Algorithm for Optimized mRNA Design Improves Stability and Immunogenicity.

Messenger RNA (mRNA) vaccines are being used to contain COVID-19 (1, 2, 3), but still suffer from the critical limitation of mRNA instability and degradation, which is a major obstacle in the storage, distribution, and efficacy of the vaccine products (4). Previous work showed that 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, due to synonymous codons, the mRNA design space is prohibitively large (e.g., ~10632 candidates for the SARS-CoV-2 Spike protein), which poses insurmountable computational challenges. Here we provide a simple and unexpected solution using a classical concept in computational linguistics, where finding the optimal mRNA sequence is akin to identifying the most likely sentence among similar sounding alternatives (6). Our algorithm LinearDesign takes only 11 minutes for the Spike protein, and can jointly optimize stability and codon usage. On both COVID-19 and varicella-zoster virus mRNA vaccines, LinearDesign substantially improves mRNA half-life and protein expression, and dramatically increases antibody titer by up to 128× in vivo, compared to the codon-optimization benchmark. This surprising 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 not only for vaccines but also for mRNA medicine encoding all therapeutic proteins (e.g., monoclonal antibodies and anti-cancer drugs (7, 8)).

A national risk analysis model (NRAM) for the assessment of COVID-19 epidemic.

COVID-19 has caused a critical health concern and severe economic crisis worldwide. With multiple variants, the epidemic has triggered waves of mass transmission for nearly 3 years. In order to coordinate epidemic control and economic development, it is important to support decision-making on precautions or prevention measures based on the risk analysis for different countries. This study proposes a national risk analysis model (NRAM) combining Bayesian network (BN) with other methods. The model is built and applied through three steps. (1) The key factors affecting the epidemic spreading are identified to form the nodes of BN. Then, each node can be assigned state values after data collection and analysis. (2) The model (NRAM) will be built through the determination of the structure and parameters of the network based on some integrated methods. (3) The model will be applied to scenario deduction and sensitivity analysis to support decision-making in the context of COVID-19. Through the comparison with other models, NRAM shows better performance in the assessment of spreading risk at different countries. Moreover, the model reveals that the higher education level and stricter government measures can achieve better epidemic prevention and control effects. This study provides a new insight into the prevention and control of COVID-19 at the national level.