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Therapeutic enzyme engineering using a generative neural network.

Andrew Giessel¹, Athanasios Dousis², Kanchana Ravichandran²

¹Moderna Therapeutics, 200 Technology Square, Cambridge, MA, 02139, USA. andrew.giessel@modernatx.com.

Scientific Reports

|January 28, 2022

Summary

This summary is machine-generated.

Developing novel mRNA therapeutics for rare diseases requires enhanced enzyme potency. This study presents a new method combining deep learning and automated design to engineer more stable and active metabolic enzymes, like ornithine transcarbamylase (OTC).

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Area of Science:

Biochemistry
Molecular Biology
Bioengineering

Background:

Enhancing messenger RNA (mRNA) therapeutics is crucial for treating rare genetic disorders, potentially allowing for reduced dosing frequency and lower doses.
Enzyme engineering can improve mRNA therapeutic potency by optimizing the expression, half-life, and catalytic efficiency of encoded enzymes.
Current methods for mapping protein sequence to function are often inaccurate, time-consuming, or labor-intensive, hindering rapid enzyme engineering.

Purpose of the Study:

To develop a broadly applicable, rapid engineering method for identifying metabolic enzyme variants with improved thermal stability and catalytic activity.
To enhance the potency of mRNA therapeutics by optimizing enzyme function for rare disease treatment.

Main Methods:

A novel engineering approach combining deep latent variable modeling of sequence co-evolution with automated protein library design and construction.
Application of the method to engineer ornithine transcarbamylase (OTC), a key enzyme in the urea cycle.

Main Results:

Identification of metabolic enzyme variants exhibiting both increased thermal stability and enhanced catalytic activity.
Successful application of the novel engineering strategy to improve the potency of ornithine transcarbamylase (OTC).

Conclusions:

The presented method offers a rapid and broadly applicable strategy for engineering enzymes with improved stability and activity.
This approach has significant potential for advancing the development of potent mRNA therapeutics for rare metabolic diseases.