Yet Axe’s experimental results presented a problem not only for scenarios involving random mutations acting alone, but also for scenarios envisioning selection and random mutation acting in concert. Further, his mutagenesis experiments cast doubt on each of the two scenarios by which evolutionary biologists might envision new protein folds (and the information necessary to produce them) arising as the result of the mutation and selection mechanism.
In theory, new genes capable of producing a new protein fold might arise either from (a) preexisting genes or from (b) nonfunctional sections of the genome. That is—to adapt Dawkins’s visual analogy—mutation and natural selection could conceivably generate a new functional gene starting from either (a) another mountain peak (a different preexisting functional gene) or (b) from the valley floor (a nonfunctional section of the genome). Yet Axe’s experimental results would show that the action of natural selection would not help solve the search problem confronting the mutation mechanism in either of these two cases. To see why, we need to understand a bit more about each of these two possible neo-Darwinian scenarios as well as Axe’s subsequent experimental findings.
From Peak to Peak
In the first case, evolutionary biologists might envision mutation and selection gradually altering a preexisting gene (and its protein product) to produce another functional gene (and a different protein product). This scenario involves moving metaphorically from one functional peak to another without dipping into a valley (a zone of diminished fitness or nonfunction).
Most evolutionary biologists reject this first scenario.8 They do so because they recognize that mutations in preexisting genes will typically degrade functional genetic information. They know, too, that when genes lose function, natural selection will eventually eliminate the organisms that possess these genes. Genes that contribute to the healthy function of an organism, that have been mutated in such a way as to diminish that function, will be subject to what evolutionary biologists call “purifying selection.” That is, natural selection will typically eliminate organisms possessing mutation-induced gene variants that diminish function or fitness. (When natural selection preserves genetic changes that enhance function or fitness, evolutionary biologists call that “positive selection.”)
Axe’s mutagenesis experiments confirmed these reasons for doubting the first of the possible neo-Darwinian scenarios, at least as an explanation for the origin of new protein folds. In work that he published in 2000, he showed that it is, indeed, exceedingly difficult to make extensive changes to functional amino-acid sequences without destabilizing a protein fold. Even best-case changes involving the most chemically similar amino acids in the exterior of proteins tended to destabilize protein folds.
In these experiments, Axe mutated a gene that produced a protein exhibiting a single fold and function. He found that as he altered this protein, multiple position changes in the exterior of the protein molecule quickly effaced or destroyed its function.9 Yet to turn one protein with a distinctive folded structure into another with a completely novel structure and function requires specified changes at many, many sites—far more than Axe altered in his experiments.10 The number of changes necessary to produce a new protein fold typically exceeds the number of changes that will result in functional loss. Given this, the probability of the evolutionary process successfully traversing a functional landscape from one functional peak to another—all the while escaping functional loss each step along the way—is extremely small, with the probability diminishing exponentially with each additional requisite change.11 Indeed, by showing that functional proteins with distinct folds are far more sensitive to functional loss than protein scientists had previously assumed, Axe’s experiments confirmed what most evolutionary biologists suspected—namely, that protein-to-protein (or functional gene–to–functional gene) evolution is a no-go where the mutation and selection mechanism must produce a new protein fold (see Fig. 10.3).
Axe had a more fundamental reason for considering the first evolutionary scenario implausible. Based on the physical principles of protein function, the vast majority of protein functions simply cannot be performed by unfolded proteins. In other words, stability of protein structure is a precondition of protein function. Destabilized protein folds not only lose the three-dimensional structures they need to perform functional tasks, they are also vulnerable to attack by other proteins called proteases that devour unfolded proteins or polypeptides in the cell.12
As one structure is degraded as the result of multiple sequence changes, it will necessarily lose structural stability, resulting in a catastrophic loss of function. Yet any diminution in the function of a protein will also diminish fitness in a way that will subject the protein (and its corresponding gene) to the purifying action of natural selection.13 Indeed, according to the equations of population genetics, the standard mathematical expression of neo-Darwinian theory, even slight losses in fitness will subject the disadvantageous traits that produce such losses to purifying selection, thus eliminating them. That means that even many protein sequences that retain a significant, though diminished, portion of their original function nevertheless will not survive the winnowing effects of the neo-Darwinian mechanism. Thus, the gradual transformation of one functional fold into another was a complete nonstarter.
FIGURE 10.3
This figure illustrates why many evolutionary biologists reject the idea that genes and proteins under selection pressure will evolve into new functional genes and proteins. Since genes, like English sentences, contain sequence-specific functional information, multiple changes in the genetic text will inevitably degrade function (or fitness) long before a new functional sequence will arise—just as random changes in a meaningful English sentence will typically destroy meaning long before such changes produce a significantly different meaningful sentence.
Research performed at the European Molecular Biology Laboratory by molecular biologist Francisco Blanco has since confirmed this conclusion. Using site-directed mutagenesis, Blanco’s team found that the sequence space between two naturally occurring protein domains is not continuously populated by folded or functional proteins. By sampling intermediate sequences between two sequences that do adopt different folds, Blanco found that the intermediate sequences “lack a well-defined three-dimensional structure.” Thus, he concluded that “the appearance of a completely new fold from an existing one is unlikely to occur by evolution through a route of folded intermediate sequences.”14
Thus, both experimental results and the physics of protein folding implied that random searches for novel proteins starting from preexisting protein-coding genes would result in functional loss long before a protein with a novel fold would emerge, as most evolutionary biologists already suspected. Although the first of the two possible evolutionary scenarios has the advantage of starting on a mountain peak—with a functional gene and protein—it also has a lethal disadvantage: randomly mutating the gene will soon destabilize a protein fold and/or generate nonfunctional intermediate sequences and structures long before a new gene (capable of generating a new fold) would arise. For this reason, this scenario involves not so much a climb up Mount Improbable, but a step out over Valley Impassable.
Scaling Mount Improbable
For all these reasons, like most evolutionary biologists, Axe thought the second neo-Darwinian scenario—in which new genes and proteins emerge from nonfunctional or neutral regions of the genome—provides a much more plausible means of producing the information necessary to construct novel protein folds. It was to this scenario that Axe turned his experimental energies.
In this scenario, neo-Darwinists envision new genetic information arising from sections of the genetic text that can vary freely without consequence to the organism. According to this scenario, noncoding sections of the genome or duplicated sections of coding regions undergo a protracted period of “neutral evolution”15 in which alterations in nucleotide sequences have no discernible effect on the fitness of the organism. Func
tional genes and proteins gradually rise from a nonfunctional valley floor to a functional mountain peak—generating a new gene. Natural selection plays a role, but not until a new functional gene has arisen.
Evolutionary biologists typically picture this process beginning with a gene duplication event. Although several different mechanisms can generate gene duplicates in DNA,16 the most common mechanism occurs during the crossing-over step of meiosis (a kind of cell division that produces sex cells, or gametes, in sexually reproducing organisms). During meiosis, homologous chromosomes swap segments of DNA. In a normal crossing-over event, corresponding chromosomal segments of equal size are swapped between the two homologous chromosomes, ensuring that both chromosomes experience no net gain or loss of genes. Sometimes, however, chromosomes swap genetic material of unequal length. When this happens, one chromosome (the one that gets the smaller piece) ends up losing some DNA, while the chromosome that receives the larger segment of genetic material ends up with a new stretch of chromosomal DNA—one that may include a gene or genes it already had. This results in duplicate copies of a gene on one chromosome.
When this occurs, one of the two genes may begin to vary—to experience mutations—without adversely affecting the function of the organism, while the other performs the original function. In the jargon of evolutionary biology, mutational changes in gene duplicates are “selectively neutral”—they initially provide no advantage or disadvantage to an organism or population. These gene-duplication events allow nature room to experiment safely. Unhelpful but harmless genetic novelties can be passed on to future generations, where additional mutations one day may render the evolving genetic material useful. Eventually, as mutational changes accumulate, a new gene sequence may arise in a new organism that can code for a novel protein fold and function. At that point, natural selection can favor the new gene and its protein product, preserving and passing it along to future generations—or so the story goes.
This scenario—which many evolutionary biologists now refer to as the “classical model” of gene evolution—has the advantage of allowing portions of the genome to vary freely through many generations, giving mutations many opportunities to “search” the space of possible base sequences without being punished for drifting into valleys of lost or diminished function.
But this scenario faces an overriding problem: the extreme rarity of sequences capable of forming stable folds and performing biological functions. Since natural selection does nothing to help generate new folded, functional sequences, but rather can only preserve such sequences once they have arisen, random mutations alone must search for the exceedingly rare folded and functional sequences within the vast sea of combinatorial possibilities.
And that is the big story associated with Axe’s experiments. His research showed that folded, functional sequences of amino acids are indeed exceedingly rare within sequence space. After his initial round of experiments, Axe performed another series of site-directed mutagenesis experiments on a 150-amino-acid protein-folding domain within a β-lactamase enzyme and published the results in the Journal of Molecular Biology.17 Recall that a folding domain is a portion of a larger protein that exhibits a distinctive fold. Since amino-acid chains must first fold into stable three-dimensional structures, Axe performed experiments that enabled him to estimate the frequency of sequences that will produce stable folds—any stable fold—before he estimated the frequency of sequences performing a specific (β-lactamase) function. His improved experimental method produced a precise quantitative result. He estimated (a) the number of 150-amino-acid-long sequences capable of folding into stable “function-ready” folded structures compared to (b) the whole set of possible amino-acid sequences of that length (recall Fig. 9.3). Based on his site-directed mutagenesis experiments, he determined that ratio to be a vanishingly small 1 in 1074. In other words, for sequences 150 amino acids long, only 1 in 1074 sequences will be capable of folding into a stable protein.
For a sequence to achieve a protein fold is only a first step, however. A protein must be folded to be functional, but a folded protein is not necessarily a functional protein. And although sequences capable of forming stable protein folds are necessary to any significant evolutionary innovation, natural selection cannot select for the presence of a fold unless it also performs a function that confers a specific functional advantage on an organism. Thus, Axe also estimated (a) the number of proteins of modest length (150 residues) that perform a specified function via any folded structure compared to (b) the whole set of possible amino-acid sequences of that size. Based on his experiments and data about the number of stable folded proteins that exist, Axe estimated that ratio to be about 1 to 1077. A telling conclusion follows from this experimental data: The probability of any given mutational trial generating (or “finding”) a specific functional protein among all the possible 150 residue amino-acid sequences is 1 chance in 1077—that is, one chance in one hundred thousand, trillion, trillion, trillion, trillion, trillion, trillion.
That is obviously an incredibly small probability, but is it small enough to justify rejecting the classical model of gene evolution? Or is it plausible to think that random mutations in the nonfunctional part of the genome could overcome these long odds to generate the genetic information necessary to produce a novel protein fold with a specific selectable function?
How Many Trials?
When statisticians or scientists assess whether a chance hypothesis provides a plausible explanation for the occurrence of an event, they do not just evaluate the probability of that particular event occurring once; they evaluate the probability of the event occurring given the number of opportunities it has to occur.
For example, if our hypothetical bike thief from the previous chapter had enough time to try more than half (more than 500 of the 1000) total combinations of a three-dial bike lock, then the probability that he will stumble upon the right combination will exceed the probability that he will fail. In that case, it will be more likely than not that he will succeed in opening the lock by chance. In that case, the chance hypothesis—the hypothesis that he will succeed in opening the lock by chance—is more likely to be true than false. On the other hand, if just after he started trying to crack the lock he heard a security guard coming around the corner and only had time to explore a small fraction of the total number of possible combinations—far fewer than half—then it will be much more likely than not that he will fail to open the lock by chance. Consequently, anyone who knew his situation could conclude that the chance hypothesis is, in that case, much more likely to turn out false than true.
When statisticians or scientists assess the probability of an event occurring by chance, they often assess what is called a conditional probability. In deciding the plausibility of a chance hypothesis, they assess the probability of the event given or “conditioned on” what else we know, especially what else is known about the number of opportunities the event has to occur. And they refer to the number of opportunities an event has to occur as “the probabilistic resources.”18
If the conditional probability of the chance hypothesis, given the number of opportunities it has to occur, is less than ½, then it is more likely than not that the event will not happen by chance. It will be viewed as implausible—more likely to be false than true. Conversely, if the conditional probability of the chance hypothesis, given the number of opportunities it has to occur, is more than ½, then it is more likely than not that the event in question will occur by chance. It will be deemed plausible—more likely to be true than false. And, of course, the smaller the conditional probability associated with a hypothesis, the more implausible the hypothesis—the more likely the chance hypothesis is to be false than true.
How then should we assess the chance hypothesis for the origin of biological information—in particular, the hypothesis that random mutations generated the information necessary to produce a novel protein fold with a selectable function? What is the conditional probability that such a folded p
rotein could arise as the result of random mutations in duplicated nonfunctional sections of a genome? Axe realized that in order to answer that question he needed a way to estimate the number of opportunities that random mutations had for producing a new protein fold with a selectable function during the whole history of life on earth.
The Biological Universal Probability Bound
Here, evolutionary theory itself provides the answer. Axe was interested in the number of times that mutations might have produced new sequences of bases in DNA that were capable of producing a new sequence of amino acids—one of the 1077 possible sequences in the relevant sequence space. Yet not every such sequence of bases that mutations might generate constituted a relevant trial. In theory, mutations may alter a gene many times during the life cycle of an organism. Nevertheless, natural selection can only act on the new sequence of bases that is actually passed on to offspring. It might seem that it would be difficult to quantify the number of mutational trials in each generation. But even if we think of mutations repeatedly shuffling and reshuffling the arrangement of bases during the life cycle of an organism, only those mutations in the genes (or DNA) in the reproductive cells of parent organisms can have any effect on the next generation. Since Axe wanted to know how many novel sequences capable of generating a selectable function might have arisen in the history of life, he only needed to concern himself with those sequences that could be transmitted during reproduction.
This meant that if he could estimate the total number of organisms that had lived during the history of life on earth and the number of new genes that mutations might produce and pass on to the next generation, he could establish an upper bound on the number of trials relevant to the evolutionary process.
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