模拟生物系统的大规模形态变化的综合方法,利用路径集体变量和新的屏障限制
István Kolossváry1, Woody Sherman1
1Psivant Therapeutics, Boston, Massachusetts 02210, United States.
The journal of physical chemistry. B
|June 6, 2023
概括
这项研究引入了一种使用路径集体变量 (PCV) 和屏障限制的生物分子构造性采样的新方法. 这种方法通过准确地建模复杂的生物运动来增强药物发现的分子动力学模拟.
科学领域:
- 计算化学和结构生物学
- 生物分子模拟和药物发现
背景情况:
- 来自AlphaFold等方法的静态蛋白质结构不足以理解动态生物过程.
- 传统的分子动力学 (MD) 模拟通常对于需要微秒到毫秒时间尺度的药物设计项目来说太慢.
- 现有的形状采样限制在模拟复杂的生物运动方面存在局限性.
研究的目的:
- 开发一种新的三阶段程序,用于构建现实的全原子路径集体变量 (PCV).
- 引入一种新的屏障抑制,适用于复杂的,由构造驱动的生物事件,如全调节.
- 在药物发现的混合采样框架内证明新PCV和克制的实用性.
主要方法:
- 从全原子MD轨迹框架中开发了一个全原子PCV.
- 引入了一种新的屏障约束,利用一个缩放的相互函数来对零容忍违规行为进行限制.
- 在混合采样框架中实现了PCV和克制,该框架结合了温和的元动力学和扩展的拉格兰奇适应偏差力 (meta-eABF).
主要成果:
- 成功地将该方法应用于三个具有药学意义的例子:在复合体中采用乌比奎样本,稳定JAK2-V617F,并诱导激活的STING状态.
- 在分子动力学模拟中证明了缩放式互惠屏障抑制的有效性.
- 为JAK2-V617F和STING示例提供了meta-eABF自由能量估计的统计分析.
结论:
- 开发的PCV和屏障限制方法显著提高了复杂生物分子的 conformational 采样.
- 这种方法为研究药物发现中由构造驱动的生物事件提供了一个强大的工具.
- 提供的代码使得该方法的可复制性和应用性能够应对类似的挑战.
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