用AI进化设计更稳定、低免疫原性的治疗性mRNA序列。
An Evolutionary Approach for Designing Stable and Highly Expressible Low-Immunogenicity Therapeutic mRNA Sequences
- 结合预训练模型与遗传算法，按密码子偏好优化序列。
- 翻译效率与结构稳定性双提升，免疫原性显著降低。
- 适合需高效表达且安全的mRNA药物研发人员使用。
治疗性mRNA序列的设计需兼顾高效翻译、结构稳定和低免疫原性。本文提出一种两阶段的体外计算框架，融合深度学习与进化计算实现理性优化。第一阶段利用预训练的CodonTransformer（类BERT大语言模型）生成生物合理的目标抗原编码mRNA序列；第二阶段通过遗传算法（GA）以密码子感知的交叉和同义突变演化候选序列，遵循人类密码子使用偏好。评估函数综合考虑翻译相关指标（CAI、tAI、密码子配对偏倚）、mRNA结构稳定性（RNAfold计算的局部与全局最小自由能MFE、GC含量）及免疫原性（CpG/UpA基序频率）。经过38、40、42代演化，CAI提升至0.73–0.74，tAI达0.63–0.64，较之前提高超6%；密码子配对偏倚保持高且一致（0.97），5'端核糖体可及性增强（未配对区占比达0.87）；全局最小自由能收敛至-346至-356 kcal/mol，结构稳定性约84%，免疫刺激基序减少，最终代平均免疫惩罚降至27.3。相比线性设计（MFE < -2000 kcal/mol导致翻译效率下降）和BiLSTM-CRF（仅追求CAI 0.96–0.98而无结构约束），本框架实现了翻译效率与结构稳定的最优平衡，验证了基于BERT-GA的体外mRNA序列设计方法的有效性与数据驱动优势。
原文摘要 · Abstract (English)
Messenger RNA (mRNA) sequences as therapeutics require optimized design to ensure efficient translation, structural stability, and minimal immunogenicity. This study presents a two-stage in-silico framework that integrates deep learning and evolutionary computation for rational mRNA optimization instead of existing state-of-the-art models. In the first stage, a pretrained CodonTransformer (BERT-like Large Language Model) generates biologically coherent mRNA sequences encoding the target antigen. In the second stage, a genetic algorithm (GA) evolves these candidate sequences through codon-aware crossover and synonymous mutation guided by human codon usage preferences. Fitness functions for evaluation combined translation-related metrics (CAI, tAI, codon-pair bias), mRNA structural stability (local and global MFE via RNAfold, GC content), and reduced immunogenicity (CpG/UpA motif frequency). Over successive generations (38th, 40th, and 42nd), the GA improved (achieved CAI values of 0.73 to 0.74 and tAI values of 0.63 to 0.64) CAI and tAI by over 6% and codon-pair bias is high and consistent (0.97 ) and improved ribosomal accessibility at the 5' end, with an unpaired_30 fraction reaching 0.87; Global Minimum Free Energy (MFE) converged to a balanced range of -346 to -356 kcal/mol, achieving approximately 84% base-paired structural stability, and reduced immune-stimulatory motifs - lowering the average immune penalty to 27.3 in the final generation. Linear Design produces hyper-stable transcripts (MFE < - 2000 kcal/mol) that risk translation inefficiency due to extreme rigidity, and BiLSTM-CRF focuses solely on high CAI (0.96 to 0.98) without structural constraints, our framework achieves an optimal translation-stability equilibrium, highlighting the proposed BERT-GA framework as an effective, data-driven approach for the design and optimization of in-silico mRNA sequences.
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