路径Gennie:通过使用超短监测轨迹的方向导向自适应采样,快速生成罕见事件路径
Dibyendu Maity1, Shaheerah Shahid1, Suman Chakrabarty1
1Department of Chemical and Biological Sciences, S. N. Bose National Centre for Basic Sciences, Kolkata 700106, India.
Journal of chemical theory and computation
|November 10, 2025
概括
通过自适应采样,PathGennie快速生成分子过渡通路,在没有外力的情况下加速对蛋白质折叠和连接体解结等罕见事件的模拟. 这种方法提高了分子动力学模拟的效率.
科学领域:
- 计算化学是一种计算化学.
- 分子动力学模拟的模拟.
- 生物物理学的生物物理.
背景情况:
- 模拟罕见的分子事件 (例如,连接体解结,蛋白质折叠) 在计算上具有挑战性.
- 传统方法通常需要大量采样或外部偏差,这可能会扭曲动态.
研究的目的:
- 介绍PathGennie,这是一个用于有效生成过渡途径的新框架.
- 克服现有方法在模拟罕见分子事件方面的局限性.
主要方法:
- 路径Gennie采用方向导向的自适应采样,采用超短,公正的轨迹.
- 它在集体变量空间中选择性地传播朝着一个定义的目标前进的轨迹.
- 该框架避免了外部偏移力和热扰动.
主要成果:
- PathGennie成功地确定了小分子 (,伊马替尼) 的多种竞争解结路径,以及蛋白质的折叠/展开过渡 (Trp-cage,Protein G).
- 从物理上有意义的路径是在皮秒时间尺度 (10-100 psi) 上生成的.
- 通过PathGennie生成的路径在随后的加权集团 (WE) 模拟中显著加快了趋同.
结论:
- PathGennie提供了一种高效且广泛适用的方法,用于在分子动力学中生成过渡路径.
- 该框架加速了对罕见分子事件的研究,并提高了途径采样方法的效率.
- 该PathGennie软件免费提供给研究社区.
相关概念视频
01:23Propagation of Action Potentials
8.8K
The propagation of an action potential refers to the process by which a nerve impulse, or "action potential," travels along a neuron.
Neurons (nerve cells) have a resting membrane potential, with a slightly negative charge inside compared to outside. This is maintained by ion channels, such as sodium (Na+) and potassium (K+) channels, which control the flow of ions. When a stimulus, like a touch or a signal from another neuron, triggers the neuron, sodium channels open, allowing sodium ions to...
Neurons (nerve cells) have a resting membrane potential, with a slightly negative charge inside compared to outside. This is maintained by ion channels, such as sodium (Na+) and potassium (K+) channels, which control the flow of ions. When a stimulus, like a touch or a signal from another neuron, triggers the neuron, sodium channels open, allowing sodium ions to...
8.8K
01:24Rapidly Varying Flow
414
Rapidly varying flow (RVF) in open channels is characterized by abrupt changes in flow depth over a short distance, with the rate of depth change relative to distance often approaching unity. These flows are inherently complex due to their transient and multi-dimensional nature, making exact analysis difficult. However, approximate solutions using simplified models provide valuable insights into their behavior.Key Features of Rapidly Varying FlowRVF is commonly observed in scenarios involving...
414