QAARM: quasi-anharmonic autoregressive model reveals molecular recognition pathways in ubiquitin
| Autor: | Pratul K. Agarwal, Virginia M. Burger, Chakra S. Chennubhotla, Andrej J. Savol, Arvind Ramanathan |
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| Rok vydání: | 2011 |
| Předmět: |
Statistics and Probability
Models Molecular Protein Conformation Protein Structure and Function Molecular Dynamics Simulation 01 natural sciences Biochemistry 03 medical and health sciences Molecular dynamics Motion 0103 physical sciences Humans Computer Simulation Ismb/Eccb 2011 Proceedings Papers Committee July 17 to July 19 2011 Vienna Austria Statistical physics Molecular Biology Simulation 030304 developmental biology Physics 0303 health sciences 010304 chemical physics Markov chain Ubiquitin Dimensionality reduction Function (mathematics) Original Papers Markov Chains Computer Science Applications Hierarchical clustering Computational Mathematics Computational Theory and Mathematics Autoregressive model Trajectory Subspace topology |
| Zdroj: | Bioinformatics |
| ISSN: | 1367-4811 |
| Popis: | 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: | OpenAIRE |
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