在Go̅Martini中改善折叠蛋白质的 conformational 集合
Maksim Kalutskii1, Carter J Wilson2, Helmut Grubmüller1
1Theoretical and Computational Biophysics Group, Max Planck Institute for Multidisciplinary Sciences, 37077 Göttingen, Germany.
Journal of chemical theory and computation
|February 25, 2026
概括
马丁尼粗粒度模拟与蛋白质动力学作斗争. 一种新的优化方法,PoGo̅,改进了基于结构的模型,以准确地复制原子模拟组合,并改善蛋白质灵活性预测.
科学领域:
- 生物分子模拟的模拟.
- 计算生物物理学的计算生物物理.
- 蛋白质动力学 蛋白质动力学
背景情况:
- 马蒂尼粗粒度 (CG) 力场提供了模拟效率,但对于折叠的蛋白质缺乏稳定性.
- 基于结构的模型,如弹性网络模型 (ENM) 和Go̅模型,通常与Martini结合用于蛋白质稳定.
- 现有的方法保留了全球折叠,但它们在复制形状动态方面的准确性是不确定的.
研究的目的:
- 评估Martini 3与ENMs或Go̅模型相结合的能力,以捕捉形状动态.
- 开发一种用于优化CG力场的自动化方法,以更好地匹配原子模拟.
- 为了提高粗粒蛋白质模拟的准确性.
主要方法:
- 在三个折叠的蛋白质上使用ENMs和Go̅模型评估Martini 3.
- 开发了PoGo̅,这是Go̅网络的基于扰动的自动化优化方法.
- 应用PoGo̅来改进不均的Go̅网络,以对抗原子化的自由能源景观.
- 证明了PoGo̅在ENM优化中的适用性.
主要成果:
- 与原子模拟相比,使用标准ENM或Go̅模型的Martini 3未能充分采样构造空间.
- 该PoGo̅方法迅速优化了Go̅网络和ENMs.
- 优化的CG组合与参考原子模拟非常一致.
- 根-平均-平方波动概况通过优化得到了改进.
结论:
- 与Martini CG力场集成的标准结构模型在重现蛋白质构造动态方面存在局限性.
- 自动化的PoGo̅优化方法有效地将CG模型与原子数据对比.
- PoGo̅显著提高了粗粒蛋白模拟的准确性,使动态和灵活性更好地预测.
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