Searching of Code Space for an Error-Minimized Genetic Code Via Codon Capture Leads to Failure, or Requires At Least 20 Improving Codon Reassignments Via the Ambiguous Intermediate Mechanism

Abstract

The standard genetic code (SGC) has a fundamental error-minimizing property which has been widely attributed to the action of selection. However, a clear mechanism for how selection can give rise to error minimization (EM) is lacking. A search through a space of alternate codes (code space) via codon reassignments would be required, to select a code optimized for EM. There are two commonly discussed mechanisms of codon reassignment; the Codon Capture mechanism, which proposes a loss of the codon during reassignment, and the Ambiguous Intermediate mechanism, which proposes that the codon underwent an ambiguous phase during reassignment. When searching of code space via the Codon Capture mechanism is simulated, an optimized genetic code can rarely be achieved (0–3.2% of the time) with most searches ending in failure. When code space is searched via the Ambiguous Intermediate mechanism, under constraints derived from empirical observations of codon reassignments from extant genomes, the searches also often end in failure. When a local minimum is avoided and optimization is achieved, 20–41 sequential improving codon reassignments are required. Furthermore, the structures of the optimized codes produced by these simulations differ from the structure of the SGC. These data are challenges for the Adaptive Code hypothesis to address, which proposes that the EM property was directly selected for, and suggests that EM is simply a byproduct of the addition of amino acids to the expanding code, as described by the alternative ‘Emergence’ hypothesis.

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References

  1. Alff-Steinberger C (1969) The genetic code and error transmission. Proc Natl Acad Sci USA 64:584–591

    Article  CAS  PubMed  Google Scholar 

  2. Ardell DH (1998) On error minimization in a sequential origin of the standard genetic code. J Mol Evol 47:1–13

    Article  CAS  PubMed  Google Scholar 

  3. Barabasi A, Albert R (1999) Emergence of scaling in random networks. Science 286:509–512

    Article  PubMed  Google Scholar 

  4. Conway Morris S (1998) Early metazoan evolution: reconciling paleontology and molecular biology. Am Zool 38:867–877

    Google Scholar 

  5. Crick FHC (1968) The origin of the genetic code. J Mol Biol 38:367–379

    Article  CAS  PubMed  Google Scholar 

  6. Di Giulio M (1989) The extension reached by the minimization of polarity distances during the evolution of the genetic code. J Mol Evol 29:288–293

    Article  PubMed  Google Scholar 

  7. Di Giulio M (2001) The origin of the genetic code cannot be studied using measurements based on the PAM matrix because this matrix reflects the code itself, making any such analyses tautologous. J Theor Biol 208:141–144

    Article  PubMed  Google Scholar 

  8. Di Giulio M, Medugno M (2001) The level and landscape of optimization in the origin of the genetic code. J Mol Evol 52:372–382

    PubMed  Google Scholar 

  9. Elena SF, Carrasco P, Daros J, Sanjuan R (2006) Mechanisms of genetic robustness in RNA viruses. EMBO Rep 7:168–173

    Article  CAS  PubMed  Google Scholar 

  10. Feng DF, Cho G, Doolittle RF (1997) Determining divergence times with a protein clock: update and reevaluation. Proc Natl Acad Sci 94:13028–13033

    Article  CAS  PubMed  Google Scholar 

  11. Freeland SJ, Hurst LD (1998a) Load minimization of the genetic code: history does not explain the pattern. Proc R Sci Lon B 265:2111–2119

    Article  CAS  Google Scholar 

  12. Freeland SJ, Hurst LD (1998b) The genetic code is one in a million. J Mol Evol 47:238–248

    Article  CAS  PubMed  Google Scholar 

  13. Freeland SJ, Knight RD, Landweber LF, Hurst LD (2000) Early fixation of an optimal genetic code. Mol Biol Evol 17:511–518

    CAS  PubMed  Google Scholar 

  14. Gilis D, Massar S, Cerf NJ, Rooman M (2001) Optimality of the genetic code with respect to protein stability and amino-acid frequencies. Genome Biol 2:11

    Article  Google Scholar 

  15. Goldman N (1993) Further results on error minimization in the genetic code. J Mol Evol 37:662–664

    CAS  PubMed  Google Scholar 

  16. Goodarzi H, Nejad HA, Torabi N (2004) On the optimality of the genetic code, with consideration of termination codons. BioSystems 77:163–173

    Article  CAS  PubMed  Google Scholar 

  17. Grantham R (1974) Amino acid difference formula to help explain protein evolution. Science 185:862–864

    Article  CAS  PubMed  Google Scholar 

  18. Haig D, Hurst LD (1992) A quantitative measure of error minimization in the genetic code. J Mol Evol 33:412–417

    Article  Google Scholar 

  19. Light S, Kraulis P, Elofsson A (2005) Preferential attachment in the evolution of metabolic networks. BMC Genomics 6:159

    Article  PubMed  Google Scholar 

  20. Liu CC, Mack AV, Tsao ML, Mills JH, Lee HS, Choe H, Farzan M, Schultz PG, Smider VV (2008) Protein evolution with an expanded genetic code. Proc Natl Acad Sci USA 105:17688–17693

    Article  CAS  PubMed  Google Scholar 

  21. Massey SE (2008a) A neutral origin for error minimization in the genetic code. J Mol Evol 67:510–516

    Article  CAS  PubMed  Google Scholar 

  22. Massey SE (2008b) The proteomic constraint and its role in molecular evolution. Mol Biol Evol 25:2557–2565

    Article  CAS  PubMed  Google Scholar 

  23. Massey SE, Garey JR (2007) A comparative genomics analysis of codon reassignments reveals a link with mitochondrial proteome size and a mechanism of genetic code change via suppressor tRNAs. J Mol Evol 64:399–410

    Article  CAS  PubMed  Google Scholar 

  24. Massey SE, Moura G, Beltrao P, Almeida R, Garey JR, Tuite MF, Santos MAS (2003) Comparative evolutionary genomics unveils the molecular mechanism of reassignment of the CTG codon in Candida spp. Genome Res 13:544–557

    Article  CAS  PubMed  Google Scholar 

  25. Miyata T, Miyazawa S, Yasunaga T (1979) Two types of amino acid substitutions in protein evolution. J Mol Evol 12:219–236

    Article  CAS  PubMed  Google Scholar 

  26. Novozhilov AS, Wolf YI, Koonin EV (2007) Evolution of the genetic code: partial optimization of a random code for robustness to translation error in a rugged fitness landscape. Biol Direct 2:24

    Article  PubMed  Google Scholar 

  27. Osawa S, Jukes TH (1989) Codon reassignment (codon capture) in evolution. J Mol Evol 28:271–278

    Article  CAS  PubMed  Google Scholar 

  28. Schultz DW, Yarus M (1994) Transfer RNA mutation and the malleability of the genetic code. J Mol Biol 235:1377–1380

    Article  CAS  PubMed  Google Scholar 

  29. Sonneborn TM (1965) Evolving genes and proteins. Academic Press, New York

    Google Scholar 

  30. Stoltzfus A, Yampolsky LY (2007) Amino acid exchangeability and the adaptive code hypothesis. J Mol Evol 65:456–462

    Article  CAS  PubMed  Google Scholar 

  31. Szollosi GJ, Derenyi I (2008) The effect of recombination on the neutral evolution of genetic robustness. Math Biosci 214:58–62

    Article  PubMed  Google Scholar 

  32. Valley JW, Cavosie AJ, Fu B, Peck WH, Wilde SA (2006) Comment on “Heterogenous Hadean hafnium: evidence of continental crust at 4.4 to 4.5 Ga”. Science 312:1139

    Article  CAS  PubMed  Google Scholar 

  33. van Nimwegen E, Crutchfield JP, Huynen M (1999) Neutral evolution of mutational robustness. Proc Natl Acad Sci USA 96:9716–9720

    Article  PubMed  Google Scholar 

  34. Woese CR (1965) On the evolution of the genetic code. Proc Natl Acad Sci USA 54:1546–1552

    Article  CAS  PubMed  Google Scholar 

  35. Wong JT (1975) A coevolution theory of the genetic code. Proc Natl Acad Sci USA 72:1909–1912

    Article  CAS  PubMed  Google Scholar 

  36. Yampolsky LY, Stoltzfus A (2005) The exchangeability of amino acids in proteins. Genetics 170:1459–1472

    Article  CAS  PubMed  Google Scholar 

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Acknowledgments

The author would like to thank two anonymous reviewers of a previous version of this manuscript for their valuable comments. This study was supported by funds provided by the Department of Biology, University of Puerto Rico, Rio Piedras.

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Correspondence to Steven E. Massey.

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Massey, S.E. Searching of Code Space for an Error-Minimized Genetic Code Via Codon Capture Leads to Failure, or Requires At Least 20 Improving Codon Reassignments Via the Ambiguous Intermediate Mechanism. J Mol Evol 70, 106–115 (2010). https://doi.org/10.1007/s00239-009-9313-7

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Keywords

  • Genetic code
  • Emergence
  • Codon reassignment