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RNASTOP: A Deep Learning Framework for mRNA Chemical Stability Prediction and Optimization
Shenggeng Lin1,2,3, Junwei Chen1,3, Heqi Sun1
1State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China.
Abstract:
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.
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