Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11889/8027
Title: Detecting transitions in protein dynamics using a recurrence quantification analysis based bootstrap method
Authors: Karain, Wael I. 
Keywords: Recurrent sequences (Mathematics);Recurrence quantification analysis;Principal components analysis.;Molecular dynamics
Issue Date: 2017
Abstract: Background: Proteins undergo conformational transitions over different timescales. These transitions are closely inter twined with the protein’s function. Numerous standard techniques such as principal component analysis are used to detect these transitions in molecular dynamics simulations. In this work, we add a new method that has the ability to detect transitions in dynamics based on the recurrences in the dynamical system. It combines boot strapping and recurrence quantification analysis. We start from the assumption that a protein has a “baseline” recurrence structure over a given period of time. Any statistically significant deviation from this recurrence structure, as inferred from complexity measures provided by recurrence quantification analysis, is considered a transition in the dynamics of the protein. Results: We apply this technique to a 132ns long molecular dynamics simulation of the β-Lactamase Inhibitory Protein BLIP. We are able to detect conformational transitions in the nanosecond range in the recurrence dynamics of the BLIP protein during the simulation. The results compare favorably to those extracted using the principal component analysis technique. Conclusions: There currence quantification analysis based boots trap technique is able to detect transitions between different dynamics states for a protein over different timescales. It is not limited to linear dynamics regimes, and can be generalized to anytime scale. It also has the potential to be used to cluster frames in molecular dynamics trajectories according to the nature of the irrecurrence dynamics. One short coming for this method is the need to have large enough time windows to insure good statistical quality for the recurrence complexity measures needed to detect the transitions.
URI: http://hdl.handle.net/20.500.11889/8027
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