Decoding Translation-Related Functional Sequences in 5'UTRs Using Interpretable Deep Learning Models

TL;DR

This paper presents UTR-STCNet, an interpretable deep learning model that accurately predicts translational efficiency from variable-length 5'UTRs and uncovers functional regulatory elements using saliency-guided clustering and attention mechanisms.

Contribution

Introduces UTR-STCNet, a novel Transformer-based architecture with saliency-aware clustering for flexible, interpretable modeling of 5'UTRs without input truncation or added computational cost.

Findings

Outperforms state-of-the-art models in predicting translational efficiency.

Recovers known regulatory elements like upstream AUGs and Kozak motifs.

Demonstrates biological interpretability of the model.

Abstract

Understanding how 5' untranslated regions (5'UTRs) regulate mRNA translation is critical for controlling protein expression and designing effective therapeutic mRNAs. While recent deep learning models have shown promise in predicting translational efficiency from 5'UTR sequences, most are constrained by fixed input lengths and limited interpretability. We introduce UTR-STCNet, a Transformer-based architecture for flexible and biologically grounded modeling of variable-length 5'UTRs. UTR-STCNet integrates a Saliency-Aware Token Clustering (SATC) module that iteratively aggregates nucleotide tokens into multi-scale, semantically meaningful units based on saliency scores. A Saliency-Guided Transformer (SGT) block then captures both local and distal regulatory dependencies using a lightweight attention mechanism. This combined architecture achieves efficient and interpretable modeling without input truncation or increased computational cost. Evaluated across three benchmark datasets, UTR-STCNet consistently outperforms state-of-the-art baselines in predicting mean ribosome load (MRL), a key proxy for translational efficiency. Moreover, the model recovers known functional elements such as upstream AUGs and Kozak motifs, highlighting its potential for mechanistic insight into translation regulation.