AI.zymes – A modular platform for evolutionary enzyme design
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Abstract
The ability to create new-to-nature enzymes would substantially advance bioengineering, medicine, and the chemical industry. Despite recent breakthroughs in protein design and structure prediction, designing biocatalysts with activities rivaling those of natural enzymes remains challenging. Here, we present AI.zymes, a modular platform integrating cutting-edge protein engineering algorithms within an evolutionary framework. By combining programs such as Rosetta, ESMFold, ProteinMPNN, and FieldTools in iterative rounds of design and selection, AI.zymes optimizes a broad range of catalytically relevant properties, such as transition state affinity and protein stability. Notably, AI.zymes can also improve properties that are not targeted by the employed design algorithms. For instance, AI.zymes enhanced electrostatic catalysis by iteratively selecting variants with stronger catalytic electric fields. AI.zymes was benchmarked by improving the promiscuous Kemp eliminase activity of ketosteroid isomerase, yielding a 7.7-fold increase in activity after zero-shot experimental validation of only 7 variants. Due to its modularity, AI.zymes can readily incorporate emerging design algorithms, paving the way for a unifying framework for enzyme design.
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Very cool study! It's great to see so many tools stitched together in such a purpose-built way. Have you thought about running your pipeline on other natural KSI homologs? It’d be interesting to see if, like in directed or natural evolution, certain starting points make it easier to explore sequence space or lead to better outcomes. This kind of pipeline seems like a great way to test that idea without requiring tons of experimental screening.
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