Does the subject of protein folding excite you? Read this to see why perhaps it should:

Protein folding is among the most important reactions in all of biology. However, 50 y after C. B. Anfinsen showed that proteins can fold spontaneously without outside help, and despite the intensive work of thousands of researchers leading to more than five publications per day in the current literature, there is still no general agreement on the most primary questions. How do proteins fold? Why do they fold in that way? How is the course of folding encoded in a 1D amino acid sequence? These questions have fundamental significance for protein science and its numerous applications. Over the years these questions have generated a large literature leading to different models for the folding process. [Emphasis added.]

In short, your life depends on protein folding, and the subject provides a classic contest between intelligent design and scientific materialism. That’s enough to make a thoughtful person take notice.

The quoted passage comes from a paper in the Proceedings of the National Academy of Sciences by two biophysicists at the University of Pennsylvania. They review the vast corpus of literature on the subject to assess the best current models for explaining how one-dimensional sequences of amino acids can end up as three-dimensional shapes that perform functional work. To appreciate the challenge, try to assemble a string of beads, some of which have electric charges or attractions to water, that will, when let go, spontaneously fold into a tool. Your cells do something like that all the time, and usually do it right.

Biologic Institute research scientist Douglas Axe has worked on the problem of protein folding for much of his career. He has been joined by another scientist, Discovery Institute’s Ann Gauger, to show why protein folding gives evidence for intelligent design. The subject is also discussed at length in Axe’s most recent book, Undeniable: How Biology Confirms Our Intuition That Life Is Designed (Harper One, 2016).

Here’s the problem for materialism in a nutshell: the number of ways you can assemble amino acids that won’t fold vastly exceeds the ways that will fold. To expect a random process to search “sequence space” (the set of all sequences of amino acids) and arrive at one that folds is so highly improbable, it will likely never occur in multiple universes. Axe followed Michael Denton’s hunch that “functional proteins could well be exceedingly rare” and put some numbers to it. He determined that there is only “one good protein sequence for every 10^74 bad ones” (Undeniable, p. 57). This was about 10 million billion billion billion times more improbable than Denton’s initial estimate.

As Axe goes on to say, materialists didn’t exactly put “out of business” signs on their doors when he published his results. That brings us to the current paper — one of the latest attempts to find a way to avoid the implications of design and find a natural, unguided means of searching sequence space for those elusive folds.

The authors, S. Walter Englander and Leland Mayne, know all too well that random search is hopeless. Even in the 1990s, “Levinthal had contributed the seminal observation that a random search could not account for known folding rates.” Most proteins find their native fold extremely rapidly — some in microseconds. Some need a little help from “chaperones” such as GRO-EL that allow the polypeptide to fold in a barrel-like chamber. In either case, the authors know that random attempts at finding the proper or “native” fold, even for a correctly-sequenced polypeptide, would be far too slow if there were many pathways to the correct fold. This led scientists early on to suspect that proteins follow an energy landscape that nudges them to the native fold, much like a funnel guides ball bearings down a narrow hole. The ball may bounce around in the funnel, but the shape of the energy landscape forces it in the right direction. This is known as energy landscape theory (ELT).

A critical feature of the funneled ELT model is that the many-pathway residue-level conformational search must be biased toward native-like interactions. Otherwise, as noted by Levinthal (57), an unguided random search would require a very long time. How this bias might be implemented in terms of real protein interactions has never been discovered.

The authors are not content with evolutionary just-so stories:

One simply asserts that natural evolution has made it so, formulates this view as a so-called principle of minimal frustration, and attributes it to the shape of the funneled energy landscape. Proteins in some unknown way “know” how to make the correct choices.

Sorry, no dice.

A calculation by Zwanzig et al. at the most primary level quantifies the energy bias that would be required. In order for proteins to fold on a reasonable time scale, the free energy bias toward correct as opposed to incorrect interactions, whatever the folding units might be, must reach 2 kT (1.2 kcal/mol). The enthalpic bias between correct and incorrect interactions must be even greater, well over 2 kcal/mol, because competition with the large entropic sea of incorrect options is so unfavorable. Known amino acid interaction energies, less than 1 kcal/mol (59), seem to make this degree of selectivity impossible at the residue–residue level.

Are we excited yet? This is getting really interesting. The suspense is growing. With randomness out of the question, what will they do?

They basically take a divide-and-conquer approach. Getting a big polypeptide to fold is too hard, but maybe if they can break the problem down into bite-size chunks, they can get to the target without intelligence. After all, it’s much easier to knit an afghan if the granny squares come ready-made so that you don’t have to make each one from scratch. “Quantized” in this manner, the problem becomes more tractable.

The structural units that assemble kinetic intermediates are much the same as the cooperative building blocks of the native protein. This strategy separates the kinetic folding puzzle into a sequence of smaller puzzles, forming pieces of the native structure and putting them into place in a stepwise pathway (Fig. 1B). This is the defined-pathway model.

They give the name “foldon” to a small chain of amino acids “perhaps 15 to 35 residues in size” that folds a little bit. If the polypeptide is composed of a number of these prefabricated foldons, maybe the whole protein will find its native fold quickly, descending the funnel in a stepwise fashion. Experiments unfolding and refolding some proteins actually show this kind of stepwise energy landscape. They like that:

The purpose of this paper is to consider the present status of these quite different models and relate them to the central questions of protein folding — how, why, and the encoding problem. We propose to rely on the solid ground of experiment rather than the countless less-definitive suggestions and inferences that have been so often used in this difficult field.

Empirical rigor; what’s not to like about that? So instead of imagining a correct sequence of amino acids from scratch, they substitute a sequence of foldons, increasing the probability of completing the search in time. Will this work in evolutionary terms?

The opposed defined-pathway model stems from experimental results that show that proteins are assemblies of small cooperative units called foldons and that a number of proteins fold in a reproducible pathway one foldon unit at a time. Thus, the same foldon interactions that encode the native structure of any given protein also naturally encode its particular foldon-based folding pathway, and they collectively sum to produce the energy bias toward native interactions that is necessary for efficient folding.

So how, exactly, did this clever solution emerge without intelligence?

Available information suggests that quantized native structure and stepwise folding coevolved in ancient repeat proteins and were retained as a functional pair due to their utility for solving the difficult protein folding problem.

“Co-evolution” again. So much for empirical rigor. They’re back to just-so storytelling mode. Let’s think this through. Each granny square in the quilt is a product of chance, according to materialist resources. Does a black granny square know that it will fit nicely into a complete quilt following a geometrical pattern of black, red and yellow squares? Unless each granny square has an immediate function, evolution will not preserve it. Similarly, no foldon will be “retained” with some future hope that it might have “utility for solving the difficult protein folding problem.” The foldon couldn’t care less! It had to be functional right when it emerged.

An intelligent designer could plan foldons as a useful strategy for constructing various complex proteins in a modular way. A designer could even preserve useful foldons, much like a computer programmer writes subroutines to use in other programs. Unless each subroutine actually does something useful for the system as a whole, though, what good is it? Say you have a subroutine that says, “Repeat whatever argument arrives in the input register.” Unless the system needs that function as part of what it’s doing, you can run the subroutine till the cows come home and nothing good will come of it.

In short, the foldon strategy doesn’t lower the probability of success, and it doesn’t solve “the difficult protein folding problem” for the evolutionist. It’s all divide and no conquer.

Englander and Mayne make a big deal out of “repeat proteins” that make up about 5 percent of the global proteome. These repeat proteins “have a nonglobular body plan made of small repeated motifs in the 20–40 residue range that are assembled in a linear array.” Are they good candidates for foldons? We know that many proteins contain repetitive structures like alpha coils and beta sheets, but the essence of a functional protein is not its repetitive parts but in its aperiodic parts. We’ve seen this requirement in other types of intelligent design, such as language. Sure; sometimes a series of dashes makes a nice separator between paragraphs, but you won’t get much meaning out of all repetitive sequences. Let’s see if they can do it:

The different families of repeat proteins are very different in detailed structure but within each family the repeats are topologically nearly identical. These observations suggest that repeat proteins arose through repeated duplication at an early stage in the evolution of larger proteins from smaller fragments. Available examples show that globular organization can arise from continued repetitive growth that closes the linear geometry, and by the fusion of nonidentical units, and so would carry forward their foldon-like properties.

The utility of foldons for the efficient folding of proteins might be seen as a dominant cause for the development and retention of a foldon-based body plan through protein evolution. In this view, contemporary proteins came so consistently to their modular foldon-based design and their foldon-based folding strategy because these linked characteristics coevolved. However, the fact that many known foldons bring together sequentially remote segments requires, at the least, some additional mechanism.

This sounds like the evolutionary story that duplicated genes became seeds of new genes. So if we duplicate the line of dashes, and then change some of the dashes to commas, will we get somewhere? Hardly. If we strip out the “mights” and “maybes” of their story, not much is left but the concluding admission that “some additional mechanism” is needed to get folded proteins. (We have one! Intelligence!) And get this: even if you get a polypeptide to fold into a globule, it’s trash unless it actually performs a function.

When scientific materialists began tackling the protein folding problem, they expected that biased energy landscapes leading to deterministic folds would soon be discovered. That didn’t happen.

However, how this propensity might be encoded in the physical chemistry of protein structure has never been discovered. One simply asserts the general proposition that it is encoded in the shape of the landscape and to an ad hoc principle named minimal frustration imposed by natural evolution.

Here they state Axe’s search challenge in their own words:

Quantitative evaluation described above shows that individual residue — residue interaction energies are inadequate for selecting native-like interactions in competition with the large number of competing nonnative alternatives. The assertion that the needed degree of energetic bias is supplied by the shape of an indefinite energy landscape because nature has made it so is — plainly said — not a useful physical — chemical explanation.

The foldon proposal that Englander and Mayne prefer, however, is not any better, despite their praise for it:

The question is what kind of conformational searching can explain the processes and pathways that carry unfolded proteins to their native state. The foldon-dependent defined-pathway model directly answers each of these challenges.

All they have done, however, is displace the challenges from amino acid sequences to foldon sequences. Since the foldons are composed of amino acid sequences, however, nothing is solved; it is still radically improbable to arrive at a sequence that will produce a functional protein without design. No amount of evolutionary handwaving changes that:

Evolutionary considerations credibly tie together the early codevelopment of foldon-based equilibrium structure and foldon-based kinetic folding.

So much for empirical rigor. Evolution did it. Problem solved.

We think not. To rub it in, consider that Axe’s calculation of one in 10^74 sequences being functional is way too generous. If we require that the amino acids be left-handed, and demand that all bonds be peptide bonds, the probability drops to one in 10^164. For a quick demonstration of why this is hoping against all hope, watch Illustra Media’s clever animation from their film Origin, titled, “The Amoeba’s Journey.”

Image: Proteins, courtesy of Illustra Media.