The New Zealand Institute for Plant and Food Research, Ltd., Canterbury Agricultural Park, Lincoln, New Zealand.
Department of Bioinformatics and Genomics, University of North Carolina Charlotte, 9201 University City Blvd, 28223, Charlotte, NC, USA.
BMC Bioinformatics. 2021 May 1;22(1):226. doi: 10.1186/s12859-021-04140-5.
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.
主成分分析（PCA）常用于生物聚合物的原子轨迹，以提取描述生物学相关运动的基本动力学。尽管 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 的功能。Java 基本动力学检查器（JEDi）软件是对以前的 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，以在各种空间分辨率和子区域内检查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，用于构建协方差（Q）、相关（R）和偏相关（P）矩阵。采用收缩和异常值阈值来准确估计协方差。使用异常值和内联过滤器来量化稀有事件的影响。在统计模型中应用稀疏阈值可识别潜在的相关运动。在层次方法中，首先使用每个残基的单独局部 cPCA 计算来计算小尺度原子运动，以获得本征残基。然后对本征残基进行 PCA 可快速准确地描述大尺度运动。对所有残基对进行局部 cPCA 可创建所有残基-残基动力学耦合的图谱。此外，还实现了核 PCA。JEDi 默认输出高质量的 PNG 图像，具有包含对齐坐标、几个量化流动性的度量、带有特征值的 PCA 模式以及将位移向量投影到主要模式上的文本文件的选项。JEDi 提供了与 PDB 文件一起的 PyMol 脚本，以可视化单个 cPCA 模式和在用户选择的时间范围内发生的基本动力学。使用几个统计度量在最相关特征向量上进行子空间比较，以量化高维向量空间的相似性/重叠。提供了 cPCA 和 dpPCA 的自由能图谱。
JEDi 是一个方便的工具包，它在多元统计中应用最佳实践，用于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可视化辅助工具帮助识别功能机制，用于检查和量化流动性和动态相关性的相似性/差异。
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