Therapeutic enzyme engineering using a generative neural network
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-enco...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2022-01-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-05195-x |
| _version_ | 1819281546302980096 |
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| author | Andrew Giessel Athanasios Dousis Kanchana Ravichandran Kevin Smith Sreyoshi Sur Iain McFadyen Wei Zheng Stuart Licht |
| author_facet | Andrew Giessel Athanasios Dousis Kanchana Ravichandran Kevin Smith Sreyoshi Sur Iain McFadyen Wei Zheng Stuart Licht |
| author_sort | Andrew Giessel |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-24T01:01:24Z |
| format | Article |
| id | doaj.art-b0325ac1eaf846a9bd3d083baf6a6ac0 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-12-24T01:01:24Z |
| publishDate | 2022-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-b0325ac1eaf846a9bd3d083baf6a6ac02022-12-21T17:23:21ZengNature PortfolioScientific Reports2045-23222022-01-0112111710.1038/s41598-022-05195-xTherapeutic enzyme engineering using a generative neural networkAndrew Giessel0Athanasios Dousis1Kanchana Ravichandran2Kevin Smith3Sreyoshi Sur4Iain McFadyen5Wei Zheng6Stuart Licht7Moderna TherapeuticsModerna TherapeuticsModerna TherapeuticsModerna TherapeuticsModerna TherapeuticsModerna TherapeuticsModerna TherapeuticsModerna TherapeuticsAbstract 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.https://doi.org/10.1038/s41598-022-05195-x |
| spellingShingle | Andrew Giessel Athanasios Dousis Kanchana Ravichandran Kevin Smith Sreyoshi Sur Iain McFadyen Wei Zheng Stuart Licht Therapeutic enzyme engineering using a generative neural network Scientific Reports |
| title | Therapeutic enzyme engineering using a generative neural network |
| title_full | Therapeutic enzyme engineering using a generative neural network |
| title_fullStr | Therapeutic enzyme engineering using a generative neural network |
| title_full_unstemmed | Therapeutic enzyme engineering using a generative neural network |
| title_short | Therapeutic enzyme engineering using a generative neural network |
| title_sort | therapeutic enzyme engineering using a generative neural network |
| url | https://doi.org/10.1038/s41598-022-05195-x |
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