蛋白质语言模型嵌入区分催化与结构结合部位,具有可解释的注意力签名
1Institute of Chemistry, Academia Sinica, 128 Academia Road, Section 2, Taipei 115201, Taiwan.
Journal of chemical information and modeling
|February 27, 2026
概括
使用蛋白序列嵌入的机器学习模型可以准确地区分催化和结构位. 这种基于序列的方法为大规模的金属蛋白质组注释提供了一个强大的新工具.
科学领域:
- 生物化学 生物化学
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
背景情况:
- 离子在蛋白质功能中起着至关重要的作用,作为催化辅因子或结构元素.
- 由于类似的协调几何形状,仅基于氨基酸序列来区分这些位功能是很困难的.
研究的目的:
- 调查蛋白序列嵌入是否可以准确地将部位分类为催化或结构性.
- 开发一种仅用于序列的计算方法,用于注释金属保护体.
主要方法:
- 利用ESM-2嵌入式,这是蛋白质序列的深度学习模型.
- 训练有素的机器学习分类器对不同序列的蛋白进行分类.
- 采用注意力分析来解释模型预测并确定关键的残留相互作用.
主要成果:
- 在使用ESM-2嵌入物对部位进行分类时,获得了高精度 (ROC-AUC 0.93-0.97).
- 显著优于传统的基于图案的方法 (AUC = 0.759).
- 注意力分析揭示了催化希斯提丁连接物和第二碳酸盐残留物之间的特定相互作用.
结论:
- 进化序列模式编码有关扩展结网络的信息,这些网络区分了催化和结构位.
- 仅序列方法为基于结构的金属蛋白质组注释技术提供了一个补充和可扩展的方法.
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