针对 S100 Ca2+ 结合蛋白的多元信息虚拟查的层次 AF2RAVE
Xinyu Gu1,2, Venkata Sai Sreyas Adury1,3, Akashnathan Aranganathan1,4
1Institute for Physical Science and Technology, University of Maryland, College Park, Maryland 20742, United States.
Journal of chemical theory and computation
|September 4, 2025
概括
我们开发了AF2RAVE, 一种结合AlphaFold2和增强样本的新计算方法, 这种方法有助于发现选择性药物抑制剂,优于现有的虚拟查测试方法.
科学领域:
- 计算生物学
- 结构生物学
- 药物发现
背景情况:
- 蛋白质功能依赖于动态的结构变化,这对药物设计构成挑战.
- 对蛋白质骨干和侧链转移稳定性的建模对于准确的基于结构的药物发现至关重要.
- 在虚拟选中生成多样化的蛋白质构造是计算要求很高的.
研究的目的:
- 介绍AF2RAVE,一个集成AlphaFold2的层次管道,用于蛋白质结构建模的增强采样.
- 系统地探索蛋白质的自由能量格局和后续稳定性.
- 为药物发现应用从序列生成多样化的全像蛋白质构造.
主要方法:
- 集成AlphaFold2与基于机器学习的增强采样 (AF2RAVE管道).
- 系统地探索无蛋白质能量景观和转化稳定性.
- 应用于结合的S100蛋白家族以产生多样化的构造.
- 使用新型Ca2+-S100B抑制剂数据集进行回顾性对接和丰富测试.
主要成果:
- AF2RAVE成功地从序列中生成了S100蛋白家族的多种全息形状.
- 与标准AlphaFold2相比,AF2RAVE生成的结构在追溯对接和丰富测试中表现出更高的性能.
- 该方法在对Ca2+-S100B抑制剂的丰富测试中超过了实验性解决的X射线结构.
结论:
- AF2RAVE提供了一个强大的方法来建模蛋白质的转移稳定性和结构动态.
- 这条管道显著增强了高通量虚拟选能力.
- AF2RAVE 显示出选择性抑制剂发现的巨大潜力,特别是对于像 S100 蛋白家族这样具有挑战性的标.
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