Researchers use AI to design proteins that block snake venom toxins

Since these two toxicities work through entirely different mechanisms, the researchers tackled them separately.

Blocking a neurotoxin

The neurotoxic three-fingered proteins are a subgroup of the larger protein family that specializes in binding to and blocking the receptors for acetylcholine, a major neurotransmitter. Their three-dimensional structure, which is key to their ability to bind these receptors, is based on three strings of amino acids within the protein that nestle against each other (for those that have taken a sufficiently advanced biology class, these are anti-parallel beta sheets). So to interfere with these toxins, the researchers targeted these strings.

They relied on an AI package called RFdiffusion (the RF denotes its relation to the Rosetta Fold protein-folding software). RFdiffusion can be directed to design protein structures that are complements to specific chemicals; in this case, it identified new strands that could line up along the edge of the ones in the three-fingered toxins. Once those were identified, a separate AI package, called ProteinMPNN, was used to identify the amino acid sequence of a full-length protein that would form the newly identified strands.

But we’re not done with the AI tools yet. The combination of three-fingered toxins and a set of the newly designed proteins were then fed into DeepMind’s AlfaFold2 and the Rosetta protein structure software, and the strength of the interactions between them were estimated.

It’s only at this point that the researchers started making actual proteins, focusing on the candidates that the software suggested would interact the best with the three-fingered toxins. Forty-four of the computer-designed proteins were tested for their ability to interact with the three-fingered toxin, and the single protein that had the strongest interaction was used for further studies.

At this point, it was back to the AI, where RFDiffusion was used to suggest variants of this protein that might bind more effectively. About 15 percent of its suggestions did, in fact, interact more strongly with the toxin. The researchers then made both the toxin and the strongest inhibitor in bacteria and obtained the structure of their interactions. This confirmed that the software’s predictions were highly accurate.