A novel deep generative model for mRNA vaccine development : Designing 5' UTRs with N1-methyl-pseudouridine modification
© 2024 The Authors..
Efficient translation mediated by the 5' untranslated region (5' UTR) is essential for the robust efficacy of mRNA vaccines. However, the N1-methyl-pseudouridine (m1Ψ) modification of mRNA can impact the translation efficiency of the 5' UTR. We discovered that the optimal 5' UTR for m1Ψ-modified mRNA (m1Ψ-5' UTR) differs significantly from its unmodified counterpart, highlighting the need for a specialized tool for designing m1Ψ-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 m1Ψ-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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:14 |
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| Enthalten in: |
Acta pharmaceutica Sinica. B - 14(2024), 4 vom: 13. Apr., Seite 1814-1826 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Tang, Xiaoshan [VerfasserIn] |
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| Links: |
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| Themen: |
5′ UTR |
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| Anmerkungen: |
Date Revised 05.04.2024 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1016/j.apsb.2023.11.003 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM370615395 |
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| 100 | 1 | |a Tang, Xiaoshan |e verfasserin |4 aut | |
| 245 | 1 | 2 | |a A novel deep generative model for mRNA vaccine development |b Designing 5' UTRs with N1-methyl-pseudouridine modification |
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| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a © 2024 The Authors. | ||
| 520 | |a Efficient translation mediated by the 5' untranslated region (5' UTR) is essential for the robust efficacy of mRNA vaccines. However, the N1-methyl-pseudouridine (m1Ψ) modification of mRNA can impact the translation efficiency of the 5' UTR. We discovered that the optimal 5' UTR for m1Ψ-modified mRNA (m1Ψ-5' UTR) differs significantly from its unmodified counterpart, highlighting the need for a specialized tool for designing m1Ψ-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 m1Ψ-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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a 5′ UTR | |
| 650 | 4 | |a COVID-19 | |
| 650 | 4 | |a Machine learning | |
| 650 | 4 | |a N1-Methyl-pseudouridine | |
| 650 | 4 | |a SARS-CoV-2 | |
| 650 | 4 | |a Sequence design | |
| 650 | 4 | |a mRNA design | |
| 650 | 4 | |a mRNA vaccine | |
| 700 | 1 | |a Huo, Miaozhe |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Yuting |e verfasserin |4 aut | |
| 700 | 1 | |a Huang, Hai |e verfasserin |4 aut | |
| 700 | 1 | |a Qin, Shugang |e verfasserin |4 aut | |
| 700 | 1 | |a Luo, Jiaqi |e verfasserin |4 aut | |
| 700 | 1 | |a Qin, Zeyi |e verfasserin |4 aut | |
| 700 | 1 | |a Jiang, Xin |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Yongmei |e verfasserin |4 aut | |
| 700 | 1 | |a Duan, Xing |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Ruohan |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Lingxi |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Hao |e verfasserin |4 aut | |
| 700 | 1 | |a Fan, Na |e verfasserin |4 aut | |
| 700 | 1 | |a He, Zhongshan |e verfasserin |4 aut | |
| 700 | 1 | |a He, Xi |e verfasserin |4 aut | |
| 700 | 1 | |a Shen, Bairong |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Shuai Cheng |e verfasserin |4 aut | |
| 700 | 1 | |a Song, Xiangrong |e verfasserin |4 aut | |
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