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arXiv · 2608.21696

A Conditional Structure-Aware Generative Transformer for Multi-Objective Design of m1Ψ-Modified RNA 5' UTRs

Narges Zarnaghinaghsh·Ahmadreza Mofayezi·Byung-Jun Yoon
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Abstract

The 5' untranslated region is a major determinant of translation initiation, and its effect becomes especially important in modified mRNA sequences, where start-codon context, cap-proximal secondary structure, upstream AUGs and upstream open reading frames, and nucleotide chemistry can alter ribosome scanning and initiation recruitment, scanning, and decoding in sequence-dependent ways. Recent computational studies have moved the field from prediction toward design, including massively trained predictive models such as Smart5UTR for m1$Ψ$-modified mRNA, broader 5' UTR generation and optimization frameworks such as UTRGAN and UTailoR, and structure-guided RNA design systems such as RhoDesign. Here, we describe a conditional generative framework for 50-nt modified-RNA 5' UTR design that optionally conditions on ribosome load, GC content, minimum free energy, and target secondary structure. The implementation uses a Transformer-based generator followed by sequence ranking and local refinement with a Smart5UTR-derived ribosome-load oracle and ViennaRNA-based folding metrics, including support for modified-base folding parameters. Across multiple simulation scenarios and experimental settings, different combinations of RL, GC, MFE, and structural constraints produced distinct performance tradeoffs, enabling ablation-based identification of the best-performing formulation.

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Narges Zarnaghinaghsh, Ahmadreza Mofayezi, Byung-Jun Yoon. 2026-08-22. A Conditional Structure-Aware Generative Transformer for Multi-Objective Design of m1Ψ-Modified RNA 5' UTRs. https://arxiv.org/abs/2608.21696

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