Parameters of proteome evolution from histograms of aminoacid sequence identities of paralogous proteins
 Jacob Bock Axelsen^{1, 2},
 KoonKiu Yan^{2, 3} and
 Sergei Maslov^{2, 3}Email author
https://doi.org/10.1186/17456150232
© Axelsen et al; licensee BioMed Central Ltd. 2007
Received: 01 November 2007
Accepted: 26 November 2007
Published: 26 November 2007
Abstract
Background
The evolution of the full repertoire of proteins encoded in a given genome is mostly driven by gene duplications, deletions, and sequence modifications of existing proteins. Indirect information about relative rates and other intrinsic parameters of these three basic processes is contained in the proteomewide distribution of sequence identities of pairs of paralogous proteins.
Results
We introduce a simple mathematical framework based on a stochastic birthanddeath model that allows one to extract some of this information and apply it to the set of all pairs of paralogous proteins in H. pylori, E. coli, S. cerevisiae, C. elegans, D. melanogaster, and H. sapiens. It was found that the histogram of sequence identities p generated by an alltoall alignment of all protein sequences encoded in a genome is well fitted with a powerlaw form ~ p^{γ}with the value of the exponent γ around 4 for the majority of organisms used in this study. This implies that the intraprotein variability of substitution rates is best described by the Gammadistribution with the exponent α ≈ 0.33. Different features of the shape of such histograms allow us to quantify the ratio between the genomewide average deletion/duplication rates and the aminoacid substitution rate.
Conclusion
We separately measure the shortterm ("raw") duplication and deletion rates ${r}_{\text{dup}}^{\ast}$, ${r}_{\text{del}}^{\ast}$ which include gene copies that will be removed soon after the duplication event and their dramatically reduced longterm counterparts r_{dup}, r_{del}. High deletion rate among recently duplicated proteins is consistent with a scenario in which they didn't have enough time to significantly change their functional roles and thus are to a large degree disposable. Systematic trends of each of the four duplication/deletion rates with the total number of genes in the genome were analyzed. All but the deletion rate of recent duplicates ${r}_{\text{del}}^{\ast}$ were shown to systematically increase with N_{genes}. Abnormally flat shapes of sequence identity histograms observed for yeast and human are consistent with lineages leading to these organisms undergoing one or more wholegenome duplications. This interpretation is corroborated by our analysis of the genome of Paramecium tetraurelia where the p^{4} profile of the histogram is gradually restored by the successive removal of paralogs generated in its four known wholegenome duplication events.
Keywords
Open peer review
This article was reviewed by Eugene Koonin, Yuri Wolf (nominated by Eugene Koonin), David Krakauer, and Eugene Shakhnovich.
Background
The recent availability of complete genomic sequences of a diverse group of living organisms allows one to quantify basic mechanisms of molecular evolution on an unprecedented scale. The part of the genome consisting of all proteincoding genes (the full repertoire of its proteome) is at the heart of all processes taking place in a given organism. Therefore, it is very important to understand and quantify the rates and other parameters of basic evolutionary processes shaping thus defined proteome. The most important of those processes are:
• Gene duplications that give rise to new proteincoding regions in the genome. The two initially identical proteins encoded by a pair of duplicated genes subsequently diverge from each other in both their sequences and functions.
• Gene deletions in which genes that are no longer required for the functioning of the organism are either explicitly deleted from the genome or stop being transcribed and become pseudogenes whose homology to the existing functional genes is rapidly obliterated by mutations.
• Changes in aminoacid sequences of proteins encoded by already existing genes. This includes a broad spectrum of processes including point substitutions, insertions and deletions (indels), and transfers of whole domains either from other genes in the same genome or even from genomes of other species.
The BLAST (blastp) algorithm [1] allows one to quickly obtain the list of pairs of paralogous proteins encoded in a given genome whose aminoacid sequences haven't diverged beyond recognition. The set of their percentage identities (PIDs) is a dynamic entity that changes due to gene duplications, deletions, and local changes of sequences. Duplication events constantly create new pairs of paralogous proteins with PID = 100%, while subsequent substitutions, insertions and deletions result in their PID drifting down towards lower values. A paralogous pair disappears from this dataset if one of its constituent genes is deleted from the genome, becomes a pseudogene, or when the PID of the pair becomes too low for it to pass the Evalue cutoff of the algorithm. Thus the PID histogram contains a valuable if indirect information about past duplications, deletions, and sequence divergence events that took place in the genome. In what follows we propose a mathematical framework allowing one to extract some of this information and quantify the average rates and other parameters of the basic evolutionary processes shaping proteincoding contents of a genome.
The list of all paralogous pairs generated by the alltoall alignment of protein sequences encoded in a given genome is generally much larger than the list of pairs of sibling proteins created by individual duplication events. For example, a family consisting of F paralogous proteins contributes up to F(F  1)/2 pairs to the alltoall BLAST output, while not more than F  1 of these pairs connect the actual siblings to each other. The identification of the most likely candidates for these "true" duplicates is in general a rather complicated task which involves reconstructing the actual phylogenetic tree for every family in a genome. This goes beyond the scope of this study, where we employ a much simpler (yet less precise) Minimum Spanning Tree algorithm to extract a putative nonredundant subset of true duplicated (sibling) pairs.
The idea of quantifying evolutionary parameters using the histogram of some measure of sequence similarity of duplicated genes in itself is not new. It was already discussed by Gillespie (see [2] and references therein) and later applied [3] to measure the deletion rate of recent duplicates. There are two important differences between our methods and those of the Ref. [3]:
• We use relatively slow changes in aminoacid sequences of proteins as opposed to much faster silent substitutions of nucleotides used in the Ref. [3]. This allows to dramatically extend the range of evolutionary times amenable to this type of analysis.
• In addition to PID distributions in the nonredundant set of true duplicated pairs used in the Ref. [3] we also study that in the highly redundant set of all paralogous pairs detected by BLAST. It turned out that both these distributions contain important and often complimentary information about the quantitative dynamics of the underlying evolutionary process. The shape of the latter (alltoall) histogram is to a first approximation independent of duplication and deletion rates and thus it allows us to concentrate on fine properties of aminoacid substitution.
The central results of our analysis are:
• The middle part of the PID histogram of all paralogous pairs detected by BLAST is well described by a powerlaw functional form with a nearly universal value of the exponent γ ≃ 4 observed in a broad variety of genomes. Our mathematical model relates this exponent to parameters of intraprotein variability of sequence divergence rates.
• The upper part of the PID histogram corresponding to recently duplicated pairs (PID>90%) deviates from this powerlaw form. It is exactly this subset of paralogous pairs that was extensively analyzed in Ref. [3]. This feature is consistent with the picture of frequent deletion of recent duplicates proposed in Ref. [3].
• The analysis of various features of the PID histogram of all paralogous pairs and that of a subset consisting of true duplicated (sibling) pairs allows us to quantify both the longterm average duplication and deletion rates in a given genome as well as a dramatic increase in those rates for recently duplicated genes.
• Abnormally flat PID histograms observed for yeast and human are consistent with lineages leading to these organisms undergoing one or more WholeGenome Duplications (WGD). This interpretation is corroborated by the genome of Paramecium tetraurelia where the PID^{4} profile of the sequence identity histogram is gradually restored by the successive removal of paralogs generated in its four known WGD events.
• Applying the same methods to large individual families of paralogous proteins allows one to study the variability of evolutionary parameters within a given genome. It is shown that larger or slower evolving families are characterized by higher interprotein variability of aminoacid substitution rates.
Results
Distribution of sequence identities of all paralogous pairs in a genome

Region I: There is a sharp and significant upturn in the PID histogram above roughly 90–95% compared to what one expects from extrapolating N_{ a }(p) from lower values of p. Apparently the constants (or possibly even mechanisms) of the dynamical process shaping N_{ a }(p) are different in this region.

Region II: This region covers the widest interval of PIDs 30% <p < 90%. N_{ a }(p) in this region can be approximated by a powerlaw form of p^{γ}with γ ≈ 4 (shown as a dashed line in Fig. 1A.) The best fits to the powerlaw form in the Region II are listed in Table 1 and (with the exception of yeast and human) they fall in the 3 – 5 range. The nearuniversality of the shape of the PID histogram is perhaps best illustrated by an approximate collapse of PID histograms in different genomes when they are normalized by the total number N_{genes}(N_{genes}  1)/2, of all (both paralogous and nonparalogous) gene pairs in the genome (Fig. 1B).
Deletion and duplication rates. The first column contains the name of the organism, the second column – N_{genes}, the number of genes in its genome, the third column is the value of the exponent γ in the best fit with p^{γ}to N_{ a }(p) in the region II. The fourth, fifth, sixth and seventh columns are correspondingly the ratios ${r}_{\text{dup}}^{\ast}/\overline{\mu}$, r_{dup}/$\overline{\mu}$, r_{del}/$\overline{\mu}$, and ${r}_{\text{del}}^{\ast}/\overline{\mu}$ defined and measured as described in the text.
Organism  Proteome size  γ  ${r}_{\text{dup}}^{\ast}/\overline{\mu}$  r_{dup}/$\overline{\mu}$  r_{del}/$\overline{\mu}$  ${r}_{\text{del}}^{\ast}/\overline{\mu}$ 

H. pylori  1590  3.1  0.73  0.032  0.16  67 
E. coli  4288  4.4  1.37  0.038  0.10  64 
S. cerevisiae  5885  1.8  1.61  0.24  0.24  27 
C. elegans  19099  4.2  3.16  0.27  0.37  41 
D. melanogaster  14015  4.4  0.35  0.084  0.22  30 
H. sapiens  25319  2.4  2.82  0.85  0.16  19 
• Region III: In this region p < 25 – 30% the histogram N_{ a }(p) starts to deviate down from the p^{γ}powerlaw behavior. This decline is an artifact of the inability of sequencebased algorithms such as BLAST to detect some of the bona fide paralogous pairs with low sequence identity. This explanation is corroborated by the observation that the exact position of the downturn of N_{ a }(p) in the region III is determined by the Evalue cutoff (see Additional file 1).
Birthanddeath model of the proteome evolution
In an attempt to interpret the empirical features of the PID distribution described above we propose a simple stochastic birth and death model of the proteome evolution. It consists of a sequence of random gene duplications, deletions, and changes in aminoacid sequences of proteins they encode. Several versions of such models were previously studied [4–7] most recently in the context of powerlaw distribution of family sizes. Our model extends these previous attempts by concentrating on evolution of sequence identities as opposed to just the number of proteins in different families.
Amino acid substitutions, insertions and deletions cause the sequence identity of any given pair of paralogous proteins to decay with time. Consider two paralogous proteins with PID = p × 100% aligned against each other. In the simplest possible case changes in their sequences happen uniformly at all amino acid positions at a constant rate μ = const. The effective "substitution" rate μ combines the effects of actual substitutions and short indels. The PID of this paralogous pair changes according to the equation dp/dt = 2μp. The factor two in the right hand side of this equation comes from the fact that substitutions can happen in any of the two proteins involved, while the factor p – from the observation that only changes in parts of the two sequences that remain identical at the time of the given change lead to a further decrease of the PID. This equation results in an exponentially decaying PID: p(t) ~ exp(2μt). More generally the drift of PID could be described by the equation dp/dt = v(p). When substitution rate varies for different amino acids within the same protein the relationship between v(p) and p would in general be nonlinear. For our immediate purposes we will leave it unspecified. The negative drift of PIDs generates a pdependent flux of paralogous pairs down the PID axis given by v(p)N_{ a }(p). The net flux into the PID bin of the width Δp centered around p is given by $\Delta p\frac{\partial}{\partial p}[v(p){N}_{a}(p)]$ (see Fig. 2A).
In our model the total number of genes N_{genes} in the genome exponentially grows (or decays) according to dN_{genes}/dt = (r_{dup}  r_{del})N_{genes}. When the genome size of an organism remains (approximately) constant with time one can find the stationary asymptotic solution of the previous equation. In this case one must have r_{dup} = r_{del} so that the second term in the right hand side is equal to zero.
In the case of an exponentially growing or shrinking genome the stationary solution for N_{ a }(p, t) does not exist. However, it exists for the histogram normalized by the total number of protein pairs: It is easy to show that since
In the steady state solution one has ∂n_{ a }(p, t)/∂t = 0 = ∂/∂p[v(p)n_{ a }(p)] or
n_{ a }(p) ~ N_{ a }(p) ~ 1/v(p).
The conjecture that the normalized PID histogram n_{ a }(p) = N_{ a }(p)/[N_{genes}(N_{genes}  1)/2] indeed is nearly stationary during the course of evolution is corroborated by the fact that all six n_{ a }(p) curves in various genomes used in our study approximately lie on top of each other in Fig. 1B (compared to unnormalized N_{ a }(p) shown in Fig. 1A).
Eq. 3 is a generalization of the previously discussed exponential decay of p(t) derived for a constant substitution rate μ. It simply weighs these exponentials by ρ(μ) – their likelihood of occurrence. For any given ρ(μ) one could exclude time from Eqs. 3 and 4 and express v as a function of p. Such v(p) dependence could then be directly compared with the empirically derived formula. In the absence of an analytical expression relating v(p) to (μ) one is limited to use a trialanderror method. We start with Gammadistributed ρ(μ) ~ μ^{α1}exp(μ/μ_{0}) which has been predominantly used in the literature [8–10]. Inserting thus defined ρ(μ) into Eqs. 3 and 4 one gets p(t) = (2μ_{0})^{α}/(t + (2μ_{0})^{1})^{ α }and v(t) = α(2μ_{0})^{α}/(t + (2μ_{0})^{1})^{α+1}which leads to v(p) ~ p^{(α+1)/α}and thus to N_{ a }(p) ~ p^{(α+1)/α}.
Robustness of the functional form of N_{ a }(p) with respect to assumptions used in the model
The birthanddeath model described above is based on a simplified picture of genome evolution. In particular it implicitly assumes:
• The neutrality of individual gene duplication and deletion events resulting in identical rates of these two processes in all paralogous families in the genome.
• Identical average aminoacid substitution rates μ_{0} in all individual proteins.
Both of these assumptions are known to be not, strictly speaking, true. Sequences of some "important" proteins (e.g. constituents of the ribosome) are known to evolve very slowly. Also, the families containing essential (lethal knockout) genes were recently shown [11] to be characterized by higher average duplication and deletion rates than those lacking such genes.
However, the validity of our main results goes well beyond the validity of the approximations that went into our birthanddeath model. The advantage of using the histogram of sequence identities generated by the alltoall alignment (N_{ a }(p)) lies in its remarkable universality and robustness. When the Eq. 2 is applied to individual families one can see that familytofamily variation of (and correlations between) the duplication rate r_{dup}, the deletion rate r_{del}, and the average substitution rate μ_{0} affect only the prefactor in the powerlaw form of N_{ a }(p). Thus the exponent γ = 1 + 1/α describing this powerlaw is very robust with respect to assumptions of the model and depends only to the exponent α quantifying the intraprotein variability of aminoacid substitution rates.
The exact mechanisms behind this apparent universality of α are not entirely clear. Chances are that it is dictated more by the protein physics rather than by organismspecific evolutionary mechanisms. A possible path towards derivation of the exponent α from purely biophysical principles starts with the results of Ref. [12], which models the effects of (correlated) multiple aminoacid substitutions on stability of the native state of a protein. However, such analysis goes beyond the scope of the present work and will be reserved for a future study.
Distribution of sequence identities of true duplicated pairs
A highly redundant dataset consisting of all paralogous pairs present in the genome enabled us to quantify the variability of intraprotein substitution rates. Another set of important parameters describing proteome evolution are average deletion/inactivation and duplication rates r_{del} and r_{dup}. As will be shown in the following, the reduced nonredundant dataset consisting only of protein pairs directly produced in duplication events allows us to estimate these rates.
To better understand the difference between those two datasets we illustrate it with a simple example. The family of four evolutionary related proteins A, B, C, D contributes six paralogous pairs to N_{ a }(p). This family was actually created by three subsequent duplication events: first A duplicated to give rise to B, then B duplicated to C and finally C duplicated to D. Thus only three out of total six paralogous pairs are directly produced in gene duplication events. The actual number of duplicated pairs could be even smaller if some intermediate genes were deleted in the course of the evolution. In general a family consisting of F proteins contributes at or around F(F  1)/2 paralogous pairs to N_{ a }(p), but only F  1 duplicated pairs to N_{ d }(p).
Statistics of datasets used in this study. The first column is the name of the organism, the second column – the number of proteincoding genes in its genome, N_{genes}, the third column – the number of proteins for which we found at least one paralogous partner, the fourth column is the percentage of proteins with at least one paralog, the fifth column – the total number of distinct BLAST hits generated before we applied subsequent filtering, the sixth column – the number of paralogous pairs included in N_{ a }(p), and the seventh column – in N_{ d }( p).
Organism  Proteome size  Number of proteins with paralogs  % of proteins with paralogs  BLASTP hits  Number of pairs in N_{ a }(p)  Number of pairs in N_{ d }(p) 

H. pylori  1590  230  14%  3228  260  148 
E. coli  4288  1428  33%  16768  2614  1013 
S. cerevisiae  5885  1689  29%  43915  2297  1025 
C. elegans  19099  6894  36%  204398  46463  5545 
D. melanogaster  14015  4153  30%  557047  17621  3238 
H. sapiens  25319  9252  37%  1330721  31078  6595 
Numerical test of analytical predictions
Fitting evolutionary parameters of real proteomes: the longterm deletion rate
To estimate the average of deletion and duplication rates we performed a twoparameter fit to the N_{ d }(p)/N_{ a }(p) ratio with A exp(B/(γ  1)p^{γ1}) (see Eqs. 2,6) in the 30% <p < 90% interval (region II in Fig. 1). Here A and B = (r_{dup} + r_{del})/(2$\overline{\mu}$) are the two free fitting parameters. The exponent γ used in the fitting formula itself was obtained from the best fit to N_{ a }(p) in the same region with the powerlaw form p^{γ}(see column 3 in Table 1). The ratio r_{del}/$\overline{\mu}$ was extracted from the bestfit value of B and the independently calculated duplication rate ratio r_{dup}/$\overline{\mu}$ (see subsection below). It is listed in the sixth column of the Table 1.
Fitting evolutionary parameters of real proteomes: the shortterm deletion rate of recent duplicates
A very pronounced and reproducible feature in all organismwide histograms is an abrupt drop as is lowered from 100% down to about 90–95% (region I in Fig. 1.) The drop is as large as 30fold in prokaryotes and is around 3to10 fold in eukaryotes. It is subsequently followed by a increase of in the region II which at low 25% (region III) turns down again only due to limitations of our ability to detect evolutionary related sequences. There exists several possible explanations for this initial drop in the region I :
• The gene conversion process. In a gene conversion process a part or the whole sequence of one of the paralogous genes is used as a template to modify the sequence of another. It happens with a reasonable frequency only if those two genes are sufficiently close to each other in their sequences so that DNA repair mechanisms might mistakenly assume that one of them is the corrupted version of the other. If gene conversion events are sufficiently common, the initial separation of a pair of freshly duplicated genes may take a long time, as one of them would be getting constantly converted back to the other. This would result in an abnormally small drift velocity v(p) for p close to 100% and hence to an abnormally high N_{ a }(p) ~ 1/v(p).
Another, more plausible explanation is that freshly duplicated genes are characterized by a much higher deletion rate ${r}_{\text{del}}^{\ast}$ ≫ r_{del} [3]. Functional roles of such genes have not had enough time to diverge from each other making each of them more disposable than an average gene in the genome. Indeed, for S. cerevisiae and C. elegans it was empirically demonstrated [13, 14] that the deletion or inactivation of genes with a highly similar paralogous partner in the genome is up to 4 times more likely to have no consequences for the survival of the organism than the deletion/inactivation of genes lacking such a partner.
Here 2$\overline{\mu}$ = v(100%) is the average substitution rate in freshly duplicated pairs and ${r}_{\text{del}}^{\ast}$ is the deletion rate inside region I. The equation has an exponentially decaying stationary solution which for ${r}_{\text{del}}^{\ast}$ ≫ r_{del} is simply given by n_{ a }(p) ~ exp(${r}_{\text{del}}^{\ast}p/\overline{\mu}$) This functional form is consistent with the empirical data for p just below 100% and the best fits to ${r}_{\text{del}}^{\ast}/\overline{\mu}$ are listed in the seventh column of the Table 1. Ref. [3] analyzed the distribution of silent substitution numbers per silent site K_{ s }between pairs of recently duplicated genes. Under the same "drift and deletion" hypothesis used to derive the Eq. 7 such K_{ s }distribution N_{ d }(K_{ s }) should also have an exponential decaying form Nd(K_{ s }) ~ exp(${r}_{\text{del}}^{\ast}{K}_{s}/{\overline{\mu}}_{s}$), where ${\overline{\mu}}_{s}$ is the average drift velocity of K_{ s }immediately following the duplication event. Fits to this exponential functional form performed in Ref. [3] resulted in ${r}_{\text{del}}^{\ast}/{\overline{\mu}}_{s}$ ~ 7 – 24. Our estimates ${r}_{\text{del}}^{\ast}/\overline{\mu}$ ~ 20 – 70 are consistent with those of [3] provided that the $\overline{\mu}/{\overline{\mu}}_{s}$s ratio is in 0.1 – 1 interval.
Fitting evolutionary parameters of real proteomes: long and shortterm duplication rates
The number of paralogous pairs with PID≃100% also contains information about the raw duplication rate ${r}_{\text{dup}}^{\ast}$ in the genome. This rate is subsequently trimmed down to its longterm stationary value r_{dup} by the removal of a large fraction of freshly created pairs as described in the previous subsection. New pairs with PID = 100% are created at a rate ${r}_{\text{dup}}^{\ast}$N_{genes}, while they leave the bin containing PID = 100% at a rate 2$\overline{\mu}$N_{ a }(100%)/Δp. Here Δp is the width of the bin and N_{ a }(100%) is the number of pairs in this last bin. The width of the bin is assumed to be small enough so that the removal of genes from the bin due to deletion is negligible in comparison to that due to the drift in their sequences. Thus ${r}_{\text{dup}}^{\ast}/\overline{\mu}$ = 2N_{ a }(100%)/(N_{genes}Δp). The average duplication rates calculated this way are presented in the fourth column of Table 1. They are compatible with ${r}_{\text{dup}}^{\ast}/{\overline{\mu}}_{s}$ calculated in [15], where the same idea was applied to N_{ d }(K_{ s }).
The rate ${r}_{\text{dup}}^{\ast}$ includes the creation of some extra duplicated pairs which are then quickly (on an evolutionary timescale) eliminated from the genome during a "trial period" while their PID>90%. We have already demonstrated that such a deletion happens at a very high rate ${r}_{\text{del}}^{\ast}$ and thus has to be treated separately from the background deletion rate r_{del}. The duplication rapidly followed by a deletion does not change the overall distribution of paralogous pairs. Therefore, the longterm average duplication rate r_{dup} used in Eqs. 2, 6 is in fact considerably lower than the raw duplication rate ${r}_{\text{dup}}^{\ast}$. An approximate way to calculate it is to use powerlaw fits to N_{ a }(p) in the region II to extrapolate it up to 100%. Such extrapolated value ${N}_{a}^{\text{ext}}$(100%) could then be used to calculate the longterm average duplication rate as r_{dup}/$\overline{\mu}$ = 2${N}_{a}^{\text{ext}}$(100%)/(N_{genes}Δp). (see the fifth column of Table 1).
Discussion and Conclusion
An estimate of the number of superfamilies in different genomes
Here A is the best fit to N_{ a }(p) with the p^{4} in the region II. Remarkably, the results of such calculation are roughly genome independent. Using the lowest theoretical limit PID_{min} = 5% (the sequence identity of two unrelated sequences composed of 20 aminoacids) results in the effective number of superfamilies N_{ F }ranging between 4.7 in C. elegans and 9.9 in D. melanogaster. A more realistic limit PID_{min} = 8% [7], which takes into account the nonuniform frequency among 20 aminoacids, somewhat increases the number of superfamilies to 36, 28, 31, 19, 40, and 35 for H. pylori, E. coli, S. cerevisiae, C. elegans, D. melanogaster, and H. sapiens correspondingly. These numbers are still respectably small compared to N_{ F }≃ 1000 one gets by using the cutoff PID_{min} = 25% imposed by the inadequacy of sequencebased methods to detect similarity of remote paralogs.
The exponent α in large individual families
The Gammadistribution ~ μ^{α1}exp(2μ/μ_{0}) was traditionally used to model and fit the distribution of substitution rates in individual families of proteins (this tradition goes back to [16]). Our approach extends this approach to a proteomewide scale and demonstrates that beyond its role as a ad hoc fitting function the Gammadistribution indeed provides an excellent quantitative description of variability of intraprotein substitution rates.
Smaller individual families do not hold sufficient statistical power to analyze the shape of N_{ a }(p). One approach would be to group them together by some shared characteristic (e.g. by their size, or by whether or not they contain an essential gene as described in Ref. [11]). However, the N_{ a }(p) histogram in such a group would depend on additional parameters such as the rate of creation and removal of families of a given type and thus will not be amenable to our type of analysis. For example the collection of families binned by their size would have additional birthanddeath events due to whole families entering or leaving the selected bin. The rates of these processes would have a nontrivial dependence on the age of a family and thus cannot be easily incorporated into our mathematical framework.
It is important to emphasize once again that the exponent α quantifies only the intraprotein variability of substitution rates at different aminoacid positions within individual proteins. Such variability should not be confused with a much larger proteintoprotein variability of average substitutions rates. Indeed, sequences of different proteins encoded in the same genome are known to evolve at vastly different rates (see [8, 10] and references therein). Some sequences, such as e.g. those of ribosomal proteins, remain virtually unchanged over billions of years of evolution, while others change at a much faster pace. In fact, the very importance of a protein is sometimes quantified by its average rate of evolution as more essential proteins involved in core cellular processes tend to evolve at slower than average rates.
Genome size dependence and other properties of long and shortterm duplication and deletion rates
Our data indicate that the longterm duplication rate r_{dup} is of the same order of magnitude as the longterm deletion rate r_{del} (see columns 5 and 6 in the Table 1). This is to be expected since any large discrepancy in these rates would generate much greater differences in genome sizes than actually observed in these model organisms. However, as was proposed by [3], both of these rates are considerably smaller than their shortterm ("raw") counterparts ${r}_{\text{dup}}^{\ast}$ and ${r}_{\text{del}}^{\ast}$ that include recently duplicated proteins.
Our results for the fruit fly D. melanogaster are consistent with an earlier observation [15] of an abnormally low average duplication rate in this organism. According to our data ${r}_{\text{dup}}^{\ast}/\overline{\mu}$ is about nine times lower than that in the genome of C. elegans. The longterm stationary duplication rate r_{dup}/$\overline{\mu}$ in the fly is also the lowest in all eukaryotic genomes used in this study but is only three times lower than that in the worm.
Intriguingly, r_{del}/$\overline{\mu}$, r_{dup}/$\overline{\mu}$, and ${r}_{\text{dup}}^{\ast}/\overline{\mu}$ ratios are all positively correlated with the complexity of the organism quantified by the total number of genes in its genome (see correspondingly filled circles, open diamonds, and open squares in Fig. 4). This means that either the pergene duplication rate in more complex organisms is consistently higher than in their simpler counterparts or that their average aminoacid substitution rate is lower. It is likely that both above trends operate simultaneously. One possible explanation for the latter trend is that the more sophisticated mechanisms of DNA copying and repair of higher organisms lead to lower average aminoacid substitution rates.
On the other hand, we find that the deletion rate of recent duplicates, ${r}_{\text{del}}^{\ast}/\overline{\mu}$, (filled triangles in Fig. 4) is negatively correlated with the number of genes in the genome. This result is in agreement with Ref. [17] where this trend was attributed to the decrease in effective population size in more complex organisms.
The effects of whole genome duplications on the histogram of sequence identities
Two of the organisms used in our study (S. cerevisiae and H. sapiens) are characterized by a dramatically lower value of the powerlaw exponent γ (1.8 for yeast and 2.4 for human) and the overall poor quality of the power law fit to N_{ a }(p). One plausible explanation of this anomaly is in terms of Whole Genome Duplications (WGD) in lineages leading to these genomes. It is well established [18] that baker's yeast underwent a WGD event, which most likely occurred about 100 Myrs ago. While the subject remains controversial, it is also commonly believed that the vertebrate lineage leading to H. sapiens (among many other vertebrate genomes) also underwent one or several largescale duplication events [19, 20]. In the immediate aftermath of a WGD event the PID distribution changes as follows: N_{ a }(p) → 4N_{ a }(p) for p < 100%, while N_{ a }(100%) → 4N_{ a }(100%) + N_{genes}. Indeed, every ancestral paralogous pair AB would give rise to 3 new pairs with the same PID: AB', A'B, and A'B'. At the same time the bin containing the PID = 100% would in addition get N_{genes} (or fewer for a large segmental duplication) of freshly created duplicated pairs of the type AA' and BB'. The subsequent spread of this sharp peak at PID = 100% towards lower values of PID accompanied by a rapid deletion of redundant copies of duplicated genes would result in an effective flattening of the N_{ a }(p) histogram in its upper range and thus in lower effective value of the exponent γ.
Methods
The details of generating lists of paralogous proteins
The proteomes of H. pylori strain 26695 and E. coli strain K12MG1655 were downloaded from the Comprehensive Microbial Resource (CMR) [22] version 1.0. Sequences of S. cerevisiae proteins are from the Saccharomyces Genome Database (SGD) [23] version number 20031001. The D. melanogaster's sequences are from the Berkeley Drosophila Genome Project [24], release 3.1. C. elegans – Wormbase [25], release WS127.H. sapiens – the NCBI database [26], build 34.1. The initial set of paralogous pairs for each of the organisms was identified by an alltoall alignment of sequences of its proteins to each other using the BLASTP program [1]. For H. pylori, E. coli, S. cerevisiae, and D. melanogaster genomes, the Evalue threshold of 10^{10} was employed. This corresponds to pvalues of the order of 10^{12} (for H. pylori) and lower. Due to larger genome sizes of C. elegans and H. sapiens an even more conservative Evalue of 10^{30} was used to reduce the number of hits generated by the algorithm.
The "raw" datasets for worm, fly and human often contain multiple overlapping protein sequences predicted by different gene models of the same gene (including but not limited to different splicing variants). To avoid spurious hits we first mapped entries in raw datasets to unique gene IDs. This was easy to accomplish in the fly and worm datasets, where names of different gene models differ from each other by the last letter. In human genome, this was done by mapping the gi numbers of sequences in the raw dataset to unique GeneID (LocusLink) identifiers from the Entrez Gene database [27]. Subsequently, if multiple BLAST hits were connecting the same pair of gene IDs we kept the one with the longest aligned region. This way we were guaranteed that one and only one pair of splicing (or gene model) variants per pair of gene IDs would contribute to the PID histogram.
In all genomes, only pairs in which the aligned region constituted at least 80% of the length of the longer protein were kept [15]. This excludes contribution from pairs of multidomain proteins paralogous over only one of their domains.
Initially, the PID histogram in S. cerevisiae had two very sharp peaks at 51% and 70%. A close inspection revealed that these peaks are produced by evolutionary related subfamilies of nearly identical transposable elements. To correct for this obvious artifact in S. cerevisiae we removed 108 proteins encoded by known transposable elements listed in the Saccharomyces Genome Database [23] and their homologs.
The overall shape of the PID histogram in regions I and II is not sensitive to the Evalue cutoff chosen. In Fig. 1S we show that when the Evalue cutoff in the fly dataset was changed from a less conservative 10^{10} to a more conservative 10^{30} value, the shape of the histogram above 40% remained virtually unchanged. Similarly, the results are nearly independent on the type of the BLOSUM substitution matrix used (in the end we opted for the BLOSUM45.) Finally, we verified that our results are independent of the alignment algorithm utilized to calculate PIDs. Indeed, in the fly dataset we have recalculated PIDs for all paralogous pairs detected by BLAST using much more sophisticated SmithWaterman algorithm [28]. The resulting histogram (shown as blue stars in Fig. 1S) is virtually indistinguishable from that based on the blastp output.
Numerical model of the proteome evolution
We numerically simulated a birth and death model mimicking the evolution of a fixedsize proteome by duplication, deletion and substitutions. We first randomly fill a 2,000 × 100 matrix with integer numbers ranging from 1 and 20 (20 types of "aminoacids"). This constitutes the initial state of our artificial genome/proteome, encoding 2,000 "proteins" of 100 "aminoacids" each. Every aminoacid position in each of the proteins is randomly assigned the substitution rate μ drawn from a Gammadistribution with α = 1/3. One evolutionary timestep consists of:
1.Duplicate a randomly selected gene in the genome and use this duplicated copy to replace another randomly selected gene (deletion). Thus in this model the deletion rate is exactly equal to the duplication rate.
2. Randomly pick 400 aminoacid positions in the whole genome and substitute aminoacids at those positions to a randomly selected new value. The probability of a particular aminoacid position to be picked is proportional to its substitution rate μ.
This choice of parameters in our model corresponds to ${r}_{del}/\overline{\mu}={r}_{del}^{\ast}/\overline{\mu}={r}_{dup}/\overline{\mu}={r}_{dup}^{\ast}/\overline{\mu}=0.25$. Indeed, the average substitution rate per amino acid during one timestep is given by 400/(100 × 2000) = 1/500. It is equal to 0.25 of the pergene per timestep duplication/deletion rate of 1/2000. In this artificial evolutionary process we have the advantage of keeping track of all the duplicated pairs. Thus, after each duplication event the list of all duplicated pairs is updated and can be directly read off. After repeating the above steps for 20,000 times the full genome alignment of all proteins is produced and stored. The distributions of duplicated and all paralogous pairs shown in Fig. 3 are generated by averaging over 20 such samples.
Identification of true duplicated (sibling) pairs by the Minimum Spanning Tree algorithm
We are naturally not in possession of the set of protein pairs that actually underwent duplication in the course of evolution of a given genome. The identification of the most likely candidates for these "true" duplicates is in general a rather complicated task which involves reconstructing the actual phylogenetic tree for every family in a genome. However, we could make a much simpler educated guess about past duplication events by connecting paralogous proteins in a given family with the Minimum Spanning Tree (MST) that is the tree maximizing the sum of PIDs along its edges (or, to agree with its name, minimizing its opposite sign value). For a family consisting of F proteins such tree has exactly F  1 edges representing our best guess about the actual duplication events. One can prove the truth of this by induction: when a freshly duplicated pair is created with PID = 100% it extends the previously existing Minimum Spanning Tree of a family by one edge. Assuming a constant rate of divergence for all paralogous pairs in a given family, the set of duplicated pairs would continue to form the Minimum Spanning Tree at all times. We used the Kruskal algorithm [29] to approximately detect the MST.
Detection of families of paralogous genes
Families of paralogous proteins used in Figure 5 are defined as mutually isolated clusters of proteins in the network in which paralogous pairs are connected by a link. Every two nodes within a family are either directly or indirectly connected to each other by at least one chain of paralogous links, while different clusters (families) are completely disconnected from each other. Because of our requirement for the length of the aligned region to be >80% of the length of the longest protein in a pair, all proteins within such families are rather homogeneous in their lengths.
Reviewers comments
Reviewer 1: Eugene V Koonin, National Center for Biotechnology Information, National Institute of Health, Bethesda, Maryland, USA
This is quite an interesting, elegant study that presents a mathematical model connecting the distribution of percent sequence identity in paralogous protein families with the parameter of the gammadistribution of intraprotein variability. The latter parameter had been explored before, and the values reported here are within the previously estimated ranges, but to my knowledge, this is the first work that derives this parameter theoretically from completely independent data. It is intriguing and, I suppose, important that the distributions of the identities between paralogs and, accordingly, the gammadistribution parameter are almost genomeindependent. It seems like the latter parameter is almost a "fundamental constant" that follows from the physics of protein structure that is, of course, universal.
I have three comments that are rather technical but bear on the robustness and generality of the conclusions.
1. A trivial point ... but, I feel it would have been helpful to increase the number of analyzed genomes, both in terms of diversity, and by including more than one genome from each of the included lineages (and others). The analysis of sets of related genomes would (hopefully) demonstrate the robustness of the obtained distributions, and would also help assessing the significance of the differences in the exponents seen among genomes. In particular, similar, flat distributions found in human and in yeast are somewhat strange given the huge difference in the size and complexity of these genomes. This is attributed to the legacy of wholegenome duplications but I find that explanation dubious. Traces of this duplication in yeast and, especially, in vertebrates are very weak. Including more genomes would help to clarify this issue. The reported genome analysis is very simple, it cannot be computationally prohibitive.
Authors response
We agree that extending our analysis to include more genomes is fairly straightforward. However, we want to save the subject of lineagedependence of the exponent γ (apart from that related to Whole Genome Duplications (WGD)) for future studies and to report it in a separate publication. To check our hypothesis that WGD are responsible for unusual profile of the PIDhistograms in human and yeast we analyzed the genome of Paramecium tetraurelia. The lineage leading to this organism underwent as many as four separately identifiable WGD events. The results of our analysis presented in Fig. 6 and the accompanying section of the manuscript have beautifully confirmed our initial hypothesis: while the alltoall N_{ a }(p) histogram in the whole proteome of Paramecium tetraurelia (solid diamonds in Fig. 6) has an unusually flat profile similar to the one we saw in human (blue ×es in Fig. 6), the removal of proteins generated in WGD events results in a stepper PIDhistogram (red stars in Fig. 6) which is in excellent agreement with the universal p^{4} functional form (dashed line in Fig. 6). This provides necessary support to our original conjecture that unusually flat PID histograms in human and baker's yeast are also due to (possibly less obvious) duplicated pairs of proteins generated in WGD events in these two lineages.
2. The mathematical model developed in the paper is a typical birthanddeath model. I wonder why the phrase is not used (it would immediately clarify the matter to those familiar with the field) and some of the relevant literature is not cited.
Authors response
We have modified our notation to incorporate this comment. We also cited the appropriate literature on birthanddeath models [4–6].
3. The "true" duplicates are identified using minimum spanning tree under the constant rate assumption. This is quite a crude method and an unrealistic assumption, too. Building actual phylogenetic trees, certainly, would be more appropriate. This might be too hard technically for this amount of material but, at least, the issues should be acknowledged, I think.
Authors response
In the Background section of the manuscript we now explicitly mention that the minimum spanning tree is just a simpler (yet less precise) alternative to reconstructing the actual phylogenetic tree for every family in a genome.
Reviewer 2: Yuri Wolf, National Center for Biotechnology Information, National Institute of Health, Bethesda, Maryland, USA (nominated by Eugene Koonin)
The authors present an elegant model of protein evolution that ties together duplication, loss of paralogs and sequence divergence. Under the assumption of the gammadistributed variation of intraprotein evolution rates the model correctly predicts the powerlaw shape of the distribution of distances between paralogs in fully sequenced genomes. Analysis of the observed distributions allows to estimate the longterm rates of duplication, retention of paralogs and the shape parameter of the intraprotein evolution rate distribution in different organisms.
The authors are very explicit and thorough about the model description. However one point should be emphasized for the sake of biologists among the Biology Direct readership: the model is based on neutral evolution of both protein sequence and the complement of paralogs in the family and assumes that all protein families behave in the same manner. This is, obviously, a gross (if necessary) simplification of reality. The good agreement between the model predictions and the observed data is quite amazing and possibly deserves some discussion.
Authors response
I believe by this comment Dr. Wolf has raised an important point which was inadequately presented in our manuscript. While the model itself indeed was built using a simplified (completely neutral) picture of real evolutionary processes the results of this analysis turned out to be independent of these assumptions. Thus they are expected to remain valid in a more realistic evolutionary scenario (such as variable average substitution, gene duplication and deletion rates in individual families) when these assumptions are relaxed. We have added a new subsection "Robustness of the functional form of N_{ a }(p) with respect to neutrality assumptions" to the Results section of the manuscript which describes in details our answer to this comment.
Probably the most interesting observation in the paper is the nearconstancy of the power of the middle part of the distribution curve (~4) and, according to the model, the shape parameter of the intraprotein evolution rate distribution (~1/3). This result is in surprising agreement with earlier estimates, including our own [Grishin et al. 2000], obtained using entirely different approaches. This might be telling us that this parameter is a "universal constant" of protein evolution and that it is dictated more by protein physics rather than organismspecific properties.
Authors response
This is now also discussed in the new section mentioned in our previous response.
The section on the numerical simulations is somewhat less justified in the eyes of this reviewer. The underlying mathematical model appears to be fully solved analytically and simulations follow the model precisely. Thus the results of the simulations are expected to agree with the analytical solution unless some really dumb mistake was made in the course of derivation or in the implementation of the simulations. If I am missing something and the impact of this section goes beyond the simple verification, it probably should be discussed in the text.
Authors response
We agree that for the most part we use numerical simulations just to confirm the validity of our analytical results. Still, we decided to keep it in the manuscript since it clearly demonstrates (see Fig. 3) that the deletion rate used in the model could be successfully reconstructed from the N_{ d }(p) histogram. This is not entirely obvious because of the approximations that went into deducing the true duplicated (sibling) pairs using the Minimum Spanning Tree algorithm.
Reviewer 3: David Krakauer, Santa Fe Institute, Santa Fe, New Mexico, USA
The paper introduces a number of new ideas, including a minimal spanning tree algorithm for eliminating redundant distance information in a full pairwise distance matrix in order to yield an estimate of the true number of paralogous genes.
I tend to view this paper as a contribution to the neutrality literature which seeks to explain large scale patterns of genomic evolution in terms of fundamental mutational processes without invoking dedicated selection pressures acting on specific genes. Having said this, selection could be playing an important role in accounting for effective rate variation in amino acid substitutions, and in establishing the parameters of duplication and deletion that prevent excessive growth or shrinking of genomes. Whatever these selection pressures might be, they would seem to have to apply across a number of species.
I have a number of questions relating to the means of establishing the empirical power law, and the interpretation of the results.
1. As the authors are aware straight lines in loglog plots are not equivalent to having demonstrated a power law distribution and least squares fitting frequently generate biased estimates. Recent research (Clauset et al 2007) presents maximum likelihood estimators for scaling parameters free from these biases. How do the authors establish confidence in their estimates of the exponent?
Authors response
Due to an unavoidably narrow range of our power law fits along the xaxis (The region II includes 0.3 <p < 0.95 or half a decade) we didn't use any sophisticated techniques in our fitting protocols. In fact, in this interval the exponential fits look only marginally worse than those with a power law. Thus for us the exponent γ of a power law fit is just a convenient single parameter quantifying the distribution which is A) consistent with the Gammadistribution traditionally used to describe substitution rates; B) nearly universal in a broad variety of organisms ranging from H. pylori to D. melanogaster.
2. I was somewhat confused by the renormalization procedure for the raw distance histograms. While I understand that this is required in order to ensure a stationary distribution, I do not see clearly what the biological implications or assumptions of this step are. Perhaps this could be clarified?
Authors response
A standard mathematical approach to describing stationary probability distributions in growing systems is to normalize the histogram in question by the sum of its elements (∑_{ p }N_{ a }(p) in our case). However, we noticed that the total number of paralogous pairs ∑_{ p }N_{ a }(p) detected by alltoall alignment of all protein sequences grows at the same exponential rate 2(r_{dup}  r_{del}) as the square of the number of genes  ${N}_{\text{genes}}^{2}$. Moreover, in real genomes the relationship ∑_{ p }N_{ a }(p) ~ ${N}_{\text{genes}}^{2}$holds very well (see Additional file 2). Thus we decided to use the total number of gene pairs N_{genes}(N_{genes}  1)/2 ~ ${N}_{\text{genes}}^{2}$(the theoretical upper bound to the number of paralogous pairs) to normalize the N_{ a }(p). The biological implication of this result is that the fraction of paralogous pairs among all gene pairs is roughly the same in all genomes (as manifested by the collapse of normalized distributions in Fig. 1B).
We have also added the new subsection "An estimate of the number of superfamilies in different genomes" in which we speculate that the p^{4} could be extended well into the region III down to its theoretical minimum of 5 – 8%. This could be (at least partially) accomplished if in addition to sequence similarity one would use structural similarity to define superfamilies of paralogous proteins. These results indicate that normalizing N_{ a }(p) by N_{genes}(N_{genes}  1)/2 is a remarkably close approximation to normalizing it by the actual (presently unknown) number of paralogous pairs contained in the genome (including the evolutionary relationships missed by sequencealignment algorithms).
3. I worried a little about the uniqueness of the Gammadistribution in generating the scaling behavior given the absence of a robust test for the scaling exponent. How many different distributions have been tested, and how robust is the result to departure from the Gamma?
Authors response
As we explained in our response to your question #1, due to an inherently narrow range of the Region II (half a decade) we never state that the power law functional form resulting from Gammadistributed substitution rates μ is the unique way to mathematically describe the N_{ a }(p) histogram in real genomes. Thus we didn't test multiple μdistributions. Our only claim is that the observed shape of N_{ a }(p) is consistent with Gammadistributed (α = 0.33) substitution rates of individual aminoacids within a protein.
4. A little more could have gone into the discussion on the mutational processes and the role of selection. Should we assume rate variation to be the outcome of selection (as in the example of the slow rate of evolution of ribosomal proteins) where perhaps an active site remains more highly conserved, or is it the contention of the authors that some purely stochastic process at the mutational level accounts for this variation? A similar argument could be made for the duplication and deletion equilibrium. As the paper reads now, I am not sure what processes the authors have in mind.
Authors response
These and other points are now explained in the new subsection "Robustness of the functional form of N_{ a }(p) with respect to assumptions used in the model" added to the Results section of the manuscript.
5. Surely the study of evolution through the properties of genetic distance histograms is older than Lynch and Conery (2000). I am aware of earlier work by Gillespie and many others.
Authors response
Thank you for pointing this out. We have added the book by Gillespie and references therein to our citation list.
6. The paper needs to be spell checked and grammar checked.
Authors response
Done.
Reviewer 4: Eugene Shakhnovich, Harvard University, Cambridge, Massachusetts, USA
Powerlaw distributions are ubiquitous in Protein Universe and were reported to describe distribution of sizes of gene families, fold families, structural similarity relationships and other properties (1–4). It had been widely accepted that the underlying reason for their emergence is in evolutionary dynamics of creation of new genes and proteins and several dynamics models have been proposed to describe it (1, 3, 5, 6). Here the authors study the distribution of amino acid sequence similarities in paralogous families and also find a regime where powerlaw describes the observed histogram well. They proposed a mathematical model akin to master equation approach which essentially assumes that the rate of divergence is nonuniform it depends on the sequence ID of genes in question. The model and the analysis are interesting and make a valuable contribution to the literature on evolutionary dynamics. The major strength of this study is in its highly quantitative character which provides interesting insights about duplication/deletion rates which are apparently dependent on past history. However I would like the authors to address the following questions:
1) The authors consider all gene families in various organisms regardless of their functional distributions. However recent work of B. Shakhnovich and Koonin (7) (which is in a sense a forerunner of the present paper) has demonstrated that evolution of paralogous families is dramatically different in the case of families containing essential (Efamilies) genes and families that do not contain such genes (Nfamilies). It would be interesting to carry out the same quantitative analysis separately for E and Nfamilies and check how different exponents of intermediate powerlaw regimes are and how does it fit into IDdependent divergence rate picture.
Authors response
We agree that it would be interesting to separately analyze the PIDhistogram E and Nfamilies. The Fig 3B of the B. Shakhnovich and E. Koonin article (Ref. [7] in the list below) essentially does that. From it one can see that while Efamilies, which are on average larger ([7]) than Nfamilies, have a sequence identity histogram similar to the wholegenome N_{ a }(p) in our study, the composite PID histogram of all Nfamilies in yeast is essentially flat. However, as we now explain in the revised version of our manuscript our birthanddeath model does not apply to collections of individual families grouped together by some shared characteristic (e.g by their size or by whether or not they contain an essential gene). Indeed, the dynamics of such a group would depend on additional parameters such as the rate of creation and removal of families of a given type. For example, the appearance of an essential gene gene in an Nfamily would turn it into a new Efamily and remove its contribution to the histogram of all Nfamilies. We feel that modification of our analysis to incorporate these extra terms goes beyond the scope of this article.
2) The major weakness of this analysis (and other phenomenological approaches) is that the "explanation" for powerlaw regime comes from an assumption of a certain form of the distribution of substitution rates in the form of Gammafunction. While assuming Gammafunction may result in good fits it is entirely mysterious why does it emerge. The authors make a very interesting hint that Gamma emerges from intraprotein variability of substitution rates but they do not dwell much further on that. In fact such variability does exist. It was quantitatively studied in a microscopic protein evolution model by Dokholyan and myself in 2001 [8]. It would be highly instructive to check whether distributions of substitution rates observed in [8] can provide additional insights into the microscopic origin of empirical fits used in this work.
Authors response
It would be indeed extremely exciting to find a truly "microscopic" explanation of the universal parameters of the Gammadistribution reported in our manuscript along the lines of the Ref. ([8]). We feel however, that this goes well beyond the scope of this article. In the new subsection of our manuscript "Robustness of the functional form of N_{ a }(p) with respect to assumptions used in the model" we now cite the Ref. ([8]) and mention that its results lead to a biophysical explanation to the remarkable universality of the exponent a reported in our manuscript.
3) The effective distribution of intraprotein variability seems to depend on family size. Why? Can it be related to functional constrains (e.g. E and N families). A comment or further analysis will be helpful.
Authors response
Soon after sending our manuscript for review we realized that our mathematical model is applicable only to whole genomes or large individual families and does not describe PID histograms in collections of many families grouped by their size. Indeed, such collections would have additional birthanddeath events due to whole families entering or leaving the selected bin of family sizes. The rates of these processes would have a nontrivial dependence on the age of a family and thus cannot be easily incorporated into our mathematical framework. Thus we removed the Fig. 5B showing the apparent systematic variation of the exponent γ with the family size, which, in the hindsight, is likely caused by these extra birthanddeath terms. For the discussion of E and Nfamilies see our response to your question 2).
4) The powerlaw regime is observed only at sufficient level of divergence. Why? How can current model be modified to account for the full histogram, not only its powerlaw part?
Authors response
We attribute the upward turn in N_{ a }(p) for p > 90% (inside the Region I) to much higher deletion rates of recently duplicated genes caused by their apparent redundancy. The crossover to power law in the Region II means that this redundancy tends to be lost below this level of sequence identity. The combination of our equations ( 1 )and ( 7 ) provide a comprehensive mathematical description valid in both Regions II and I (as we explained the crossover in the region III is an artifact caused by some bona fide paralogous pairs being missed by sequence alignment algorithms).
References used by Eugene Shakhnovich: 1. Qian, J., Luscombe, N. M. & Gerstein, M. (2001) J Mol Biol 313, 673–81.
2. Koonin, E. V., Wolf, Y. I. & Karev, G. P. (2002) Nature 420, 218–23.
3. Huynen, M. A. & van Nimwegen, E. (1998) Mol Biol Evol 15, 583–9.
4. Dokholyan, N. V., Shakhnovich, B. & Shakhnovich, E. I. (2002) Proc Natl Acad Sci U S A 99, 14132–6.
5. Karev, G. P., Wolf, Y. I., Rzhetsky, A. Y., Berezovskaya, F. S. & Koonin, E. V. (2002) BMC Evol Biol 2, 18.
6. Roland, C. B. & Shakhnovich, E. I. (2007) Biophys J 92, 701–16.
7. Shakhnovich, B. E. & Koonin, E. V. (2006) Genome Res 16, 1529–36.
8. Dokholyan, N. V. & Shakhnovich, E. I. (2001) J Mol Biol 312, 289–307.
Declarations
Acknowledgements
Work at Brookhaven National Laboratory was carried out under Contract No. DEAC0298CH10886, Division of Material Science, U.S. Department of Energy. JBA thanks the Institute for Strongly Correlated and Complex Systems at Brookhaven National Laboratory for hospitality and financial support during visits when the majority of this work was done. JBA thanks the Lundbeck Foundation for sponsorship of PhD studies at the Niels Bohr Institute. JBA and SM visit to Kavli Institute for Theoretical Physics where this work was initiated was supported by the National Science Foundation under Grant No. PHY0551164.
Authors’ Affiliations
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