Assessing the laboratory performance of ai-generated enzymes

A set of 20 computational metrics was evaluated to determine whether they could predict the functionality of synthetic enzyme sequences produced by generative protein models, resulting in

the development of a computational filter, COMPSS, that increased experimental success rates by 50–150%, tested in over 500 natural and AI-generated enzymes. Access through your institution

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ADDITIONAL ACCESS OPTIONS: * Log in * Learn about institutional subscriptions * Read our FAQs * Contact customer support REFERENCES * Repecka, D. et al. Expanding functional protein

sequence spaces using generative adversarial networks. _Nat. Mach. Intell_. 3, 324–333 (2021). AMONG THE FIRST EXPERIMENTALLY VALIDATED GENERATIVE MODELS OF PROTEIN SEQUENCES DEMONSTRATING

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https://doi.org/10.1101/2021.07.09.450648 (2021). THE PAPER PRESENTS ONE OF THE TOP-PERFORMING MODELS THAT ENDED UP IN THE COMPSS FILTER. * Dauparas, J. et al. Robust deep learning-based

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published maps and institutional affiliations. THIS IS A SUMMARY OF: Johnson, S. R. et al. Computational scoring and experimental evaluation of enzymes generated by neural networks. _Nat.

Biotechnol_. https://doi.org/10.1038/s41587-024-02214-2 (2024). RIGHTS AND PERMISSIONS Reprints and permissions ABOUT THIS ARTICLE CITE THIS ARTICLE Assessing the laboratory performance of

AI-generated enzymes. _Nat Biotechnol_ 43, 308–309 (2025). https://doi.org/10.1038/s41587-024-02239-7 Download citation * Published: 23 April 2024 * Issue Date: March 2025 * DOI:

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