RNASTOP：预测并优化 mRNA 化学稳定性的深度学习框架
RNASTOP: A Deep Learning Framework for mRNA Chemical Stability Prediction and Optimization
研究团队提出 RNASTOP，一个把深度学习与启发式搜索结合的框架，可同时预测和优化 mRNA 化学稳定性。在 Stanford OpenVaccine 竞赛数据集上，其准确率比表现最好的模型提升 13%，并在预测全长 mRNA 降解时表现出较好的泛化能力。用于 mRNA 密码子优化时，RNASTOP 将水痘-带状疱疹病毒疫苗序列的最低自由能降低 75.73%，同时保持较高的翻译效率。作者认为该工具可加速 mRNA 疗法开发，源代码已在 GitHub 开源。
来源：Genomics, Proteomics & Bioinformatics · doi.org
Messenger RNA (mRNA) vaccines offer promising therapeutic potential for combating various diseases, yet their inherent chemical instability hampers long-term efficacy. Although several methods have been developed to predict mRNA degradation, they often suffer from limited accuracy and lack the capability for rational codon optimization. Here, we propose RNASTOP, a novel framework that integrates deep learning with heuristic search to simultaneously predict and optimize mRNA chemical stability. RNASTOP achieves a 13% improvement in accuracy over the top-performing model on the Stanford OpenVaccine competition dataset and demonstrates robust generalization in predicting full-length mRNA degradation. When applied to mRNA codon optimization, RNASTOP reduces the minimum free energy of the varicella-zoster virus vaccine sequence by 75.73% while maintaining high translation efficiency. Overall, RNASTOP serves as a powerful tool for predicting and optimizing mRNA chemical stability, accelerating the development of mRNA therapeutics. The source code of RNASTOP is available at https://github.com/xlab-BioAI/RNASTOP.