Researchers say AI vastly improves a technique used to engineer proteins. As a proof of concept, they redesigned the Botox enzyme to snip a protein linked to ALS.
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Building new enzymes is a labor of love. These proteins are the body's chemical workhorses, speeding up the reactions that make life possible. Researchers use them in gene editing and synthetic biology, and they’re involved in many medical treatments.
But enzymes are also extremely finicky. Even tiny changes to their structures can jeopardize how well they work. To grow or improve their capabilities, scientists usually begin with a natural enzyme. In a process called directed evolution, they slowly nudge the enzyme towards new versions with tailored properties. The process is tedious, time-consuming, and despite best efforts, it may never yield the desired result.
“Laboratory evolution requires the commitment of time and resources. So what you start with is incredibly important as a major determinant of what you end up with,” said David Liu at the Broad Institute of Harvard and MIT in a press release.
Natural enzymes don’t always make good starting points. During directed evolution, they can collapse and stop working. But upgraded designs could be far more resilient.
Now, Liu and colleagues have redrawn the starting line. As a proof of concept, they redesigned the enzyme behind Botox with the help of a popular AI model to create more stable variants for directed evolution.
The evolved enzymes were far more stable and specific at cutting a protein linked to neurodegeneration compared to enzymes evolved from their natural counterparts. The strategy could expand the universe of designer enzymes, making it possible to target protein sequences that are currently out of reach because no suitable natural enzyme exists.
“The most important finding is that using AI to stabilize natural proteins can provide much better starting points for laboratory protein evolution than what we and other researchers have been using for decades,” said Liu. “This insight could change the way researchers conduct protein evolution.”
Evolutionary Bottleneck
Liu is no stranger to reprogramming proteins. As the pioneer of base editing—an offshoot of CRISPR gene editing that swaps single DNA letters—his team has long pursued enzymes with better stability and precision.
One way researchers do this is by speeding up evolution. Like all proteins, enzymes have evolved over eons. Some copy, repair, or modify DNA. Others convert nutrients into energy, break down toxins and drugs in the liver, or relay messages inside cells.
Researchers have long tried to make enzymes that do even more by evolving them in the lab. Success is largely tied to the number of generations they can produce. The more rounds, the greater the chances of producing the desired results. This is why these experiments are so tedious. Each round takes time and careful monitoring.
In 2011, Liu’s lab reported a system called PACE that could perform dozens of rounds of evolution a day without intervention. The system grows bacteriophages—viruses that infect bacteria—in vessels that are continuously diluted of certain molecules. Only viruses carrying improved proteins survive the selection pressure.
Using PACE, the researchers created more efficient prime editors, highly precise RNA-targeting enzymes, therapeutic antibody fragments, and tiny gene editing “scissor” proteins.
Then they hit a wall. Nearly all of the team’s successes began with natural proteins. These were effective to a point, but their descendants would often lose stability as they evolved.
Proteins work by docking with their targets, called substrates, like keys fitting into locks. But evolving new abilities requires them to mutate, which increases the chances their structures warp. Rather than fitting the intended locks, the resulting altered proteins instead clump together and become useless. Precision can also suffer. Even if enzymes have been evolved to recognize new substrates, they may still unintentionally act on their original targets.
Proteins that become less stable during the process can require additional work to make them usable, wrote the team.
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There are a few workarounds. In one such strategy, researchers adds chaperones—these are proteins that help other proteins fold correctly—to buffer the effects of harmful mutations. While this can work, it adds another layer of complexity to an already intricate process. In another method, scientists first evolve a natural enzyme to enhance its stability and then use that version as a starting point. But this costs more time, labor, and frustration.
New Beginning
The team turned to AI. Over the past decade, powerful AI models for biology have emerged that can predict and design protein structures from their underlying molecular sequence alone. One example is ProteinMPNN, developed by Nobel laureate David Baker and colleagues at the University of Washington. The model dreams up new protein sequences that preserve overall structure while altering the underlying building blocks—all in seconds.
Liu’s team reasoned the AI could generate more stable enzymes to kick off directed evolution. To test their theory, they turned to natural botulinum neurotoxin proteases. These molecular scissors paralyze muscles by snipping specific proteins and are the main active component in Botox.
ProteinMPNN generated 58 designs predicted to be more stable. The top three candidates, when produced in E. coli bacteria, were highly soluble, meaning they didn’t aggregate inside cells. Some even had higher activity than their natural counterparts.
The team fed the redesigned enzymes into PACE, evolving them to slice away a mutated region of a protein associated with neuron health. But in diseases such as ALS (Lou Gehrig’s disease), a repetitive stretch expands, causing the protein to clump together and gradually damage neurons. Although the protein is an attractive therapeutic target, naturally occurring enzymes have had limited success cutting the mutant version before it forms toxic aggregates.
Compared with enzymes evolved from natural botulinum neurotoxin, those descended from the AI-redesigned versions were nearly 80 times more efficient at cutting the target protein, and over 56 times more selective for the intended region on the protein. Across three different types of the neurotoxin and multiple substrates, the AI-designed starting points consistently excelled at producing more stable and effective enzymes.
By mathematically mapping their evolutionary paths, the team found the redesigned enzymes tolerated more mutations while gaining new functions. That extra flexibility could open the door to larger reprogramming efforts, such as targeting substrates that lack natural enzymes.
“If you start with a more stable protein, it has more stability to spare, so it can afford larger changes in pursuit of new functions,” said study author Nicholas Krasnow.
The team worked with immortalized human cells for the study, so whether the proteins perform as well in more complex environments remains to be seen. But the work showcases the power of coupling AI and laboratory evolution to rapidly reprogram nature’s molecular machines, endowing them with functions evolution never produced. The team is already applying the strategy to finessing prime editors and other molecular tools.
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What we’re reading
Facts Only
* Researchers redesigned the Botox enzyme using an AI model as a proof of concept.
* The goal was to create more stable variants for directed evolution.
* Natural enzymes often collapse or stop working during directed evolution.
* The AI-redesigned enzymes were more stable and specific at cutting a protein linked to neurodegeneration.
* Enzymes evolved from the AI designs were nearly 80 times more efficient at cutting the target protein.
* The AI designs were over 56 times more selective for the intended region on the protein compared to natural counterparts.
* The team found that starting with a more stable protein allowed for larger changes in pursuit of new functions.
* ProteinMPNN generated 58 designs predicted to be more stable from natural botulinum neurotoxin proteases.
* The evolved enzymes tolerated more mutations while gaining new functions.
Executive Summary
Full Take
The narrative highlights a critical tension between the inherent complexity of molecular evolution and the speed of modern computational design. The core implication is that traditional laboratory evolution, while powerful, suffers from an evolutionary bottleneck where natural starting points impose severe structural constraints on subsequent beneficial mutations. The introduction of AI serves not merely as a tool for prediction but as a mechanism to bypass this bottleneck by generating more resilient initial states. This reframes the process of biological engineering: instead of slow, iterative refinement relying solely on physical selection pressure, the system can leverage predictive power to explore a vastly larger design space simultaneously.
The shift from evolutionary dead ends—where natural enzyme descendants often lose stability upon mutation—to AI-guided evolution suggests that the fundamental limit in protein engineering may not be the difficulty of searching for functional sequences but the structural integrity of the initial scaffold provided. This points toward a paradigm where computational scaffolding can precede physical experimentation, enabling researchers to target molecular functions currently inaccessible due to the lack of suitable natural enzymes. The system is moving toward coupling predictive modeling with iterative evolution, suggesting that future breakthroughs in synthetic biology will rely less on brute-force generational cycling and more on intelligent initialization.
Bridge Questions: How does this AI-stabilization strategy scale when dealing with multi-protein systems or complex cellular environments rather than single enzyme targets? What are the ethical considerations regarding accelerating the rate of molecular design, and who bears responsibility for the unforeseen consequences of rapid, AI-driven evolutionary pathways? What limitations exist on the predictive power of models like ProteinMPNN when extrapolating to novel biological functions not represented in existing training data?
Sentinel — Human
This text reads as a well-informed journalistic summary of specific, complex scientific research findings involving protein engineering and AI application, exhibiting characteristics of expert reporting rather than pure synthetic generation.