Mimicking Protein Dynamics by the Integration of Elastic Network Model with Time Series Analysis

Abstract

Anisotropic network model (ANM) is a coarse-grained normal mode analysis that is widely used for describing the collective motions of proteins around their native structure. In this work, protein dynamics along ANM modes are constructed by linear stochastic time series models extracted from molecular dynamics (MD) simulations, and these models are simulated to mimic the dynamics of a relatively small protein, tendamistat. It is found that ANM modes are at least as successful as principal components analysis in explaining different conformational subspaces, and the nonstationary character of ANM modes is a further advantage. The significant reduction in computation time makes time series simulations a promising alternative to the standard requirement of performing multiple long MD runs especially in water, provided that a single short MD is available for extracting the models and some energy restrictions are performed during the moves.

Keywords

Molecular Dynamics Principal Components Analysis Stochastic Models

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