优化mRNA设计的算法可提高稳定性和免疫原性
Algorithm for Optimized mRNA Design Improves Stability and Immunogenicity
AI总结：
本研究提出LinearDesign算法，将mRNA设计简化为计算语言学问题，同时优化稳定性和密码子使用，显著提高mRNA半衰期、蛋白质表达及体内抗体反应（达23倍），为疫苗和治疗性mRNA药物提供高效工具。
摘要：
信使RNA（mRNA）疫苗正被用于COVID-19，但仍面临mRNA不稳定和降解的关键问题，这是疫苗储存、分发和效力的主要障碍。先前的研究表明，优化二级结构稳定性可延长mRNA半衰期，结合最优密码子，可提高蛋白质表达。因此，一个合理的mRNA设计算法必须同时优化结构稳定性和密码子使用，以提高mRNA效率。然而，由于同义密码子的存在，mRNA设计空间极其庞大，例如，SARS-CoV-2刺突蛋白约有~10^632种mRNA，这对先前的方法构成了不可逾越的挑战。在这里，我们通过将这一难题简化为计算语言学中的一个经典问题，提供了一个令人惊讶的简单解决方案，其中找到最优mRNA类似于在发音相似的备选方案中找到最可能的句子。我们的算法名为LinearDesign，仅需11分钟即可处理刺突蛋白，并能同时优化稳定性和密码子使用。实验上，无需化学修饰，我们的设计在体外显著提高了mRNA半衰期和蛋白质表达，并在体内将抗体反应大幅提升至密码子优化基准的23倍。我们的工作使得探索以前无法实现的高度稳定和高效的设计成为可能，不仅为疫苗，也为编码所有治疗性蛋白质（如单克隆抗体和抗癌药物）的mRNA药物提供了及时的工具。
英文摘要：
Messenger RNA (mRNA) vaccines are being used for COVID-19, but still suffer from the critical issue of mRNA instability and degradation, which is a major obstacle in the storage, distribution, and efficacy of the vaccine. Previous work showed that optimizing secondary structure stability lengthens mRNA half-life, which, together with optimal codons, increases protein expression. Therefore, a principled mRNA design algorithm must optimize both structural stability and codon usage to improve mRNA efficiency. However, due to synonymous codons, the mRNA design space is prohibitively large, e.g., there are $\sim\!10^{632}$ mRNAs for the SARS-CoV-2 Spike protein, which poses insurmountable challenges to previous methods. Here we provide a surprisingly simple solution to this hard problem by reducing it to a classical problem in computational linguistics, where finding the optimal mRNA is akin to finding the most likely sentence among similar sounding alternatives. Our algorithm, named LinearDesign, takes only 11 minutes for the Spike protein, and can jointly optimize stability and codon usage. Experimentally, without chemical modification, our designs substantially improve mRNA half-life and protein expression in vitro, and dramatically increase antibody response by up to 23$\times$ in vivo, compared to the codon-optimized benchmark. Our work enables the exploration of highly stable and efficient designs that are previously unreachable and 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).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
