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Abstract
BackgroundPrincipal 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.ResultsEmploying 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.ConclusionJEDi 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.
Highlights
Principal component analysis (PCA) is commonly applied to the atomic trajectories of biopolymers to extract essential dynamics that describe biologically relevant motions
[4] Principal component analysis (PCA) is a method from multivariate statistics to reduce the dimensionality of the vector space, allowing the essential dynamics (ED) [5] of large molecules to be expressed in terms of a small number of collective motions. [3, 6, 7]
Key features of Java Essential Dynamics inspector (JEDi) are illustrated by analyzing molecular dynamics (MD) trajectories for two beta-lactamase proteins
Summary
Key features of JEDi are illustrated by analyzing MD trajectories for two beta-lactamase proteins. For the same computational cost of an alpha carbon analysis, using three eigenresidues yields a very high (> 0.95 ) RMSIP to the explicit all atom approach This example clearly shows that HPCA is an excellent approximation for a brute force all atom PCA while significantly reducing compute times. When multiple PCA models are being analyzed for the same subset of atoms (including sparsification results), or if explicit PCA and HPCA are selected for the same subset (and resolution), a subspace analysis will be automatically done for comparison, and the output is directed to labeled sub-directories. Visualization of molecular motion JEDi includes a program which takes eigenvectors from a Cartesian PCA and generates a high quality movie of the dynamics described by each mode and the essential subspace.
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