DERNA Enables Pareto Optimal RNA Design-猫眼课题宝

DERNA 实现帕累托最优 RNA 设计

DOI：

10.1089/cmb.2023.0283

发表时间：

2024

期刊：

Journal of Computational Biology

影响因子：

1.7

通讯作者：

中科院分区：

生物学4区

文献类型：

作者：

Gu, Xinyu;Qi, Yuanyuan;El-kebir, Mohammed

设计一个编码输入靶蛋白序列的RNA序列是信使RNA（mRNA）疫苗开发的一个关键方面。由于密码子简并性，对于单个靶蛋白存在指数数量的可能的RNA序列。这些潜在的RNA序列可以呈现各种二级结构构象，每种构象具有不同的最小自由能（MFE），影响热力学稳定性和mRNA半衰期。此外，物种特异性密码子使用偏好的存在，量化的密码子适应指数（CAI），在翻译效率中起着至关重要的作用。虽然早期的研究集中在优化MFE或CAI，但最近的研究强调了同时优化两个目标的优势。然而，优化一个目标是以牺牲另一个目标为代价的。在这项工作中，我们提出了thePareto OptimalRNADesignproblem，旨在确定一组Pareto最优解，其中没有其他解决方案存在，表现出更好的MFE和CAI值。我们的算法DESign RNA（DERNA）使用的加权和方法，通过优化两个目标的凸组合来枚举帕累托前沿。我们使用动态规划来解决每个凸组合在时间和空间。与CDSfold（以前仅优化MFE的方法）相比，我们在基准数据集上表明，DERNA获得了具有相同MFE但具有更高上级CAI的解决方案。此外，我们表明，DERNA匹配性能的解决方案的质量LinearDesign，最近的方法，同样寻求平衡MFE和CAI。最后，我们证明了我们的方法的潜力mRNA疫苗设计的SARS-CoV-2刺突蛋白。

The design of an RNA sequencethat encodes an input target protein sequenceis a crucial aspect of messenger RNA (mRNA) vaccine development. There are an exponential number of possible RNA sequences for a single target protein due to codon degeneracy. These potential RNA sequences can assume various secondary structure conformations, each with distinct minimum free energy (MFE), impacting thermodynamic stability and mRNA half-life. Furthermore, the presence of species-specific codon usage bias, quantified by the codon adaptation index (CAI), plays a vital role in translation efficiency. While earlier studies focused on optimizing either MFE or CAI, recent research has underscored the advantages of simultaneously optimizing both objectives. However, optimizing one objective comes at the expense of the other. In this work, we present thePareto OptimalRNADesignproblem, aiming to identify the set of Pareto optimal solutions for which no alternative solutions exist that exhibit better MFE and CAI values. Our algorithm DEsign RNA (DERNA) uses the weighted sum method to enumerate the Pareto front by optimizing convex combinations of both objectives. We use dynamic programming to solve each convex combination intime andspace. Compared with a CDSfold, previous approach that only optimizes MFE, we show on a benchmark data set that DERNA obtains solutions with identical MFE but superior CAI. Moreover, we show that DERNA matches the performance in terms of solution quality of LinearDesign, a recent approach that similarly seeks to balance MFE and CAI. We conclude by demonstrating our method's potential for mRNA vaccine design for the SARS-CoV-2 spike protein.