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在显式和隐式溶剂中模拟蛋白质折叠的分散粒子动力学:原子分辨率的粗粒度模型

Ruzhuang Zhang¹, Li Yang², Xingqing Xiao¹

¹Department of Chemistry, School of Chemistry and Chemical Engineering, Hainan University, Haikou City, Hainan Province 570228, PR China.

Journal of chemical theory and computation

|July 25, 2024

概括

分散粒子动力学 (DPD) 模拟用于研究蛋白质折叠. 隐式力DPD模型提高了粗粒蛋白模拟的结构精度和计算效率.

科学领域:

计算化学是一种计算化学.
生物物理学的生物物理.
分子动力学分子动力学

背景情况:

分散粒子动力学 (DPD) 是一种粗粒度 (CG) 模拟方法.
研究蛋白质折叠需要准确和高效的模拟技术.

研究的目的:

在明确和隐性溶剂中研究小蛋白折叠的DPD.
建立模型参数化并提高CG蛋白模型的计算效率.

主要方法:

从原子模拟和热力学理论中开发出外力和强力DPD参数.
使用氨基酸方案将全原子蛋白映射到CG模型中.
研究了使用ex-DPD和im-DPD的四种小蛋白质的折叠路径.

主要成果:

与前DPD相比,Im-DPD模拟显示出与原生蛋白质的结构相似性更大.
实现了安格斯特罗姆 (Å) 分辨率,与原子模型可比.
Im-DPD显著加速了模拟和提高了效率.

结论:

开发的im-DPD方法提高了蛋白质结构预测的准确性.
这种方法为大规模分子模拟提供了一个计算效率高的策略.
对于详细的蛋白质研究,DPD模型可以达到原子级分辨率.

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