Algorithm for optimized mRNA design improves stability and immunogenicity

He Zhang, Liang Zhang, Ang Lin, Congcong Xu, Ziyu Li, Kaibo Liu, Boxiang Liu, Xiaopin Ma, Fanfan Zhao, Huiling Jiang, Chunxiu Chen, Haifa Shen, Hangwen Li (), David H. Mathews (), Yujian Zhang () and Liang Huang ()
Additional contact information
He Zhang: Baidu Research USA
Liang Zhang: Baidu Research USA
Ang Lin: StemiRNA Therapeutics
Congcong Xu: StemiRNA Therapeutics
Ziyu Li: Baidu Research USA
Kaibo Liu: Baidu Research USA
Boxiang Liu: Baidu Research USA
Xiaopin Ma: StemiRNA Therapeutics
Fanfan Zhao: StemiRNA Therapeutics
Huiling Jiang: StemiRNA Therapeutics
Chunxiu Chen: StemiRNA Therapeutics
Haifa Shen: StemiRNA Therapeutics
Hangwen Li: StemiRNA Therapeutics
David H. Mathews: University of Rochester Medical Center
Yujian Zhang: StemiRNA Therapeutics
Liang Huang: Baidu Research USA

Nature, 2023, vol. 621, issue 7978, 396-403

Abstract: Abstract Messenger RNA (mRNA) vaccines are being used to combat the spread of COVID-19 (refs. 1–3), but they still exhibit critical limitations caused by mRNA instability and degradation, which are major obstacles for the storage, distribution and efficacy of the vaccine products4. Increasing secondary structure lengthens mRNA half-life, which, together with optimal codons, improves protein expression5. Therefore, a principled mRNA design algorithm must optimize both structural stability and codon usage. However, owing to synonymous codons, the mRNA design space is prohibitively large—for example, there are around 2.4 × 10632 candidate mRNA sequences for the SARS-CoV-2 spike protein. This poses insurmountable computational challenges. Here we provide a simple and unexpected solution using the classical concept of lattice parsing in computational linguistics, where finding the optimal mRNA sequence is analogous to identifying the most likely sentence among similar-sounding alternatives6. Our algorithm LinearDesign finds an optimal mRNA design for the spike protein in just 11 minutes, and can concurrently optimize stability and codon usage. LinearDesign substantially improves mRNA half-life and protein expression, and profoundly increases antibody titre by up to 128 times in mice compared to the codon-optimization benchmark on mRNA vaccines for COVID-19 and varicella-zoster virus. This result reveals the great potential of principled mRNA design and enables the exploration of previously unreachable but highly stable and efficient designs. Our work is a timely tool for vaccines and other mRNA-based medicines encoding therapeutic proteins such as monoclonal antibodies and anti-cancer drugs7,8.

Date: 2023
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DOI: 10.1038/s41586-023-06127-z

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