AI 中文总结
研究利用含反应坐标相关质量和摩擦记忆函数的广义朗之万方程，通过条件沃尔泰拉方程从分子动力学数据中提取记忆函数，发现六种快速折叠蛋白的记忆摩擦强烈依赖反应坐标，改进了对蛋白质折叠动力学的描述。
AI 中文摘要
通常将蛋白质的完整原子分辨率表示投影到一维反应坐标（RC）上以捕捉蛋白质折叠动力学。这种降维的直接结果是，在广义朗之万方程（GLE）框架中出现非马尔可夫摩擦。以往GLE在蛋白质折叠中的应用都采用与RC无关的摩擦记忆函数，未考虑折叠态和未折叠态的不同摩擦。我们使用最近推导的具有RC相关质量和摩擦记忆函数的GLE，通过条件沃尔泰拉方程从时间序列数据中提取记忆函数的新方法。应用于六种快速折叠蛋白质的分子动力学（MD）数据时，发现记忆摩擦强烈依赖于RC，符合因蛋白质内部摩擦折叠态摩擦高于未折叠态的直观预期。我们模拟GLE的数值有效方法通过与MD数据比较证实了GLE参数提取的准确性。表明RC相关的记忆摩擦不仅为折叠过程增添物理见解，还显著改进了使用低维RC对蛋白质折叠动力学的描述。
英文摘要
It is common to project the full atomic-resolution representation of a protein onto a one-dimensionalreaction coordinate (RC) to capture the protein-folding kinetics. As a direct consequence ofthis dimensionality reduction, non-Markovian friction emerges in the framework of the general-ized Langevin equation (GLE). All previous applications of GLEs to protein folding employed anRC-independent friction memory function and therefore did not account for the different frictionin the folded and unfolded states. Using a recently derived GLE with RC-dependent mass andfriction memory function, we introduce a novel method to extract memory functions from timeseries data via a conditional Volterra equation. When applied to molecular dynamics (MD) data ofsix fast-folding proteins, we find strongly RC-dependent memory friction in line with the intuitiveexpectation that friction is higher in the folded than in the unfolded state due to internal proteinfriction. Our numerically efficient method to simulate the GLE confirms the accuracy of the GLEparameter extraction by comparison with the MD data. We show that RC-dependent memoryfriction not only adds physical insight into the folding process but also significantly improves thedescription of protein folding kinetics using low-dimensional RCs.
