QAARM: quasi-anharmonic autoregressive model reveals molecular recognition pathways in ubiquitin.

Autor:	Savol AJ; Joint Carnegie Mellon University-University of Pittsburgh Ph.D. Program in Computational Biology, Department of Computational and Systems Biology, University of Pittsburgh, PA 15260, USA., Burger VM, Agarwal PK, Ramanathan A, Chennubhotla CS
Jazyk:	angličtina
Zdroj:	Bioinformatics (Oxford, England) [Bioinformatics] 2011 Jul 01; Vol. 27 (13), pp. i52-60.
DOI:	10.1093/bioinformatics/btr248
Abstrakt:	Motivation: Molecular dynamics (MD) simulations have dramatically improved the atomistic understanding of protein motions, energetics and function. These growing datasets have necessitated a corresponding emphasis on trajectory analysis methods for characterizing simulation data, particularly since functional protein motions and transitions are often rare and/or intricate events. Observing that such events give rise to long-tailed spatial distributions, we recently developed a higher-order statistics based dimensionality reduction method, called quasi-anharmonic analysis (QAA), for identifying biophysically-relevant reaction coordinates and substates within MD simulations. Further characterization of conformation space should consider the temporal dynamics specific to each identified substate. Results: Our model uses hierarchical clustering to learn energetically coherent substates and dynamic modes of motion from a 0.5 μs ubiqutin simulation. Autoregressive (AR) modeling within and between states enables a compact and generative description of the conformational landscape as it relates to functional transitions between binding poses. Lacking a predictive component, QAA is extended here within a general AR model appreciative of the trajectory's temporal dependencies and the specific, local dynamics accessible to a protein within identified energy wells. These metastable states and their transition rates are extracted within a QAA-derived subspace using hierarchical Markov clustering to provide parameter sets for the second-order AR model. We show the learned model can be extrapolated to synthesize trajectories of arbitrary length. Contact: ramanathana@ornl.gov; chakracs@pitt.edu.
Databáze:	MEDLINE
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