Gly>Ser>Asn>Pro>Met, Val>Ile, Leu for poly(A) poly(U) and Ala, Gly, Ser>Asn>Pro>Val>Ile, Met, Leu for poly(I)poly(C). The special effects of hydrophobic amino acids may be related to the similarity of the codons for these amino acids. A simple model for assignment of codons to amino acids is proposed.     

Prediction of site-specific amino acid distributions and limits of divergent evolutionary changes in protein sequences

We derive an analytic expression for site-specific stationary distributions of amino acids from the structurally constrained neutral (SCN) model of protein evolution with conservation of folding stability. The stationary distributions that we obtain have a Boltzmann-like shape, and their effective temperature parameter, measuring the limit of divergent evolutionary changes at a given site, can be predicted from a site-specific topological property, the principal eigenvector of the contact matrix of the native conformation of the protein. These analytic results, obtained without free parameters, are compared with simulations of the SCN model and with the site-specific amino acid distributions obtained from the Protein Data Bank. These results also provide new insights into how the topology of a protein fold influences its designability, i.e., the number of sequences compatible with that fold. The dependence of the effective temperature on the principal eigenvector decreases for longer proteins, as a possible consequence of the fact that selection for thermodynamic stability becomes weaker in this case.     

Comparative modeling of the conformational stability of chymotrypsin inhibitor 2 protein mutants using amino acid sequence autocorrelation (AASA) and amino acid 3D autocorrelation (AA3DA) vectors and ensembles of Bayesian-regularized genetic neural networks

Predicting protein stability changes upon point mutation is important for understanding protein structure and designing new proteins. Autocorrelation vector formalism was extended to amino acid sequences and 3D conformations for encoding protein structural information with modeling purpose. Protein autocorrelation vectors were weighted by 48 amino acid/residue properties selected from the AAindex database. Ensembles of Bayesian-regularized genetic neural networks (BRGNNs) trained with amino acid sequence autocorrelation (AASA) vectors and amino acid 3D autocorrelation (AA3DA) vectors yielded predictive models of the change of unfolding Gibbs free energy change (ΔΔG) of chymotrypsin Inhibitor 2 protein mutants. The ensemble predictor described about 58 and 72% of the data variances in test sets for AASA and AA3DA models, respectively. Optimum sequence and 3D-based ensembles exhibit high effects on relevant structural (volume, solvent-accessible surface area), physico-chemical (hydrophilicity/hydrophobicity-related) and thermodynamic (hydration parameters) properties.