ArticleScientific reports2022
Therapeutic enzyme engineering using a generative neural network.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- AI-Driven Enzyme Engineering: Emerging Models and Next-Generation Biotechnological Applications.Molecules (Basel, Switzerland) · 2025Review
- codonGPT: reinforcement learning on a generative language model enables scalable mRNA design.Nucleic acids research · 2025Article
- ProT-VAE: Protein Transformer Variational AutoEncoder for functional protein design.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Boosting the catalytic efficiency of UGT51 for efficient production of rare ginsenoside Rh2.Folia microbiologica · 2025Article
- Biochemical and Computational Characterization of Haloalkane Dehalogenase Variants Designed by Generative AI: Accelerating the SJournal of the American Chemical Society · 2025Article
- Dirichlet latent modelling enables effective learning and sampling of the functional protein design space.Nature communications · 2024Article
- Deep-learning-based design of synthetic orthologs of SH3 signaling domains.Cell systems · 2024Article
- Article
- Simultaneous enhancement of multiple functional properties using evolution-informed protein design.Nature communications · 2024Article
- Machine learning for functional protein design.Nature biotechnology · 2024Review
- ProtWave-VAE: Integrating Autoregressive Sampling with Latent-Based Inference for Data-Driven Protein Design.ACS synthetic biology · 2023Article
- Harnessing generative AI to decode enzyme catalysis and evolution for enhanced engineering.National science review · 2023Review
- Enhancing luciferase activity and stability through generative modeling of natural enzyme sequences.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Accelerating Biocatalysis Discovery with Machine Learning: A Paradigm Shift in Enzyme Engineering, Discovery, and Design.ACS catalysis · 2023Review
- Harnessing Generative AI to Decode Enzyme Catalysis and Evolution for Enhanced Engineering.bioRxiv : the preprint server for biology · 2023Article
- Lipid nanoparticle-targeted mRNA formulation as a treatment for ornithine-transcarbamylase deficiency model mice.Molecular therapy. Nucleic acids · 2023Article
- Simultaneous enhancement of multiple functional properties using evolution-informed protein design.bioRxiv : the preprint server for biology · 2023Article
- Data-driven enzyme engineering to identify function-enhancing enzymes.Protein engineering, design & selection : PEDS · 2023Review
- Prediction of designer-recombinases for DNA editing with generative deep learning.Nature communications · 2022Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Enhancing the potency of mRNA therapeutics is an important objective for treating rare diseases, since it may enable lower and less-frequent dosing. Enzyme engineering can increase potency of mRNA therapeutics by improving the expression, half-life, and catalytic efficiency of the mRNA-encoded enzymes. However, sequence space is incomprehensibly vast, and methods to map sequence to function (computationally or experimentally) are inaccurate or time-/labor-intensive. Here, we present a novel, broadly applicable engineering method that combines deep latent variable modelling of sequence co-evolution with automated protein library design and construction to rapidly identify metabolic enzyme variants that are both more thermally stable and more catalytically active. We apply this approach to improve the potency of ornithine transcarbamylase (OTC), a urea cycle enzyme for which loss of catalytic activity causes a rare but serious metabolic disease.
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Registered trials
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