Therapeutic enzyme engineering using a generative neural network

• Published in: Scientific reports Vol. 12; no. 1; pp. 1536 - 17
• Main Authors: Giessel, Andrew, Dousis, Athanasios, Ravichandran, Kanchana, Smith, Kevin, Sur, Sreyoshi, McFadyen, Iain, Zheng, Wei, Licht, Stuart
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 27-01-2022; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114/1305; 631/114/469; Amino acid sequence; Coevolution; Enzymes; Humanities and Social Sciences; Humans; Metabolic disorders; Metabolism; mRNA; multidisciplinary; Neural networks; Neural Networks, Computer; Ornithine; Ornithine Carbamoyltransferase - genetics; Ornithine Carbamoyltransferase - metabolism; Protein Engineering - methods; Rare diseases; RNA, Messenger - genetics; RNA, Messenger - metabolism; Science; Science (multidisciplinary); Urea
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-022-05195-x

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.