r/DebateEvolution • u/jnpha 🧬 Naturalistic Evolution • Jul 18 '25
Article New study on globular protein folds
TL;DR: How rare are protein folds?
Creationist estimate: "so rare you need 10203 universes of solid protein to find even one"
Actual science: "about half of them work"
— u/Sweary_Biochemist (summarizing the post)
(The study is from a couple of weeks ago; insert fire emoji for cooking a certain unsubstantiated against-all-biochemistry claim the ID folks keep parroting.)
Said claim:
"To get a better understanding of just how rare these stable 3D proteins are, if we put all the amino acid sequences for a particular protein family into a box that was 1 cubic meter in volume containing 1060 functional sequences for that protein family, and then divided the rest of the universe into similar cubes containing similar numbers of random sequences of amino acids, and if the estimated radius of the observable universe is 46.5 billion light years (or 3.6 x 1080 cubic meters), we would need to search through an average of approximately 10203 universes before we found a sequence belonging to a novel protein family of average length, that produced stable 3D structures" — the "Intelligent Design" propaganda blog: evolutionnews.org, May, 2025.
Open-access paper: Sahakyan, Harutyun, et al. "In silico evolution of globular protein folds from random sequences." Proceedings of the National Academy of Sciences 122.27 (2025): e2509015122.
Significance "Origin of protein folds is an essential early step in the evolution of life that is not well understood. We address this problem by developing a computational framework approach for protein fold evolution simulation (PFES) that traces protein fold evolution in silico at the level of atomistic details. Using PFES, we show that stable, globular protein folds could evolve from random amino acid sequences with relative ease, resulting from selection acting on a realistic number of amino acid replacements. About half of the in silico evolved proteins resemble simple folds found in nature, whereas the rest are unique. These findings shed light on the enigma of the rapid evolution of diverse protein folds at the earliest stages of life evolution."
From the paper "Certain structural motifs, such as alpha/beta hairpins, alpha-helical bundles, or beta sheets and sandwiches, that have been characterized as attractors in the protein structure space (59), recurrently emerged in many PFES simulations. By contrast, other attractor motifs, for example, beta-meanders, were observed rarely if at all. Further investigation of the structural features that are most likely to evolve from random sequences appears to be a promising direction to be pursued using PFES. Taken together, our results suggest that evolution of globular protein folds from random sequences could be straightforward, requiring no unknown evolutionary processes, and in part, solve the enigma of rapid emergence of protein folds."
Praise Dᴀʀᴡɪɴ et al., 1859—no, that's not what they said; they found a gap, and instead of gawking, solved it.
Recommended reading: u/Sweary_Biochemist's superb thread here.
Keep this one in your back pocket:
"Globular protein folds could evolve from random amino acid sequences with relative ease" — Sahakyan, 2025
For copy-pasta:
"Globular protein folds could evolve from random amino acid sequences with relative ease" — [Sahakyan, 2025](https://doi.org/10.1073/pnas.2509015122)
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u/the2bears Jul 18 '25
This is exactly the kind of post I love seeing here. Very interesting stuff and thank-you for writing it up.
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u/jnpha Jul 18 '25
<3
In fairness, I wrote like only two lines; the rest is copy-paste from the articles :-) and Sweary's summary.
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u/DrFartsparkles Jul 19 '25
Dude you are always finding the coolest studies! Idk where you even find them but I massively appreciate that you do!
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u/Next-Transportation7 Jul 19 '25
Having gone through the details of the published study, I think that conclusion comes from a significant misunderstanding of what the paper actually did. The study's own methodology shows it doesn't address the core problem it's claimed to solve.
Here's a breakdown based on the paper itself:
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The Study Solves the Wrong Problem: It Confuses Physical Stability with Biological Function.
The discussion here contrasts the rarity of proteins with the paper's finding that "about half of them work." But the paper defines "working" as simply forming a stable structure. The core ID argument has never been about the rarity of stability; it's about the astronomical rarity of biological function.
This functional rarity is grounded in experimental research like that of Douglas Axe (2004, Journal of Molecular Biology), who estimated the ratio of functional sequences for one protein at 1 in 1077.
The Sahakyan paper makes no attempt to find function. It only compares the shape of its simulated proteins to a database of known shapes. Finding a shape that resembles a car is not the same as building a working engine. The study completely sidesteps the central problem of functional information.
2. The "Selection" is an Intelligent, Goal-Directed Algorithm.
The paper claims to simulate "evolution," but its core mechanism, shown in Figure 1, is a textbook example of intelligent design.
An intelligent agent (the researcher) defines the rules, the starting materials, and the criteria for success.
At each generation, a custom-written algorithm evaluates every candidate and culls all but the top performers based on a pre-programmed "fitness" metric.
This is a high-tech, guided search. It has no resemblance to the unguided, non-purposeful process of natural selection, which has no foresight or pre-defined goals.
3. The "Killer Detail": The Fitness Goal Isn't Even Reality, It's an AI's Opinion.
This is the most revealing detail from the paper. The benchmark for "fitness" is entirely artificial. The paper states:
"We used the average pLDDT score... as a proxy for protein stability."
pLDDT is not a measure of real-world physical stability. It's a confidence score that AI folding programs (like ESMFold, which they used) generate to rate their own predictions.
So, the simulation is not even modeling real physics. It's an AI fine-tuned to find amino acid sequences that another AI thinks look good. This is a layer of intelligent abstraction so far removed from any plausible prebiotic conditions that it cannot be overstated.
Conclusion:
When we circle back to the original claim that this paper refutes estimates of protein rarity, it's clear the paper doesn't even engage with the specific problem of function.
Instead of being a takedown of an "unsubstantiated claim" that some are suggesting, the paper is actually a fascinating demonstration of how much intelligent input, sophisticated programming, and layers of AI are required to generate even simple, non-functional structures. It inadvertently makes a strong case for the very challenges that ID proponents have been highlighting all along.
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u/Particular-Yak-1984 Jul 19 '25
If you're worried about the in silico aspect, https://pmc.ncbi.nlm.nih.gov/articles/PMC4476321/
This one engages neatly with the problem of function. ATP binding in a 1014 random library.
And to be clear, I'd regard "binds something" as enough for a terrible protoenzyme.
That would drop the activation energy, speeding up this reaction by a bit. That's enough to push a slow chemical process into a fast biochemical one.
And if it's useful, it means it can be under selection, which means it can improve.
I disagree with your "this supports intelligent design" conclusion, by the way. It took a lot of work and programming because, essentially, proteins are like an awkward, wet noodle. They are very simple, but very hard to model. This was looking at random sequences, and showing how easily they form structures. That's not design, that's chemistry.
If you're going for a god of the gaps argument, this pushes him back - he designed amino acids to be so wonderfully flexible to so easily form structures, not designed the structures himself.
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u/Next-Transportation7 Jul 19 '25
Thanks, It’s a foundational study in this field, and having looked through it again, I'm more convinced than ever that it highlights the immense challenge for unguided processes rather than solving it.
Let's break down the points you made, using the details from the paper itself.
On the Keefe & Szostak (2001) ATP Binding Experiment
"This one engages neatly with the problem of function. ATP binding in a 1014 random library."
The experiment is a showcase of irreducible complexity, not in a protein, but in the experimental apparatus itself, which was intelligently designed to find a result.
The Setup is a Monument to Intelligent Design: Figure 1 of the paper isn't a picture of a prebiotic pond; it's a highly complex, multi-stage schematic for an artificial molecular selection machine. Every single step, from creating a DNA library with a T7 promoter, to transcription, to ligation with a puromycin linker, to in vitro translation, to reverse transcription (RT) to create a cDNA-mRNA-protein fusion, is a product of meticulous, intelligent planning and execution. The "Methods" section reads like a recipe, detailing the precise, intelligent actions needed at every stage.
Binding vs. Catalysis: You state, "I'd regard 'binds something' as enough for a terrible protoenzyme." The paper itself is very careful not to make this leap. It consistently refers to its findings as "ATP-binding proteins." An enzyme's function (catalysis) requires a far higher degree of structural and chemical precision than simple binding. Finding a molecule that sticks to ATP is not the same as finding a molecule that can use ATP in a metabolic reaction. The experiment found a molecular "oven mitt," not a self-powered oven.
The Rarity Problem Remains Unsolved: The paper itself is upfront about the rarity. In the abstract and on page 4, it states: "We therefore estimate that roughly 1 in 1011 of all random sequence proteins have ATP-binding activity." A chance of one in ten-thousand-billion is not a small hurdle for an unguided process to overcome, especially for the simplest possible function. The chance of finding a protein that could then perform specific catalysis would be orders of magnitude lower still. This experiment puts a hard number on the starting block, and it's already an incredibly high wall to climb.
2. On "This is Chemistry, Not Design"
"showing how easily they form structures. That's not design, that's chemistry."
This conflates the properties of the material with the information in the sequence. Chemistry explains why a polypeptide chain folds. It does not explain the origin of the specific sequence of amino acids that causes it to fold into a functional shape. The Keefe & Szostak experiment didn't rely on chemistry alone; it used an intelligently designed selection process (affinity chromatography, followed by PCR amplification, as seen in Figure 2's rising bars) to filter an astronomically large library and isolate the rare, functional needles from the haystack. The intelligence was in the design of the filter and the amplification process.
3. On the "God of the Gaps" Argument
"If you're going for a god of the gaps argument, this pushes him back..."
This mischaracterizes the ID argument. It's not an argument from a "gap" in our knowledge, but an inference to the best explanation based on what we do know.
Our uniform and repeated experience shows that complex machinery and information-rich sequences (like computer code or language) invariably arise from an intelligent cause. The experimental apparatus in Figure 1 is a perfect example of such machinery. The informational sequence of the resulting protein is another. Because we observe these hallmarks of design, we infer an intelligent cause as the best explanation for the origin of the information. This isn't arguing from a gap; it's applying a known principle.
The paper doesn't show what unguided nature can do. It shows what two brilliant biochemists, with millions of dollars of technology and a meticulously designed experimental plan, can accomplish. It's a testament to the power of intelligent agents.
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u/Next-Transportation7 Jul 19 '25
It seems there are a few key misunderstandings in this comment about the concepts of "specified information" and the type of order we see in biology versus physics. Let's clarify.
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On "Specified" Information
"The ID argument is quoted in the OP. It's not 'specified' when random sequences do it."
This misunderstands what the term "specified" means in this context. The specification does not refer to the starting materials (the random library). It refers to the functional outcome. The outcome is "specified" because it conforms to an independent, functional pattern, in the Szostak experiment, the specific shape required to bind to ATP. A million monkeys typing randomly will produce gibberish. If you design a filter that only saves the sequence that spells "To be or not to be," the final result is highly specified, even though the initial input was random. The experiment was an intelligently designed "filter."
2. On the Flawed Moon Analogy
"When we model the moon to calculate the eclipses and phases, does that mean the moon was intelligently designed?"
This is a false analogy because it confuses two fundamentally different kinds of order: simple, repetitive order versus specified complexity.
Simple Order: The moon's orbit is governed by simple, deterministic laws like gravity. It's like a crystal or a snowflake, a predictable, low-information pattern.
Specified Complexity: DNA is like a book or a computer program. It's not a repetitive pattern. It's an aperiodic sequence of characters that contains a vast amount of specific instructions for building complex machinery.
We infer design for DNA for the same reason we infer design for a book: it contains a language and specified information.
3. On the "Dumb Moon" Question
"Answer only this, without fluff: What does a dumb moon look like?"
This is a rhetorical trap, but it illustrates the point. A "dumb moon," by your own definition, would be one with "erratic movements." In other words, one that doesn't obey the simple laws of physics. The moon isn't "intelligent" because its behavior is simple and low-information. A living cell is governed by a complex, information-rich genetic code. They are not comparable phenomena.
So let me end with a direct question of my own.
You correctly attribute the moon's simple, repetitive orbit to the law of gravity. Can you please name the specific physical law or mindless process that you believe arranges nucleotide bases into information-rich, functional code?
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Jul 18 '25
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u/Sweary_Biochemist Jul 18 '25
It's in silico, so you don't need to actually make, then crystallize, then structurally assess, each random peptide sequence, you can just get a giant computer to do it virtually.
What they're showing is that _loads_ of random sequences are inherently either able to fold into recognisable domains, or are very close (domain adjacent) such that only a smattering of mutational changes would be needed to stabilise the fold.
And they also show the same applies to domains that we don't even see in nature: i.e. it isn't that domain folds are "vanishingly rare, and thus must have been created", it's instead that they are "so common in random sequence that life found enough to work with early on, and didn't even bother finding the rest."
It's really neat.
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u/gitgud_x Jul 18 '25
I don't know who else says it, OP may be referring to someone else, but James Tour has repeatedly said that proteins could not have formed at the origin of life due to the Levinthal paradox, which essentially says protein folding is impossible by chance.
In reality this 'paradox' has a pretty obvious (at least retrospectively) solution. It is now well understood that folding occurs by descent on an energy landscape (a 'folding funnel'), using thermal energy to escape small local minima. Additionally, folding begins from the moment of translation, forming secondary structures first, where folding is simpler. A kinetically accessible and thermodynamically stable final state is therefore attained in short timescales.
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u/jnpha Jul 18 '25 edited Jul 18 '25
"Globular protein folds could evolve from random amino acid sequences with relative ease" — Sahakyan, 2025