Non-targeted Parallel Cascade Selection Molecular Dynamics Using Time-localised Prediction of Conformational Transitions in Protein Dynamics.

08:00 EDT 14th August 2019 | BioPortfolio

Summary of "Non-targeted Parallel Cascade Selection Molecular Dynamics Using Time-localised Prediction of Conformational Transitions in Protein Dynamics."

Non-targeted parallel cascade selection molecular dynamics (nt-PaCS-MD) is an enhanced conformational sampling method of proteins, which does not rely on knowledge of the target structure. It makes use of cyclic resampling from some relevant initial structures to expand the searched conformational subspace. The efficiency of nt-PaCS-MD depends on the selections of these initial structures. They are usually stochastically occurring perturbed structures at which larger conformation transitions are about to happen. Reliable identification of these is the key to using nt-PaCS-MD. Two new parameters, the moving root mean square deviation (mRMSD) and the inner products of the backbone dihedral angles Φ and Ψ are introduced as indicators of conformational outliers in MD trajectories. Both are based on the analysis of a time-localised set of coordinates, overcoming the need for a target structure while still capturing the complexity of the conformational transition. The reference to which the mRMSD relates is the close surrounding of the ith conformation, often the (i-1)st one. Hence the name 'time-localized' analysis. In this work, we focus on its interplay with nt-PaCS-MD and show that it increases its effectiveness compared to older versions. The target system is the mid-sized protein T4 lysozyme (in explicit water) on which we demonstrate the open-closed transition without referring to any target configuration. Additionally, we show that the short MD trajectories can be used for the construction of a free energy landscape of the conformational transition based on the Markov state model.


Journal Details

This article was published in the following journal.

Name: Journal of chemical theory and computation
ISSN: 1549-9626


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