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A novel deep generative model for mRNA vaccine development: Designing 5′ UTRs with N1-methyl-pseudouridine modification
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作者 Xiaoshan Tang Miaozhe Huo +16 位作者 Yuting Chen Hai Huang Shugang qin Jiaqi Luo zeyi qin Xin Jiang Yongmei Liu Xing Duan Ruohan Wang Lingxi Chen Hao Li Na Fan Zhongshan He Xi He Bairong Shen Shuai Cheng Li Xiangrong Song 《Acta Pharmaceutica Sinica B》 SCIE CAS CSCD 2024年第4期1814-1826,共13页
Efficient translation mediated by the 5'untranslated region(5'UTR)is essential for the robust efficacy of mRNA vaccines.However,the N1-methyl-pseudouridine(m1)modification of mRNA can impact the translation ef... Efficient translation mediated by the 5'untranslated region(5'UTR)is essential for the robust efficacy of mRNA vaccines.However,the N1-methyl-pseudouridine(m1)modification of mRNA can impact the translation efficiency of the 5'UTR.We discovered that the optimal 5'UTR for m1y-modified mRNA(m1y-5'UTR)differs significantly from its unmodified counterpart,high-lighting the need for a specialized tool for designing mly-5'UTRs rather than directly utilizing high-expression endogenous gene 5'UTRs.In response,we developed a novel machine learning-based tool,Smart5UTR,which employs a deep generative model to identify superior m1y-5'UTRs in silico.The tailored loss function and network architecture enable Smart5UTR to overcome limitations inherent in existing models.As a result,Smart5UTR can successfully design superior 5'UTRs,greatly benefiting mRNA vaccine development.Notably,Smart5UTR-designed superior 5'UTRs significantly enhanced antibody titers induced by COVID-19 mRNA vaccines against the Delta and Omicron variants of SARS-CoV-2,surpassing the performance of vaccines using high-expression endogenous gene 5'UTRs. 展开更多
关键词 mRNA vaccine Machine learning 5'UTR mRNA design Sequence design N1-Methyl-pseudouridine COVID-19 SARS-CoV-2
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