Kenneth Loi shows how VIPR RNA (in pink) snakes around the double helix of DNA (yellow and green) to form a unique triplex structure.
Photo: Glenn Ramit.
One of the recurring mistakes in creationist arguments is to assume that a complex biological system must always have performed its present function. If its components now work together, the argument goes, they must have been created together for that purpose. Evolution, however, can recruit existing machinery for new roles. Two papers in *Science* now provide evidence that this process may help explain the origin of an important form of microbial immunity—with the intriguing possibility that bacteria acquired the precursors of their antiviral weapons from viruses themselves.
To appreciate the discovery, it helps to understand what CRISPR immunity actually is. Although CRISPR is familiar as a tool for editing genes, its natural role is defence. Many bacteria and archaea possess CRISPR–Cas systems that retain molecular records of past invaders. Short pieces of foreign DNA are incorporated into the cell’s genome, between repeated sequences. These stored fragments provide templates for small RNA molecules, which guide CRISPR-associated—or Cas—proteins to matching genetic material during subsequent infections. Depending on the system, the resulting response can destroy invading DNA or RNA. This is adaptive immunity: protection directed against particular threats, with a genetic memory that can pass to descendants.
CRISPR stands for “clustered regularly interspaced short palindromic repeats”, a description of the DNA arrangement in which those memories are stored. The best-known gene-editing protein, Cas9, belongs to class 2, whose targeting machinery centres on one large protein. Class-1 systems instead use assemblies of several proteins to recognise their targets. It is the evolutionary origin of this latter machinery that the new research addresses.
The studies, published on 17 September 2026 investigate a compact system called VIPR, short for viral interference programmable repeat. Found in viruses and bacteria, it combines a small protein with a guide RNA. The researchers propose that an ancestral VIPR-like system involved in competition between viruses was recruited into bacterial defence, eventually contributing to the evolution of class-1 CRISPR. This would be evolutionary co-option: machinery favoured in one context becoming useful in another.
VIPR also has an unusual way of recognising DNA. Its guide RNA pairs with the target in separated stretches, skipping every third DNA nucleotide. The structural paper shows how repeated copies of the protein organise the RNA into a filament, allowing the RNA and DNA to form an unusual three-stranded arrangement. VIPR can suppress gene activity through this binding mechanism; it does not depend on cutting DNA in the manner familiar from Cas9 gene editing.
The evolutionary interpretation in the accompanying paper should not be mistaken for a complete reconstruction of every historical step. Discovering a plausible precursor is not the same as demonstrating the entire transition to a modern immune system. Nevertheless, the proposed relationship gives researchers a concrete, testable route to investigate.
For advocates of “irreducible complexity”, that is precisely the difficulty. A simpler precursor need not have performed every task of its more elaborate descendants to have been useful and subject to natural selection. Nor did a viral competitor need to anticipate the eventual evolution of bacterial immunity. Immediate advantages, inherited variation and the recruitment of existing components are enough to make such evolutionary transitions possible. The scientific question is how that history unfolded—a question that declaring the finished machinery “designed” does nothing to answer.
How evolution builds complex molecular machinery. A molecular machine is an assembly of interacting molecules, usually proteins, that carries out a biological task. Its components may now depend on one another, but that does not mean they originated together or have always performed their present roles. Evolution modifies existing systems, and several processes can contribute to increasing complexity.The papers were accompanied by a description in an Innovative Genomics Institute news release:
What does this mean for “irreducible complexity”?
- Co-option: an existing mechanism acquires a new role
A molecule that performs one task may also have properties useful in another context. If those properties confer an advantage, natural selection can favour changes that enhance the new role. This recruitment is called co-option. It requires neither foresight nor a component waiting uselessly for the rest of a future system to appear.
The proposed VIPR–CRISPR relationship illustrates this possibility: machinery involved in competition between viruses may have been recruited into bacterial defence and subsequently modified into part of the targeting apparatus of class-1 CRISPR systems.
- Gene duplication and divergence: copies become different
When a gene is duplicated, the resulting copies can accumulate different mutations. Sometimes one retains an ancestral function while another acquires a new activity. In other cases, the copies divide the ancestral gene’s functions between them. The proteins they encode can consequently become specialised partners within a larger assembly. Many duplicates are lost; duplication creates opportunities, not a guaranteed increase in complexity.
- Horizontal gene transfer: useful machinery moves between lineages
Genes do not pass only from parent to offspring. Bacteria and archaea can also acquire genetic material from other organisms, including through processes involving viruses. This horizontal gene transfer can introduce a useful component or an entire functional system into a new genetic background. The recipient lineage can then retain and modify it. Thus, evolution can draw on innovations that arose elsewhere.
- Increasing interdependence: once-optional parts become essential
After components begin working together, subsequent changes can make each dependent on the others. For example, duplicated proteins may lose different ancestral capabilities, so that both are eventually needed to perform what an ancestral protein could previously accomplish. Removing one modern component therefore does not reconstruct the ancestral condition: the remaining components have changed too.
This has experimental support. In a 2012 study of the V-ATPase proton pump, researchers reconstructed ancestral proteins and tested their functions. Their results showed how a fungal ring containing three distinct protein types evolved from an ancestral ring containing two. Following duplication, the descendant proteins lost different interaction capabilities, making both necessary within the ring. Greater complexity arose without an apparent new function for the machine as a whole.
Showing that a present-day system fails when a component is removed establishes its present-day dependence on that component. It does not establish that simpler ancestors were non-functional. Those ancestors may have used less specialised components, performed a different task, or operated in a different genetic background.
Nor must every evolutionary change improve a system. Natural selection can preserve advantageous variants, while genetic drift can spread variants that are effectively neutral. Complexity is a possible outcome of evolution, not its predetermined goal.
What is established, and what remains a hypothesis?
Co-option, gene duplication, horizontal gene transfer and the evolution of new dependencies are documented processes. The specific proposal that ancestral VIPR-like machinery contributed to class-1 CRISPR remains a reconstruction supported by the new studies. It is not a complete account of every intermediate step, nor does it establish that all four processes described here occurred in this particular transition. Modern VIPR systems are clues to that history, not unchanged ancestral specimens.
Brief glossary
- Co-option
- The recruitment of an existing biological feature for a new role.
- Gene duplication
- The production of an additional copy of a gene, allowing the copies to follow different evolutionary paths.
- Horizontal gene transfer
- The movement of genetic material between organisms outside ordinary parent-to-offspring inheritance.
- Homology
- A relationship attributable to shared ancestry. Homologous proteins can retain similar structures even after their sequences and functions have diverged.
- Natural selection
- Differences in survival and reproduction associated with heritable variation, causing some variants to become more common.
- Genetic drift
- Changes in the frequency of genetic variants caused by chance sampling across generations.
VIPR Systems Break the Rules of the Genetic Code to Target and Twist Around DNA
IGI researchers discover an ancient progenitor of CRISPR using a combination of AI tools and human ingenuity to solve a genomic mystery.
When scientists first started to decode the language of the genome, the intuitive simplicity made the discovery feel especially profound. The language of our cells works much like our own languages. The code is read consecutively from beginning to end, every three characters like a word in a sentence, every gene a complete story. Biology, however, is never content to be quite so neat and tidy.
In two connected papers published today in the journal Science (Paper 1 | Paper 2), researchers from the lab of Jennifer Doudna at the Innovative Genomics Institute (IGI) at UC Berkeley describe the surprising discovery of an ancient progenitor of CRISPR systems that had been overlooked because it uses a never-before-seen coding system to find and bind to DNA.
In nature, CRISPR is an adaptive immune system that bacteria and archaea use to fight viruses. CRISPR systems collect snippets of viral DNA, and they use matching pieces of RNA like mugshots to recognize invaders. The newly discovered system, named “VIPR” (Viral Interference Programmable Repeat), likely predates CRISPR and was instead found in viruses. The new studies rewrite CRISPR’s origin story with a surprising twist: bacteria appear to have stolen a weapon made by viruses to fight other viruses, and they turned it back on their enemy.
The case of the confusing code
As often happens in science, the discovery of VIPR started with an observation that didn’t make sense.
Since the discovery of CRISPR-Cas9’s ability to be harnessed as a gene-editing tool, scientists have searched for other related molecules in nature. CRISPR-Cas9 is what is known as a “Class 2” CRISPR system. In evolutionary terms, these are the latest models, composed of a single large protein. Because of the utility for life science research and gene editing for health and other applications, Class 2 systems have been deeply studied in recent years.
Peter Yoon and Kenneth Loi in the Doudna lab at the IGI were instead digging into the origins of Class 1 CRISPR systems, which are much more common in nature but less studied. Class 1 systems are composed of multiple proteins that come together to form a complex, and are also significantly older than Class 2 systems — in fact, it is thought that the last universal common ancestor of all cellular life on Earth, aka “LUCA,” had a Class 1 CRISPR system.
In an earlier paper, the team developed a process using AI tools to improve on older methods for finding related proteins. With this AI-assisted process, they could uncover molecules with similar structures and functions that were missed by earlier methods that relied on comparing the underlying sequences.
The further you go back in evolutionary history, the more challenging it becomes to find related proteins. For organisms that diverged relatively recently, DNA sequences will be relatively similar. As divergence times get farther and farther back on the timeline, the sequences have had more time to evolve away from each other. The sequences can look unrecognizable while key parts of the protein structure remain comparable.
“If you want to find something truly ancient, you need to look for something with a particular shape, not a particular sequence,” says Doudna.
Using their AI-assisted approach to search through roughly 2.3 million structures for proteins that might be related to early CRISPR systems, Yoon and Loi found a few hundred candidates. One particular protein stood out: it had the same shape as CRISPR proteins but was never found next to CRISPR RNAs. Sequencing this new system revealed that the protein was paired with a mysterious RNA. CRISPR RNA contains an exact match of a section of virus DNA. This new RNA didn’t match anything they could identify.
“We were scratching our heads. It didn’t look like any RNA we had ever seen before. That’s where the project was stuck for months,” says Doudna. “I told Peter and Kenneth, this is either something very interesting or very boring.”
It turned out to be very interesting, it just needed the right combination of human ingenuity and cutting edge AI tools working together to crack the code.
The mystery of the mismatched RNA
To gain insights into the structure and function of VIPR, the team turned to colleagues in the Doudna and Brohawn labs, Terry Zhang and Trevor Docter.
“Part of the mystery was that we know that VIPRs look similar to some CRISPR systems, but we didn’t know their biological function. We also didn’t know their biochemical properties, so that made them very hard to study,” says Zhang.
Yoon, Zhang and Docter initially imaged a sample with a low resolution electron microscope, and confirmed that the VIPR system was assembled from multiple proteins. When they treated it with an enzyme that removes RNA, the complex disappeared, showing that it was likely assembled along its RNA partner.
“After a lot of optimization we were able to finally get a high resolution structure. And then everything started coming together quickly. In the first set of structures, we could clearly resolve each protein subunit and the VIPR RNA, which really opened the door for us,” says Docter.
The picture was coming into focus, but the team was stuck on the mysterious VIPR RNA that matched no known sequence. What could it possibly do? And why did its sequence make no sense?
This time, with the aid of a different AI language model, the team was able to spot a unique pattern.
“I reasoned that if a genomic language model has been trained on enough of these sequences, it should have an idea of how to generate one on its own,” says Loi.
Loi’s intuition turned out to be correct: the AI model picked up patterns in the training sequences and was able to generate sequences that were variations on what the team had found in nature. A language model you might use to write an email is good at predicting what word should follow another in a sentence based on probabilities. Similarly, the genomic language model scores and predicts which base should come next in a string of code.
In the VIPR RNA, three bases in a row were easy for the model to predict: they were almost always GGT with very high probability. This would repeat consistently in the sequence like beads on a string, but in between these repeats there were two bases that were almost impossible to predict.
“I looked at this for a very long time. I never picked it up by eye. It was only these language models that were able to pull out a pattern and make it visible to us,” says Yoon.
Instead of a standard consecutive string of bases like CRISPR RNA, the VIPR RNA appeared to be using what is called a “skip cipher” in cryptography. In between the repeating GGTs was a hidden code. Skip 3, read 2, skip 3, read 2, and repeat.
The team pulled out this hidden code in a consecutive string to see if it matched any known sequence. No match. If the VIPR RNA has two letters of code and three gaps, perhaps that same pattern would be found in the target sequence? Again no match. They tried a gap of two bases in the target sequence, and then a single-base gap, and finally they found matches in viral DNA.
“This was really bizarre,” says Yoon. “We were able to observe that if you follow this one predictive pattern from the structure, that we start to find clear matches. But if we follow any other pattern — just nothing.”
As the team looked more closely at how the VIPR RNA matched with virus genes, their opinion of this system quickly changed. What looked bizarre at first glance was actually an ingenious solution to a problem: virus DNA mutates rapidly, so if you want to attack a virus’ genome, your tool needs to be adaptable to change.
Finding the signal, ignoring the noise
At some point you might have come across a viral message (in the internet meaning of the term) that looked something like this:
Aoccdrnig to rscheearch at Cmabrigde Uinervtisy, it deosn’t mttaer in waht oredr the ltteers in a wrod are, the olny iprmoetnt tihng is taht the frist and lsat ltteer be in the rghit pclae.This strange-looking sentence is a demonstration of the phenomenon humorously called “typoglycemia,” where most people can understand a sentence made of jumbled words as long as the first and last letters of each word remain unchanged. It might slow us down a little, but our brains can deal with some noise and still recognize the words and understand the sentence.
What the VIPR RNA is doing is comparable, but it follows a different pattern. Instead of relying on the first and last letters of a word to remain stable while the other letters change, VIPR looks at the genetic code in sets of three letters, relying on the first two to be stable, while the third can change in any way it likes:
Using VIPR’s rules, YES OLD BUD would be the same as YEP OLE BUB.
With CRISPR’s rules — and the standard way genetic strands are known to pair — these two sentences would have to be a perfect, letter-for-letter match.
To say that VIPR’s gapped code is not the way scientists have come to expect the genetic code to work is an understatement. There are sometimes large gaps in coding regions of DNA, but the code itself is supposed to be consecutive, read letter-by-letter, just like the letters in this sentence.
For a quick refresher on basic genetics, the genetic code in DNA is made of four nucleotide bases — A, C, G, and T, for short. Within the coding regions of the genome, sets of three of these bases in a row make up a codon, each one corresponding to an amino acid building block for proteins. For example, TCA translates to the amino acid serine, but there is flexibility built into the system: TCC, TCG, and TCT also translate to serine. The third letter is sometimes called the “wobble position” because in many cases changing it has no impact.
Changes to the third position of a codon in a gene — aka the “wobble position” — are often synonymous and don’t affect the translation into the amino acids that make up a protein.
The most likely place to find a mutation in a gene is in these wobble positions — and those are exactly what the VIPR’s unique system ignores. If the targeted virus mutates, the VIPR RNA will still find and pair to the more stable parts of the code.
“No known system does this, and this provides a very clear evolutionary rationale,” says Yoon. “This is a mechanism that intentionally masks out the parts of the code that are likely to change. It’s a de-noising algorithm. It makes it very difficult for the targeted virus to evolve out of danger.”
The VIPR attacks
VIPR RNA (top) has a unique gapped coding system that pairs with two bases on the target strand of viral DNA (bottom), but skips every third base. The skipped base is also known as the “wobble” position, which can evolve rapidly. Even if the target DNA changes, the VIPR RNA will still be able to identify and bind to its target.
With the remarkable gapped coding system cracked, the research team could move on to figuring out how VIPR worked in nature, and what they found were signs of an ancient war between viruses.
“The viruses that are encoding these VIPR systems are targeting related viruses, often viruses that infect the same host, so it’s part of viral competition,” says Yoon.
Once they understood how to search for them, they started finding VIPR systems everywhere.
“In metagenomic data, where researchers sequence environmental samples from soils and other places, we could find hundreds of thousands easily,” says Yoon. “They are widespread enough that we found them in bacteriophage viruses that were literally sitting in our fridge.”
Nature has found various ways to deploy and repurpose VIPR systems. In some cases, the viruses being targeted by VIPR carry their own VIPR RNAs that serve to lead the other VIPR system astray. In other cases, VIPR systems target other VIPR systems, in a never-ending VIPR-on-VIPR war.
Unlike CRISPR, which pushes the two strands of DNA apart to find and cut the targeted DNA, VIPR takes a different approach that inspired its name: it twists itself around the DNA double helix like a snake.
“CRISPR systems like Cas9 have to overcome the challenge of double-stranded DNA, which is very stable and well-protected. They use their guide RNA to invade the DNA duplex and force it to open up so that the pairing can happen, and they use a continuous string of bases that creates a stable pairing,” says Zhang.
VIPR’s RNA, on the other hand, is full of gaps, and it wouldn’t be stable in the same scenario.
“From our observations, the DNA strand is not being replaced by the RNA strand, but rather the VIPR filament is destabilizing the DNA in a way that the DNA gives up one of its pairing strands to interact with the VIPR RNA guide and forms a triplex,” says Zhang.
This serpentine triplex structure is key to VIPR’s attack. It doesn’t use a protein to cut the DNA like CRISPR, instead it wraps around the targeted DNA, effectively turning off the gene.
“One of the things we demonstrate in these papers is that we can reprogram VIPR to silence a gene by binding upstream to the gene’s promoter region,” says Loi.
CRISPR has been used to silence genes as well, but VIPR has several advantages as a potential tool. First, it’s incredibly small — the smallest RNA-guided system that has been found. This makes it significantly easier to deliver into cells, often a challenge for the large CRISPR enzymes. VIPR also has no limitations on where it can target in a genome. CRISPR-Cas9 requires a short sequence called a “PAM” to be near the place where it binds and cuts DNA; VIPR can be reprogrammed to target anywhere in a genome.
“There’s no system that wraps around DNA like this. We suspect that this unusual geometry should allow for different kinds of applications people couldn’t do before,” says Yoon.
Intriguingly, the team found cases where fully functional VIPR systems were transferred to bacteria. The bacteria picked up a weapon used by viruses and turned it back on viruses, and it was this act that eventually led to the evolution of Class 1 CRISPR systems at some point before LUCA, the last universal common ancestor of all cellular life.
We’ve been calling it the ultimate anime betrayal. These viruses are fighting each other, and the host just goes, ‘Oh, boop, let me take that gun from you guys’. It’s crazy to think that this one act could be the origins of adaptive immune systems broadly for life on planet Earth.
Kenneth Loi, co-author (Paper 2)
Department of Molecular and Cell Biology
University of California, Berkeley
Berkeley, CA, USA.
Projects like this keep Doudna endlessly curious about the next discovery down the road.
“This work is a great reminder of how complex biology is, and how little we still know,” says Doudna. “Fundamental concepts are still out there to be discovered. I think that’s wonderful.”
Publications:Peter H. Yoon et al.
A noncontiguous code for RNA-guided DNA recognition at the origin of CRISPR-Cas. Science 393, 1230-1235 (2026). DOI: 10.1126/science.aei0498
Peter H. Yoon et al.
VIPR RNA-guided DNA recognition by noncontiguous geometric triplex formation. Science 393, 1236-1240 (2026). DOI: 10.1126/science.aei3472
The significance of this research is that it gives scientists a concrete route to investigate how complex microbial defences could have evolved from machinery with an earlier, different function. The proposed connection between VIPR and class-1 CRISPR does not yet supply every intermediate step, but it places the question firmly within experimental biology. Protein structures, molecular interactions and evolutionary relationships provide evidence that can be tested, challenged and refined. An unexplained detail is an invitation to further research.
For creationists invoking “irreducible complexity”, the central problem remains their assumption that present-day interdependence proves simultaneous creation. It does nothing of the sort. Components can acquire new roles, become specialised and eventually depend on partners that their ancestors did not need. Removing a component from a modern system cannot reverse all the changes that occurred during its evolution. A machine that falls apart when dismantled today may still have descended from a simpler, functional predecessor.
The proposed history also illustrates evolution’s complete lack of foresight. A mechanism that helped one virus compete with another could subsequently have benefited a bacterial host. Neither the original viral advantage nor its later recruitment required anticipation of an immune system. Variations arose without regard to future needs; their consequences in particular circumstances determined whether natural selection favoured them. What eventually became useful for defence may have begun in competition among the very agents against which that defence now operates.
There is an awkward implication here for intelligent design, too. Anyone determined to attribute this molecular machinery to a designer must also account for the viral threats and competing countermeasures that make it necessary. Evolution explains such conflicts through the divergent interests of hosts and viruses, without requiring a designer to equip opposing sides. As this research shows, that explanation generates productive questions and discoveries. Declaring the machinery designed supplies neither its history nor a testable account of how it arose.
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