增强粗粒度分子动力学模拟与光滑的混合潜力使用神经网络模型
Ryo Kanada1, Atsushi Tokuhisa1, Yusuke Nagasaka2
1RIKEN Center for Computational Science, Kobe 650-0047, Japan.
Journal of chemical theory and computation
|December 26, 2023
概括
这项研究引入了一种新的AI驱动的混合潜力,以加速生物分子模拟. 该方法准确地预测能量,并增强了对状态之间的蛋白质动态的探索,克服了现有模型的局限性.
科学领域:
- 计算生物学 计算生物学
- 生物物理学的生物物理.
- 科学中的人工智能.
背景情况:
- 全原子 (AA) 分子动力学 (MD) 模拟在复制生物分子结构变化方面面临着挑战,这是由于崎的能量配置和长时间尺度造成的.
- 现有的粗粒度 (CG) 模型往往过于简化了能源景观,限制了远离初始结构的超稳定状态的探索.
研究的目的:
- 开发一种混合潜力,结合人工智能 (AI) 和粗粒度 (CG) 方法,以加速生物分子动力学模拟.
- 为了使得在保持蛋白质的基本特征的同时,能够探索元稳定状态之间的过渡.
主要方法:
- 开发了一种混合潜力,将AI潜力与最小的CG潜力 (统计债券长度,不包括体积) 整合在一起.
- 通过使用能量匹配来训练AI潜力,与AA力场能量相匹配,来自通过多规范 (Mc) MD模拟采样的多种结构.
- 在将其应用于CGMD模拟之前,通过能源最小化平滑了能量配置.
主要成果:
- 人工智能潜力在预测奇诺林和TrpCage的AA能量 (R值>0.89) 中表现出很高的准确性.
- 使用光滑混合潜力的CGMD模拟显著增强了元稳定状态之间的过渡动态.
- 与传统的CGMD和AAMD方法相比,增强的动力学保留了蛋白质特性.
结论:
- 开发的AI-CG混合潜力有效地加速了生物分子结构动态的探索.
- 这种方法克服了传统的MD和CG方法的局限性,用于研究遥远的元稳定状态之间的过渡.
- 该方法为推进结构生物学和药物发现的计算研究提供了一个有前途的工具.
更多相关视频
09:17Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion
Published on: March 1, 2022
3.2K
07:41Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0
Published on: June 5, 2017
9.9K
相关概念视频
01:02¹H NMR: Interpreting Distorted and Overlapping Signals
1.0K
Spin systems where the difference in chemical shifts of the coupled nuclei is greater than ten times J are called first-order spin systems. These nuclei are weakly coupled, and their chemical shifts and coupling constant can generally be estimated from the well-separated signals in the spectrum.
As Δν decreases and the signals move closer, the doublets appear increasingly distorted. The intensities of the inner lines increase at the cost of those of the outer lines as the signals are...
As Δν decreases and the signals move closer, the doublets appear increasingly distorted. The intensities of the inner lines increase at the cost of those of the outer lines as the signals are...
1.0K
01:29Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
56
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
56