首页> 外文期刊>Journal of chemical theory and computation: JCTC >All-Atom Calculation of the Normal Modes of Bacteriorhodopsin Using a Sliding Block Iterative Diagonalization Method
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All-Atom Calculation of the Normal Modes of Bacteriorhodopsin Using a Sliding Block Iterative Diagonalization Method

机译:使用滑动块迭代对角线化方法对细菌视紫红质的正常模式进行全原子计算

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Conventional normal-mode analysis of molecular vibrations requires computation and storage of the Hessian matrix.For a typical biological system such storage can reach several gigabytes posing difficulties for straightforward implementation.In this work we discuss an iterative block method to carry out full diagonalization of the Hessian while only storing a few vectors in memory.The iterative approach is based on the conjugate gradient formulation of the Davidson algorithm for simultaneous optimization of L roots,where in our case 10 < L < 300.The procedure is modified further by automatically adding a new vector into the search space for each locked (converged) root and keeping the new vector orthogonal to the eigenvectors previously determined.The higher excited states are then converged with the orthonormality constraint to the locked roots by applying a projector which is carried out using a read-rewind step done once per iteration.This allows for convergence of as many roots as desired without increasing the computer memory.The required Hessian-vector products are calculated on the fly as follows,Kp=dg_p/dt,where K is the mass weighted Hessian,and g_p is the gradient along p.The method has been implemented into the TINKER suite of molecular design codes.Preliminary results are presented for the normal modes of bacteriorhodopsin (bR) up to 300 cm~(-1) and for the high frequency range between 2840 and 3680 cm~(-1).There is evidence of a highly localized,noncollective mode at approx 1.4 cm~(-1),caused by long-range interactions acting between the cytoplasmic and extracellular domains of bR.
机译:传统的分子振动正态分析需要计算和存储Hessian矩阵,对于典型的生物系统而言,这种存储可能达到数GB,这给直接实现带来了困难。在这项工作中,我们讨论了一种迭代块方法来实现对角线的完全对角化Hessian,而仅将几个向量存储在内存中。该迭代方法基于Davidson算法的共轭梯度公式,用于同时优化L根,在我们的情况下为10

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