Optimizing 5’UTRs for mRNA-delivered gene editing using deep learning

Sebastian Castillo-Hair, Stephen Fedak, Ban Wang, Johannes Linder, Kyle Havens, Michael Certo and Georg Seelig ()
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Sebastian Castillo-Hair: University of Washington
Stephen Fedak: 2seventy bio
Ban Wang: Stanford University
Johannes Linder: University of Washington
Kyle Havens: 2seventy bio
Michael Certo: 2seventy bio
Georg Seelig: University of Washington

Nature Communications, 2024, vol. 15, issue 1, 1-15

Abstract: 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 megaTALTM 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.

Date: 2024
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DOI: 10.1038/s41467-024-49508-2

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