To overcome this improbability, Maynard Smith proposed a model of protein evolution. While admitting that the origin of the first proteins remained a mystery, he suggested that one protein could evolve into another as the result of small incremental changes in amino-acid sequences, provided each sequence maintained some function at each step along the way. Maynard Smith compared protein-to-protein evolution to changing one letter in an English word in order to generate a different word (while at each step generating a different meaningful word). He used this example to convey how he thought protein evolution might work:
WORD → WORE → GORE → GONE → GENE
He explained:
The words [in this analogy] represent proteins; the letters represent amino acids; the alteration of a single letter corresponds to the simplest evolutionary step, the substitution of one amino acid for another; and the requirement of meaning corresponds to the requirement that each unit step in evolution should be from one functional protein to another.15
As a self-professed “convinced Darwinist,” Maynard Smith realized that natural selection and random mutation could only build new biological structures from preexisting structures if each intermediate structure along the way conferred some adaptive advantage. He thought that this requirement applied as much to the evolution of new genes and proteins as it did to the evolution of new phenotypic traits or larger-scale anatomical structures.16
Nevertheless, the essentially digital or alphabetic character of the genetic information that directs protein synthesis suggested a problem to Maynard Smith. How, he asked, could one gene or protein evolve into another if such a transformation required multiple simultaneous changes in the bases of the genetic text (or arrangement of amino acids)? If building new genes required multiple coordinated mutations, then the probability of generating a new gene or protein would drop precipitously, since such a transformation would require not just one improbable mutational event, but two or three or more, occurring more or less at once. Here’s how he described the potential problem:
Suppose that a protein ABCD … exists, and that a protein abCD … would be favoured by selection if it arose. Suppose further that the intermediates aBCD … and AbCD … are nonfunctional. These forms would arise by mutation, but would usually be eliminated by selection before a second mutation could occur. The double step from abCD … to ABCD would thus be very unlikely to occur.17
In Maynard Smith’s view, the improbability associated with “double-step” or multiple-step coordinated mutations presented a significant potential problem for molecular evolution. In the end, however, he concluded that such mutations were so improbable that they must not have played a significant role in the evolution of novel structures. As he explained, “Such double steps … may occasionally occur, but are probably too rare to be important in evolution.”18
For several decades, the problem he flagged receded into obscurity. As biochemist H. Allen Orr pointed out in 2005 in the journal Nature Reviews Genetics, “Although Maynard Smith’s work appeared early in the molecular revolution,” his ideas about problems facing protein evolution “were almost entirely ignored for two decades.”19 Thus, Orr noted that evolutionary biologists stopped thinking about molecular evolution as a consequence of adaptive changes at the amino-acid level. Not until the first decade of the twenty-first century would biologists confront the challenge of making a rigorous quantitative analysis of the plausibility of protein-to-protein evolution.
Waiting for Complex Adaptations
In 2004, Lehigh University biochemist Michael Behe (see Fig. 12.2), introduced briefly at the end of Chapter 9, and University of Pittsburgh physicist David Snoke published a paper in the journal Protein Science that returned to the problem first described by Maynard Smith.20 By this time, Behe had established himself as a prominent critic of neo-Darwinism by arguing that the neo-Darwinian mechanism did not provide an adequate explanation for the origin of functionally integrated “irreducibly complex” molecular machines. In his 2004 paper, Behe sought to extend his critique of neo-Darwinism by assessing its adequacy as an explanation for new genes and proteins. He and Snoke attempted to assess the plausibility of protein evolution in the case that it does indeed require multiple coordinated mutations. They applied standard neo-Darwinian modes of analysis derived from population genetics to make their evaluation. They considered the plausibility of the main neo-Darwinian model of gene evolution in which evolutionary biologists envision new genes arising by gene duplication and subsequent mutations in the duplicated gene.
Behe and Snoke assessed the plausibility of this model for multicellular organisms in the case that multiple (two or more) point mutations must occur simultaneously in order to generate a new selectable gene or protein. Whereas Maynard Smith saw the need for multiple coordinated mutations as a potential problem, one that ultimately needn’t trouble evolutionary biologists, Behe and Snoke argued that evolutionary biologists do need to worry about it, and they quantified its severity.
Behe and Snoke first noted that many proteins, as a condition of their function, require unique combinations of amino acids interacting in a coordinated way. For example, ligand binding sites on proteins—places where small molecules bind to large proteins to form larger functional complexes—typically require a combination of several amino acids. Behe and Snoke argued that in such cases the combinations of amino acids would have to arise in a coordinated fashion since the capacity for ligand binding depends on all the necessary amino acids being present together. In support of this inference, they cited an authoritative textbook, Molecular Evolution, by University of Chicago evolutionary biologist Wen-Hsiung Li. In it, Li notes that evolving ligand binding capacity in proteins such as hemoglobin may require “many mutational steps,”21 even though the first steps on the way to building such capacity would confer no selective advantage. As Li explains, “Acquiring a new function may require many mutational steps, and a point that needs emphasis is that the early steps might have been selectively neutral [non-advantageous] because the new function might not be manifested until a certain number of steps had already occurred.”22
FIGURE 12.2
Michael Behe. Courtesy Laszlo Bencze.
Behe and Snoke point out that this observation implies that a series of separate mutations could not generate a ligand binding function in a protein that previously did not have this capacity, since individual amino-acid changes would initially confer no selectable advantage on the protein lacking this function. Instead, evolving ligand binding capability would require multiple coordinated mutations. Behe and Snoke make a similar argument about the requirements for the evolution of protein-to-protein interactions. They note that for proteins to interact with each other in specific ways, typically at least several individually necessary amino acids must be present in combination in each protein, again, suggesting the need for multiple coordinated mutations.
So Many Changes, So Little Time
Behe and Snoke used the principles of population genetics to assess the likelihood of various numbers of coordinated mutational changes occurring in a given period of time. They asked: Is it probable that there was enough time in evolutionary history to generate coordinated mutations? If so, how many coordinated mutations is it reasonable to expect in a period of time given various population sizes, mutation rates, and generation times? Then, for different combinations of these various factors, they assessed how long it would typically take to generate two or three or more coordinated mutations. They determined that generally the probability of multiple mutations arising in close (functionally relevant) coordination to each other was “prohibitively” low—it would likely take an immensely long time, typically far longer than the age of the earth.
The Powerball Lottery—Population Genetics Made Easy
Before going on, it might be helpful to understand a bit more about how the equations and principles of population genetics can be used to calculate what evolutionary biologists call “waiting times,” the expecte
d time that it will take for a given trait to arise by various evolutionary processes. In his book The Edge of Evolution, Michael Behe illustrates these principles using a charming analogy to the Powerball lottery game that many American state governments use to raise money.
To win at Powerball, contestants must purchase tickets with six numbers that match the numbers printed on six balls drawn from two drums. Five of the balls are selected from a drum containing 59 white balls, numbered 1 through 59. A sixth red ball, the so-called power ball, is chosen out of a drum of 35 red balls numbered 1 to 35. To win the jackpot—which can exceed $100 million—a player must purchase a ticket listing all six of the chosen numbers in any order. The Powerball website lists the probability of matching all six balls at roughly 1 in 175 million. Depending on how many tickets have been purchased and how frequently drawings occur, it may take a very long time for someone to win.
Behe asked his readers first to consider how long it will take, on average, to generate a lottery ticket with the winning numbers. He notes that knowing the probability of drawing such a winning ticket isn’t sufficient. The calculation also requires knowing how often drawings occur and how many tickets are sold. As Behe explains: “If the odds of winning are one in a hundred million, and if a million people play every time, then it will take on average about a hundred drawings for someone to win.” If there are about a hundred drawings per year, with a million people playing per drawing, “then it would take about a year before someone won. But if there were only one drawing per year, on average it would take a century to hit the jackpot.”23 More frequent drawings produce shorter waiting times. Less frequent drawings tend to require longer waiting times. Similarly, more players will decrease the average time necessary to produce a winner, while fewer players result in longer waits.
Similar mathematical principles apply when calculating the expected waiting times for the evolution of biological features by mutation and selection. Biologists first need to assess the complexity of the system—or its inverse, the improbability of the feature occurring. As in Powerball, however, knowing the probability of an event by itself does not allow someone to calculate how long it will likely take for that event to occur. Such a calculation requires also knowing the size of the population (equivalent to how many people are playing Powerball) and how frequently new genetic sequences arise (equivalent to how frequently drawings are held).
In Powerball, a new sequence of numbers arises in every drawing. But when organisms reproduce, they do not always generate a new sequence of nucleotide bases in their individual genes. For this reason, to calculate the rate at which new sequences arise in living organisms requires knowing two factors: the generation time and the mutation rate. More rapid rates of mutation and/or shorter generation times will increase the rate at which new genetic sequences arise, resulting in shorter waiting times. Slower rates of mutation and/or longer times between generations produce longer waiting times. Also, as in Powerball, the number of “players” is important. Larger populations generate new genetic sequences more frequently than smaller ones and, thus, decrease expected waiting times. Smaller populations reduce the rate at which new sequences are generated, increasing waiting times.
Now, under the rules of Powerball, you can “win” without picking all six numbers correctly—you just won’t win the entire jackpot.24 If you pick just the red “power ball” correctly, you win $4. Pick three white balls correctly, and you win $7. If you correctly pick the numbers of four white balls, you win $100. Guess all five white balls correctly (but not the red ball), and you can win a cool $1 million.
With each additional ball necessary to secure a new level of winnings, the probability of winning decreases exponentially, while the values of the prizes increase dramatically. The Powerball website lists the probability of winning a $4 prize at just 1 in 55, the probability of winning $1 million dollars at roughly 1 in 5 million, and the probability of winning the jackpot as 1 in 175 million (see Fig. 12.3).25
FIGURE 12.3
A chart showing the probability of winning and the corresponding payouts for different combinations of balls in the Powerball lottery game.
Neo-Darwinists have long assumed that biological evolution works something like matching one number in Powerball. In their view, natural selection acts to reward or preserve small but relatively probable changes in gene sequences—like winning the small but more likely $4 prize in Powerball over and over again. They assume the mutation and selection mechanism doesn’t depend on winning extremely unlikely “prizes” (like the whole Powerball jackpot) all at once.
But what if, to produce a functional advantage at the genetic level, the mutation and selection mechanism had to generate the biological equivalent of all six (or more) correct balls in the Powerball lottery with no reward for guessing a smaller number of balls correctly first? Clearly, the probability of this would be extremely small. And the waiting time for winning such a lottery could become prohibitively long.
Back to the Biology
That brings us back to Behe and Snoke’s conclusion. In their 2004 paper, they argued that generating a single new protein will often require many improbable mutations occurring at once. They took into account the improbability of multiple functionally necessary mutations appearing together—the equivalent of needing to get a Powerball ticket matching several numbers to win any money at all. Then they sought to determine how long it would take and/or how large the population sizes would need to be to generate a new gene via multiple coordinated mutational changes—the genetic equivalent of the “jackpot scenario.”
Behe and Snoke found that if generating a new gene required multiple coordinated mutations, then the waiting time would grow exponentially with each additional necessary mutational change. They also assessed how population sizes affected how long it would take to generate new genes, if multiple coordinated mutations were necessary to produce those genes. They found, not surprisingly, that just as larger populations diminished expected waiting times, smaller populations dramatically increased them.
More important, they found that even if building a new gene required just two coordinated mutations, the neo-Darwinian mechanism would likely either require huge population sizes or extremely long waiting times or both. If coordinated mutations were necessary, then evolution at the genetic level faced a catch-22: for the standard neo-Darwinian mechanism to generate just two coordinated mutations, it typically either needed unreasonably long waiting times, times that exceeded the duration of life on earth, or it needed unreasonably large population sizes, populations exceeding the number of multicellular organisms that have ever lived. To get population sizes that were reasonable, they had to have waiting times that were unreasonable. To get waiting times that were reasonable, they had to have population sizes that were unreasonable. As they put it, either way the “numbers seem prohibitive.”26
Behe and Snoke found that mutation and selection could generate two coordinated mutations in a mere 1 million generations, a reasonable length of time given the age of the earth. But that was only in a population of 1 trillion or more multicellular organisms, a number that exceeds the size of the effective breeding populations of practically all individual animal species that have lived at any given time.27 Conversely, they found that mutation and selection could generate two coordinated mutations in a population of only 1 million organisms, but only if the mechanism had 10 billion generations at its disposal. Yet on the assumption that each multicellular organism lived only one year, 10 billion generations computes to 10 billion years—more than twice the age of the earth. This is clearly an unreasonable length of time to wait for the emergence of a single gene, let alone more significant evolutionary innovations.
Behe and Snoke did, however, find one tiny “sweet spot” in which a gene requiring only two coordinated mutations could arise (see Fig. 12.4). Such a gene could conceivably arise from 1 billion organisms in a “mere” 100 million generations. Since many more than 1 billion multicellular organi
sms have lived on earth during its history and since multicellular life on earth has existed for more than 500 million years, these numbers offer (assuming, again, one year per generation) the prospect of enough time and organisms to generate one new gene—if only two coordinated mutations are necessary. (Of course, if the population evolving a two-mutation trait had fewer than 1 billion organisms, then the waiting times again increased to unreasonable lengths.)
FIGURE 12.4
This diagram shows the population sizes and times (measured in number of generations) required to produce a gene or trait if building that gene or trait requires multiple coordinated mutations. The shaded gray area shows the “sweet spot”—population sizes and available time sufficient to generate the coordinated mutations necessary to produce a new gene. Note that any multi-mutation feature requiring more than two mutations could not, in all probability, evolve by gene duplication and subsequent coordinated mutation in a population of multicellular organisms, however large. Note also that for most normal population sizes and reasonable generation times, even evolving two mutations lies beyond the reach of gene duplication, mutation, and selection. Courtesy John Wiley and Sons and The Protein Society.
Nevertheless, these numbers only apply to the case in which only two coordinated mutations are necessary to build a new gene. Behe and Snoke found that if generating a new functional gene or trait required more than two coordinated mutations, then excessively long waiting times were necessary regardless of the size of the population. If three or more coordinated mutations were necessary, their calculations generated no “sweet spots” at all. Thus, they concluded that “the mechanism of gene duplication and point mutation alone would be ineffective, at least for multicellular … species.”28
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