The spatial distribution of fixed mutations within genes coding for proteins (original) (raw)

Summary

We have examined the extensive amino acid sequence data now available for five protein families — the α crystallin A chain, myoglobin, alpha and beta hemoglobin, and the cytochromes_c_ — with the goal of estimating the true spatial distribution of base substitutions within genes that code for proteins. In every case the commonly used Poisson density failed to even approximate the experimental pattern of base substitution. For the 87 species of beta hemoglobin examined, for example, the probability that the observed results were from a Poisson process was the minuscule 10−44. Analogous results were obtained for the other functional families. All the data were reasonably, but not perfectly, described by the negative binomial density. In particular, most of the data were described by one of the very simple limiting forms of this density, the geometric density. The implications of this for evolutionary inference are discussed. It is evident that most estimates of total base substitutions between genes are badly in need of revision.

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References

• Baba ML, Darga LL, Goodman M, Czelusniak J (1981) Evolution of cytochrome_c_ investigated by the maximum parsimony method. J Mol Evol 17:197–213
  PubMed Google Scholar
• Barnabas J, Goodman M, Moore GW (1972) Descent of mammalian alpha globin chain sequences investigated by the maximum parsimony method. J Mol Biol 69:249–278
  PubMed Google Scholar
• De Jong WW, Goodman M (1982) Mammalian phylogeny studied by sequence analysis of the eye lens protein α-crystallin. Z Säugetierkunde 47:257–276
  Google Scholar
• Fitch W, Markowitz E (1970) An improved method for determining codon variability in a gene and its application to the rate of fixation of mutations in evolution. Biochem Genet 4:579–593
  PubMed Google Scholar
• Goodman M (1981) Decoding the pattern of protein evolution. Progr Biophys Mol Biol 38:105–164
  Google Scholar
• Goodman M, Moore GW, Barnabas J, Matsuda G (1974) The phylogeny of human globin genes investigated by the maximum parsimony method. J Mol Evol 3:1–48
  PubMed Google Scholar
• Goodman M, Czelusniak J, Moore GW, Romero-Herrera AE, Matsuda G (1979) Fitting the gene lineage into its species lineage, a parsimony strategy illustrated by cladograms constructed from globin sequences. Syst Zool 28:132–163
  Google Scholar
• Goodman M, Romero-Herrera AE, Dene H, Czelusniak J, Tashian RE (1982) Amino acid sequence evidence on the phylogeny of primates and other eutherians. In: Goodman M (ed) Macromolecular sequences in systematic and evolutionary biology. Plenum Press, New York, pp 115–191
  Google Scholar
• Hoel PG (1954) Testing goodness of fit. In: Introduction to Mathematical Statistics, Chapter 9, Chapman and Hall, Ltd. (London), John Wiley & Sons (New York)
  Google Scholar
• Holmquist R (1980) The method of parsimony: an experimental test and theoretical analysis of the adequacy of molecular restoration studies. J Mol Biol 135:939–958
  Google Scholar
• Holmquist R (1978) A measure of the denseness of a phylogenetic network. J Mol Evol 11:225–231
  PubMed Google Scholar
• Holmquist R, Pearl D (1980) Theoretical foundations for quantitative paleogenetics Part III: the molecular divergence of nucleic acids and proteins for the case of genetic events of unequal probability. J Mol Evol 16:211–267
  PubMed Google Scholar
• Holmquist R, Pearl D, Jukes TH (1982) Nonuniform molecular divergence. In: Goodman M (ed) Macromolecular sequences in systematic and evolutionary biology. Plenum Press, New York, pp 281–315
  Google Scholar
• Moore GW, Barnabas J, Goodman M (1973) A method for constructing maximum parsimony ancestral amino acid sequences on a given network. J Theor Biol 38:459–485
  PubMed Google Scholar
• Nei M, Li W-H (1979) Mathematical model for studying genetic variation in terms of restriction endonucleases. Proc Natl Acad Sci USA 76:5269–5273
  PubMed Google Scholar
• Tajima F, Nei M (1982) Biases of the estimates of DNA divergence obtained by the restriction enzyme technique. J Mol Evol 18:115–120
  PubMed Google Scholar
• Uzzell T, Corbin KW (1971) Fitting discrete probability distributions to evolutionary events. Science 172:1089–1096
  PubMed Google Scholar

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Authors and Affiliations

1. Space Sciences Laboratory, University of California at Berkeley, 94720, Berkeley, CA, USA
   Richard Holmquist
2. Department of Anatomy, Wayne State University, 48105, Detroit, MI, USA
   Morris Goodman & John Czelusniak
3. Department of Electrical Engineering (Graduate Division), University of California at Berkeley, 94720, Berkeley, CA, USA
   Thomas Conroy

Authors

1. Richard Holmquist
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2. Morris Goodman
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3. Thomas Conroy
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4. John Czelusniak
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In memory of Margaret Dayhoff, who charted the course of molecular evolution

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Holmquist, R., Goodman, M., Conroy, T. et al. The spatial distribution of fixed mutations within genes coding for proteins.J Mol Evol 19, 437–448 (1983). https://doi.org/10.1007/BF02102319

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• Received: 10 February 1983
• Issue Date: November 1983
• DOI: https://doi.org/10.1007/BF02102319

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