基于变压器的Sec和Tat类信号的预测,用于增强细菌蛋白质分泌
Seongmo Kang1, Seong Min Lee1, Ryu Hong Park1
1Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.
Biotechnology journal
|March 4, 2026
概括
研究人员开发了一种人工智能模型,用于设计细菌中蛋白质分泌的信号. 这种数据驱动的方法显著提高了生物制造的效率,绕过了传统的选方法.
科学领域:
- 生物技术是生物技术.
- 分子生物学分子生物学
- 生物信息学是一种生物信息学.
背景情况:
- 为高效的重组蛋白质分泌设计信号 (SPs) 是至关重要的,但具有挑战性.
- 目前的方法严重依赖于劳动密集型查过程.
研究的目的:
- 为合理的信号设计开发一个计算框架.
- 为了产生类型特定的Sec或Tat类型的SPs用于重组蛋白质分泌在格拉姆阳性细菌中.
主要方法:
- 开发了一个基于变压器的AI模型,在158,768个SP-蛋白对上进行训练.
- 利用定制的令牌化策略,包括区域划分令牌,用于区域意识的序列设计.
- 采用光束搜索解码和采样方法,以实现序列多样性和强大的生成.
主要成果:
- 该模型实现了精确的in silico SP分类,具有高序列一致性 (>60%) 与本地SPs.
- 在Corynebacterium glutamicum中进行的实验验证显示,在16个设计的SP中,有15个SP的分泌成功.
- 仅使用格拉姆阳性数据进行训练显示出优异的性能,这表明宿主特异性的机制差异.
结论:
- 建立了一个实用的,数据驱动的框架,用于合理的信号设计.
- 人工智能模型能够有效地生成用于蛋白质生物制造的功能信号.
- 这种方法支持在阳性宿主中增强蛋白质分泌.
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