Literature DB >> 18005995

A quantitative investigation of the chemical space surrounding amino acid alphabet formation.

Yi Lu1, Stephen J Freeland.   

Abstract

To date, explanations for the origin and emergence of the alphabet of amino acids encoded by the standard genetic code have been largely qualitative and speculative. Here, with the help of computational chemistry, we present the first quantitative exploration of nature's "choices" set against various models for plausible alternatives. Specifically, we consider the chemical space defined by three fundamental biophysical properties (size, charge, and hydrophobicity) to ask whether the amino acids that entered the genetic code exhibit a higher diversity than random samples of similar size drawn from several different definitions of amino acid possibility space. We found that in terms of the properties studied, the full, standard set of 20 biologically encoded amino acids is indeed significantly more diverse than an equivalently sized group drawn at random from the set of plausible, prebiotic alternatives (using the Murchison meteorite as a model for pre-biotic plausibility). However, when the set of possible amino acids is enlarged to include those that are produced by standard biosynthetic pathways (reflecting the widespread idea that many members of the standard alphabet were recruited in this way), then the genetically encoded amino acids can no longer be distinguished as more diverse than a random sample. Finally, if we turn to consider the overlap between biologically encoded amino acids and those that are prebiotically plausible, then we find that the biologically encoded subset are no more diverse as a group than would be expected from a random sample, unless the definition of "random sample" is adjusted to reflect possible prebiotic abundance (again, using the contents of the Murchison meteorite as our estimator). This final result is contingent on the accuracy of our computational estimates for amino acid properties, and prebiotic abundances, and an exploration of the likely effect of errors in our estimation reveals that our results should be treated with caution. We thus present this work as a first step in quantifying and thus testing various origin-of-life hypotheses regarding the origin and evolution of life's amino acid alphabet, and advocate the progress that would add valuable information in the future.

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Year:  2007        PMID: 18005995     DOI: 10.1016/j.jtbi.2007.10.007

Source DB:  PubMed          Journal:  J Theor Biol        ISSN: 0022-5193            Impact factor:   2.691


  7 in total

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Authors:  Claudia Alvarez-Carreño; Arturo Becerra; Antonio Lazcano
Journal:  Orig Life Evol Biosph       Date:  2013-09-08       Impact factor: 1.950

2.  Modern diversification of the amino acid repertoire driven by oxygen.

Authors:  Matthias Granold; Parvana Hajieva; Monica Ioana Toşa; Florin-Dan Irimie; Bernd Moosmann
Journal:  Proc Natl Acad Sci U S A       Date:  2017-12-19       Impact factor: 11.205

3.  A Closer Look at Non-random Patterns Within Chemistry Space for a Smaller, Earlier Amino Acid Alphabet.

Authors:  Christopher Mayer-Bacon; Markus Meringer; Riley Havel; José C Aponte; Stephen Freeland
Journal:  J Mol Evol       Date:  2022-06-06       Impact factor: 3.973

Review 4.  Origin and evolution of the genetic code: the universal enigma.

Authors:  Eugene V Koonin; Artem S Novozhilov
Journal:  IUBMB Life       Date:  2009-02       Impact factor: 3.885

5.  Why twenty amino acid residue types suffice(d) to support all living systems.

Authors:  Robert P Bywater
Journal:  PLoS One       Date:  2018-10-15       Impact factor: 3.240

6.  A novel method for achieving an optimal classification of the proteinogenic amino acids.

Authors:  Andre Then; Karel Mácha; Bashar Ibrahim; Stefan Schuster
Journal:  Sci Rep       Date:  2020-09-18       Impact factor: 4.379

Review 7.  Evolution as a Guide to Designing xeno Amino Acid Alphabets.

Authors:  Christopher Mayer-Bacon; Neyiasuo Agboha; Mickey Muscalli; Stephen Freeland
Journal:  Int J Mol Sci       Date:  2021-03-10       Impact factor: 5.923

  7 in total

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