Molecular clock in neutral protein evolution
 Claus O Wilke^{1, 2}Email author
https://doi.org/10.1186/14712156525
© Wilke; licensee BioMed Central Ltd. 2004
Received: 09 June 2004
Accepted: 27 August 2004
Published: 27 August 2004
Abstract
Background
A frequent observation in molecular evolution is that aminoacid substitution rates show an index of dispersion (that is, ratio of variance to mean) substantially larger than one. This observation has been termed the overdispersed molecular clock. On the basis of in silico proteinevolution experiments, Bastolla and coworkers recently proposed an explanation for this observation: Proteins drift in neutral space, and can temporarily get trapped in regions of substantially reduced neutrality. In these regions, substitution rates are suppressed, which results in an overall substitution process that is not Poissonian. However, the simulation method of Bastolla et al. is representative only for cases in which the product of mutation rate μ and population size N_{e} is small. How the substitution process behaves when μN_{e} is large is not known.
Results
Here, I study the behavior of the molecular clock in in silico protein evolution as a function of mutation rate and population size. I find that the index of dispersion decays with increasing μN_{e}, and approaches 1 for large μN_{e} . This observation can be explained with the selective pressure for mutational robustness, which is effective when μN_{e} is large. This pressure keeps the population out of lowneutrality traps, and thus steadies the ticking of the molecular clock.
Conclusions
The molecular clock in neutral protein evolution can fall into two distinct regimes, a strongly overdispersed one for small μN_{e}, and a mostly Poissonian one for large μN_{e}. The former is relevant for the majority of organisms in the plant and animal kingdom, and the latter may be relevant for RNA viruses.
Background
Kimura has argued that the majority of nucleotide substitutions that accumulate in genes over time are selectively neutral, and go to fixation purely by chance [1]. One major prediction of Kimura's neutral theory is that the substitution process should be a Poisson process, with the mean number of substitutions per unit time equal to the variance. In contrast to this theory, empirical studies often find the variance to be significantly larger than the mean [2–8]. This observation has been termed the "overdispersed molecular clock". (For an excellent review of both empirical evidence and mathematical theories, see Ref. [9].) It is possible to reconcile Kimura's theory with the overdispersed molecular clock via Takahata's fluctuating neutral space model [10–12]. If the neutral mutation rate fluctuates slowly, then the substitution process ceases to be Poissonian, and becomes indeed overdispersed. However, the problem with the fluctuating neutral space model is that it does not offer any argument for why the neutral mutation rate should fluctuate, and thus ultimately fails to explain the observed substitution patterns.
An explanation for fluctuations in neutral mutation rate was recently proposed by Bastolla et al. [13–16]. Different proteins with identical structure naturally vary in their neutrality, that is, in the fraction of singlepoint mutants that are viable. Therefore, as a gene slowly drifts through sequence space, the neutral mutation rate will fluctuate in correlation to the changing neutrality, and this fluctuation alone could be sufficient to explain the overdispersed molecular clock. Bastolla et al. studied the substitution process in a variety of models of neutral protein evolution in silico, and found significant overdispersion in all cases they considered.
However, the simulations that Bastolla et al. carried out were limited to cases in which the product of mutation rate μ and population size N_{e} is small (because Bastolla et al. used only a single sequence as the representative of the whole population, an approach that is justified for μN_{e} ≲ 1). Since population size and mutation rate can be substantial in some species (most notably in RNA viruses), it is justified to ask how general this result is for arbitrary values of μN_{e}. It is known that large populations and populations evolving under high mutation pressure experience a strong selective pressure to avoid regions of low neutrality, an effect that has been termed "evolution of mutational robustness" [17–20]. In equilibrium, such populations settle in areas of sequence space that have aboveaverage neutrality. As a result, regions of low neutrality are not represented in the population, and the distribution of neutralities in the population is much narrower than the total distribution of neutralities in sequence space. Therefore, we should expect that the neutral mutation rate does not fluctuate strongly under these conditions, and that the molecular clock will not be significantly overdispersed.
For the present paper, I have studied the behavior of the substitution process under neutral protein evolution as a function of mutation rate μ and population size N_{e}. I have found that the accumulation of nonsynonymous mutations is substantially overdispersed for small μN_{e}, in agreement with the results of Bastolla et al., but approaches a Poisson process when μN_{e} ≫ 1. The accumulation of synonymous substitutions is always Poissonian, regardless of the value of μN_{e}.
Results
I carried out simulations with DNA sequences of length L = 75. I determined the fitness of a DNA sequence by translating it into the corresponding aminoacid sequence, and determining its native fold within the framework of a latticeprotein model. (A sequence would have fitness 1 if it folded into a predetermined target structure, and fitness 0 otherwise.) I used a simple model of maximally compact proteins on a 5 × 5 lattice. This proteinfolding model is much simpler than the ones used by Bastolla et al. [13, 14], but has been shown to produce realistic distributions of folding free energies and neutralities [21–23]. The advantage of the simpler model is that entire populations of evolving sequences can be simulated, instead of just individual sequences.
First, I have found that my model produces overdispersion (that is, an index of dispersion R substantially above 1) for nonsynonymous substitutions, but not for synonymous substitutions. The finding for synonymous mutations is not surprising, because changes in the protein's neutrality do not affect the probability with which a synonymous mutation is neutral (which is always one). Neutral evolution could produce overdispersion in the synonymous substitutions only if the number of synonymous sites in the sequence were undergoing significant fluctuations. While these fluctuations do occur, they are apparently not large enough to affect the index of dispersion.
Discussion
My results show that the size of the product μN_{e} has a substantial effect on the index of dispersion under neutral evolution. The substitution process is strongly overdispersed for small μN e, but approaches a Poisson process as μN_{e} grows large. Therefore, the next question is which of the two regimes has more biological relevance. As discussed by Cutler [9], the biggest problem in explaining the overdispersed molecular clock is not to come up with mechanisms that produce overdispersion, but to find a general mechanism that does not depend on special conditions or finelytuned parameters.
To assess the likelihood that fluctuations in neutrality contribute to the overdispersed molecular clock, we have to know the mutation rate and population size for the species of interest. It is notoriously difficult to obtain accurate data for these parameters, and only a few species have been studied in depth. One of the best data sets available is probably the one for Drosophila. Keightley and EyreWalker estimated the pernucleotide substitution rate in Drosophila to be u = 2.2 × 10^{9} [24]. If we assume that the average gene in Drosophila is 1770 bp long [24], and that 76% of the nucleotides are nonsynonymous (this number stems from averaging the number of nonsynonymous sites over all codons with equal weight), then the average number of nonsynonymous sites per gene is 1345 bp. Thus, the average rate of nonsynonymous mutations per gene is μ_{n} = 3.0 × 10^{6}. With an effective population size of approximately 3 × 10^{5} [25], we get a product of population size and pergenenonsynonymous mutation rate of approximately 1. Since selection for mutational robustness starts to take effect when this product is substantially larger than 1, Drosophila lies well within the parameter region in which we expect overdispersion to be caused by neutral evolution. For other higher organisms, in particular mammals, which tend to have comparatively small population sizes, we can expect that the product μ_{n}N_{e} falls into the same parameter region. On the other hand, for microorganisms, which can have very large population sizes, mutational robustness may play a role in their evolution. In particular, RNA viruses have genomic mutation rates on the order of one [26, 27] and their genomes consist typically of only a handful of genes. Because RNA viruses undergo severe bottlenecks on a regular basis, their effective population size N_{e} is much smaller than the number of virus particles in infected individuals (which can exceed 10^{12}), and is more closely related to the number of infected individuals. For HIV1, N_{e} has been estimated to be approximately 10^{2} for subtype A, and 10^{5} for subtype B [28].
The preceeding paragraph shows that neutral evolution of proteins is probably one source of overdispersed nonsynonymous substitutions in Drosophila and other organisms. However, overdispersion has been observed in synonymous substitutions as well. For example, Zeng et al. [29] found an index of dispersion R significantly above one for synonymous, but not for nonsynonymous substitutions in Drosophila. For mammals, some studies found R significantly above one for both synonymous and nonsynonymous substitutions [8], while others found only the nonsynonymous substitution process to be overdispersed [30]. Therefore, it is likely that other processes than neutral protein evolution also contribute to overdispersion. Such processes can be selection for optimal codon usage in the case of synonymous mutations, and positive selection on the amino acid level in the case of nonsynonymous mutations.
I have demonstrated that large μN_{e} results in a substitution process with little overdispersion. However, I have not yet given an explanation for how overdispersion is reduced in populations with large μN_{e}. There are two elements: First, selection for mutational robustness reduces the fraction of sequences with low neutrality, and increases the fraction of sequences with high neutrality, thus making the population more homogeneous and reducing the overall range of neutralities [17–20]. Second, a sequence with low neutrality will experience a real selective disadvantage in comparison to a sequence with high neutrality for large μN_{e}, and will therefore have a reduced probability to end up on the line of descent. While this selective disadvantage is often small, it can nevertheless determine the evolutionary fate of a sequence in a large population. The larger the population, the more sensitive it becomes to small fitness differences, so that in a very large population a sequence with only a moderate reduction in neutrality will have a small probability to end up on the line of descent. (The fact that the mean substitution rate increases with the population size, as seen in Fig. 2, is also consistent with this reasoning. The larger the population size, the more highneutrality sequences end up on the line of descent, which is reflected in the increase in the mean substitution rate.)
Throughout this paper, I have considered only neutral or lethal mutations. It is a reasonable question to ask if and how deleterious mutations would change my results. The answer is that they probably have only a minor impact, and the less so the larger N_{e}, unless they are very slightly deleterious. In order to affect the molecular clock, the deleterious mutations must end up on the line of descent, that is, they must go to fixation. The probability of fixation p_{fix} of deleterious mutants drops exponentially with the population size, p_{fix} = [1  exp(2s)]/ [1  exp(2sN_{e})], where s is the selective disadvantage of the deleterious mutation [31]. Therefore, for reasonable population sizes, only very slightly deleterious mutations can go to fixation and thus affect the molecular clock. This reasoning is independent of the size of μN_{e}, as long as N_{e} is large in comparison to s.
Conclusions
The present study supports the following conclusions:

Neutral drift of proteins can lead to an overdispersed substitution process for nonsynonymous mutations, but not for synonymous mutations.

The amount of overdispersion in the nonsynonymous substitution process depends strongly on the product of mutation rate and population size. As this product increases, the substitution process becomes more and more Poissonian. The transition region starts at μN_{e} ≈ 10, and extends to values well above 100.

It is not clear whether there are any species that have a sufficiently large population size and mutation rate to prevent overdispersion through neutral drift. In Drosophila, the product of mutation rate and population size is close to one, which is well below the parameter region in which the substitution process turns Poissonian.
Methods
Lattice protein model
I implemented a version of the 5 × 5 lattice protein model put forward by Goldstein and coworkers [21–23, 32]. In this model, proteins are sequences of n = 25 residues that fold into a maximally compact structure on a twodimensional grid of 5 × 5 lattice points. There are 1081 distinct possible conformations in this model, and the partition function can be evaluated exactly by summing over the contact energies of all distinct conformations.
The contact energy of a conformation i is
where the sum runs over all 1081 conformations. A sequence folds into conformation f if the contact energy for that conformation is lower than the contact energies of all other formations, E_{ f }<E_{ i }for all i ≠ f, and if the free energy of folding, which is defined as
is smaller than some cutoff ΔG_{cut}. Throughout this study, I used kT = 0.6 and ΔG_{cut} = 0. The contact energies where taken from Table VI in Ref. [33].
Sequence evolution
I simulated the evolution of populations of DNA sequences in discrete, nonoverlapping generations. Population size is denoted by N_{e}. The fitness of a sequence was 1 if the DNA sequence translated into a peptide sequence that could fold into a chosen target structure, and had a free energy of folding smaller than G_{cut}. Otherwise, the fitness of the sequence was 0. All sequences had length L = 75. In each successive generation, sequences with fitness 1 were randomly chosen to reproduce, until the new generation had N_{e} members. At reproduction, the sequences were mutated, with an average of μ base pair substitutions per sequence. I let each population evolve for several thousand generations, and kept track of the full genealogic information of all sequences in the population. In order to measure the molecular clock of fixed mutations only, I studied the pattern of base substitutions in a window of τ generations along the line of descent backwards in time, starting from the most recent common ancestor of the final population.
I varied the parameters N_{e} (10, 33, 100, 330, 1000, 3300), μ (0.0075, 0.075, 0.75), and τ (500, 1000). For each set of parameters, I carried out 500 replicates (each with a different, randomly chosen target structure), to obtain a distribution for the number of synonymous and nonsynonymous substitutions S_{d} and N_{d}. Since there was some variation in the number of synonymous and nonsynonymous sites across different target structures (on the order of approximately ± 5% variation from the mean), I then applied a correction factor to S_{d} and N_{d} to bring them into comparable units: I calculated the corrected number of synonymous substitutions as Here, S is the mean number of synonymous sites for the given replicate, and (S) is the average of S over all 500 replicates. Likewise, I calculated (Indices of dispersion calculated without this correction factor are slightly larger than the ones reported here, because the variation in S and N creates additional variance in S_{d} and N_{d}). Similar correction factors have been used in sequence analysis [7], and are generally referred to as lineage adjustments. They control for differences among lineages that are primarily related to the expected number of substitutions in a lineage, and thus should not enter the index of dispersion.
To obtain an estimate for mean and standard error of the index of dispersion, I subdivided the 500 results into 10 blocks of 50 each, and calculated mean and variance of the number of substitutions for each block. The ratio of variance to mean for a given set of substitutions (synonymous or nonsynonymous) in a block is the index of dispersion for this data set. I then calculated mean and standard error for the index of dispersion from the individual results of the 10 blocks.
The total CPU time needed to carry out all simulations was several months on a small cluster of Pentium II 500 MHz machines.
Calculation of synonymous and nonsynonymous substitutions and sites
I calculated the number of synonymous and nonsynonymous sites S and N and the number of synonymous and nonsynonymous substitutions S_{d} and N_{d} according to the method proposed by Nei and Gojobori [34]. In short, under this method the number of synonymous sites s_{ i }of a codon i is the fraction of possible substitutions to that codon that leave the residue unchanged. The number of nonsynonymous sites n_{ i }for the same codon is n_{ i }= 3  s_{ i }. For the complete sequence, S and N are calculated as and where i runs over all codons in the sequence. The number of synonymous or nonsynonymous substitutions s_{d,i}or n_{d,i}between two codons is the average number of such substitutions, where the average is taken over all paths that lead from one codon to the other. The total number of synonymous or nonsynonymous substitutions between two sequences is the sum over all individual constributions, and (again, i runs over all codons in the sequence).
To calculate the number of synonymous or nonsynonymous substitutions along the line of descent, I simply summed up all synonymous or nonsynonymous substitutions that occurred from generation to generation. Because the full evolutionary history was known, a correction for multiple mutations such as the JukesCantor correction [35] was not necessary. I also averaged the number of synonymous and nonsynonymous sites over all sequences along the line of descent, to get the mean number of synonymous and nonsynonymous sites for the given evolutionary trajectory.
Declarations
Acknowledgements
This work was supported in part by the NSF under Contract No. DEB9981397. I thank Ugo Bastolla for helpful comments on an earlier version of this paper.
Authors’ Affiliations
References
 Kimura M: The Neutral Theory of Molecular Evolution. 1983, Cambridge: Cambridge University PressView ArticleGoogle Scholar
 Ohta T, Kimura M: On the constancy of the evolutionary rate of cistrons. J Mol Evol. 1971, 1: 1825.View ArticleGoogle Scholar
 Langley CH, Fitch WM: An estimation of the constancy of the rate of molecular evolution. J Mol Evol. 1974, 3: 161177.View ArticlePubMedGoogle Scholar
 Gillespie JH: The molecular clock may be an episodic clock. Proc Natl Acad Sci USA. 1984, 81: 80098013.PubMed CentralView ArticlePubMedGoogle Scholar
 Gillespie JH: Natural selection and the molecular clock. Mol Biol Evol. 1986, 3: 138155.PubMedGoogle Scholar
 Gillespie JH: Variability of evolutionary rates of DNA. Genetics. 1986, 113: 10771091.PubMed CentralPubMedGoogle Scholar
 Gillespie JH: Lineage effects and the index of dispersion of molecular evolution. Mol Biol Evol. 1989, 6: 636647.PubMedGoogle Scholar
 Ohta T: Synonymous and nonsynonymous substitutions in mammalian genes and the nearly neutral theory. J Mol Evol. 1995, 40: 5663.View ArticlePubMedGoogle Scholar
 Cutler DJ: Understanding the overdispersed molecular clock. Genetics. 2000, 154: 14031417.PubMed CentralPubMedGoogle Scholar
 Takahata N: On the overdispersed molecular clock. Genetics. 1987, 116: 169179.PubMed CentralPubMedGoogle Scholar
 Takahata N: Statistical models of the overdispersed molecular clock. Theor Popul Biol. 1991, 39: 329344.View ArticlePubMedGoogle Scholar
 Cutler DJ: The index of dispersion of molecular evolution: slow fluctuations. Theor Popul Biol. 2000, 57: 177186. 10.1006/tpbi.1999.1445.View ArticlePubMedGoogle Scholar
 Bastolla U, Roman HE, Vendruscolo M: Neutral evolution of model proteins: Diffusion in sequence space and overdispersion. J Theor Biol. 1999, 200: 4964. 10.1006/jtbi.1999.0975.View ArticlePubMedGoogle Scholar
 Bastolla U, Porto M, Roman HE, Vendruscolo M: Lack of selfaveraging in neutral evolution of proteins. Phys Rev Lett. 2002, 89: 2080110.1103/PhysRevLett.89.208101.View ArticleGoogle Scholar
 Bastolla U, Porto M, Roman HE, Vendruscolo M: Connectivity of neutral networks, overdispersion, and structural conservation in protein evolution. J Mol Evol. 2003, 56: 243254. 10.1007/s0023900223500.View ArticlePubMedGoogle Scholar
 Bastolla U, Porto M, Roman HE, Vendruscolo M: Statistical properties of neutral evolution. J Mol Evol. 2003, 57: S103S119. 10.1007/s0023900300134.View ArticlePubMedGoogle Scholar
 van Nimwegen E, Crutchfield JP, Huynen M: Neutral evolution of mutational robustness. Proc Natl Acad Sci USA. 1999, 96: 97169720. 10.1073/pnas.96.17.9716.PubMed CentralView ArticlePubMedGoogle Scholar
 BornbergBauer E, Chan HS: Modeling evolutionary landscapes: Mutational stability, topology, and superfunnels in sequence space. Proc Natl Acad Sci USA. 1999, 96: 1068910694. 10.1073/pnas.96.19.10689.PubMed CentralView ArticlePubMedGoogle Scholar
 Wilke CO: Adaptive evolution on neutral networks. Bull Math Biol. 2001, 63: 715730. 10.1006/bulm.2001.0244.View ArticlePubMedGoogle Scholar
 Wilke CO, Adami C: Evolution of mutational robustness. Mutat Res. 2003, 522: 311. 10.1016/S00275107(02)00307X.View ArticlePubMedGoogle Scholar
 Taverna DM, Goldstein RA: The distribution of structures in evolving protein populations. Biopolymers. 2000, 53: 18. 10.1002/(SICI)10970282(200001)53:1<1::AIDBIP1>3.3.CO;2O.View ArticlePubMedGoogle Scholar
 Taverna DM, Goldstein RA: Why are proteins so robust to site mutations?. J Mol Biol. 2002, 315: 479484. 10.1006/jmbi.2001.5226.View ArticlePubMedGoogle Scholar
 Taverna DM, Goldstein RA: Why are proteins marginally stable?. Proteins. 2002, 46: 105109. 10.1002/prot.10016.View ArticlePubMedGoogle Scholar
 Keightley PD, EyreWalker A: Deleterious mutations and the evolution of sex. Science. 2000, 290: 331333. 10.1126/science.290.5490.331.View ArticlePubMedGoogle Scholar
 Li YJ, Satta Y, Takahata N: Paleodemography of the Drosophila melanogaster subgroup: application of the maximum likelihood method. Genes Genet Syst. 1999, 74: 117127. 10.1266/ggs.74.117.View ArticlePubMedGoogle Scholar
 Drake JW: Rates of spontaneous mutation among RNA viruses. Proc Natl Acad Sci USA. 1993, 90: 41714175.PubMed CentralView ArticlePubMedGoogle Scholar
 Drake JW, Holland JJ: Mutation rates among RNA viruses. Proc Natl Acad Sci USA. 1999, 96: 1391013913. 10.1073/pnas.96.24.13910.PubMed CentralView ArticlePubMedGoogle Scholar
 Grasslya NC, Harveya PH, Holmes EC: Population dynamics of HIV1 inferred from gene sequences. Genetics. 1999, 151: 427438.Google Scholar
 Zeng LW, Comeron JM, Chen B, Kreitman M: The molecular clock revisited: the rate of synonymous vs. replacement change in Drosophila. Genetica. 1998, 102/103: 369382. 10.1023/A:1017035109224.View ArticleGoogle Scholar
 Smith NG, EyreWalker A: Partitioning the variation in mammalian substitution rates. Mol Biol Evol. 2003, 20: 1017.View ArticlePubMedGoogle Scholar
 Kimura M: On the probability of fixation of mutant genes in a population. Genetics. 1962, 47: 713719.PubMed CentralPubMedGoogle Scholar
 Buchler NEG, Goldstein RA: Effect of alphabet size and foldability requirements on protein structure designability. Proteins. 1999, 34: 113124. 10.1002/(SICI)10970134(19990101)34:1<113::AIDPROT9>3.3.CO;2A.View ArticlePubMedGoogle Scholar
 Miyazawa S, Jernigan RL: Estimation of effective interresidue contact energies from protein crystal structures: quasichemical approximation. Macromolecules. 1985, 18: 534552.View ArticleGoogle Scholar
 Nei M, Gojobori T: Simple methods for estimating the numbers of synonymous and nonsynonymous nucleotide substitutions. Mol Biol Evol. 1986, 3: 418426.PubMedGoogle Scholar
 Jukes TH, Cantor CR: Evolution of protein molecules. In Mammalian protein metabolism III. Edited by: Munro HN. 1969, New York: Academic Press, 21132.View ArticleGoogle Scholar
Copyright
This article is published under license to BioMed Central Ltd. This is an openaccess article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.