Therapeutic enzyme engineering using a generative neural network.

Journal: Scientific reports
Published Date:

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.

Authors

  • Andrew Giessel
    Moderna Therapeutics, 200 Technology Square, Cambridge, MA, 02139, USA. andrew.giessel@modernatx.com.
  • Athanasios Dousis
    Moderna Therapeutics, 200 Technology Square, Cambridge, MA, 02139, USA.
  • Kanchana Ravichandran
    Moderna Therapeutics, 200 Technology Square, Cambridge, MA, 02139, USA.
  • Kevin Smith
  • Sreyoshi Sur
    Moderna Therapeutics, 200 Technology Square, Cambridge, MA, 02139, USA.
  • Iain McFadyen
    Moderna Therapeutics, 200 Technology Square, Cambridge, MA, 02139, USA.
  • Wei Zheng
    School of Computer Engineering, Jinling Institute of Technology, Nanjing, 211169, China. zhengwei@jit.edu.cn.
  • Stuart Licht
    Moderna Therapeutics, 200 Technology Square, Cambridge, MA, 02139, USA. Stuart.Licht@modernatx.com.