通过将超粗粒度模型集成到多尺度工作流程中,加强对蛋白质形态空间的探索
Fikret Aydin1, Konstantia Georgouli1, Loïc Pottier2
1Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States.
The journal of physical chemistry. B
|May 8, 2025
概括
超粗粒度 (UCG) 模型是为了高效模拟大型生物系统而开发的. 将这些模型集成到MuMMI中可以增强蛋白质构造样本,并提供对蛋白质动态的洞察力.
科学领域:
- 生物物理学的生物物理.
- 计算生物学 计算生物学
- 分子建模分子建模
背景情况:
- 全原子 (AA) 分子动力学 (MD) 模拟提供了详细的见解,但计算密集.
- 粗粒度 (CG) 模型简化了分子表示,以研究更大的系统和更长的时间尺度.
- 超粗粒度 (UCG) 模型进一步降低复杂性,以提高计算效率.
研究的目的:
- 开发并将UCG模型集成到多尺度机器学习建模基础架构 (MuMMI).
- 能够有效地采样蛋白质结构和动态.
- 调查蛋白质膜相互作用和形状变化.
主要方法:
- 开发了使用基本动力学粗粒度 (EDCG) 和异质弹性网络建模 (hENM) 的UCG模型,并进行了无调修改.
- 利用来自更高分辨率的Martini CG模拟的波动来参数化UCG模型.
- 纳入了一种隐性膜模型,用于蛋白质-膜动态.
- 开发了一种基于机器学习的反向映射方法,用于UCG到Martini CG转换.
主要成果:
- 实现了蛋白质配置和长距离构造变化的精确采样.
- 通过隐性膜模型对蛋白质膜动态进行了增强的探索.
- 使用新型机器学习后映射方法提高了预测准确度.
- 证明了RAS-RBDCRD蛋白质复合物的高效构造采样.
结论:
- 集成到MuMMI中的UCG模型显著推进了蛋白质配置的探索.
- 这种方法提供了对蛋白质动态在生物过程中的作用的关键见解.
- 开发的方法为大规模分子模拟提供了强大的工具.
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