ML-driven design of 3' UTRs for mRNA stability

Abstract

Using mRNA as a therapeutic has received enormous attention in the last few years, but instability of the molecule remains a hurdle to achieving long-lasting therapeutic levels of protein expression. In this study, we describe our approach for designing stable mRNA molecules by combining machine learning-driven sequence design with high-throughput experimental assays. We developed a high-throughput massively parallel reporter assay (MPRA) that, in a single experiment, measures the half-life of tens of thousands of unique mRNA sequences containing designed 3 UTRs. Over multiple design-build-test iterations, we have accumulated mRNA stability measurements for 180,000 unique genomic and synthetic 3 UTRs, representing the largest such dataset of sequences. We trained highly-accurate machine learning models to map from 3 UTR sequence to mRNA stability, and used them to guide the design of synthetic 3 UTRs that increase mRNA stability in cell lines. Finally, we validated the function of several ML-designed 3 UTRs in mouse models, resulting in up to 2-fold more protein production over time and 30-100-fold higher protein output at later time points compared to a commonly used benchmark. These results highlight the potential of ML-driven sequence design for mRNA therapeutics.