来自深度马尔科夫状态模型的内在无序蛋白质的折叠结合路径
Thomas R Sisk1, Paul Robustelli1
1Department of Chemistry, Dartmouth College, Hanover, NH 03755.
概括
内在无序的蛋白质通过不同的途径在结合时折叠,深度学习揭示了这一点. 本研究详细介绍了多步诱导的适合机制,并确定了四个动态稳定的绑定状态.
科学领域:
- 生物物理学的生物物理.
- 结构生物学 结构生物学
- 计算生物学 计算生物学
背景情况:
- 内在无序蛋白 (IDP) 在理解它们的结合机制方面存在独特的挑战.
- 描述流离失所者与他们的伴侣之间的动态相互作用对于理解生物功能至关重要.
研究的目的:
- 阐明麻疹病毒核蛋白NTAIL及其蛋白复合体的折叠结合路径.
- 描述结合过程中所涉及的中间状态和动力障碍.
主要方法:
- 利用基于深度学习的马尔科夫状态建模方法.
- 进行了长时间的分子动力学模拟.
- 分析了结合方向,螺旋内容和形状异质性.
主要成果:
- 确定了两种不同的碰撞复合体,这些复合体控制了折叠-绑定.
- 观察到一个多步骤的诱导适合机制,而不是正规的形状选择.
- 具有四个动态分离的特征,具有明显的接触形成的本地类绑定状态.
结论:
- 提供了原子分辨率的洞察力,了解折叠-结合的中间状态.
- 阐明了动态"模糊"蛋白质复合体中的动力障碍物的性质.
- 揭示了IDP绑定过程中结构变化的复杂相互作用.
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