National Biomedical Center for Advanced ESR Technology and Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853, USA.
Department of Computer Science, Cornell University, Ithaca, New York 14853, USA.
J Chem Phys. 2021 Feb 28;154(8):084115. doi: 10.1063/5.0042441.
Two-dimensional electron-electron double resonance (2D-ELDOR) provides extensive insight into molecular motions. Recent developments permitting experiments at higher frequencies (95 GHz) provide molecular orientational resolution, enabling a clearer description of the nature of the motions. In previous work, we provided simulations for the case of domain motions within proteins that are themselves slowly tumbling in a solution. In order to perform these simulations, it was found that the standard approach of solving the relevant stochastic Liouville equation using the efficient Lanczos algorithm for this case breaks down, so algorithms were employed that rely on the Arnoldi iteration. While they lead to accurate simulations, they are very time-consuming. In this work, we focus on a variant known as the rational Arnoldi algorithm. We show that this can achieve a significant reduction in computation time. The stochastic Liouville matrix, which is of very large dimension, N, is first reduced to a much smaller dimension, m, e.g., from N ∼ O(10) to m ∼ 60, that spans the relevant Krylov subspace from which the spectrum is predicted. This requires the selection of the m frequency shifts to be utilized. A method of adaptive shift choice is introduced to optimize this selection. We also find that these procedures help in optimizing the pruning procedure that greatly reduces the dimension of the initial N dimensional stochastic Liouville matrix in such subsequent computations.
二维电子-电子双共振(2D-ELDOR)为分子运动提供了广泛的深入了解。最近的发展允许在更高频率(95GHz)下进行实验,从而提供了分子取向分辨率,能够更清楚地描述运动的性质。在以前的工作中,我们针对在溶液中本身缓慢旋转的蛋白质内的域运动的情况提供了模拟。为了执行这些模拟,发现使用 Lanczos 算法求解相关随机 Liouville 方程的标准方法在此情况下失败,因此采用了依赖 Arnoldi 迭代的算法。虽然它们导致了准确的模拟,但它们非常耗时。在这项工作中,我们专注于一种称为有理 Arnoldi 算法的变体。我们表明,这可以大大减少计算时间。随机 Liouville 矩阵的维度非常大,N,首先被降低到更小的维度,m,例如,从 N∼O(10)到 m∼60,它覆盖了从预测谱的相关 Krylov 子空间。这需要选择要利用的 m 个频移。引入了自适应移位选择方法来优化这种选择。我们还发现,这些过程有助于优化剪枝过程,从而大大减少了后续计算中初始 N 维随机 Liouville 矩阵的维数。