用于m1Ψ修饰RNA 5'非翻译区多目标设计的条件结构感知生成Transformer
A Conditional Structure-Aware Generative Transformer for Multi-Objective Design of m1Ψ-Modified RNA 5' UTRs
浏览论文内容
中文总结 AI 辅助
本研究提出一种条件结构感知生成Transformer框架，用于50nt m1Ψ修饰RNA 5' UTR的多目标设计，结合Smart5UTR与ViennaRNA实现序列优化，通过消融确定最优约束组合。
中文摘要 AI 辅助
5'非翻译区是翻译起始的主要决定因素，其效应在修饰mRNA序列中尤为重要，其中起始密码子上下文、帽近端二级结构、上游AUG和上游开放阅读框以及核苷酸化学性质可通过序列依赖方式改变核糖体扫描、起始招募、扫描及解码。近期计算研究已将该领域从预测推进至设计，包括针对m1Ψ修饰mRNA的大规模训练预测模型Smart5UTR、更广泛的5' UTR生成与优化框架UTRGAN和UTailoR，以及结构引导的RNA设计系统RhoDesign。本文描述了一种用于50nt修饰RNA 5' UTR设计的条件生成框架，该框架可选择性地以核糖体负载、GC含量、最小自由能（MFE）及目标二级结构为条件。实现采用基于Transformer的生成器，随后通过源自Smart5UTR的核糖体负载预测器（oracle）及基于ViennaRNA的折叠指标进行序列排序与局部优化，包括对修饰碱基折叠参数的支持。在多种模拟场景与实验设置中，核糖体负载（RL）、GC、MFE及结构约束的不同组合产生了不同的性能权衡，通过消融研究确定了性能最优的方案。
英文摘要
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.
