Optimizing 5’UTRs for mRNA-delivered gene editing using deep learning
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Abstract
mRNA therapeutics are revolutionizing the pharmaceutical industry, but methods to optimize the primary sequence for increased expression are still lacking. Here, we design 5’UTRs for efficient mRNA translation using deep learning. We perform polysome profiling of fully or partially randomized 5’UTR libraries in three cell types and find that UTR performance is highly correlated across cell types. We train models on our datasets and use them to guide the design of high-performing 5’UTRs using gradient descent and generative neural networks. We experimentally test designed 5’UTRs with mRNA encoding megaTAL TM gene editing enzymes for two different gene targets and in two different cell lines. We find that the designed 5’UTRs support strong gene editing activity. Editing efficiency is correlated between cell types and gene targets, although the best performing UTR was specific to one cargo and cell type. Our results highlight the potential of model-based sequence design for mRNA therapeutics.
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The Pfizer-BioNTech mRNA vaccine contains the 5′ UTR of human α-globin and the 3′ UTR AES-mtRNR1 combination.
“the Pfizer-BioNTech mRNA vaccine contains the 5′ UTR of human α-globin, which was identified as one of the top 20% 5′ UTRs in massive screening studies,10,11,13 and the 3′ UTR AES-mtRNR1 combination of two genes, AES (Amino Enhancer of Split) and mtRNR1 (12S mitochondrial RNA).12”
From Evaluation of synthetic mRNA with selected UTR sequences and alternative poly(A) tail, in vitro and in vivo (2025) · cites this paper as [10]
The Introduction says many mRNA therapies use alpha- or beta-globin UTRs. The Results section on de novo designed 5′UTRs places the commonly used beta-globin UTR—not alpha-globin—in the top ~20% of short endogenous UTRs. This paper therefore does not establish the cited ranking for alpha-globin or identify it as the Pfizer-BioNTech vaccine’s 5′UTR; the sentence cites a different source for the 3′UTR combination. (LLM, conf=0.84)
Experimental — accuracy not yet measuredIt is widely suggested that cell-type specificity of mRNA translation is primarily dictated by 3′ UTR, but emerging evidence points to significant regulatory role of 5′ UTR as well.
“While it is widely suggested that the cell-type specificity of mRNA translation is primarily dictated by the 3′ UTR [31], emerging evidence points to a significant regulatory role of the 5′ UTR as well.”
From Identification of novel 5’ UTR variants for enhanced mRNA translation and vaccine immunogenicity (2026) · cites this paper as [31]
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Medjmedj, A., Genon, H., Hezili, D., Loth, A. N., Clemençon, R., Guimpied, C., Mollet, L., Bigot, A., Wien, F., Hamacek, J., et al. (2025) · Molecular Therapy Nucleic Acids · doi:10.1016/j.omtn.2025.102648 · Cited by 19 · see citing works
Identification of novel 5’ UTR variants for enhanced mRNA translation and vaccine immunogenicity
Mazunina, E. P., Dzharullaeva, A. S., Kleymenov, D. A., Bykonia, E. N., Kozlova, S. R., Usachev, E. V., Panova, E. A., Konopleva, M. V., Telegina, D. V., Kukushkina-Zvezdova, A. A., et al. (2026) · Scientific Reports · doi:10.1038/s41598-026-59586-5
- Authors
- Castillo-Hair, S., Fedak, S., Wang, B., Linder, J., Havens, K., Certo, M., & Seelig, G.
- Affiliations
- University of Washington (US)
- Stanford University (US)
- Corresponding author
- Seelig, Georg
- Venue
- Nature Communications
- Published
- 2024-06-20
- Volume
- 15
- Issue
- 1
- Pages
- 5284-5284
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC11189900
- OpenAlex
- W4399848621
- Cited by
- 98
- References
- 17
- Citations by year
- 2026: 55 · 2025: 36 · 2024: 7
- FWCI
- 12.68
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (R01GM149631)
- National Science Foundation (2021552)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA Interference and Gene Delivery
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA regulation and disease
- Keywords
- 5' untranslated region, mRNA therapeutics, genome editing, mRNA delivery, deep learning, sequence design, generative neural networks, polysome profiling, translation efficiency, TALEN, cell type specificity
- MeSH terms
- Gene Editing, Deep Learning, Cell Line, Humans, Polyribosomes, RNA, Messenger, Protein Biosynthesis, 5' Untranslated Regions, HEK293 Cells
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