RNASTOP: A Deep Learning Framework for mRNA Chemical Stability Prediction and Optimization
Shenggeng Lin, Junwei Chen, Heqi Sun, Yufang Zhang, Wenyi Yang, Hong Tan, Dong-Qing Wei, Qinghua Jiang, Yi XiongAbstract
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.