Rapid Calculation of Molecular Kinetics Using Compressed Sensing
Rapid Calculation of Molecular Kinetics Using Compressed Sensing
复制标题
利用压缩感知快速计算分子动力学
DOI：
10.1021/acs.jctc.8b00089
复制
发表时间：
2018
影响因子：
5.5
通讯作者：
Clementi, Cecilia
中科院分区：
文献类型：
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作者：
Litzinger, Florian;Boninsegna, Lorenzo;Wu, Hao;Nüske, Feliks;Patel, Raajen;Baraniuk, Richard;Noé, Frank;Clementi, Cecilia
从大量分子动力学（MD）数据分析分子动力学的最新方法依赖于甚大特征值问题的求解。在这里，我们基于压缩感知领域的最新成果，开发了光谱oASIS方法，这是一种高效的方法，可以近似大型广义特征值问题的主要特征值和特征向量，而无需评估完整的矩阵。该方法将问题的维数降低了1到2个数量级，直接节省了必要矩阵的计算和存储，并将求解特征值问题的速度提高了2到4个数量级。我们利用构象动力学（VAC）和时滞独立成分分析（TICA）的变分方法，在蛋白质构象变化和蛋白质配体结合的广泛数据集上展示了该方法。我们的方法也可以应用于VAC， TICA和扩展动态模态分解（EDMD）的核公式。
Recent methods for the analysis of molecular kinetics from massive molecular dynamics (MD) data rely on the solution of very large eigenvalue problems. Here we build upon recent results from the field of compressed sensing and develop the spectral oASIS method, a highly efficient approach to approximate the leading eigenvalues and eigenvectors of large generalized eigenvalue problems without ever having to evaluate the full matrices. The approach is demonstrated to reduce the dimensionality of the problem by 1 or 2 orders of magnitude, directly leading to corresponding savings in the computation and storage of the necessary matrices and a speedup of 2 to 4 orders of magnitude in solving the eigenvalue problem. We demonstrate the method on extensive data sets of protein conformational changes and protein–ligand binding using the variational approach to conformation dynamics (VAC) and time-lagged independent component analysis (TICA). Our approach can also be applied to kernel formulations of VAC, TICA, and extended dynamic mode decomposition (EDMD).