通过基于转移学习的生成深度学习模型对人工促进体的特定物种设计
Yan Xia1, Xiaowen Du1, Bin Liu2
1Key Laboratory of Molecular Medicine and Biotherapy, School of Life Science, Beijing Institute of Technology, Beijing 100081, China.
Nucleic acids research
|May 23, 2024
概括
PromoGen使用核酸语言模型来创建特定物种的促进剂,即使是对研究不足的微生物. 这种深度学习工具有效地为代谢工程等应用程序生成功能促进器.
科学领域:
- 微生物学 微生物学
- 生物信息学是一种生物信息学.
- 合成生物学 合成生物学
背景情况:
- Prokaryotic 促销者表现出特定物种的序列模式,使促销者预测和设计变得复杂.
- 对于许多物种,有限的数据阻碍了准确评估现有的促销者或设计新鲜物种的能力.
研究的目的:
- 开发PromoGen,这是一个核酸语言模型的集合,用于高效的,特定物种的功能性促进物生成.
- 为代谢工程提供一种有价值的工具,特别是对于具有有限基因组数据的微生物.
主要方法:
- 微调预训练的多种促进模型以创建27个特定物种的模型.
- 开发一个回归模型来得分和比较生成的促销者与原生和其他人工促销者.
- 在 * Bacillus subtilis * * 中生成的促进体的实验验证.
主要成果:
- 促销基因生成的促销剂 (PGPs) 用于*大肠杆菌*和*细菌*反映了原生促销剂分布模式.
- 通过竞争性神经网络产生的PGP比促进者获得更高的分数.
- 对22种*B. subtilis*PGPs的实验性表征显示,其中4种达到强的促进剂水平,所有均表现出活性.
- 推出了一个用户友好的网站,用于为27种物种生成物种特异性促进剂.
结论:
- PromoGen提供了一种高效的深度学习策略,用于新的特定物种的促销者生成,即使数据集有限.
- 这种方法为推进研究不足的微生物的代谢工程提供了必要的促进器工具箱.
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