Utrgan: Learning to generate 5’ UTR sequences for optimized translation efficiency and gene expression

Abstract

The 5’ untranslated region (5’ UTR) of the messenger RNA plays a crucial role in the translatability and stability of the molecule. Thus, it is an important component in the design of synthetic biological circuits for high and stable expression of intermediate proteins. Several UTR sequences are patented and used frequently in laboratories. We present a novel model, UTRGAN, which is a Generative Adversarial Network (GAN)-based model designed to generate 5’ UTR sequences coupled with an optimization procedure to ensure a target feature such as high expression for a target gene sequence or high ribosome load and translation efficiency. We rigorously analyze and show that the model can generate sequences that mimic various properties of natural UTR sequences. Then, we show that the optimization procedure yields sequences that are expected to yield (i) up to 5-fold higher average expression on a set of target genes, (ii) up to 2-fold higher mean ribosome load on average, and (iii) a 34-fold higher average translation efficiency, compared to the initially generated UTR sequences. We compare our method with other approaches and find that UTRGANgenerated sequences contain motives that show higher sequence similarity to known regularity motives in various regions such as (i) internal ribosome entry sites, (ii) upstream open reading frames, (iii) G-quadruplexes, (iv) Kozak and initiation start codon regions. Finally, we show in-vitro that the UTR sequences we designed yield a higher translation rate for the human TNF-α protein compared to the human Beta Globin 5’ UTR, which is a 5’ UTR with high production capacity.