Leveraging AI tools like RFDiffusion and PLACER, researchers were able to design a novel enzyme capable of breaking down plastic by targeting ester bonds, a key component in polyester. Ars Technica reports: The researchers started out by using the standard tools they developed to handle protein design, including an AI tool named RFDiffusion, which uses a random seed to generate a variety of protein backgrounds. In this case, the researchers asked RFDiffusion to match the average positions of the amino acids in a family of ester-breaking enzymes. The results were fed to another neural network, which chose the amino acids such that they'd form a pocket that would hold an ester that breaks down into a fluorescent molecule so they could follow the enzyme's activity using its glow.

Of the 129 proteins designed by this software, only two of them resulted in any fluorescence. So the team decided they needed yet another AI. Called PLACER, the software was trained by taking all the known structures of proteins latched on to small molecules and randomizing some of their structure, forcing the AI to learn how to shift things back into a functional state (making it a generative AI). The hope was that PLACER would be trained to capture some of the structural details that allow enzymes to adopt more than one specific configuration over the course of the reaction they were catalyzing. And it worked. Repeating the same process with an added PLACER screening step boosted the number of enzymes with catalytic activity by over three-fold.

Unfortunately, all of these enzymes stalled after a single reaction. It turns out they were much better at cleaving the ester, but they left one part of it chemically bonded to the enzyme. In other words, the enzymes acted like part of the reaction, not a catalyst. So the researchers started using PLACER to screen for structures that could adopt a key intermediate state of the reaction. This produced a much higher rate of reactive enzymes (18 percent of them cleaved the ester bond), and two -- named "super" and "win" -- could actually cycle through multiple rounds of reactions. The team had finally made an enzyme.

By adding additional rounds alternating between structure suggestions using RFDiffusion and screening using PLACER, the team saw the frequency of functional enzymes increase and eventually designed one that had an activity similar to some produced by actual living things. They also showed they could use the same process to design an esterase capable of digesting the bonds in PET, a common plastic. The research has been published in the journal Science.

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