生成语法
理论(学习稳定性)
信使核糖核酸
生成模型
翻译(生物学)
计算生物学
计算机科学
生物
人工智能
遗传学
机器学习
基因
作者
He Zhang,Hailong Liu,Yushan Xu,Haoran Huang,Yiming Liu,Jia Wang,Yan Qin,Haiyan Wang,Lili Ma,Zhiyuan Xun,Xianzhi Hou,Timothy K. Lu,Jicong Cao
出处
期刊:Science
[American Association for the Advancement of Science]
日期:2025-08-28
标识
DOI:10.1126/science.adr8470
摘要
Despite the success of mRNA COVID-19 vaccines, extending this modality to more diseases necessitates substantial enhancements. We present GEMORNA, a generative RNA model that utilizes Transformer architectures tailored for mRNA coding sequences (CDSs) and untranslated regions (UTRs), to design novel mRNAs with enhanced expression and stability. GEMORNA-designed full-length mRNAs exhibited up to a 41-fold increase in firefly luciferase expression compared to an optimized benchmark in vitro. GEMORNA-generated therapeutic mRNAs achieved up to a 15-fold enhancement in human erythropoietin (EPO) expression and substantially elicited antibody titers of COVID vaccine in mice. Additionally, GEMORNA’s versatility extends to circular RNA, substantially enhancing circular EPO expression and boosting anti-tumor cytotoxicity in CAR-T cells. These advancements highlight deep generative AI’s vast potential for mRNA therapeutics.
科研通智能强力驱动
Strongly Powered by AbleSci AI