How are my objections misguided?
How can it be fair when his method would indicate that not a single human protein has function?
If there parameters were set in favor of Darwinian evolution, then he would have tested more than one substrate. Those are the facts.
Thats why you need to read the reply from Douglas Axe. Your objections are not even mentioned. I will let the man himself answer. These are the objections raised by Hunt in Panda thumbs. Objection three probably most relevant.
Objection 1: Axe's paper doesn't claim to support intelligent design or challenge Darwinism, so it's a mistake to use it for those purposes.
Objection 2: Although Axe makes a case that functional sequences are rare in sequence space, this has no bearing on whether new protein folds can evolve. The evolution of new protein folds simply requires that functional sequences not be isolated in sequence space, which has nothing to do with how rare functional sequences are.
Objection 3: Because Axe measured mutational sensitivity from a weakly functional starting sequence rather than the fully functional natural enzyme, the mutants he generated were inappropriately disadvantaged, and this is why he arrived at such a low value for the prevalence of functional sequences.
Objection 4: Axe's experiment doesn't reflect how evolution really works. He mutated amino acids in groups of ten, whereas evolution sifts mutations one at a time. Consequently, Axe's results tell us nothing about whether protein folds can or cannot evolve.
Objection 3: Because Axe measured mutational sensitivity from a weakly functional starting sequence rather than the fully functional natural enzyme, the mutants he generated were inappropriately disadvantaged, and this is why he arrived at such a low value for the prevalence of functional sequences.
According to Hunt, I "molded a variant that would be exquisitely sensitive to mutation." Hunt, seem to think the outcome would have been more favorable (i.e., functional sequences would have been more prevalent) had I used the highly proficient natural enzyme as a starting point rather than the handicapped version. Actually, as a demonstration will show, the opposite is true.
Suppose we want to estimate the proportion of 42-character strings that can replace it. One way to approach this is to generate a large collection of strings that are randomized at the first seven positions, and another randomized at the second run of seven positions, and so on, for a total of six collections.
Here are a few examples of 'mutant' strings from the first collection:
npfzbifogical information by natural means
tnagyllogical information by natural means
zkjubdbogical information by natural means
sodlwdjogical information by natural means
and here are a few from the second collection:
no biolfaryewrinformation by natural means
no biolupbmjmginformation by natural means
no biolurxacryinformation by natural means
no biolfjrqgatinformation by natural means
Assuming we have an unlimited pool of readers who can examine the mutant strings, how should we proceed? If we instruct the readers to accept only those sequences that match the original, rejecting all others, then we know what the result will be. Of ten billion possible variants in each collection (27^7 = 10^10), only one will match the original. If we raise this proportion to the power six (because there are six collections) we get the expected answer: Of all possible 42-character strings, only one in 10^60 match the original.
But suppose we use a less strict approach. Suppose we simply ask the readers whether they can discern a meaningful reading, and we count all cases where the discerned reading is correct. This will increase the number of accepted mutants dramatically. If, with a bit of squinting, most mutants with two typos can be interpreted correctly -- mutants like these:
ng bhological information by natural means
no biolojijal information by natural means
no biological infosmntion by natural means
Then each collection will have about fifteen thousand accepted mutants instead of only one. This makes the prevalence of functional sequences in each collection about one in a million, which is much higher than the previous value of one in ten billion.
But we want to know how prevalent acceptable sequences are among all possible sequences. Let's call this fraction P. The question is, how do we calculate P now that we are using this less strict approach to accepting mutants? In answering this question we will discover two major problems with Objection 3.
It's certainly true that we will arrive at a much higher value for P if we simply raise the prevalence found for the individual collections to the power six. That calculation gives us one in (10^6)^6 , which is one in 10^36. This is a tiny fraction, but it's a whole lot larger than the one we got using the strict approach (one in 10^60).
Here's the problem, though. In calculating this higher P value, we have effectively assumed that the typo rate we found to be tolerable in the individual collections (two typos per seven positions) remains tolerable when we apply it to the full 42-character string. However, when we try this by generating full-length strings with twelve typos (maintaining the 2-in-7 ratio), we get completely unreadable gibberish:
nohbimlogicaemicrxrmation synnaludalnmeans
no giolotida binuprmazion by nktumai me ns
go hiozfgicac infeemadion zyknatural weans
Evidently we have made a mistake, because mutants that ought to be readable according to our calculation clearly aren't readable.
As you may have guessed, the mistake is that the 2-in-7 typo rate was tolerable only because it was restricted to a narrow section of seven positions. The fact that the remaining 35 positions were without error compensated in large measure for the errors in the mutated section.
The remedy is to use a different starting sequence. Specifically, we need the starting sequence to be of the same quality that we intend to require of mutant sequences in order for them to be accepted. Otherwise the mismatch in quality will skew our results.
The first problem with Objection 3, then, is that it fails to recognize the importance of applying the same quality standard to the starting sequence that will be applied to the mutants derived from it. Objection 3 focuses on the fact that I used a weakly functional starting sequence
without recognizing that this was called for by the fact that weakly functional variants of that sequence were accepted as 'functional'.
But there's another problem with Objection 3, having to do with the choice of a quality standard. We now know that the same standard has to be applied consistently if our results are to be meaningful, but we are still free to set that standard at any level. So, what difference does the level make?
As we've seen, if we take perfection to be the standard (i.e., no typos are tolerated) then Phas a value of one in 10^60. If we lower the standard by allowing, say, four mutations per string, then mutants like these are considered acceptable:
no biologycaa ioformation by natutal means
no biologicaljinfommation by natcrll means
no biolojjcal information by natiral myans
and if we further lower the standard to accept five mutations, we allow strings like these to pass:
no ziolrgicgl informationpby natural muans
no biilogicab infjrmation by naturalnmaans
no biologilah informazion by n turalimeans
The readability deteriorates quickly, and while we might disagree by one or two mutations as to where we think the line should be drawn, we can all see that it needs to be drawn wellbelow twelve mutations.
If we draw the line at four mutations, we find P to have a value of about one in 10^50, whereas if we draw it at five mutations, the P value increases about a thousand-fold, becoming one in 10^47.
Notice two things. First, both of these P values are far more favorable than one in 10^60, and second, lowering the standard always increases the P value
. This makes perfect sense -- it has to become easier to meet the standard as the standard is lowered.
Having understood this, we now see that Objection 3 has things inverted. In the work described in the 2004 JMB paper,
I chose to apply the lowest reasonable standard of function knowing this would produce the highest reasonable value for P,
which in turn provides the most optimistic assessment of the feasibility of evolving new protein folds.
Had I used the wild-type level of function as the standard, the result would have been a much lower P value which would present an even greater challenge for Darwinism. In other words, contrary to Objection 3, the method I used was deliberately generous toward Darwinism.
![[serious]](/data/avatars/m/160/160873.jpg?1431539545){kind=avatar}
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