Protein design tools may be getting increasingly sophisticated, but you still need a sophisticated understanding of protein structure for them to work. This can include knowing enzyme mechanisms down to the positions of specific amino acids, as well as the water molecules that also occupy the area where catalysis takes place. Once you specify these, the tools can build the rest of a protein around this target.

A large collaboration of researchers based in China, however, decided to bring protein design to the masses, making a tool that can take natural language prompts and use them to produce proteins with the function you specify. If your measure for success is a working protein, then the success rate may not be great. But they still manage to get the job done. And, in a separate announcement, Hugging Face shared that it's now hosting a tool that does similar things on the DNA level.

The equivalent of vibe coding may have come to protein design—at least if you have access to a lab where you can sort the good designs from the dozens of failed ones.

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The UW Medicine Institute for Protein Design (IPD) today released a new open-source model for creating proteins with specific functions, called RFD4-Proteina. It is the first AI protein model the University of Washington has co-developed with semiconductor giant Nvidia.

The release comes on the heels of a major computing boost. Earlier this month, IPD announced it had received a large grant of computing power from Nvidia CEO Jensen Huang and his wife, Lori Huang, through their foundation. In June, the institute began accessing Nvidia graphics processing units, or GPUs, through cloud company CoreWeave. The grant is expected to support the institute for a year.

“RFD4-Proteina will open the door to new solutions in medicine, sustainability, materials and biotechnology,” David Baker, director of IPD and a 2024 Nobel laureate in chemistry, said in a statement. He earlier told GeekWire that the foundation’s grant has been “transformational” for the institute’s work.

IPD has used deep learning to develop and release a series of tools, including RoseTTAFold for predicting protein structures and RFdiffusion (RFD) for designing new proteins. The new model’s name nods to RFD and references Nvidia’s Proteina-Complexa computational models.

The team is touting RFD4-Proteina as “a considerable improvement over previous methods” that is faster and requires significantly less computational power.

The models have allowed researchers to design proteins never imagined before, with potential as drugs for challenging diseases or as tools for managing environmental waste such as toxic chemicals and plastics, among other applications. The IPD lists 11 spinout companies, including vaccine developer Icosavax and protein-design startup Archon Biosciences.

Seth Woodbury used RFD4-Proteina in his enzyme work as a , a UW bioengineering predoctoral candidate. “In just a few days we designed a pesticide-degrading enzyme that performs many fold better than my previous best, which took months to design,” he said. Woodbury last year published a paper in Nature on enzyme research.

At IPD, the RFD4-Proteina work was led by UW doctoral students Nathaniel Corley, Maxwell Kazman and Rafi Brent, along with Kieran Didi, a visiting student from the University of Oxford.

RFD4-Proteina is available through Berkeley Software Distribution. 

Researchers at UW Medicine’s Institute for Protein Design (IPD) have created new proteins that act like molecular scissors, chopping up targets associated with Alzheimer’s disease and amyotrophic lateral sclerosis, also known as ALS or Lou Gehrig’s disease.

The University of Washington scientists are using sophisticated AI models developed at IPD to design proteins called proteases — a class of enzymes that cut specific proteins at specific locations in their structure. The proteases can make those cuts over and over again.

“You have this catalytic capability to destroy one target, and then immediately your molecule can hop over to the second, third and thousands of different targets,” said Anqi Chen, a postdoctoral fellow at the institute.

Many pharmaceuticals bind to a single target, inactivate it and stay there. That approach requires high doses of medicine to have an effect on disease. These enzymatic proteins, by contrast, have an amplified impact.

“There’s this potential to use your drug at very, very low concentrations,” Chen said.

The researchers have made proteases that attack proteins associated with neurodegenerative disease and are now working on targets for malaria, chronic diseases and cancer. They also could make proteins to defang viruses or bacterial toxins.

“There’s super exciting applications,” said David Baker, director of IPD and a 2024 Nobel laureate in chemistry.

Baker is the senior author of two papers describing this work that were published today in the journal Nature. Chen and Kejia Wu are the first authors of one paper, and Hojae Choi, an IPD graduate student, is the first author of the other.

Baker said the research is significant because of how well the enzymes work and the range of potential applications, which beyond medicine include breaking down hazardous pollutants and plastic waste.

Chen and her colleagues designed proteases that use a metal ion, rather than an amino acid, as the cutting blade. More than one-third of the new proteases that they created precisely cut their target, a protein implicated in Alzheimer’s disease, and did so 100 million times faster than cuts made without the enzymes.

The work by Choi and his colleagues used an activated amino acid called cysteine as the blade. The proteins designed by the scientists had a 19% success rate in cutting their targets and were up to 30 million times faster than uncatalyzed reactions.

Much work remains before these newly designed proteins can become drugs for humans. One challenge is ensuring that the proteases don’t trigger a response in which a patient’s immune system attacks the therapeutic before it has a beneficial effect. Another potential hurdle is turning the enzymes off and on to modulate their impact as needed.

Choi is studying that immune system issue, called immunogenicity. He’s working to combine the catalytic, cutting component of the enzyme with a benign-appearing antibody that won’t alert the immune system — sort of a protease wolf in antibody clothing.

“It is a newly launched project, so we don’t have any official data to show to public,” Choi said. “But we’re really working on it.”

The Nature publications are titled: “Computational design of metalloproteases” and “De novo design of cysteine proteases”

Other institutions contributing to the research are: University of Oxford, NVIDIA, University of Cambridge, Imperial College London, Universitat Ramon Llull, Howard Hughes Medical Institute, Zhejiang University, Max Planck Institute of Molecular Cell Biology and Genetics, ETH Zurich, Lawrence Berkeley National Laboratory, Stanford University and Korea Advanced Institute of Science and Technology.

A protein is a chain of amino acids held together by what are called peptide bonds. On their own, those bonds are extremely stable; they're estimated to have a half-life of several hundred years. But in Earth's environment, most proteins don't last nearly that long. That's because organisms have evolved a handful of ways to catalyze the break-up of peptide bonds, which have diversified into countless specialized protein-digesting enzymes. Proteases are everywhere and generally make short work of any proteins that find their way into the environment.

Now, researchers have basically short-circuited the diversification process, using AI-based protein design tools to take the catalytic core of two types of protease and replace the entire rest of the protein. The resulting enzymes could be tuned to cut specific proteins in half, and the researchers demonstrated their effectiveness by using them to cut proteins associated with neurological diseases.

Cutting proteins

The basic peptide bond isn't especially interesting-looking—it's what you get when you link an amino group to an acid, releasing a water molecule in the process. As their name implies, amino acids have both an amino and an acid group, allowing them to form peptide bonds on two sides of the amino acid. This is critical for building up the long chains found in proteins. (The amino acids also have side chains with distinctive chemistry, which gives a backbone specific properties.)

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The UW Medicine Institute for Protein Design (IPD) has announced a giant grant of computing power from the Jen-Hsun and Lori Huang Foundation, supercharging its research into new protein discovery.

“We have made more rapid progress on methods development than any time in my career,” said David Baker, director of the IPD and a 2024 Nobel laureate in chemistry. “I mean, it’s just been totally mind-boggling.”

In June, the IPD began accessing Nvidia graphics processing units, or GPUs, through the cloud computing company CoreWeave. The grant from the foundation, which was co-founded by Nvidia CEO Jensen Huang and his wife, Lori Huang, totals nearly 7 million GPU hours. The allocation is expected to support the institute for a year.

The IPD’s work has long been rooted in computation. In 2003, Baker and fellow University of Washington researchers used their Rosetta software to design a protein with a structure unlike any known in nature. In recent years, the institute has embraced deep learning, releasing tools such as RoseTTAFold for predicting protein structures and RFdiffusion for designing new proteins.

With this technology, the institute designs previously unimagined proteins with potential for treating intractable diseases and tackling environmental challenges such as plastic waste and toxic pollutants. The tools are open source and available for public use.

Running them takes vast computing power, though, and until now the institute has lacked the resources to pay for outside cloud services. It has relied instead on its own computer cluster, which works but is limited. Baker said IPD scientists meet every Tuesday at 5 p.m. to decide what to test over the coming week, keeping capacity in mind.

To advance their protein research, the team experiments with different architectures for the AI models and tweaks the datasets used for training, sometimes expanding them or adding synthetic data. With compute restrictions, however, researchers historically have made multiple changes at once rather than testing each alteration individually, a more systematic approach that yields cleaner results.

Despite the hurdles, the IPD has produced a steady stream of breakthroughs with its models and protein designs, publishing regularly in leading scientific journals such as Nature and Science.

Google DeepMind is pursuing similar AI-driven protein research. Baker said he knew the tech giant’s team had much more computing power than the IPD, but he didn’t fully appreciate what that could unlock.

That capacity “spawns more ideas because you know you can test them,” Baker said. “So it just creates this incredibly fertile, creative environment.”

Jensen Huang, the foundation’s co-president, said in a statement that the extra computing power allows the UW researchers to train AI “on tens of millions of molecular structures, learning from virtual experiments, and finding novel protein designs that have eluded nature.”

Huang predicted that many of the most important future medicines “will come from proteins that have never existed.”

Baker said the IPD still holds its Tuesday meetings, but the new compute capabilities — which he called “transformational” — open up more options.

“The team is iterating fast,” Baker said. “We get feedback on all these different computational architecture explorations, and there’s just so many different things you can try.”