Literature DB >> 19393096

A four-column theory for the origin of the genetic code: tracing the evolutionary pathways that gave rise to an optimized code.

Paul G Higgs1.   

Abstract

BACKGROUND: The arrangement of the amino acids in the genetic code is such that neighbouring codons are assigned to amino acids with similar physical properties. Hence, the effects of translational error are minimized with respect to randomly reshuffled codes. Further inspection reveals that it is amino acids in the same column of the code (i.e. same second base) that are similar, whereas those in the same row show no particular similarity. We propose a 'four-column' theory for the origin of the code that explains how the action of selection during the build-up of the code leads to a final code that has the observed properties.
RESULTS: The theory makes the following propositions. (i) The earliest amino acids in the code were those that are easiest to synthesize non-biologically, namely Gly, Ala, Asp, Glu and Val. (ii) These amino acids are assigned to codons with G at first position. Therefore the first code may have used only these codons. (iii) The code rapidly developed into a four-column code where all codons in the same column coded for the same amino acid: NUN = Val, NCN = Ala, NAN = Asp and/or Glu, and NGN = Gly. (iv) Later amino acids were added sequentially to the code by a process of subdivision of codon blocks in which a subset of the codons assigned to an early amino acid were reassigned to a later amino acid. (v) Later amino acids were added into positions formerly occupied by amino acids with similar properties because this can occur with minimal disruption to the proteins already encoded by the earlier code. As a result, the properties of the amino acids in the final code retain a four-column pattern that is a relic of the earliest stages of code evolution.
CONCLUSION: The driving force during this process is not the minimization of translational error, but positive selection for the increased diversity and functionality of the proteins that can be made with a larger amino acid alphabet. Nevertheless, the code that results is one in which translational error is minimized. We define a cost function with which we can compare the fitness of codes with varying numbers of amino acids, and a barrier function, which measures the change in cost immediately after addition of a new amino acid. We show that the barrier is positive if an amino acid is added into a column with dissimilar properties, but negative if an amino acid is added into a column with similar physical properties. Thus, natural selection favours the assignment of amino acids to the positions that they occupy in the final code.

Entities:  

Mesh:

Substances:

Year:  2009        PMID: 19393096      PMCID: PMC2689856          DOI: 10.1186/1745-6150-4-16

Source DB:  PubMed          Journal:  Biol Direct        ISSN: 1745-6150            Impact factor:   4.540


  55 in total

1.  Physicochemical optimization in the genetic code origin as the number of codified amino acids increases.

Authors:  M Di Giulio; M Medugno
Journal:  J Mol Evol       Date:  1999-07       Impact factor: 2.395

2.  Consensus temporal order of amino acids and evolution of the triplet code.

Authors:  E N Trifonov
Journal:  Gene       Date:  2000-12-30       Impact factor: 3.688

3.  tRNomics: analysis of tRNA genes from 50 genomes of Eukarya, Archaea, and Bacteria reveals anticodon-sparing strategies and domain-specific features.

Authors:  Christian Marck; Henri Grosjean
Journal:  RNA       Date:  2002-10       Impact factor: 4.942

Review 4.  The case for an error minimizing standard genetic code.

Authors:  Stephen J Freeland; Tao Wu; Nick Keulmann
Journal:  Orig Life Evol Biosph       Date:  2003-10       Impact factor: 1.950

5.  The role of protein associated amino acid precursor molecules in the organization of genetic codons.

Authors:  A Miseta
Journal:  Physiol Chem Phys Med NMR       Date:  1989

6.  On the optimality of the genetic code, with the consideration of termination codons.

Authors:  Hani Goodarzi; Hamed Ahmadi Nejad; Noorossadat Torabi
Journal:  Biosystems       Date:  2004-11       Impact factor: 1.973

7.  The characterization of amino acid sequences in proteins by statistical methods.

Authors:  J M Zimmerman; N Eliezer; R Simha
Journal:  J Theor Biol       Date:  1968-11       Impact factor: 2.691

8.  Hydrophobicity of amino acid residues in globular proteins.

Authors:  G D Rose; A R Geselowitz; G J Lesser; R H Lee; M H Zehfus
Journal:  Science       Date:  1985-08-30       Impact factor: 47.728

9.  A simple method for displaying the hydropathic character of a protein.

Authors:  J Kyte; R F Doolittle
Journal:  J Mol Biol       Date:  1982-05-05       Impact factor: 5.469

10.  Identification of conflicting selective effects on highly expressed genes.

Authors:  Paul G Higgs; Weilong Hao; G Brian Golding
Journal:  Evol Bioinform Online       Date:  2007-02-14       Impact factor: 1.625

View more
  48 in total

1.  Unassigned codons, nonsense suppression, and anticodon modifications in the evolution of the genetic code.

Authors:  Peter T S van der Gulik; Wouter D Hoff
Journal:  J Mol Evol       Date:  2011-11-11       Impact factor: 2.395

2.  Metabolic basis for the self-referential genetic code.

Authors:  Romeu Cardoso Guimarães
Journal:  Orig Life Evol Biosph       Date:  2010-11-06       Impact factor: 1.950

3.  Evolution of the genetic code by incorporation of amino acids that improved or changed protein function.

Authors:  Brian R Francis
Journal:  J Mol Evol       Date:  2013-06-07       Impact factor: 2.395

4.  A realistic model under which the genetic code is optimal.

Authors:  Harry Buhrman; Peter T S van der Gulik; Gunnar W Klau; Christian Schaffner; Dave Speijer; Leen Stougie
Journal:  J Mol Evol       Date:  2013-07-23       Impact factor: 2.395

5.  Selection on GGU and CGU codons in the high expression genes in bacteria.

Authors:  Siddhartha Sankar Satapathy; Bhesh Raj Powdel; Malay Dutta; Alak Kumar Buragohain; Suvendra Kumar Ray
Journal:  J Mol Evol       Date:  2013-11-23       Impact factor: 2.395

Review 6.  Pathways of Genetic Code Evolution in Ancient and Modern Organisms.

Authors:  Supratim Sengupta; Paul G Higgs
Journal:  J Mol Evol       Date:  2015-06-09       Impact factor: 2.395

7.  Two perspectives on the origin of the standard genetic code.

Authors:  Supratim Sengupta; Neha Aggarwal; Ashutosh Vishwa Bandhu
Journal:  Orig Life Evol Biosph       Date:  2015-01-15       Impact factor: 1.950

8.  The Self-Referential Genetic Code is Biologic and Includes the Error Minimization Property.

Authors:  Romeu Cardoso Guimarães
Journal:  Orig Life Evol Biosph       Date:  2015-03-14       Impact factor: 1.950

Review 9.  Understanding the Genetic Code.

Authors:  Milton H Saier
Journal:  J Bacteriol       Date:  2019-07-10       Impact factor: 3.490

10.  Exceptional error minimization in putative primordial genetic codes.

Authors:  Artem S Novozhilov; Eugene V Koonin
Journal:  Biol Direct       Date:  2009-11-19       Impact factor: 4.540

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.