Coarse-graining protein structures with local multivariate features from molecular dynamics.
Coarse-graining protein structures with local multivariate features from molecular dynamics.
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DOI：
10.1021/jp806291p
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发表时间：
2008-11-06
期刊：
影响因子：
--
通讯作者：
Wriggers W
中科院分区：
文献类型：
--
作者：
Zhang Z;Wriggers W
一个多元统计理论，局部特征分析（LFA），提取功能相关领域的分子动力学（MD）的轨迹。LFA表示，像那些主成分分析（PCA），是低维的，并提供了一个减少的基础集的集体运动模拟蛋白质，但局部特征稀疏分布和空间局部化，在全球PCA模式。局部特征分配中的一个关键问题是通过种子原子对冗余LFA输出函数进行粗粒度化。人们可以通过将种子原子一个接一个地添加到增长集合中来解决组合问题，从而使每次添加时的重建误差最小化。这允许有效的实现，但是顺序算法不能保证顺序分配的特征的最佳相互关联。在这里，我们提出了一种新的粗粒化算法的蛋白质，直接最大限度地减少种子原子的Monte Carlo（MC）模拟的相互关联。两个生物系统，噬菌体T4溶菌酶和肌球蛋白II电机域S1的MD轨迹的测试表明，新的算法提供了统计上可重复的结果，并描述了功能相关的动力学。在我们的模型中，短MD模拟对大规模运动的众所周知的欠采样也很明显，但新的粗粒度提供了优于PCA的主要优势;收敛特征在轨迹的多个窗口中是不变的，将蛋白质划分为收敛区域和少量局部欠采样区域。除了其在结构分类中的使用之外，所提出的粗粒度化因此提供了MD采样效率的本地化测量。
A multivariate statistical theory, local feature analysis (LFA), extracts functionally relevant domains from molecular dynamics (MD) trajectories. The LFA representations, like those of principal component analysis (PCA), are low dimensional and provide a reduced basis set for collective motions of simulated proteins, but the local features are sparsely distributed and spatially localized, in contrast to global PCA modes. One key problem in the assignment of local features is the coarse-graining of redundant LFA output functions by means of seed atoms. One can solve the combinatorial problem by adding seed atoms one after another to a growing set, minimizing a reconstruction error at each addition. This allows for an efficient implementation, but the sequential algorithm does not guarantee the optimal mutual correlation of the sequentially assigned features. Here, we present a novel coarse-graining algorithm for proteins that directly minimizes the mutual correlation of seed atoms by Monte Carlo (MC) simulations. Tests on MD trajectories of two biological systems, bacteriophage T4 lysozyme and myosin II motor domain S1, demonstrate that the new algorithm provides statistically reproducible results and describes functionally relevant dynamics. The well-known undersampling of large-scale motion by short MD simulations is apparent also in our model, but the new coarse-graining offers a major advantage over PCA; converged features are invariant across multiple windows of the trajectory, dividing the protein into converged regions and a smaller number of localized, undersampled regions. In addition to its use in structure classification, the proposed coarse-graining thus provides a localized measure of MD sampling efficiency.
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影响因子：
64.8
作者：
FABER, HR;MATTHEWS, BW
通讯作者：
MATTHEWS, BW
影响因子：
64.8
作者：
Holmes, KC;Angert, I;Schröder, RR
通讯作者：
Schröder, RR
DOI：
10.1073/pnas.92.8.3288
发表时间：
1995-04-11
影响因子：
11.1
作者：
CLARAGE, JB;ROMO, T;PHILLIPS, GN
通讯作者：
PHILLIPS, GN
影响因子：
2.9
作者：
Scott, WRP;Hünenberger, PH;van Gunsteren, WF
通讯作者：
van Gunsteren, WF
DOI：
10.1073/pnas.040569697
发表时间：
2000-03-28
影响因子：
11.1
作者：
Hernández, G;Jenney, FE;LeMaster, DM
通讯作者：
LeMaster, DM