arXiv：cs.LG· 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·· 3 小时前AI 评分34
LSCO：预测器引导的潜在空间密码子优化，提升蛋白表达
Predictor-Guided Latent Space Codon Optimization for Maximizing Protein Expression
AI 导读
研究者提出潜在空间密码子优化（LSCO），将离散密码子选择问题映射到预训练 mRNA 语言模型的潜在空间，实现基于梯度的搜索。LSCO 结合不确定性感知预测器的表达目标、最小自由能正则项、蛋白到密码子回译模型的自然度先验以及约束解码。在真实湿实验抗体表达数据集上，LSCO 的预测表达优于频率基线及现代深度生成基线，并保持合适的生物物理性质。
正文
Authors: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
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 space is discrete and combinatorially large, precluding gradient-based methods, and existing tools rely on heuristic proxies (e.g., Codon Adaptation Index or GC-content) that poorly capture true expression. We introduce Latent-Space Codon Optimization (LSCO), which recasts this discrete problem as a continuous one by mapping sequences into the latent space of a pretrained mRNA language model, enabling efficient gradient-based search. LSCO combines four components: a data-driven expression objective from an uncertainty-aware predictor, a Minimum-Free-Energy regularizer for structural stability, a naturalness prior from a protein-to-codon back-translation model, and constrained decoding for protein fidelity. On a real-world, wet-lab antibody expression dataset, LSCO outperforms simple frequency-based, as well as modern deep generative baselines in predicted expression, while retaining suitable biophysical properties.
| Subjects: | Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.03098 [cs.AI] |
| (or arXiv:2610.03098v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.03098 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Alberto Caron [view email]
[v1]
Fri, 2 Oct 2026 10:18:50 UTC (2,170 KB)
来源：arXiv：cs.LG · arxiv.org