Broad distributions of transition-path times are fingerprints of multidimensionality of the underlying free energy landscapes
Broad distributions of transition-path times are fingerprints of multidimensionality of the underlying free energy landscapes
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DOI：
10.1073/pnas.2008307117
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发表时间：
2020-11-03
影响因子：
11.1
通讯作者：
Makarov, Dmitrii E.
中科院分区：
文献类型：
--
作者：
Satija, Rohit;Berezhkovskii, Alexander M.;Makarov, Dmitrii E.
最近的单分子实验观察到了过渡路径，即分子（特别是生物分子）在克服激活障碍的过程中被捕获的短暂事件。这些测量为生物分子折叠和结合、分子机器和生物膜通道的动力学提供了前所未有的机械见解。这些研究的一个关键挑战是从固有的低维实验信号中推断出过渡路径所遍历的多维能源景观的复杂细节。尝试这样做的常见极简模型是沿反应坐标的一维扩散模型，但其有效性受到质疑。在这里，我们表明，转变路径时间的分布是常见的实验可观测值，可用于区分一维扩散模型描述的动力学与多维性至关重要的动力学。具体来说，我们证明，对于任何一维扩散模型，从该分布获得的变异系数都不可能超过 1，无论其基础自由能景观多么崎岖：换句话说，该分布不可能比单指数分布更宽。因此，超过 1 的变异系数是多维动力学的指纹。对蛋白质原子模拟中跃迁路径的分析表明，该系数通常超过 1，表明这些系统本质上是多维的。
Recent single-molecule experiments have observed transition paths, i.e., brief events where molecules (particularly biomolecules) are caught in the act of surmounting activation barriers. Such measurements offer unprecedented mechanistic insights into the dynamics of biomolecular folding and binding, molecular machines, and biological membrane channels. A key challenge to these studies is to infer the complex details of the multidimensional energy landscape traversed by the transition paths from inherently low-dimensional experimental signals. A common minimalist model attempting to do so is that of one-dimensional diffusion along a reaction coordinate, yet its validity has been called into question. Here, we show that the distribution of the transition path time, which is a common experimental observable, can be used to differentiate between the dynamics described by models of onedimensional diffusion from the dynamics in which multidimensionality is essential. Specifically, we prove that the coefficient of variation obtained from this distribution cannot possibly exceed 1 for any one-dimensional diffusive model, no matter how rugged its underlying free energy landscape is: In other words, this distribution cannot be broader than the single-exponential one. Thus, a coefficient of variation exceeding 1 is a fingerprint of multidimensional dynamics. Analysis of transition paths in atomistic simulations of proteins shows that this coefficient often exceeds 1, signifying essential multidimensionality of those systems.