Algorithm for optimized mRNA design improves stability and immunogenicity

作者：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, Liang Huang · 发表于：Nature · 年份：2023 · DOI：10.1038/s41586-023-06127-z · 被引用次数：471 · 研究领域：RNA and protein synthesis mechanisms、RNA Interference and Gene Delivery、Viral Infections and Immunology Research

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 products 4 . Increasing secondary structure lengthens mRNA half-life, which, together with optimal codons, improves protein expression 5 . 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 × 10 632 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 alternatives 6 . 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 explorat...