基于蛋白质的多目标强化学习用于全长mRNA设计
Protein-Conditioned Multi-Objective Reinforcement Learning for Full-Length mRNA Design
AI总结：
本文提出ProMORNA框架，通过多目标强化学习生成全长mRNA，优化稳定性、翻译效率和免疫安全。在火萤虫荧光素酶目标上验证，提升了预测半衰期和翻译效率的帕累托前沿。
摘要：
设计治疗性mRNA需要生成平衡稳定性、翻译效率和免疫安全的全长转录本。为解决此挑战，我们提出ProMORNA，一种多目标生成框架，直接从目标蛋白序列生成完整mRNA转录本。我们的方法首先在超过600万自然蛋白-mRNA配对上训练BART式编码器-解码器模型。然后引入多目标群体相对策略优化（MO-GRPO）以统一优化多种生物学目标。在火萤虫荧光素酶目标上进行案例研究，排除其在监督训练数据和提示池中。结果表明，ProMORNA相比标准监督基线提升了预测半衰期和翻译效率的帕累托前沿。此外，在相同评估流程下，其预测功能得分高于最先进的基线。这些计算发现证明了使用多目标强化学习进行未见过目标的全长mRNA设计的可行性。
英文摘要：
Designing therapeutic messenger RNA (mRNA) requires creating full-length transcripts that carefully balance stability, translation efficiency, and immune safety. To address this challenge, we propose ProMORNA, a multi-objective generation framework that produces complete mRNA transcripts \textit{de novo} directly from a target protein sequence. Our approach begins by training a BART-style encoder-decoder model on over 6 million natural protein-mRNA pairs. We then introduce Multi-Objective Group Relative Policy Optimization (MO-GRPO) to simultaneously optimize for various biological objectives in a unified way. As a case study, we evaluated ProMORNA on the widely used firefly luciferase target, excluding it from both our supervised training data and the prompt pool. The results indicate that ProMORNA improves the \textit{in silico} Pareto frontier for predicted half-life and translation efficiency relative to standard supervised baselines. Additionally, it achieves higher predicted functional scores than a state-of-the-art baseline under the same evaluation pipeline. These computational findings demonstrate the feasibility of using multi-objective reinforcement learning for full-length mRNA design on unseen targets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
