The lab of Nobel laureate David Baker keeps pushing the limits of what’s possible in making proteins with computers, opening up new possibilities in using AI to make drugs.
The latest advance
arrived Thursday in
Science
, with a team of 21 scientists using AI models to make enzymes from scratch that perform some fairly complicated chemical feats. The technology isn’t yet as simple as pushing a button and getting a protein, but the progress shows these models rapidly improving in tackling tougher protein design problems. Though these AI-made enzymes aren’t quite as good as the best ones found in nature, it’s still a step ahead for the AI protein field.
Running what is likely
the hottest lab in the protein design world,
Baker has been studying and designing proteins with software since the 1990s. But AI advances have driven an explosion in progress these past few years. That culminated in winning the Nobel Prize in Chemistry in October, paired with a growing list of startups and spinouts from his lab. Baker has co-founded over 20 companies, including
the billion-dollar debut of Xaira Therapeutics
last year, while his lab has been able to make minibinders,
antibodies
and now enzymes from scratch.
In an interview with
Endpoints News
, Baker laid out possibilities for using these latest enzyme advances in drug discovery, especially to design site-specific enzymes that cut DNA or RNA strands. That could give scientists a new level of precision and control in designing gene-editing tools.
“The bigger picture is we can now use deep learning, ML diffusion methods, to make really active enzymes,” Baker said, adding his lab is now working on making nucleases, or enzymes that cut nucleic acids, and thinking about base editors, another type of gene editing.
This latest Science project was led by a trio of scientists in Baker’s lab — Anna Lauko, Sam Pellock and Kiera Sumida —who wanted to make new hydrolases, or a type of enzyme that acts like scissors in cutting molecules into two pieces. These are well-studied, common enzymes that have been found across nature, from eating up plastics to allowing our muscles to contract, relax and move.
Hydrolases are seen as a hard challenge for the protein design field. Their activity requires getting a multistep process just right, which can be harder than getting a protein to glom onto a target. The team started out with little progress in searching for active enzymes by making and screening thousands of designs. That changed when another Baker lab group developed RFdiffusion, a diffusion model that creates proteins.
“There was a long period where we didn’t make anything that worked,” Lauko said in an interview. “And then RFdiffusion basically became available to us because people in our lab were developing it. Almost immediately, we made far more progress than we had in the past three years by completely changing how we were doing the design.”
Once they got access to RFdiffusion in the winter of 2022, the team changed their approach. Rather than starting with the scaffold of an enzyme and trying to mutate it to get what they wanted, they could basically tell the model what they desired. The AI model then created the possible proteins.
Their paper details rounds of testing and iterating on better enzyme designs. That includes implementing another, new AI model called PLACER, which acted like a quality control check in vetting the AI-made proteins. PLACER judged how compatible they were for the desired chemical reaction, helping filter out ones unlikely to succeed and not worth making and testing on the lab bench.
These AI-made enzymes aren’t as active as the best hydrolases found in nature, according to their lab results. At least for now, the AI models still aren’t coming up with better solutions than a billion years of evolutionary pressures.
“They’re still not as good, and that’s another future direction: is trying to understand what’s missing there to make these better and faster,” Lauko said.
Looking ahead, Pellock described two major areas they are focusing future research on: making plastic-degrading enzymes and exploring the therapeutic potential for AI-made enzymes with other functions. In the therapeutic world, Pellock said proteases, or enzymes that break down proteins or peptides, are an area to watch. A long-term hope is being able to degrade any protein in the human body by building the right enzyme, he said, which could lead to new, more precise therapies that could also tackle currently unreachable targets.
A provisional patent to the PLACER model was licensed to the Baker-founded biotech startup Vilya, according to the paper. Vilya has licensed other AI models from Baker’s lab,
including an exclusive license to RFpeptides
, a diffusion model for macrocyclic peptide design.