阿尔法折叠-随机步行和阿尔法折叠-组合:采样替代蛋白质构造与阿尔法折叠的扰乱版本
Ishan Taneja1, Manuel A Llanos1, Monica L Fernández-Quintero1
1Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California 92037, United States.
Journal of chemical information and modeling
|December 31, 2025
概括
新的机器学习方法AlphaFold-RandomWalk和AlphaFold-Ensemble产生了多样化的蛋白质结构. 将这些与分子动力学模拟相结合,可以有效地探测蛋白质结构异质性.
科学领域:
- 结构生物学 结构生物学
- 计算生物学 计算生物学
- 生物物理学的生物物理.
背景情况:
- 蛋白质构成的多样性对于生物功能至关重要.
- 机器学习 (ML) 方法,如AlphaFold,可以预测蛋白质结构.
- ML独立生成多种蛋白质构造的能力仍在研究中.
研究的目的:
- 开发基于ML的新方法,用于生成多样化的蛋白质构造.
- 创建一个计算管道,将ML与分子动力学 (MD) 模拟集成在一起.
- 评估ML产生的构造在探索蛋白质构造异质性的有用性.
主要方法:
- 开发AlphaFold-RandomWalk (AF-RW) 通过在模型重量上添加噪声.
- 通过微调模型组合,开发AlphaFold-Ensemble (AF-Ensemble).通过微调模型组合.
- 集成到多阶段管道中,从ML构造中播种不受偏见的MD模拟.
主要成果:
- 与传统方法相比,AF-RW显著增加了形状的多样性.
- 该管道为十种蛋白质产生了生物学上有意义的替代构造.
- 来自ML生成的形状的模拟对K-Ras和核糖酶结合蛋白的近似自由能量景观进行了模拟.
结论:
- ML方法可以有效地产生各种蛋白质构造.
- 将ML预测的多样性构造与MD模拟相结合,可以有效地探测蛋白质构造异质性.
- 这种方法为研究蛋白质动态和功能提供了一个强大的工具.
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