JEDi: java essential dynamics inspector - a molecular trajectory analysis toolkit.
JEDi: java essential dynamics inspector - a molecular trajectory analysis toolkit.
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DOI：
10.1186/s12859-021-04140-5
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发表时间：
2021-05-01
影响因子：
3
通讯作者：
Jacobs DJ
中科院分区：
文献类型：
--
作者：
David CC;Avery CS;Jacobs DJ
主成分分析（PCA）通常应用于生物聚合物的原子轨迹，以提取描述生物相关运动的基本动力学。虽然PCA的应用很简单，但缺乏专门的软件来促进分子动力学模拟数据的工作流程和分析，以充分利用PCA的力量。Java Essential Dynamics inspector（JEDi）软件是以前JED软件的重大升级。JEDi采用多线程，具有用户友好的界面，可控制快速工作流程，以各种空间分辨率和子区域内询问生物聚合物的构象运动，包括多链蛋白质。JEDi具有基于笛卡尔坐标（cPCA）和内部距离对坐标（dpPCA）的选项，以构建协方差（Q），相关性（R）和部分相关性（P）矩阵。收缩和离群阈值的准确估计的协方差。使用离群值和内值过滤器量化罕见事件的影响。在统计模型中应用稀疏阈值识别潜在的相关运动。在分层方法中，首先使用每个残差的单独局部cPCA计算来计算小尺度原子运动，以获得本征残差。然后PCA的特征残基产生快速和准确的描述大规模的运动。所有残基对上的局部cPCA创建所有残基-残基动态耦合的映射。此外，内核PCA的实现。默认情况下，JEDi输出提供高质量的PNG图像，文本文件的选项包括对齐坐标，量化移动性的几个指标，PCA模式及其特征值，以及到顶部主模式的位移矢量投影。JEDi提供PyMol脚本和PDB文件，以可视化各个cPCA模式和用户选择的时间尺度内发生的基本动态。使用若干统计度量对最相关的特征向量执行的子空间比较量化高维向量空间的相似性/重叠。cPCA和dpPCA都有自由能景观。JEDi是一个方便的工具包，应用多元统计的最佳实践，对类似生物聚合物的基本动力学进行比较研究。JEDi通过许多集成工具和视觉辅助工具帮助识别功能机制，用于检查和量化移动性和动态相关性的相似性/差异。在线版本包含补充材料，可通过10.1186/s12859-021-04140-5获得。
Principal component analysis (PCA) is commonly applied to the atomic trajectories of biopolymers to extract essential dynamics that describe biologically relevant motions. Although application of PCA is straightforward, specialized software to facilitate workflows and analysis of molecular dynamics simulation data to fully harness the power of PCA is lacking. The Java Essential Dynamics inspector (JEDi) software is a major upgrade from the previous JED software. Employing multi-threading, JEDi features a user-friendly interface to control rapid workflows for interrogating conformational motions of biopolymers at various spatial resolutions and within subregions, including multiple chain proteins. JEDi has options for Cartesian-based coordinates (cPCA) and internal distance pair coordinates (dpPCA) to construct covariance (Q), correlation (R), and partial correlation (P) matrices. Shrinkage and outlier thresholding are implemented for the accurate estimation of covariance. The effect of rare events is quantified using outlier and inlier filters. Applying sparsity thresholds in statistical models identifies latent correlated motions. Within a hierarchical approach, small-scale atomic motion is first calculated with a separate local cPCA calculation per residue to obtain eigenresidues. Then PCA on the eigenresidues yields rapid and accurate description of large-scale motions. Local cPCA on all residue pairs creates a map of all residue-residue dynamical couplings. Additionally, kernel PCA is implemented. JEDi output gives high quality PNG images by default, with options for text files that include aligned coordinates, several metrics that quantify mobility, PCA modes with their eigenvalues, and displacement vector projections onto the top principal modes. JEDi provides PyMol scripts together with PDB files to visualize individual cPCA modes and the essential dynamics occurring within user-selected time scales. Subspace comparisons performed on the most relevant eigenvectors using several statistical metrics quantify similarity/overlap of high dimensional vector spaces. Free energy landscapes are available for both cPCA and dpPCA. JEDi is a convenient toolkit that applies best practices in multivariate statistics for comparative studies on the essential dynamics of similar biopolymers. JEDi helps identify functional mechanisms through many integrated tools and visual aids for inspecting and quantifying similarity/differences in mobility and dynamic correlations. The online version contains supplementary material available at 10.1186/s12859-021-04140-5.
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影响因子：
3
作者：
KAISER, HF
通讯作者：
KAISER, HF
影响因子：
5.5
作者：
Roe, Daniel R.;Cheatham, Thomas E., III
通讯作者：
Cheatham, Thomas E., III
影响因子：
3.8
作者：
CERNY, BA;KAISER, HF
通讯作者：
KAISER, HF
影响因子：
3.4
作者：
Trivedi, Darshan V.;David, Charles;Yengo, Christopher M.
通讯作者：
Yengo, Christopher M.
影响因子：
16.2
作者：
Hollingsworth SA;Dror RO
通讯作者：
Dror RO