基于嵌入的对齐:将蛋白质语言模型与动态编程对齐相结合,以检测在黄昏区的结构相似性
Lorenzo Pantolini1,2, Gabriel Studer1,2, Joana Pereira1,2
1Biozentrum, University of Basel, Basel 4056, Switzerland.
Bioinformatics (Oxford, England)
|January 4, 2024
概括
蛋白质语言模型 (pLM) 提供了新的生物信息学工具. 我们的基于嵌入的蛋白质序列对齐 (EBA) 方法有效地识别了遥远的蛋白质关系,优于现有的方法.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 结构生物学 结构生物学
背景情况:
- 蛋白质语言模型 (pLMs) 是先进的AI工具,越来越多地应用于生物序列.
- pLMs产生残留水平嵌入,捕捉蛋白质内的氨基酸的功能上下文.
- 这些嵌入已经显示出下游任务的前景,包括识别同类蛋白质.
研究的目的:
- 引入一种新的方法来产生蛋白质序列对齐,使用pLMs的嵌入.
- 评估这种新方法在检测结构相似性的表现,特别是在具有挑战性的同类学"暮色区".
- 为了证明将plm与对齐技术结合用于蛋白质关系发现的实用性.
主要方法:
- 基于嵌入的蛋白质序列对齐 (EBA) 方法的开发.
- 使用蛋白质语言模型产生的每残留嵌入.
- 与经典对齐方法和其他基于pLM的方法进行EBA对比.
主要成果:
- EBA成功地捕捉了蛋白质之间的结构相似性,即使在黄昏区.
- 该方法实现了高精度,不需要训练或参数优化.
- 在检测远距离关系方面,EBA的表现优于传统的序列对齐和其他基于pLM的方法.
结论:
- 蛋白质语言模型与对齐策略的整合是生物信息学的一个强有力的方法.
- EBA提供了一个强大的,准确的工具来识别同类蛋白质,特别是那些具有低序列相同性的蛋白质.
- 这种方法有助于检测蛋白序列中的进化和结构关系.
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