Spencer E. McMinn*, Danielle V. Miller*, Daniel Yur, Kevin Stone, Yuting Xu, Ajit Vikram, Shashank Murali, Jessica Raffaele, David Holland, Sheng-Ching Wang and Joseph P. Smith,
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High-Throughput Algorithmic Optimization of In Vitro Transcription for SARS-CoV-2 mRNA Vaccine Production
The in vitro transcription (IVT) of messenger ribonucleic acid (mRNA) from the linearized deoxyribonucleic acid (DNA) template of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Delta variant (B.1.617.2) was optimized for total mRNA yield and purity (by percent intact mRNA) utilizing machine learning in conjunction with automated, high-throughput liquid handling technology. An iterative Bayesian optimization approach successfully optimized 11 critical process parameters in 42 reactions across 5 experimental rounds. Once the optimized conditions were achieved, an automated, high-throughput screen was conducted to evaluate commercially available T7 RNA polymerases for rate and quality of mRNA production. Final conditions showed a 12% yield improvement and a 50% reduction in reaction time, while simultaneously significantly decreasing (up to 44% reduction) the use of expensive reagents. This novel platform offers a powerful new approach for optimizing IVT reactions for mRNA production.
期刊介绍：
Biochemistry provides an international forum for publishing exceptional, rigorous, high-impact research across all of biological chemistry. This broad scope includes studies on the chemical, physical, mechanistic, and/or structural basis of biological or cell function, and encompasses the fields of chemical biology, synthetic biology, disease biology, cell biology, nucleic acid biology, neuroscience, structural biology, and biophysics. In addition to traditional Research Articles, Biochemistry also publishes Communications, Viewpoints, and Perspectives, as well as From the Bench articles that report new methods of particular interest to the biological chemistry community.
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