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Can I read A novel deep generative model for mRNA vaccine development: Designing 5′ UTRs with N 1-methyl-pseudouridine modification on EtoBox?
A novel deep generative model for mRNA vaccine development: Designing 5′ UTRs with N 1-methyl-pseudouridine modification by Xiaoshan Tang; Miaozhe Huo; 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 is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.
What is A novel deep generative model for mRNA vaccine development: Designing 5′ UTRs with N 1-methyl-pseudouridine modification about?
Efficient translation mediated by the 5′ untranslated region (5′ UTR) is essential for the robust efficacy of mRNA vaccines. However, the __N__1-methyl-pseudouridine (m1__Ψ__) modification of mRNA can impact the translation efficiency of the 5′ UTR. We discovered that the optimal 5′ UTR for m1__Ψ__-modified mRNA (m1__Ψ__**–**5′ UTR) differs significantly from its unmodified counterpart, highlighting the need for a specialized tool for designing m1__Ψ__**–**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 m1__Ψ__**–**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.
Who reads A novel deep generative model for mRNA vaccine development: Designing 5′ UTRs with N 1-methyl-pseudouridine modification?
It is typically read by researchers, students, and practitioners in Biochemistry, Genetics and Molecular Biology.
- Author
- Xiaoshan Tang; Miaozhe Huo; 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
- Publisher
- Elsevier
- Published
- 2023
- Language
- EN
- Field
- Biochemistry, Genetics and Molecular Biology (Life Sciences)