So the thing is @John_Detwiler, there are many ways to build up complexity that Behe neglects. Neutral and even destructive evolution can be very constructive at a bimolecular level. You might want to brush up on this: Constructive Neutral Evolution. The only way that devolution becomes a problem is if you ignore all the ways that functional complexity is built up.
do you mean “on the whole”?
No, I mean that it does not demonstrate a “negative genotypic effect” in any way. He misunderstands the output of the program.
Polyphen does run the results against pdb for structure and even a database for secondary structure. This obviously returns results that will highly correlate with stability.
If proteins mostly lose stability or binding affinity each generation but never gain an equal amount in the same or other proteins, that is obviously not sustainable or “beneficial”
Please produce evidence of this claim. It appears to be entirely incorrect. Have you worked with polyphen before? Did you know there are other programs that can predict stability? Would you like to see the results from that analysis?
Unless there are other ways to regain it. There are.
I remember reading some of it. This quote in particular confirms my belief that people keep focusing only on the effect the protein has on the organism; “In other words, **these mutations may not have damaged the protein at all, but quite possibly improved one of its activities, namely the clearance of cholesterol from the blood of a species that subsists on an extremely high-fat diet”
It’s quite obvious that organisms adapt. No one here is questioning that. Reproving it says nothing about the underlying sustainability of the proteins involved from a stability standpoint. Creationists do not require organisms to always have minimal fitness which, if damaged will instantly kill an organism. Everyone keeps talking about fitness and “new function”. But what does that have to do with how close a protein gets to breaking? Conversations about long term evolution need to recon with sustainability, not transient effects.
It is true that the majority of mutations that occur are likely to destabilize a protein, as opposed to enhance it’s function or make it more stable. So since all the software can do is attempt to estimate the likelihood that the mutation will affect the protein’s structure, and thus it’s stability or function, it will simply always err on the side of assuming ANY function-affecting mutation is damaging. The software doesn’t know how the proteins actually work or what they do.
However, since it actually IS possible for a mutation to increase stability, or somehow enhance function, but the software doesn’t know how to determine whether the mutation will have this effect, it has been coded to just assume it will be damaging.
Is it likely to affect structure, or is it associated with a disease state in human? If yes → assume damagning, if no → assume benign.
It is in this fact that Behe’s reliance on positively selected mutations actually being damaging as estimated by this software, where the problem lies. Because positive, or purifying selection will actually some times get rid of damaging mutations, and therefore only allow neutral or even function-improving ones. For that reason reliance on this software is a mistake, and that is why we actually have to look at what genes these mutations happen in, and what these genes actually do.
It does make sense for the function-affecting mutations in the LYST gene, for example, to actually be damaging to it’s function, because this gene is involved in fur pigmentation and is, at least in part, a membrane transporter gene. This at least implies that turning off it’s pigmentation transport ability would be beneficial to the polar bear, and so mutations that “damage” this ability are likely to have been beneficial.
In contrast though is the APOB gene, involved in fatty acid transport in blood. Here again the software just assumes the mutations are damaging, because they’re likely to affect the protein’s structure and thus it’s function. But this time, because of what we know about what the gene actually does, transporting fatty acid out of blood and into important tissues, it is more likely that the mutations actuallly improved the function of the gene, because polar bears eat so much fat. So the blind reliance on the software’s output is a problem. We simply can’t take it at face value when we are actually dealing with mutations evolving under strong selection in a new environment, as the polar bear’s have been.
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You misread the quote @John_Detwiler. We are saying that the biochemical activity likely increased, because it would help organismal function. You are making a different claim than Behe, that the biochemical activity might have increased, but the protein was less stable. That is a different claim, and also ends up not being correct.
We can, of course, examine protein stability. What not?
I’m not sure that is the case @Rumraket. Demonstrate it with evidence. Mutations should be about 50/50 in their ability to affect stability.
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from overview [PolyPhen-2 Wiki]
“Mapping of amino acid replacement to the known 3D structure reveals whether the replacement is likely to destroy the hydrophobic core of a protein, electrostatic interactions, interactions with ligands or other important features of a protein. If the spatial structure of a query protein is unknown, one can use the homologous proteins with known structure.”
The scoring method is described just below under “Mapping of the substitution site to known protein 3D structures”
And the comparison for secondary structure is described below that.
I will never not look at data.
That doesn’t seem obvious to me at all. It’s also perfectly clear from my own work that more stability does not correlate with improved function. That’s your most untenable assumption.
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Well this is one of the conclusions of those experiments detailed in the Tawfik papers discussed in Miller: Axe Decisively Confirmed? thread. This one in particular: http://www.ira.cinvestav.mx/portals/0/Estudiantes/Tokuriki_2009.pdf
In the absense of purifying selection, mutations in protein coding genes that affect amino acid sequence will quickly degrade protein stability. Generally, the conclusion of the paper is that evolved proteins have some “threshold of stability” where they can initially tolerate some accumulation of mutations, but once this threshold is exhausted, the vast majority of mutations negatively affect protein stability and thus function, and become significantly deleterious for that reason.
If what you’re saying is really true, it should generally be the case that mutations in proteins are just as likely to enhance stability, as to degrade it. But that isn’t what is observed. The key player here is purifying selection against stability-affecting mutations.
You are mixing issues here. A protein can loose function while increasing stability. I’d point to the work on P53s to show that it is about 50/50. The studies you are showing are not measuring stability, they are measuring function, right?
So how about this. Let’s do the experiment now. Can you obtain the sequence for polar bear ApoB and the key mutations? @evograd, @T_aquaticus, and @davecarlson might be able to point you in the right direction if you don’t know how to do it yourself. Once we have the sequences, I’ll show you some programs you can use to look at to predict stability.
Sure, but that just implies most natural proteins exist in some zone of stability above too unstable, and below too rigid. But apparently mutations towards higher stability are less likely on average compared to mutations towards lower stability. It is true that stability increasing mutations are not necessarily function improving.
The key point is that in general, if proteins lose stability and go below this zone or “threshold of stability” with the highest fitness effects, it negatively affects their function. And some random mutation is initially likely to be neutral because the protein is in this “threshold” zone where it is somewhat buffered against the stability-affecting effects of mutations, but invariably mutations do affect the stability, and are more likely to be downwards than upwards, so after a few mutations have occured, it is on the border of beneficial stability (most likely downwards towards more unstable), and will become function-affectingly unstable with further mutations. Again, in the absense of purifying selection of course.
I’d point to the work on P53s to show that it is about 50/50. The studies you are showing are not measuring stability, they are measuring function, right?
Well it depends, the study I linked above is essentially a review and theoretical discussion based on lots of different empirical experiments. Some of which measured stability, others measured function as a proxy for stability. I think you have to read the paper to get the full picture. There’s this figure which does a pretty good job of explaining it though:
No. Not at all.
A mutation can obliterate a specific function without affecting stability one bit, or increasing or decreasing it. Visa versa too. Stability is not the same thing as function. Exaptation becomes important. A mutation could leave a protein equally stable, obliterate function 1, and confer function 2. Maintaining the initial function (function 1) has little to do with protein function in most cases.
I don’t see how this contradicts what I wrote. I have not stated that an increase in stability necessarily implies an improvement in function.
I don’t think this relationship between protein function and protein stability is one of necessity, it is however one of generality. And it is generally the case that there is some zone between too rigid, and too unstable, where most natural proteins sit and function “best”.
And if we were to count the stability-effects of all possible mutations for some protein, more of them will reduce stability than increase stability. So in general, when mutations are allowed to accumulate in the absense of purifying selection, a protein is more likely to eventually become unstable and move out of this zone of function, than it is to become more stable and move out of this zone of function.
I’m really just stating the conclusions of the papers I referenced above here.
I’m just saying, take an average protein, and make a missense mutation. If the mutation changes stability, it is about 50/50 whether it is an increase or decrease in stability. Is it an increase in function? That is a totally different question. So you can’t measure this by measuring function.
Do you agree?
I don’t agree with this part. If that is true, it contradicts with what I have read in the papers linked earlier.
Is it an increase in function? That is a totally different question.
I completely agree with this part.
So you can’t measure this by measuring function.
A priori, no. I agree. You give me a protein with some level of function, and a mutant with a different level of function, I can’t know from that alone whether the mutation affected the stability of the protein up or down.
But there is actually some relationship there that can be elucidated by studying the epistatic effects of multiple mutations in combination, which is one of the implications of the paper. And it is a priori more likely that IF the mutation that affects function also affects protein stability, then it will have made the protein more unstable, than more stable. That’s a statement about relative probabilities, not meant to imply it will always be the case.
Well let’s test it then with some proteins, missense mutations, and stability predictors. Are you game?