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Vibe genes? Natural(ish) language comes to protein, genome design software
You can apparently now tell AI tools what sort of protein you want them to make.
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.
Some of the best protein design tools we have available right now work through a two-step process. First, you specify a set of constraints, such as the need to bind another molecule or to have a specific arrangement of amino acids. One AI tool then generates a protein shape that meets those constraints. Once that’s done, a separate tool populates that shape with a specific sequence of amino acids that can ensure the protein folds into the appropriate configuration to match the shape.
Obviously, doing that often requires a fair bit of technical knowledge. If you’re designing a catalyst, for example, then you not only need to know what amino acids are critical to get a chemical reaction to happen, but you have to know where they should be located within a 3D space. That, not surprisingly, requires a fairly sophisticated understanding of protein chemistry and the ability to express that understanding in whatever form that the protein design tools require.
The thing is, if two experts were talking to each other, they wouldn’t need to get that specific. If one said “serine protease,” for example, the other would know a lot of what was implied by that in terms of the specifics of the reaction mechanism. We need to get specific when communicating with an AI tool because, while our current generation of tools are great at working through the 3D arrangements of amino acids, they’re kind of stupid about the functions that those 3D arrangements enable.
In the work of the team behind the new tool, called Pinal, this doesn’t necessarily have to be the case. We have databases full of millions of protein sequences tagged with notes about the functions those proteins perform. With the proper training, the researchers reasoned, it should be possible to just use the same terminology in those tags to get a system that outputs a sequence that performs exactly like what you’re looking for.
Pinal, on some levels, works quite like the standard tools, following a two-step process of building a backbone for the protein and then filling in the details of the amino acids that populate that backbone through a separate process. But there are very significant differences—beginning with what you need to do to start the process.
Rather than specifying some details of a few amino acids in the design product, you simply tell the system what you want. “The first stage is handled by T2struct, a text-to-structure module that translates a natural language prompt into a structural blueprint,” as the system’s developers describe it. T2struct doesn’t output a 3D shape, either; it simply produces a linear array of tokens that correspond to the spatial relationship between each amino acid and its nearest neighbors and assign a probability to each.