JEDi: java essential dynamics inspector — a molecular trajectory analysis toolkit

Abstract

Background 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. Results 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. Conclusion 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.

Protein Function Analysis through Machine Learning

“…Normally, it is best to use only a select set of heavy atoms, such as carbon alpha atoms, along the backbone. PCA has been implemented directly in many MD packages, including GROMACS, as well as part of standalone MD simulation analysis software such as JEDi [ 129 ], MDAnalysis [ 130 ] or ModeTask [ 131 ]. These methods are excellent for extracting the large-scale motions from a protein.…”

“…A common approach is to use the RMSD and TM score [ 168 ]. The RMSD distance metric compares the euclidean distance between the 3D coordinates of two structurally aligned structures [ 129 , 169 , 170 , 171 ]. Although commonly used due to its simplicity, RMSD is sensitive to the size of the systems being compared.…”

“…The traditional approach of atom to atom comparison makes it difficult to compare conformational ensembles across larger sets of proteins that have large variations in sequence identity. This paradigm shift would require replacing structural alignments between corresponding atoms [ 129 ] in terms of physical properties that go beyond atomic coordinates. Such an approach would allow environmental and dynamic effects to be quantified, assist protein evolution studies and benefit ML methods for structure prediction.…”

From Data to Knowledge: Systematic Review of Tools for Automatic Analysis of Molecular Dynamics Output

“…Dimensionality reduction of MD data with the use of PCA was also first used in the early 90s ( Ichiye and Karplus, 1991 ; Amadei et al, 1993 ) and since that time its application in MD output analysis has been constantly growing ( Das and Mukhopadhyay, 2007 ; Chiappori et al, 2010 ; Kim et al, 2010 ; Casoni et al, 2013 ; Ng et al, 2013 ; Novikov et al, 2013 ; Bhakat et al, 2014 ; Sittel et al, 2014 ; Ernst et al, 2015 ; Chaturvedi et al, 2017 ; Cossio-Pérez et al, 2017 ; Fakhar et al, 2017 ; Chen, 2018 ; Cholko et al, 2018 ; An et al, 2019 ; Barletta et al, 2019 ; Girdhar et al, 2019 ; Karnati and Wang, 2019 ; Lipiński et al, 2019 ; Martínez-Archundia et al, 2019 ; Wu et al, 2019 ; Magudeeswaran and Poomani, 2020 ; David et al, 2021 ; Majumder and Giri, 2021 ). Although PCA is the most popular approach applied to handle MD trajectories, other data dimensionality reduction methods are also used in the MD field.…”