Optimizing 5'UTRs for mRNA-delivered gene editing using deep learning.
Sebastian Castillo-HairStephen FedakBan WangJohannes LinderKyle HavensMichael CertoGeorg SeeligPublished in: Nature communications (2024)
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.
Keyphrases
- deep learning
- single cell
- binding protein
- cell therapy
- rna seq
- neural network
- poor prognosis
- crispr cas
- genome wide
- stem cells
- gene expression
- double blind
- clinical trial
- risk assessment
- convolutional neural network
- high speed
- mesenchymal stem cells
- long non coding rna
- climate change
- transcription factor
- phase ii
- amino acid
- genome wide identification
- phase iii
- placebo controlled