UTR-Insight: integrating deep learning for efficient 5′ UTR discovery and design - Saichao Pan; Hanyu Wang; Hang Zhang; Zan Tang; Lianqiang Xu; Zhixiang Yan; Yong Hu (BMC Genomics)
Abstract The 5′ UTR is critical for mRNA stability and translation efficiency in therapeutics. We developed UTR-Insight, a model integrating a pretrained language model with a CNN-Transformer architecture, explaining 89.1% of the mean ribosome load (MRL) variation in random 5′ UTRs and 82.8% in endogenous 5′ UTRs, surpassing existing models. Using UTR-Insight, we performed high-throughput in silico screening of hundreds of thousands of endogenous 5′ UTRs from primates, mice, and viruses. The screened sequences increased protein expression by up to 319% compared to the human α-globin 5′ UTR, and UTR-Insight-designed sequences achieved even greater expression levels than high-performing endogenous 5′ UTRs.