mRNAid, an open-source platform for therapeutic mRNA design and optimization strategies
mRNAid，用于治疗性 mRNA 设计和优化策略的开源平台

Abstract 抽象

Introduction 介绍

mRNA-based therapeutics continue to revolutionize vaccine development (1), immunotherapy (2) and targeted degradation methodologies (3), advancing the battle of modern medicine against infectious diseases, genetic disorders and cancer [4–6, reviewed in (7)]. Irrespective of the various indications and the diverse mechanisms of action of mRNA-based drugs, all derived therapeutics and vaccines share the same underlying principles. First, the mRNA sequence is designed and optimized in silico. Then, the optimized sequence is transcribed in vitro, often with selected chemical modifications. Finally, the synthetic transcript is packaged and delivered to the cytoplasm of host cells, where it is translated into a protein that exerts the desired cellular effect. Therefore, in silico mRNA design is undeniably instrumental to the success of any mRNA-based therapeutic. Transcript design is typically initiated with a decoration of the coding sequence (CDS) with flanking 5′- and 3′-UTRs (untranslated regions) and other signals (e.g. translational ramps, miRNA-binding sites, etc.) that can improve stability and translation efficiency, and enable tissue-specific expression (8,9). Then, rigorous sequence engineering is required to eliminate immunogenic properties (10) and further enhance transcript stability and translation (11–13). During in vitro transcription, chemical modifications such as a 5′-cap and nucleoside analogs can be incorporated to protect against degradation and evade host immune surveillance (14,15). At present, mRNA design is largely dependent on expert knowledge, manual sequence editing, distributed optimization and visualization tools that are often proprietary. There is no freely available tool specifically tailored for therapeutic mRNA design that combines multiple optimization strategies. Here, we present mRNAid, an open-source, integrated software that bundles several modified and extended algorithms and tools for constraint propagation, sequence optimization and secondary structure visualization. Via an intuitive and user-friendly interface, mRNAid orchestrates simultaneous optimization of several sequence and structural properties including codon usage, GC content, minimum free energy (MFE), uridine depletion and exclusion of specific motifs and/or rare codons, thereby providing a powerful platform for therapeutic mRNA design. mRNAid is available at https://github.com/MSDLLCpapers/mRNAid and as a web application at https://mrnaid.dichlab.org.
基于mRNA的疗法继续革新疫苗开发（1）、免疫疗法（2）和靶向降解方法（3），推进了现代医学对抗传染病、遗传疾病和癌症的斗争[4-6，见（7）]。无论基于mRNA的药物具有不同的适应症和不同的作用机制，所有衍生疗法和疫苗都具有相同的基本原理。首先，在计算机中设计和优化mRNA序列。然后，在体外转录优化的序列，通常进行选定的化学修饰。最后，合成转录本被包装并递送至宿主细胞的细胞质中，在那里它被翻译成一种蛋白质，发挥所需的细胞效应。因此，不可否认，计算机mRNA设计对于任何基于mRNA的疗法的成功都起着重要作用。转录本设计通常从编码序列 （CDS） 的修饰开始，其中包含侧翼 5′- 和 3′-UTR（非翻译区）和其他信号（例如翻译斜坡、miRNA 结合位点等），这些信号可以提高稳定性和翻译效率，并实现组织特异性表达 （ 8， 9）。然后，需要严格的序列工程来消除免疫原性 （10） 并进一步增强转录本稳定性和翻译 （ 11–13）。在体外转录过程中，可以掺入化学修饰，例如 5′-帽和核苷类似物，以防止降解并逃避宿主免疫监视 （ 14， 15）。目前，mRNA设计很大程度上依赖于专业知识、手动序列编辑、分布式优化和可视化工具，这些工具通常是专有的。 目前尚无专门针对治疗性 mRNA 设计量身定制的结合多种优化策略的免费工具。在这里，我们介绍了 mRNAid，这是一款开源的集成软件，它捆绑了几种经过修改和扩展的算法和工具，用于约束传播、序列优化和二级结构可视化。通过直观且用户友好的界面，mRNAid可同时优化多个序列和结构特性，包括密码子使用、GC含量、最小自由能（MFE）、尿苷耗竭以及排除特定基序和/或稀有密码子，从而为治疗性mRNA设计提供强大的平台。mRNAid 可在 https://github.com/MSDLLCpapers/mRNAid 上获得，并在 https://mrnaid.dichlab.org 上作为 Web 应用程序提供。

Materials and methods 材料和方法

Dinucleotides, codon-pair, CAI and MatchCodonUsage optimizations
二核苷酸、密码子对、CAI 和 MatchCodon使用优化

Dinucleotides and codon-pair are custom objectives derived from usage tables from the CoCoPUTs database (17). They account for the difference between dinucleotide or codon-pair frequencies in the host organism (Homo sapiens or Mus musculus) and the current sequence. The score is calculated by the following formula:
二核苷酸和密码子对是从 CoCoPUTs 数据库的使用表中得出的自定义目标 （ 17）。它们解释了宿主生物体（智人或肌肉人）中二核苷酸或密码子对频率与当前序列之间的差异。分数由以下公式计算：

where is the score for a given nucleotide pair or codon-pair, is the total score being the mean of all the individual scores, is the frequency for a given pair, is a corresponding frequency from the database and is the total number of pairs across the sequence. The total score is maximized by the DNA Chisel optimization algorithm. Codon Adaptation Index (CAI) optimization is the built-in objective, used if a user specifies so. CAI optimization is a common optimization strategy introduced in (18). MatchCodonUsage is a built-in constraint which minimizes the sum of discrepancies over all possible codon frequencies in a given sequence and in the target organism, set as default. All codon optimization objectives are considered mutually exclusive, so it is not possible to use any combination of these in our tool.
其中 是给定核苷酸对或密码子对的分数， 是所有单个分数的平均值的总分， 是给定对的频率，是数据库中的相应频率， 是整个序列中的对总数。总分通过DNA凿子优化算法最大化。密码子适应指数 （CAI） 优化是内置目标，如果用户指定，则使用。CAI优化是（18）中引入的一种常用优化策略。MatchCodonUsage 是一个内置约束，它最小化给定序列和目标生物体中所有可能的密码子频率的差异总和，设置为默认值。所有密码子优化目标都被认为是相互排斥的，因此在我们的工具中不可能使用这些目标的任何组合。

Scoring function 评分功能

A scoring function which evaluates sequences for different criteria was applied to the list of optimized sequences. The final score used in ranking was:
根据不同标准评估序列的评分函数被应用于优化序列列表。排名中使用的最终分数是：

where are individual weights of each score (), is the uridine depletion score, is the GC content score, is the CAI score, is the total MFE score and is the MFE 5′-end score. The weights were assigned to accommodate the order of importance for different objectives, as defined by the experimental scientists and based on previous reports. These weights were later fine-tuned based on the experimental data. As more data become available, these parameters can be further optimized and changed accordingly.
其中 是每个分数的单独权重 （ ）， 是尿苷耗竭分数， 是 GC 含量分数， 是 CAI 分数，是总 MFE 分数， 是 MFE 5′ 结束分数。分配权重以适应不同目标的重要性顺序，由实验科学家定义并基于以前的报告。这些权重后来根据实验数据进行了微调。随着更多数据的出现，这些参数可以进一步优化并相应地更改。

GC score GC 评分

GC content is calculated for the whole sequence and checked to be within the user-defined range (GC_min and GC_max). The score is calculated as a growing linear function of GC content value to favor sequences with larger GC values:
计算整个序列的GC含量，并检查是否在用户定义的范围（GC_min和GC_max）内。分数计算为 GC 含量值的递增线性函数，以支持具有较大 GC 值的序列：

The score is bounded in the range 0 to 1. As GC content has an influence on properties and expression rates of mRNA, we optimize the sequence to fit the GC content in a specified window.
分数的边界范围为 0 到 1。由于GC含量对mRNA的特性和表达率有影响，因此我们优化了序列以适合指定窗口中的GC含量。

Total MFE score MFE 总分

It is preferable to have sequences with a lower value of MFE. To enable efficient sorting of the sequences according to this requirement, we use the following score:
最好具有具有较低 MFE 值的序列。为了能够根据此要求对序列进行有效排序，我们使用以下分数：

A value of 5000 was chosen based on the observed value of many input sequences that did not exceed an MFE value of 3500. In that way, the score remains between 0 and 1, where the score tends to zero when MFE goes to zero, and does not exceed 1 for MFE values around 3500 bp.
根据许多输入序列的观测值选择值 5000，这些序列的 MFE 值不超过 3500。这样，分数保持在 0 和 1 之间，当 MFE 变为零时，分数趋于零，而对于 3500 bp 左右的 MFE 值，分数不超过 1。

5′-MFE score (mfe_5_score)
5′-MFE 评分 （mfe_5_score）

The MFE of the 5′ end is calculated using RNAfold. MFE has a theoretical maximum of 0, but in practice does not reach that value. The score is calculated as a decreasing exponential function of the 5′-MFE:
5′末端的MFE使用RNAfold计算。MFE 的理论最大值为 0，但实际上没有达到该值。分数计算为 5′-MFE 的递减指数函数：

In this case when and when . The score is now bound in the range 0 to 1, with the aim to minimize the 5′-MFE to 0.
在这种情况下 ，何时 和 何时 .分数现在限制在 0 到 1 的范围内，目的是将 5′-MFE 最小化为 0。

Results and discussion 结果与讨论

The core backbone for sequence optimization in mRNAid is based on the DNA Chisel framework (16) (Supplementary Figure S1). DNA Chisel permits global and local optimization of hard and soft constraints, which in turn enables the adjustment of the desired sequence properties along the entire transcript. Hard constraints refer to criteria that must be satisfied in the final sequence, whereas soft constraints refer to criteria whose score must be maximized. Furthermore, the ability to flexibly define new constraints makes DNA Chisel an ideal sandbox for probing the effect of a multitude of sequence properties on stability and expression.
mRNAid 中序列优化的核心骨架基于 DNA 凿子框架 （ 16） （补充图 S1）。DNA 凿子允许对硬约束和软约束进行全局和局部优化，从而能够在整个转录本上调整所需的序列特性。硬约束是指在最终序列中必须满足的标准，而软约束是指其分数必须最大化的标准。此外，灵活定义新约束的能力使 DNA Chisel 成为探索多种序列特性对稳定性和表达影响的理想沙箱。

mRNAid piggybacks on several hard constraints implemented in DNA Chisel that enforce global GC content and translation and avoid rare codons and specific motifs. mRNAid extends this list with an important uridine depletion constraint that avoids codons with uridine at their third position, which reportedly improves expression and reduces immunogenicity (10).
mRNAid 搭载了 DNA Chisel 中实现的几个硬约束，这些约束强制执行全局 GC 含量和翻译，并避免罕见的密码子和特定基序。mRNAid 扩展了此列表，具有重要的尿苷耗竭限制，该约束避免了尿苷位于第三位的密码子，据报道，这会改善表达并降低免疫原性 （10）。

Codon usage optimization aims to improve expression by systematic replacement of synonymous codons based on the organism's codon frequency table. Numerous proprietary and freely available codon optimization algorithms have been reported to date (11) and many show a strong preference towards the CAI (18) since it highly correlates with gene expression (23). The CAI reflects the deviation of codon frequencies in a sequence from those observed in a reference set of highly expressed genes in the target host organism (18). DNA Chisel includes CAI and Matched Codon Usage optimizations as soft constraints. The latter method ensures that the relative frequencies of the codons in the sequence match the overall codon usage of the target organism (16). While the above methods consider each codon independently, a recent study reported a bias in codon-pair utilization and dinucleotide usage that are inevitably inter-related (17)
密码子使用优化旨在通过基于生物体密码子频率表的同义密码子系统替换来改善表达。迄今为止，已经报道了许多专有和免费提供的密码子优化算法 （ 11） 并且许多算法表现出对 CAI （ 18） 的强烈偏好，因为它与基因表达高度相关 （ 23）。CAI 反映了序列中密码子频率与在目标宿主生物体中一组高表达基因的参考中观察到的密码子频率的偏差 （18）。DNA 凿子包括 CAI 和匹配密码子使用优化作为软约束。后一种方法确保序列中密码子的相对频率与目标生物体的整体密码子使用率相匹配 （ 16）。虽然上述方法独立地考虑每个密码子，但最近的一项研究报告称，密码子对利用率和二核苷酸使用存在偏差，这不可避免地相互关联 （ 17）

The CoCoPUTs database provides pre-computed codon-pair and dinucleotide frequency tables in various organisms that can be used for sequence optimization. We implemented the dinucleotide and codon-pair usage optimizations since they have been shown to affect translation fidelity and efficiency (17,24). There is a significant codon-pair usage bias in all three domains of life, and between proteins expressed at a low and high level within a species (25) that cannot be explained by individual codon bias, pointing towards a distinct mechanism of translation modulation. It has been suggested that codon-pair effects on the translation rate may be mediated by interactions of adjacent aminoacyl-tRNA molecules bound to ribosomes (25). To account for structural properties, we also incorporated the Vienna-RNA MFE optimization (19) and the correlated stem–loop prediction approach (20), given the pivotal role that mRNA secondary structures play in regulating translation efficiency (26). The Vienna-RNA method uses a thermodynamic energy model to compute the MFE of a given RNA sequence to identify the most thermodynamically stable secondary structure (19). To reduce the complexity of the MFE computation, the stem–loop method (20) estimates a pseudo-MFE as the average energy of all possible stem–loop conformations for a given sequence, increasing the computation efficiency of sequence optimization. Multiple studies reported correlation between highly structured features in the CDS and functional mRNA half-life (27,28). In contrast, the region around the translation start site is less structured in highly expressed genes (29), which presumably facilitates ribosome loading and prevents jamming. Thus, different transcript regions possess different structural properties that must be reflected in the optimization strategy. Furthermore, transcripts are often fused to already optimized UTRs, obviating the need for optimization of these regions. Given the above, and the fact that MFE optimization is extremely computationally expensive, we provide users with the flexibility to optimize MFE within an adjustable window in the 5′ end of the CDS, while accounting for global MFE in our ranking approach. To this end, we ensure that the combination of multiple constraints generates optimal sequence properties by applying a novel scoring formula that ranks sequences based on weighted scores of uridine depletion, GC content, CAI, local 5′ end and global MFEs.
CoCoPUTs 数据库提供了各种生物体中预先计算的密码子对和二核苷酸频率表，可用于序列优化。我们实施了二核苷酸和密码子对的使用优化，因为它们已被证明会影响翻译保真度和效率 （ 17， 24）。在生命的所有三个领域中，以及在一个物种中以低水平和高水平表达的蛋白质之间，都存在显着的密码子对使用偏倚（25），这不能用单个密码子偏倚来解释，这表明了一种独特的翻译调节机制。有人认为，密码子对对翻译速率的影响可能是由与核糖体结合的相邻氨酰基-tRNA 分子的相互作用介导的 （ 25）。为了解释结构特性，我们还结合了 Vienna-RNA MFE 优化 （ 19） 和相关的茎环预测方法 （ 20），因为 mRNA 二级结构在调节翻译效率方面起着关键作用 （ 26）。Vienna-RNA 方法使用热力学能量模型来计算给定 RNA 序列的 MFE，以确定最热力学稳定的二级结构 （19）。为了降低MFE计算的复杂度，茎-环方法（20）将伪MFE估计为给定序列下所有可能的茎-环构象的平均能量，从而提高了序列优化的计算效率。多项研究报告了 CDS 中高度结构化的特征与功能性 mRNA 半衰期之间的相关性 （ 27， 28）。相比之下，翻译起始位点周围的区域在高表达基因中的结构较少 （ 29），这可能有助于核糖体上样并防止干扰。 因此，不同的转录区域具有不同的结构特性，这些特性必须反映在优化策略中。此外，转录本通常与已经优化的UTR融合，从而避免了对这些区域进行优化的需要。鉴于上述情况，以及 MFE 优化在计算上极其昂贵这一事实，我们为用户提供了在 CDS 5' 端的可调窗口内优化 MFE 的灵活性，同时在我们的排名方法中考虑了全球 MFE。为此，我们通过应用一种新的评分公式来确保多个约束的组合产生最佳序列特性，该评分公式根据尿苷耗竭、GC 含量、CAI、局部 5' 末端和全局 MFE 的加权分数对序列进行排名。

Next, we selected five distinct optimization strategies in mRNAid that are based on a codon optimization approach coupled with uridine depletion, GC content and MFE optimizations (Strategies 1–5: dinucleotide, matched codon-pair usage, matched codon usage with or without uridine depletion and CAI, respectively) (Table ​(Table1).1). To experimentally validate these strategies, we adopted NanoLuciferase-PEST (Nluc-PEST) as the reporter system since the short half-lives of the individually produced luciferase proteins prevent confounding effects on interrogation of mRNA properties as they relate to translation efficiency or mRNA stability. Indeed, these effects can be conveniently measured through kinetic tracking of the target protein's luminescence. Area under the curve (AUC) and luminescence at 48 h (RLU @ 48 h) are represented as indicators of total protein output and functional mRNA stability, respectively, given that protein sequence and other mRNA features are kept constant. A de-optimized mRNA version of Nluc-PEST encoded by the least frequent codon for each amino acid (Rare, red, Figure ​Figure1A1A–C) was used as the input for mRNAid and the top four ranked mRNA sequences generated under each software setting were selected (Supplementary Tables S1 and S2). These were benchmarked against a proprietary sequence from Promega (Promega, blue, Figure ​Figure1A1A–C) as the codon-optimized control.
接下来，我们在mRNAid中选择了五种不同的优化策略，这些策略基于密码子优化方法与尿苷耗竭、GC含量和MFE优化相结合（策略1-5：二核苷酸、匹配密码子对使用、匹配密码子使用量，分别有或没有尿苷耗竭和CAI）（表（表1）.1）。为了通过实验验证这些策略，我们采用纳米荧光素酶-PEST （Nluc-PEST） 作为报告系统，因为单独产生的荧光素酶蛋白的半衰期较短，可以防止对 mRNA 特性的询问产生混杂效应，因为它们与翻译效率或 mRNA 稳定性有关。事实上，这些效应可以通过对靶蛋白发光的动力学跟踪来方便地测量。曲线下面积 （AUC） 和 48 小时的发光 （RLU @ 48 h） 分别表示为总蛋白质输出和功能 mRNA 稳定性的指标，前提是蛋白质序列和其他 mRNA 特征保持不变。使用由每个氨基酸（稀有，红色，图1A1A-C）最不频繁的密码子编码的Nluc-PEST的去优化mRNA版本作为mRNAid的输入，并选择在每个软件设置下生成的前四个排名靠前的mRNA序列（补充表S1和S2）。这些是以 Promega 的专有序列（Promega，蓝色，图 Figure1A1A-C）作为密码子优化对照的基准测试。

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Figure 1. 图 1.

Impact of various sequence optimization strategies in mRNAid on NanoLuc-PEST expression. Effects of five different sequence optimization strategies on NanoLuc-PEST expression in MIA PaCa-2 cells are represented as (A) area under the curve (AUC) of luminescence over 48 h and (B) relative luminescence (RLU) at 48 h post-transfection. Rare, red denotes the de-optimized input; and Promega, blue denotes the codon-optimized control. The top four outputs from each strategy were tested at a 6.25 ng dose of the respective mRNA. Scatter plots with bars represent the mean from two independent biological replicates. (C) Western blot analysis of the sequence variants from 24 h post-transfection in MIA PaCa-2 cells. GAPDH was used as a loading control. Band intensities for NanoLuc were normalized to GAPDH and represented as fold change over the Promega control. Two additional outputs for each strategy were evaluated. Data from repeat experiment are included in Supplementary Figure S2. (D) Correlation plots for AUC (top) and RLU @ 48 h (bottom) versus MFE (kcal/mol). Mean values of AUC and RLU @ 48 h were determined for the 6.25 ng mRNA dose in (A and B) and represented as fold change over the respective mean values of Promega control (blue dashed line). The red dot denotes the de-optimized Rare input. Pearson’s r is indicated as determined by GraphPad Prism.
mRNAid中各种序列优化策略对NanoLuc-PEST表达的影响。五种不同序列优化策略对 MIA PaCa-2 细胞中 NanoLuc-PEST 表达的影响表示为 （A） 转染后 48 h 发光曲线下面积 （AUC） 和 （B） 转染后 48 h 的相对发光 （RLU）。罕见，红色表示去优化的输入;和 Promega，蓝色表示密码子优化的对照。每种策略的前四个输出在 6.25 ng 剂量的相应 mRNA 下进行测试。带条形的散点图表示两个独立生物学重复的平均值。（C） 对 MIA PaCa-2 细胞转染后 24 小时的序列变异进行蛋白质印迹分析。GAPDH被用作上样对照。将 NanoLuc 的条带强度归一化为 GAPDH，并表示为 Promega 对照的倍数变化。对每项战略的另外两项产出进行了评价。来自重复实验的数据包含在补充图S2中。（D） AUC（顶部）和 RLU @ 48 小时（底部）与 MFE（kcal/mol）的相关性图。确定 （A 和 B） 中 6.25 ng mRNA 剂量的 AUC 和 RLU @ 48 小时的平均值，并表示为 Promega 对照相应平均值的倍数变化（蓝色虚线）。红点表示去优化的稀有输入。Pearson 的 r 表示为 GraphPad Prism 确定。

All 20 mRNAid-optimized sequences resulted in significantly higher expression relative to the de-optimized input (Figure ​(Figure1A1A–C; Supplementary Figure S2), attesting to the robustness of the tool. Among the five optimization strategies, all four sequences optimized by Strategy-5 (CAI optimization) were consistently better than or comparable with Promega and exhibited the highest GC content, lowest global MFE and lowest uridine content (Figure ​(Figure1A1A–C; Supplementary Table S2). Similarly, sequences that were optimized by Strategy-2 (matched codon-pair optimization) exhibited expression comparable with Promega. The importance of codon-pair context for enhanced gene expression was also recognized in previous studies (30). Conversely, all the four sequences that were optimized by Strategy-1 (dinucleotide optimization) were expressed less well than in Promega (Figure ​(Figure1A1A–C; Supplementary Table S2; Supplementary Figure S2). Further analyses revealed that both AUC and RLU @ 48 h were strongly correlated with global MFE (r = –0.87 and r = –0.92, respectively, Figure ​Figure1D),1D), in line with previous reports on the impact of secondary structures on mRNA half-lives and ultimately final protein output (27). Correlations were also observed with GC (%) and U (%), but not 5′-MFE (Supplementary Figure S3). Sequence optimization using mRNAid has a strong impact on rate of decay but not translation initiation as compared with the de-optimized input (Supplementary Figure S4). These findings highlight the importance of combining a multitude of hard, soft, local and global constraints to achieve balanced sequence and structural properties that cooperatively define total protein expression.
所有 20 个 mRNAid 优化序列的表达量均显著高于去优化的输入（图 （图 1A1A–C;补充图S2），证明了该工具的坚固性。在五种优化策略中，通过策略-5（CAI优化）优化的所有四个序列都始终优于或与Promega相当，并且表现出最高的GC含量，最低的全局MFE和最低的尿苷含量（图（图1A1A-C;附表S2）。同样，通过策略-2（匹配密码子对优化）优化的序列表现出与Promega相当的表达。密码子对环境对增强基因表达的重要性在以前的研究中也得到了认可 （ 30）。相反，通过策略-1（二核苷酸优化）优化的所有四个序列的表达都不如Promega（图（图1A1A-C;附表S2;补充图S2）。进一步的分析表明，AUC 和 RLU @ 48 h 都与整体 MFE 密切相关（分别为 r = –0.87 和 r = –0.92，图 1D），1D），这与之前关于二级结构对 mRNA 半衰期和最终蛋白质输出影响的报道一致 （ 27）。还观察到与GC（%）和U（%）的相关性，但与5′-MFE无关（补充图S3）。与去优化的输入相比，使用 mRNAid 进行序列优化对衰变速率有很大影响，但对翻译起始没有影响（补充图 S4）。这些发现强调了结合多种硬、软、局部和全局约束的重要性，以实现平衡的序列和结构特性，从而共同定义总蛋白表达。

An in silico optimization of the native SARS-CoV-2 surface glycoprotein gene using mRNAid Strategy-5 (CAI optimization) generated a transcript with 96.5% and 90.9% similarity to the putative assembled Moderna and Pfizer/BioNtech vaccine sequences, respectively (Table ​(Table2)2) (21,22). We also compared the expression of the mRNAid-optimized spike CDS with the putative Moderna and Pfizer/BioNtech sequences in SJCRH30 (muscle, relevant for vaccine testing) and MIA PaCa-2 cells. In both cell lines, mRNAid optimization significantly boosted expression of spike protein as compared with the native input sequence (Figure ​(Figure2).2). Although the mRNAid sequence is inferior to the putative Moderna sequence in both cell lines, it yielded expression comparable with the putative Pfizer vaccine sequence in the muscle cell line (Figure ​(Figure2).2). These data confirm that mRNAid sequence optimization can be applied to therapeutically relevant proteins whose expression can be modulated based on the sequence optimization parameters applied.
使用 mRNAid Strategy-5 对天然 SARS-CoV-2 表面糖蛋白基因进行计算机优化（CAI 优化）产生的转录本分别与假定组装的 Moderna 和 Pfizer/BioNtech 疫苗序列相似度为 96.5% 和 90.9% （表 （Table2）2） （ 21， 22）。我们还比较了 mRNAid 优化的刺突 CDS 与假定的 Moderna 和 Pfizer/BioNtech 序列在 SJCRH30（肌肉，与疫苗测试相关）和 MIA PaCa-2 细胞中的表达。在两种细胞系中，与天然输入序列相比，mRNAid优化显著提高了刺突蛋白的表达（图（图2）.2）。尽管mRNAid序列在两种细胞系中都不如假定的Moderna序列，但它在肌肉细胞系中的表达与假定的辉瑞疫苗序列相当（图（图2）.2）。这些数据证实，mRNAid序列优化可以应用于治疗相关蛋白质，其表达可以根据应用的序列优化参数进行调节。

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Figure 2. 图2.

Impact of mRNAid sequence optimization on spike protein expression. Luminescence signals (normalized to a HiBiT control protein) of HiBiT-tagged spike CDS optimized by mRNaid (teal) or from the Moderna (black) and Pfizer (pink) vaccines are compared with the native spike CDS (blue) at (A) 24 h and (B) 48 h in SJCRH30 and MIA PaCa-2 cell lines at the 12.5 ng dose of the respective mRNA. Scatter plots, with bars representing the mean from two independent biological replicate experiments. (C) Western blot analysis of the spike sequence variants 24 or 48 h post-transfection in MIA PaCa-2 and SJCRH30 cells. HSP90 was used as a loading control. A representative blot is shown from two independent biological replicates. (D) Band intensities from two biological replicate experiments for HiBiT–spike (full-length, FL; and cleaved S2, S2) were normalized to HSP90 and represented as fold change over the native control. The presence of the expected product and the addition of a poly(A) tail for all mRNA constructs synthesized in this study were confirmed by gel electrophoresis (Supplementary Table S4; Supplementary Figure S5).
mRNAid序列优化对刺突蛋白表达的影响。将 mRNaid（蓝绿色）或来自 Moderna（黑色）和辉瑞（粉红色）疫苗优化的 HiBiT 标记刺突 CDS 的发光信号（归一化为 HiBiT 对照蛋白）与天然刺突 CDS（蓝色）在 （A） 24 小时和 （B） 48 小时在 SJCRH30 和 MIA PaCa-2 细胞系中以 12.5 ng 剂量的相应 mRNA 进行比较。散点图，条形代表两个独立的生物重复实验的平均值。（C） MIA PaCa-2 和 SJCRH30 细胞转染后 24 或 48 小时对刺突序列变体进行蛋白质印迹分析。HSP90 用作上样对照。从两个独立的生物学重复中显示了具有代表性的印迹。（D） 来自HiBiT刺突的两个生物学重复实验（全长，FL;和裂解的S2，S2）的条带强度被归一化为HSP90，并表示为天然对照的倍数变化。通过凝胶电泳证实了本研究中合成的所有 mRNA 构建体存在预期产物并添加了 poly（A） 尾部（补充表 S4;补充图S5）。

We next explored additional features that can be incorporated into mRNAid-optimized sequences to yield optimal therapeutic mRNAs. The discovery that uridine analogs dramatically reduce immune stimulation (10) and increase protein production from synthetic mRNA (14) marked a breakthrough in mRNA-based therapeutics. We evaluated the impact of substituting uridine (U) with pseudouridine (pU), 5-methoxyuridine (5moU) or N¹-methylpseudouridine (N1m) on Nluc-PEST protein expression and pro-inflammatory cytokine (IFN-β) release in human BJ fibroblasts. For the de-optimized input (Rare) and codon-optimized control (Promega) sequences, pU and N1m modifications significantly improved protein expression as compared with U (Figure ​(Figure3A),3A), in line with previous reports. Instead of improving expression, U substitution with 5moU reduced protein output from the Promega sequence. Out of 20 mRNAid sequences, we picked the best expressed sequence (Strategy-5:output-2, Figure ​Figure1C)1C) and were able to recapitulate this improvement compared with Rare and Promega in BJ fibroblast using U (black, Figure ​Figure3A).3A). However, protein levels were not further enhanced using uridine analogs in Strategy-5:output-2 sequence. We postulate that since Strategy-5:output-2 sequence had the lowest uridine content (14% in Strategy-5:output-2; 21% in Promega; 27% in Rare), the effect of uridine substitution on expression may be minimal. In terms of immunogenicity, unmodified mRNAs (U) caused the highest cytokine release, as expected (comparable with the positive control poly I:C), followed by pU, N1m and 5moU, respectively (Figure ​(Figure3B).3B). Thus, our findings demonstrate how sequence optimization in addition to incorporation of uridine analogs can reduce undesired innate immune responses while maintaining high target protein expression.
接下来，我们探索了可以整合到 mRNAid 优化序列中的其他功能，以产生最佳的治疗性 mRNA。尿苷类似物显着减少免疫刺激 （10） 并增加合成 mRNA 的蛋白质产量 （14） 的发现标志着基于 mRNA 的疗法的突破。我们评估了用假尿苷 （pU）、5-甲氧基尿苷 （5moU） 或 N ¹ -甲基假尿苷 （N1m） 替代尿苷 （U） 对人 BJ 成纤维细胞中 Nluc-PEST 蛋白表达和促炎细胞因子 （IFN-β） 释放的影响。对于去优化的输入（Rare）和密码子优化对照（Promega）序列，与U相比，pU和N1m修饰显著改善了蛋白质表达（图（图3A），3A），与之前的报道一致。用 5moU 替换 U 非但没有改善表达，反而减少了 Promega 序列的蛋白质输出。在 20 个 mRNAid 序列中，我们选择了表达最好的序列（Strategy-5：output-2，图 Figure1C）1C），并且能够使用 U（黑色，图 3A）概括了与 Rare 和 Promega 相比在 BJ 成纤维细胞中的这种改善。然而，在策略-5：output-2序列中使用尿苷类似物并未进一步提高蛋白质水平。我们假设，由于 Strategy-5：output-2 序列的尿苷含量最低（Strategy-5：output-2 为 14%;Promega 为 21%;Rare 为 27%），因此尿苷替代对表达的影响可能很小。在免疫原性方面，未修饰的mRNA（U）引起最高的细胞因子释放，正如预期的那样（与阳性对照poly I：C相当），其次分别是pU，N1m和5moU（图（图3B）.3B）。 因此，我们的研究结果表明，除了掺入尿苷类似物外，序列优化如何减少不需要的先天免疫反应，同时保持高靶蛋白表达。

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Figure 3. 图3.

Additional ways to engineer mRNA for therapeutic use. (A) Effect of modified nucleotides on NanoLuc-PEST expression in BJ fibroblasts. U, uridine; pU, pseudouridine; 5moU, 5-methoxyuridine; N1m, N¹-methyl-pseudouridine. Individual values from two independent biological replicate experiments have been plotted. (B) Effect of modified nucleotides on innate immune activation in BJ fibroblasts. Cytokine release assay 48 h post-transfection with 50 ng of the indicated mRNAs. IFN-β levels were normalized to the Promega sequence with pU incorporated. OOR (out-of-range), where values are below the detection limits of the assay. The dashed line represents the positive control poly I:C. Scatter plot, with bars representing the mean from two biological replicate experiments. (C) Effect of AG versus GG initiator sequence after the T7 promoter on NanoLuc protein expression in MIA PaCa-2 cells 24 h post-mRNA transfection. Individual values from two independent biological replicate experiments have been plotted. (D) Effect of different UTRs on NanoLuc protein expression in MIA PaCa-2 cells 24 h post-mRNA transfection.
设计用于治疗用途的 mRNA 的其他方法。（A） 修饰核苷酸对BJ成纤维细胞中NanoLuc-PEST表达的影响。U： 尿苷;pU，假尿苷;5moU，5-甲氧基尿苷;N1m，N ¹ -甲基假尿苷。已经绘制了两个独立的生物重复实验的单个值。（B） 修饰核苷酸对BJ成纤维细胞先天免疫活化的影响。用 50 ng 指定的 mRNA 转染后 48 小时进行细胞因子释放测定。将 IFN-β 水平归一化为掺入 pU 的 Promega 序列。OOR（超出范围），其中值低于检测的检测限。虚线表示阳性对照多边形 I：C.散点图，条形表示两个生物重复实验的平均值。（C） T7 启动子后 AG 与 GG 起始子序列对 mRNA 转染后 24 小时 MIA PaCa-2 细胞中 NanoLuc 蛋白表达的影响。已经绘制了两个独立的生物重复实验的单个值。（D） 不同 UTR 对 mRNA 转染后 24 小时 MIA PaCa-2 细胞中 NanoLuc 蛋白表达的影响。

mRNAs are capped at the 5′ end to protect against degradation, facilitate ribosome loading and evade innate immune responses (31) Compared with legacy cap analogs such as ARCA, the proprietary co-transcriptional capping reagent CleanCap AG from TriLink was shown to have higher capping efficiency, increased RNA yield and reduced immunogenicity. We modified the conventional GG initiator sequence after the T7 promoter to AG and synthesized mRNAs using CleanCap AG. Indeed, the AG initiator significantly improved NanoLuc expression (Figure ​(Figure3C)3C) and total RNA yield (data not shown). This highlights how protein output can be further boosted and emphasizes the importance of tailoring template design to the desired capping technique. As noted, the role of 5′- and 3′-UTRs in modulating mRNA stability and translation is well established (28). To this end, we selected four pairs of UTR sequences that have been reported to boost protein expression in human cells (Supplementary Table S3). Indeed, all four UTRs increased NanoLuc expression compared with the plasmid in which default sequences flank its CDS (Figure ​(Figure3D).3D). Together, we present various opportunities to enhance mRNA potency by optimizing key mRNA components.
mRNA 被封盖在 5' 端以防止降解、促进核糖体加载和逃避先天免疫反应 （ 31） 与 ARCA 等传统帽类似物相比，TriLink 专有的共转录加帽试剂 CleanCap AG 被证明具有更高的加帽效率、更高的 RNA 产量和降低的免疫原性。我们将 T7 启动子后的传统 GG 起始子序列修改为 AG，并使用 CleanCap AG 合成 mRNA。事实上，AG引发剂显著改善了NanoLuc的表达（图（图3C）3C）和总RNA产量（数据未显示）。这突出了如何进一步提高蛋白质产量，并强调了根据所需的加帽技术定制模板设计的重要性。如前所述，5′- 和 3′-UTR 在调节 mRNA 稳定性和翻译中的作用已得到充分证实 （ 28）。为此，我们选择了四对UTR序列，这些序列已被报道为促进人类细胞中蛋白质表达（补充表S3）。事实上，与默认序列位于其 CDS 两侧的质粒相比，所有四种 UTR 都增加了 NanoLuc 表达（图 （图3D）.3D）。我们共同提供了各种机会，通过优化关键的 mRNA 成分来增强 mRNA 效力。

In summary, this study represents a first attempt to create a comprehensive playbook for rational design of therapeutic mRNA transcripts. mRNAid is an open-source software that offers advanced sequence and structural optimization strategies that generate transcripts with desired expression properties. We also experimentally demonstrate that incorporation of certain uridine analogs, and inclusion of key mRNA components can further enhance stability, boost protein output and mitigate undesired immunogenicity effects.
总之，这项研究代表了为合理设计治疗性 mRNA 转录本创建综合剧本的首次尝试。mRNAid 是一款开源软件，可提供先进的序列和结构优化策略，可生成具有所需表达特性的转录本。我们还通过实验证明，掺入某些尿苷类似物和包含关键 mRNA 成分可以进一步增强稳定性、提高蛋白质输出并减轻不良免疫原性作用。

Despite the encouraging results of mRNAid, it is important to note its limitations. mRNAid does not optimize for MFE along the entire transcript, and the thermodynamic parameters of uridine analogs are not accounted for. Yet the experimental data we presented here clearly indicate that global MFE optimization is worthwhile, albeit computationally expensive. However, the flexible backbone of mRNAid presents an opportunity for the broader scientific community to make additional enhancements to the tool. These improvements may involve extending support to other species, incorporating MFE calculations that account for uridine analogs, implementing new constraints or optimization strategies, or considering any other sequence features that might influence stability, immunogenicity and expression.
尽管mRNAid取得了令人鼓舞的结果，但重要的是要注意其局限性。mRNAid 不针对整个转录本的 MFE 进行优化，并且不考虑尿苷类似物的热力学参数。然而，我们在这里提供的实验数据清楚地表明，尽管计算成本高昂，但全局多微一体化优化是值得的。然而，mRNAid的灵活骨架为更广泛的科学界提供了对该工具进行额外改进的机会。这些改进可能涉及将支持扩展到其他物种，纳入考虑尿苷类似物的MFE计算，实施新的约束或优化策略，或考虑可能影响稳定性、免疫原性和表达的任何其他序列特征。

Supplementary Material 补充材料

Acknowledgements 确认

Contributor Information 贡献者信息

Nikita Vostrosablin, Discovery Informatics, MSD Czech Republic s.r.o., Prague, 150 00, Czech Republic.
Nikita Vostrosablin， Discovery Informatics， MSD Czech Republic s.r.o.， 布拉格， 150 00， 捷克共和国.

Shuhui Lim, Quantitative Biosciences, MSD Singapore, 138665, Singapore.
Shuhui Lim，定量生物科学，默沙东新加坡，138665，新加坡。

Pooja Gopal, Quantitative Biosciences, MSD Singapore, 138665, Singapore.
Pooja Gopal，定量生物科学，默沙东新加坡，138665，新加坡。

Kveta Brazdilova, Discovery Informatics, MSD Czech Republic s.r.o., Prague, 150 00, Czech Republic. Department of Informatics and Chemistry, Faculty of Chemical Technology, University of Chemistry and Technology, Prague, 160 00, Czech Republic.
Kveta Brazdilova， Discovery Informatics， MSD Czech Republic s.r.o.， 布拉格， 150 00， 捷克共和国.化学技术大学化学技术学院信息学和化学系，布拉格，160 00，捷克共和国。

Sushmita Parajuli, Discovery Informatics, MSD Czech Republic s.r.o., Prague, 150 00, Czech Republic.
Sushmita Parajuli， Discovery Informatics， MSD Czech Republic s.r.o.， 布拉格， 150 00， 捷克共和国.

Xiaona Wei, Bioinformatics, MSD Singapore, 138665, Singapore.
Xiaona Wei，生物信息学，默沙东新加坡，138665，新加坡。

Anna Gromek, Discovery Informatics, MSD Czech Republic s.r.o., Prague, 150 00, Czech Republic.
Anna Gromek， Discovery Informatics， MSD Czech Republic s.r.o.， 布拉格， 150 00， 捷克共和国.

David Prihoda, Discovery Informatics, MSD Czech Republic s.r.o., Prague, 150 00, Czech Republic.
David Prihoda， Discovery Informatics， MSD Czech Republic s.r.o.， 布拉格， 150 00， 捷克共和国.

Martin Spale, Discovery Informatics, MSD Czech Republic s.r.o., Prague, 150 00, Czech Republic.
Martin Spale， Discovery Informatics， MSD Czech Republic s.r.o.， 布拉格， 150 00， 捷克共和国.

Anja Muzdalo, Discovery Informatics, MSD Czech Republic s.r.o., Prague, 150 00, Czech Republic.
Anja Muzdalo， Discovery Informatics， MSD Czech Republic s.r.o.， 布拉格， 150 00， 捷克共和国.

Jamie Greig, Quantitative Biosciences, MSD Singapore, 138665, Singapore.
Jamie Greig，定量生物科学，默沙东新加坡，138665，新加坡。

Constance Yeo, Quantitative Biosciences, MSD Singapore, 138665, Singapore.
Constance Yeo，定量生物科学，默沙东新加坡，138665，新加坡。

Joanna Wardyn, Quantitative Biosciences, MSD Singapore, 138665, Singapore.
Joanna Wardyn，定量生物科学，默沙东新加坡，138665，新加坡。

Petr Mejzlik, Discovery Informatics, MSD Czech Republic s.r.o., Prague, 150 00, Czech Republic.
Petr Mejzlik， Discovery Informatics， MSD Czech Republic s.r.o.， 布拉格， 150 00， 捷克共和国.

Brian Henry, Quantitative Biosciences, MSD Singapore, 138665, Singapore.
Brian Henry，定量生物科学，默沙东新加坡，138665，新加坡。

Anthony W Partridge, Quantitative Biosciences, MSD Singapore, 138665, Singapore.
Anthony W Partridge，定量生物科学，默沙东新加坡，138665，新加坡。

Danny A Bitton, Discovery Informatics, MSD Czech Republic s.r.o., Prague, 150 00, Czech Republic.
Danny A Bitton， Discovery Informatics， MSD Czech Republic s.r.o.， 布拉格， 150 00， 捷克共和国.

References 引用