Physics v. OOL science.

Tornadoes, Ice, and Cells: The Challenge from Thermodynamics to Origin-of-Life Scenarios
Evolution News | @DiscoveryCSC

On a new episode of ID the Future, physicist and Center for Science & Culture research coordinator Brian Miller talks with host Sarah Chaffee about the thermodynamics of the origin of life. Dr. Miller has been unfolding a four-part series on the subject here at Evolution News, concluding on Monday.

Explaining why materialist theories of origins hit a wall when examining the physics of abiogenesis. Dr. Miller discusses the difference between systems such tornadoes, ice, and living cells. Learn more about equilibrium, self-organization, and how the cell defies natural tendencies towards high entropy and low energy.

Listen to the podcast here, or download it here.

As Dr. Miller concludes, referring to the origin of the genetic code and the information it bears at the heart of the cell:

The encoding and the decoding and the information had to be there all at once, which means it had to preexist the existence of the cell, because it had to exist before it was embodied in physical reality. But the only place that information in a code can exist outside of physical reality is in a mind, and that points very clearly to intelligent design.

Second only to cosmic fine-tuning, this would seem to be the most fundamental challenge to materialism there could be. With no original life, without the guidance and intervention of a designing agent, obviously Darwinian evolution is absolutely nothing even to begin to work with. This is a subject on which materialists are largely silent, and with good reason.

Unsettled science?

There's No Grand Unity Called "Science"
Ralph Dave Westfall 

Editor's note: We are delighted to welcome Dr. Westfall as a new contributor. He is an emeritus professor in the Computer Information Systems Department at California Polytechnic University, Pomona.

Doug Axe's piece at Evolution News the other day was very good ("Public Opinion Is the Ultimate Peer Review"), but it implicitly supports the misconception that science is one big thing. That idea is the primary basis for all pejorative propaganda attacking dissenters as being "anti-science."

It is false to say that there is one single activity to be identified as "Science." In truth, there are only individual fields of study, some of which deserve being called sciences, while others arguably do not. They don't all fit into one overarching category because the methodologies and criteria for what count as valid findings vary so greatly among them. (A cynic might suggest that in contrast to people who do research in psychology, physicists function in a different and not very parallel universe.)

The panorama can be taxonomized as follows. First, divide the fields of study into: (A) the natural or physical sciences, and (B) the social sciences. Then divide the natural sciences, separating (A1) those concerned with homogeneous entities and deterministic (at least in the aggregate) relationships, from (A2) the ones that deal with chaotic processes (like climatology).

Most of the progress in knowledge and technology comes from the (A1) category. Although researchers in the other categories would like you to think they are making comparable contributions to society, they are not.

In the public eye, most of the credibility of "Science" comes from tangible products resulting from the findings of computer science, physics, and chemistry -- for example, computers, jumbo jets, medical technologies such as MRI scanners, etc. Very few question the accomplishments of these kinds of sciences. But that doesn't mean that other sciences produce comparably valid results.

You can take this even further. Throughout history much of progress initially came from the tinkerers, inventors, and engineers. The relevant sciences were discovered or substantially elaborated after the fact to understand why the things they created actually worked. The Romans built great aqueducts two thousand years ago and the church produced grand cathedrals in the Middle Ages before materials science was developed. "The era of the steam engine ... was well into its second century before a fully formed science of thermodynamics had been developed." (See "Engineering Is Not Science.")

And unlike science, replication is not an issue in engineering. People may be able to get away with "scientific findings" that can't be reproduced, but not with bridges that collapse.

The climate change alarmists, for one, seem to think that just calling their opponents anti-science should be more than enough to shut them up, or at least convince others to ignore evidence contrary to their catastrophic warming narrative. However there is an implicit assumption in the "anti-science" epithet: that all sciences produce comparably reliable results. Say, that the science of climatology can be trusted as much as the science of aeronautics.

Here are a couple of thought-provoking questions to ask anyone who accuses others of being anti-science: Would you book a flight on an airplane that was as unreliable as weather forecasts more than ten days in advance? Or whose landings were as inconsistent as the frequently changing dietary recommendations from nutritional research?

No, there is no grand unity called "Science." It might clarify the public debate on these topics if we could help people understand this important point.

Protein folds v. Darwin.

Escape from Randomness: Can Foldons Explain Protein Functional Shapes?
Evolution News | @DiscoveryCSC

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.”

Darwinism's quest for a free lunch rolls on.

Free Energy and the Origin of Life: Natural Engines to the Rescue
Brian Miller

In previous articles, I outlined the thermodynamic challenges to the origin of life  and attempts to address them by evoking self-organizing processes. Now, I will address attempts to overcome the free-energy barriers through the use of natural engines. To summarize, a fundamental hurdle facing all origin-of-life theories is the fact that the first cell must have had a free energy far greater than its chemical precursors. And spontaneous processes always move from  higher free energy to lower free energy.  More specifically, the origin of life required basic chemicals to coalesce into a state of both lower entropy and higher energy, and no such transitions ever occur without outside help in any situation, even at the microscopic level.

Attempted solutions involving external energy sources fail since the input of raw energy actually increases the entropy of the system, moving it in the wrong direction. This challenge also applies to all appeals to  self-replicating molecules, auto-catalytic chemical systems, and self-organization. Since all of these processes proceed spontaneously, they all move from higher to lower free energy, much like rocks rolling down a mountain. However, life resides at the top of the mountain. The only possible solutions must assume the existence of machinery that processes energy and directs it toward performing the required work to properly organize and maintain the first cell.

Modern cells perform these tasks using a host of molecular assemblies, such as ATP synthase and chloroplasts. Ancient cells may not have used these tools, but they had to possess some analogous ones that could extract free energy from such sources as high-energy chemicals, heat, or sunlight. The problem is that this machinery could only be assembled in cells that had such machinery already in full operation. But, no such machinery on the early earth could have existed.

Recognizing this problem, many origins researchers have proposed the existence of naturally occurring settings that effectively functioned as  thermodynamic engines (cycles) or their close equivalent. Proposed systems drive a constantly repeating cyclical that includes three basic components:

Energy and/or material is collected from an outside source.

Energy and/or material is released into the surrounding environment.

Energy is extracted from the flow of energy and matter through the system and redirected toward driving chemical reactions or physical processes that advance the formation of the first cell.

A prime example is the  proposal by geologist Anthonie Muller that thermal cycling generated  ATP molecules, which are a primary source of energy for cellular metabolism. Muller argues that volcanic hot springs heated nearby water which drove a convection cycle with heated water moving away from the spring, then cooling, and then reentering the region near the spring to reheat. The water fortuitously contained ADP molecules, phosphate, and an enzyme (pF1) which combines the ADP and phosphate to form ATP. The thermal cycle synchronized with the enzyme/reaction cycle as follows (components from the thermal cycle described above are labeled):

The pF1 enzyme bound to the ADP and to the phosphate, and then the enzyme folded to chemically bond the two molecules together to form ATP. This reaction moves toward higher free energy, so it would not normally occur spontaneously. However, the folding of the enzyme provides the needed energy (Component 3).

The conformational change of the enzyme gives off heat in the process (Component 2).

The bound complex of the ATP and the enzyme enter the heated region near the hot spring. The heat causes the enzyme to unfold and release the ATP, and in the process of unfolding the enzyme absorbs heat (Component 1). The enzyme is again able to bind to ADP and phosphate, thus restarting the cycle.

The net result is that energy is extracted from the heat flow and redirected toward the production of ATP. The ATP could then provide the needed free energy to organize the first cell.

This scenario, however, has many obvious problems. First, the abiotic production of ADP would have been in extremely small quantities, if anything, due to the challenges of producing its key components, particularly  adenine and ribose,and then linking all of the molecules together properly. Next, the existence of any long amino acid chains is highly unlikely near a hot spring, so the needed enzyme would not have existed. Even if such chains were in abundance, the chances of the amino acids stumbling across the proper sequence to form the correct 3D structure to drive the ATP reaction are next to nil.

Even if all of these problems are ignored, thermal cycling would still not prove a viable source of energy. The existence of ATP does nothing to help promote life unless the energy released by ATP breaking down into ADP and phosphate could be coupled directly to useful reactions, such as combining amino acids into chains. However, such coupling is only possible if aided by information-rich enzymes with the precise structure to bind to the correct molecules associated with the target reactions. For the reasons mentioned above, no such enzymes would have existed.

Another scenario is advanced by biochemist Nick Lane and geochemist Michael Russell.  In their proposal, alkaline hydrothermal vents in acidic oceans could have served as the incubators for life. Their theory is that some membrane-like film formed on the surface of a vent, and a proton gradient (difference in concentration) formed between the acidic outside ocean and the basic interior. Protons would have then transported across the membrane (Component 1 of a thermodynamic cycle) through some crevice or micro-pore, which happened to have a ready supply of catalysts such as iron-sulfur minerals, and then exiting into the vent’s interior (Component 2). The catalysts could then have driven chemical reactions that accessed energy from the proton gradient to build cellular structures and drive a primitive cellular metabolism (Component 3). This process would mimic the modern cell’s ability to access the energy from proton gradients across its membrane using machinery such as ATP synthase. Eventually, a fully functional cell would emerge with its own suite of protein enzymes and the ability to create proton gradients and harvest their energy.

To call this scenario unlikely would be generous. It faces all of the challenges of the previous theory plus the implausibility of random chemical catalysts driving the precise reactions needed for life. Origins researchers will undoubtedly come up with many further creative stories of how natural processes could access energy and how life could form in general. However, they will all face the same basic problems:

Natural Tendencies: The natural tendencies of organic chemical reactions are to move in directions contrary to those needed for the origin of life. For instance, smaller organic chemicals are favored over the larger ones needed for cellular structures. When larger ones do form, they tend toward biologically inert tars. Similarly, chains of life’s building blocks tend to break apart, not grow longer.

Specificity: Countless molecules could form through innumerable chemical pathways. Life requires that a highly specific set are selected and others are avoided. Such selectively requires a precise set of enzymes that each contain highly specified amino acid sequences. A membrane must also form that has a highly specified structure to allow the right materials in and out.

Choreography: Any scenario requires many actions to take place in a highly specific order, in the right locations, and in the right ways. Life’s building blocks must be formed in their own special environments with the correct initial conditions. After they form, they then need to migrate at the right times to the right locations with a proper collection of other molecules to assist in the next stage of development. (See Shapiro’s Origins.)

Efficiency: All proposed makeshift scenarios for energy production are highly inefficient. They would be fortunate to access miniscule amounts of useful energy over extended periods of time. In contrast, bacteria can form billions of high-energy molecules every hour. Their overall energy production when scaled is comparable to that of a high-performance sports car. No natural process could reach the required efficiencies.

Localization: The energy production must be localized inside a cell membrane. No imaginable process could scale down anything like thermal cycling or protein gradient production to fit inside such a small, enclosed volume.

As science advances, the need for intelligent direction becomes increasingly clear. The more successful experiments are at generating the products of life, the greater the need for investigator intervention and the more highly specified the required initial conditions and experimental protocols. This trend will only continue until researchers honestly acknowledge the evidence for design that stares them in the face.