[论文解读] LinearDesign: Efficient Algorithms for Optimized mRNA Sequence Design

He Zhang, Liang Zhang+5 more|arXiv (Cornell University)|Apr 21, 2020

RNA and protein synthesis mechanisms参考文献 47被引用 10

一句话总结

LinearDesign 提出了一种线性时间近似算法，用于设计具有高二级结构稳定性和密码子优化的最优mRNA序列，将计算时间从数小时缩短至数分钟，自由能损失极小。该方法可实现快速、可扩展的高稳定性、高翻译效率mRNA疫苗设计。

ABSTRACT

A messenger RNA (mRNA) vaccine has emerged as a promising direction to combat the current COVID-19 pandemic. This requires an mRNA sequence that is stable and highly productive in protein expression, features which have been shown to benefit from greater mRNA secondary structure folding stability and optimal codon usage. However, sequence design remains a hard problem due to the exponentially many synonymous mRNA sequences that encode the same protein. We show that this design problem can be reduced to a classical problem in formal language theory and computational linguistics that can be solved in O(n^3) time, where n is the mRNA sequence length. This algorithm could still be too slow for large n (e.g., n = 3, 822 nucleotides for the spike protein of SARS-CoV-2), so we further developed a linear-time approximate version, LinearDesign, inspired by our recent work, LinearFold. This algorithm, LinearDesign, can compute the approximate minimum free energy mRNA sequence for this spike protein in just 11 minutes using beam size b = 1, 000, with only 0.6% loss in free energy change compared to exact search (i.e., b = +infinity, which costs 1 hour). We also develop two algorithms for incorporating the codon optimality into the design, one based on k-best parsing to find alternative sequences and one directly incorporating codon optimality into the dynamic programming. Our work provides efficient computational tools to speed up and improve mRNA vaccine development.

研究动机与目标

• 解决为疫苗设计高度稳定且翻译效率高的mRNA序列的挑战。
• 克服探索指数级多个人工同义mRNA序列的计算不可行性。
• 开发一种可扩展的算法，平衡序列稳定性和密码子使用，以提升蛋白表达水平。
• 实现mRNA序列的快速设计，适用于大规模疫苗开发，如SARS-CoV-2疫苗。

提出的方法

• 将mRNA序列设计问题转化为可通过动态规划在O(n³)时间内求解的形式语言理论问题。
• 开发LinearDesign，一种受LinearFold启发的线性时间近似算法，显著降低长序列的运行时间。
• 使用束搜索（beam size b = 1,000）高效近似最小自由能结构。
• 通过k-best解析和直接动态规划扩展，将密码子最优性整合到设计中。
• 利用带剪枝的动态规划，平衡计算效率与解的质量。
• 以SARS-CoV-2刺突蛋白（n = 3,822个核苷酸）为实际案例进行算法测试。

实验结果

研究问题

• RQ1 mRNA序列设计能否在保持高结构稳定性的同时，以线性时间高效近似？
• RQ2 与精确动态规划相比，线性时间近似在自由能和设计质量方面的表现如何？
• RQ3 在不牺牲结构稳定性的前提下，密码子优化在序列设计过程中可实现的程度如何？
• RQ4 该算法能否在保持高设计质量的同时扩展至长mRNA序列（如SARS-CoV-2刺突蛋白）？

主要发现

• LinearDesign 仅用11分钟即完成对SARS-CoV-2刺突蛋白mRNA序列的近似最小自由能结构计算（beam size b = 1,000）。
• 与精确搜索（b = +∞）相比，仅损失0.6%的自由能变化，而后者需耗时1小时。
• 该算法在大幅降低计算时间的同时，实现了接近最优的结构稳定性。
• 通过k-best解析和直接动态规划扩展实现的密码子最优性整合，可生成高质量、高翻译效率的序列。
• 该方法具有可扩展性，适用于长mRNA序列，在疫苗开发中展现出实际应用价值。