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Another instance of fine-tuning in biology?

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Abstract:The standard genetic code (SGC) is a mapping between the 64 possible arrangements of the four RNA nucleotides (C, A, U, G) into triplets or codons, where 61 codons are assigned to a specific amino acid and the other three are stop codons for terminating protein synthesis. Aminoacyl-tRNA synthetases (aaRSs) are responsible for implementing the SGC by specifically amino-acylating only its cognate transfer RNA (tRNA), thereby linking an amino acid with its corresponding anticodon triplets. tRNAs molecules bind each codon with its anticodon. To understand the meaning of symmetrical/asymmetrical properties of the SGC, we designed synthetic genetic codes with known symmetries and with the same degeneracy of the SGC. We determined their impact on the substitution rates for each amino acid under a neutral model of protein evolution. We prove that the phenotypic graphs of the SGC for codons and anticodons for all the possible arrangements of nucleotides are asymmetric and the amino acids do not form orbits. In the symmetrical synthetic codes, the amino acids are grouped according to their codonicity, this is the number of triplets encoding a given amino acid. Both the SGC and symmetrical synthetic codes exhibit a probability of occurrence of the amino acids proportional to their degeneracy. Unlike the SGC, the synthetic codes display a constant probability of occurrence of the amino acid according to their codonicity. The asymmetry of the phenotypic graphs of codons and anticodons of the SGC, has important implications on the evolutionary processes of proteins.

José, M.V.; Zamudio, G.S. On the Importance of Asymmetry in the Phenotypic Expression of the Genetic Code upon the Molecular Evolution of Proteins. Symmetry 2020, 12, 997.

Paper. (open access)

Friends say it’s complex but yes. Fine-tuning in biology shouldn’t be surprising. Why should biology be different from the rest of the universe?

See also: Biological fine-tuning goes to extremes. It turns out that biological fine-tuning goes to the very extremes of physics. In this lecture, William Bialek shows that eyes can detect individual photons, and a number of other phenomena where biology operates on the very edge of what is possible in physics. Jonathan Bartlett

One Reply to “Another instance of fine-tuning in biology?

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    bornagain77 says:

    Related notes:

    Does Life Use a Non-Random Set of Amino Acids? – Jonathan M. – April 2011
    Excerpt: The authors compared the coverage of the standard alphabet of 20 amino acids for size, charge, and hydrophobicity with equivalent values calculated for a sample of 1 million alternative sets (each also comprising 20 members) drawn randomly from the pool of 50 plausible prebiotic candidates. The results? The authors noted that: “…the standard alphabet exhibits better coverage (i.e., greater breadth and greater evenness) than any random set for each of size, charge, and hydrophobicity, and for all combinations thereof.”

    Extreme genetic code optimality from a molecular dynamics calculation of amino acid polar requirement – 2009
    Excerpt: A molecular dynamics calculation of the amino acid polar requirement is used to score the canonical genetic code. Monte Carlo simulation shows that this computational polar requirement has been optimized by the canonical genetic code, an order of magnitude more than any previously known measure, effectively ruling out a vertical evolution dynamics.

    The Finely Tuned Genetic Code – Jonathan M. – November 2011
    Excerpt: Summarizing the state of the art in the study of the code evolution, we cannot escape considerable skepticism. It seems that the two-pronged fundamental question: “why is the genetic code the way it is and how did it come to be?,” that was asked over 50 years ago, at the dawn of molecular biology, might remain pertinent even in another 50 years. Our consolation is that we cannot think of a more fundamental problem in biology.
    – Eugene Koonin and Artem Novozhilov

    “The coding system used for living beings is optimal from an engineering standpoint.”
    – Werner Gitt – In The Beginning Was Information – p. 95

    Collective evolution and the genetic code – 2006
    Excerpt: The genetic code could well be optimized to a greater extent than anything else in biology and yet is generally regarded as the biological element least capable of evolving.

    “The more we learn about the chemical basis of life and the intricacy of the genetic code, the more unbelievable the standard historical account becomes.”
    – Thomas Nagel – “Mind & Cosmos”

    “Because of Shannon channel capacity that previous (first) codon alphabet had to be at least as complex as the current codon alphabet (DNA code), otherwise transferring the information from the simpler alphabet into the current alphabet would have been mathematically impossible”
    – Donald E. Johnson – Bioinformatics: The Information in Life

    The Optimal Design of the Genetic Code – Fazale Rana – 2018
    Subsequent analysis performed later that decade incorporated additional factors. For example, some types of substitution mutations (called transitions) occur more frequently in nature than others (called transversions). As a case in point, an A-to-G substitution occurs more frequently than does either an A-to-C or an A-to-T mutation. When researchers included this factor into their analysis, they discovered that the naturally occurring genetic code performed better than one million randomly generated genetic codes. In a separate study, they also found that the genetic code in nature resides near the global optimum for all possible genetic codes with respect to its error-minimization capacity.3
    It could be argued that the genetic code’s error-minimization properties are more dramatic than these (one in a million) results indicate. When researchers calculated the error-minimization capacity of one million randomly generated genetic codes, they discovered that the error-minimization values formed a distribution where the naturally occurring genetic code’s capacity occurred outside the distribution. Researchers estimate the existence of 10^18 (a quintillion) possible genetic codes possessing the same type and degree of redundancy as the universal genetic code. Nearly all of these codes fall within the error-minimization distribution. This finding means that of 10^18 possible genetic codes, only a few have an error-minimization capacity that approaches the code found universally in nature.

    Genetic Code Complexity Just Tripled – April 21, 2017
    Excerpt: Each protein is made of a series of amino acids, and each amino acid is coded for by sets of “triplets,” which are sets of three informational DNA units (codons), in the genetic code.,,,
    “We realized that these two codons, although separated by a codon, were talking to each other,” Hughes says. “The effective code might be a triplet of triplets.”,,,
    To understand the genetic code,,, geneticists are going to have to understand the context of each triplet,,,
    One doesn’t just tinker with a particular codon and expect to get the same result in a different organism that has a different expression context.,,,
    “selection pressure on a single codon is exerted over five successive codons, which represent 61^5 or 844,596,301 codon combinations.”,,,
    “represents a significant challenge in designing genes for maximal expression whether by natural selection or in the laboratory.”,,,
    “there are 845,596,301 codon combinations to worry about, it’s like having to get many more numbers right in Powerball than you thought when you bought your lottery ticket.”

    Though the DNA code is found to be optimal from an error minimization standpoint, it is also now found that the fidelity of the genetic code, of how a specific amino acid is spelled, is far greater than had at first been thought:

    Synonymous Codons: Another Gene Expression Regulation Mechanism – September 2010
    Excerpt: There are 64 possible triplet codons in the DNA code, but only 20 amino acids they produce. As one can see, some amino acids can be coded by up to six “synonyms” of triplet codons: e.g., the codes AGA, AGG, CGA, CGC, CGG, and CGU will all yield arginine when translated by the ribosome. If the same amino acid results, what difference could the synonymous codons make? The researchers found that alternate spellings might affect the timing of translation in the ribosome tunnel, and slight delays could influence how the polypeptide begins its folding. This, in turn, might affect what chemical tags get put onto the polypeptide in the post-translational process. In the case of actin, the protein that forms transport highways for muscle and other things, the researchers found that synonymous codons produced very different functional roles for the “isoform” proteins that resulted in non-muscle cells,,, In their conclusion, they repeated, “Whatever the exact mechanism, the discovery of Zhang et al. that synonymous codon changes can so profoundly change the role of a protein adds a new level of complexity to how we interpret the genetic code.”,,,

    Codon Degeneracy Discredited Again by Jeffrey P. Tomkins, Ph.D. – 2016
    Excerpt: One of the main themes of evolution is the belief that certain types of DNA sequences freely mutate and develop new functions that allow for new creatures to evolve. This mostly mythical concept was applied to the protein-coding regions of genes, but in recent years this idea was discredited by the discovery of multiple codes embedded in the same sequence—because the disruption of these codes is typically harmful, mutations are not tolerated. And now another critical embedded code was discovered, further discrediting the idea of pervasive mutable DNA in genes.1,,,
    We have three different types of codes specified by codons that not only overlap each other, but play key roles in diverse types of cellular function. To sum things up, full codon utility (all three bases, besides specifying which amino acid is produced) controls: 1) transcription factor binding, 2) protein production rate and protein folding, and 3) gene transcription rates and levels.
    While human generated computer code is linear with only one meaning, the genetic code created by an Omnipotent God has multiple complex meanings and functions—all in the same sequence. The complexity of the genetic code points directly to a Divine Engineer instead of random purposeless evolution.

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