新的LSCO方法利用AI语言模型优化蛋白质表达

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-05 04:00

研究人员开发了一种名为潜在空间密码子优化（LSCO）的新方法，以提高蛋白质表达的效率。该技术通过将序列映射到预训练的mRNA语言模型的潜在空间，将密码子选择的离散问题重塑为连续问题，从而实现基于梯度的优化。LSCO集成了表达预测器、稳定性正则化器、自然度先验和约束解码，以确保蛋白质的保真度。在抗体表达数据实验中，LSCO与现有的启发式和深度生成方法相比，表现出了优越的性能。 AI

影响这种AI驱动的方法可以通过提高表达效率，加速治疗性蛋白质和mRNA疫苗的开发。

排序理由该集群包含一篇详细介绍蛋白质表达优化新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Alberto Caron, Tianyu Cui, Dmytro S. Lituiev, Mangal Prakash, Artem Moskalev, Amina Mollaysa, Bo Zhai, Hirsh Nanda, Daniel M. Poole, Zhongyin Liu, Iman Farasat, Robert Davidson, Nikolay V. Manyakov, Tommaso Mansi, Scott Oloff, Rui Liao · 2026-10-05 04:00

预测引导的潜在空间密码子优化以最大化蛋白质表达

arXiv:2610.03098v1 Announce Type: new Abstract: Codon optimization, the process of selecting synonymous codons to improve mRNA translation efficiency and protein expression, is central to therapeutic protein production and mRNA vaccines, yet it remains a hard problem. The design …

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预测引导的潜在空间密码子优化以最大化蛋白质表达

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