Table 8. Observed amino acid replacements in SOD and expected values, assuming a covarion model

 

Amino acid differences

Comparison

My

Observed

Expected

1. Drosophila subgenera

55±10

18±3

19±3

2. Drosophila-Chymomyza

60±10

23±2

20±4

3. Mammalian orders

70±10

27±2

22±4

4. Drosophila-Ceratitis

100±20

31±2

28±3

5. Monocot-dicot

125±20

28±3

31±5

6. Angiosperm-gymnosperm

220±30

29±7

42±5

7. Mammal-amphibian

350±50

49±2

53±6

8. Tetrapod-fish

400±50

44±4

56±7

9. Vertebrate-insect

600±100

59±3

60±6

10. Animal-yeast

1100±200

67±4

66±7

The expected values are obtained usin the covarion model with the parameter values given in the text; they are averages of 40 computer simulations for each entry. The data are modified from Fitch and Ayala (19).

of sites that can accept amino acid replacements and the particular replacements that can occur at each site. It remains obscure why greater constraints would occur in Drosophila than in the Chymomyza or Ceratitis lineages (or, indeed, in other animals, plants, and fungi). But, in any case, the issue is not whether biologically ascertainable processes are at work, which of course they are, in GPDH, SOD, or any other enzymes. The issue rather is whether the processes are of such regularity that some sort of molecular clock may be assumed to be at work. The stark contrast between the pattern of evolution of GPDH and SOD may be an aberration rather than representative of prevailing modes of protein evolution, since protein evolution seems so often to behave in a clocklike manner. But the congruence between observations and the clock predictions are often obtained due to the fact that the data collected do not have sufficient resolution to exhibit likely discrepancies.

The operational risks of assuming that protein clocks are fairly reliable are made evident in Table 9. The rate of GPDH evolution is nearly 4 times faster between animals and plants than between Drosophila species, whereas the rate of SOD evolution is 1/5 as fast. If we were to use the observed rate of Drosophila evolution to estimate the time of divergence between plants and animals, GPDH would yield an estimate of 3,990 My, SOD an estimate of 224 My, both grossly erroneous. The practical conclusions to be drawn are that (i) protein clocks should be used cautiously and weighed against any other available evidence, rather than considered decisive; (ii) several protein clocks should be used whenever feasible, particularly

Table 9. Rates of evolution of GPDH and SOD and estimates of divergence time derived from the Drosophila rate

 

Rate of evolution

Normalized rate

Clock estimates, My

Taxa compared

GPDH

SOD

GPDH

SOD

GPDH

SOD

1. Drosophila subgenera

1.1

16.2

1.0

1.0

55

55

2. Mammalian orders

5.3

17.2

4.8

1.1

340

74

3. Dipteran families

4.7

15.9

4.3

1.0

470

98

4. Animal phyla

4.2

5.3

3.8

0.33

2.500

211

5. Kingdoms

4.0

3.3

3.6

0.20

3,990

224

The rate of evolution is in units of 10−10 amino acid replacements per site per year. The normalized rate is relative to the rate between the Drosophila subgenera. The clock estimates of time divergence use the average amino acid replacements between the particular organisms and assume that they are evolving as a molecular clock that ticks at the Drosophila rate.

when important evolutionary events need to be determined (44); (iii) whenever possible, synonymous rather than nonsynonymous nucleotide substitutions should be used, given that substitutions that yield amino acid replacements are more constrained by natural selection. The rapid rate of synonymous nucleotide substitutions becomes, however, a problem whenever long evolutionary spans are at stake, because many superimposed substitutions will have occurred so that the differences observed have little statistical reliability for estimating the multiple hits concealed behind the observed differences. The strategy of using as many separate molecular clocks as feasible is grounded on the convergence expected from the “law of large numbers;” statistical and other biases will tend to cancel as the number of observations increases.

I am grateful to Walter M.Fitch and Richard R.Hudson for valuable comments and to the members of my laboratory who participated in the research herein reported, particularly Kevin Bailey, Eladio Barrio, Michal Jaworski, Michal Krawczyk, Jan Kwiatowski, and Douglas Skarecky. Stephen Rich’s help with computer graphics is much appreciated. This research is supported by National Institutes of Health Grant GM42397.

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