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.
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Fig. 1: Benchmarking in silico metrics for prediction of enzyme functionality.
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 that AI can generate diverse functional enzymes.
Meier, J. et al. Language models enable zero-shot prediction of the effects of mutations on protein function. Preprint at bioRxiv 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 protein sequence design using ProteinMPNN. Science 378, 49–56 (2022). The paper presents one of the top-performing models that ended up in the COMPSS filter.
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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).
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Assessing the laboratory performance of AI-generated enzymes.
Nat Biotechnol (2024). https://doi.org/10.1038/s41587-024-02239-7
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Published: 23 April 2024
DOI: https://doi.org/10.1038/s41587-024-02239-7
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