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Updated: Feb 19, 2026

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Identifying Amino Acid Overproducers Using Rare-Codon-Rich Markers
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皮奇亚-CLM:基于Komagataella phaffii的语言模型的编码子优化管道
Harini Narayanan1, J Christopher Love1,2
1Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139.
概括
科学家们使用语言模型来改善酵母 Komagataella phaffii.酵母中的蛋白质生产. 这种编码子使用偏差优化增强了蛋白质产量高达三倍,超过了现有的工具.
科学领域:
- 生物技术是生物技术.
- 计算生物学 计算生物学
- 基因组学就是基因组学.
背景情况:
- 同义符号使用偏差 (CUB) 影响基因表达和蛋白质生产.
- 诸如宿主生物体,基因功能和代码子位置等因素会影响CUB.
- 优化编码子使用对于高效的异质蛋白表达至关重要.
研究的目的:
- 开发一种基于语言模型的管道,用于学习和应用codon使用偏差.
- 增强工业宿主 Komagataella phaffii.中异质蛋白质的产生.
- 将开发管道的性能与现有的编码子优化工具进行比较.
主要方法:
- 利用语言模型从宿主基因组中学习编码子使用模式.
- 开发了Pichia-Codon语言模型 (Pichia-CLM) 管道.
- 应用了Pichia-CLM来优化K. phaffii中异质蛋白表达的序列.
- 评估蛋白质生产水平,并将结果与商业工具进行比较.
主要成果:
- 与原生序列相比,异质蛋白质生产达到三倍的增强.
- 皮奇亚-CLM持续提高了不同复杂度的蛋白质的生产率.
- 生成的序列模仿了宿主细胞蛋白质的使用特性.
- 成功学习并避免负面的 cis-regulatory 和重复元素.
结论:
- 语言模型可以有效地从基因组数据中学习代码的使用模式.
- 皮奇亚-CLM提供了一种强大而高效的编码子优化方法.
- 这种方法具有显著的潜力,可以改善工业生物技术中的异质蛋白质生产.
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