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

Abstract

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 0.5µs 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.more » We show the learned model can be extrapolated to synthesize trajectories of arbitrary length.« less

Authors:
 [1];  [1];  [2];  [2];  [2]
+ Show Author Affiliations
  1. Univ. of Pittsburgh, PA (United States). Joint Carnegie Mellon Univ.; Univ. of Pittsburgh, PA (United States). Dept. of Computational and Systems Biology
  2. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Computational Biology Inst. Computer Science and Mathematics Division
Publication Date:
Research Org.:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER). Biological Systems Science Division; National Institutes of Health (NIH)
OSTI Identifier:
1625275
Grant/Contract Number:  
AC05-00OR22725; T32 EB009403; R01 GM086238
Resource Type:
Accepted Manuscript
Journal Name:
Bioinformatics
Additional Journal Information:
Journal Volume: 27; Journal Issue: 13; Journal ID: ISSN 1367-4803
Publisher:
International Society for Computational Biology - Oxford University Press
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; 97 MATHEMATICS AND COMPUTING; Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology; Computer Science; Mathematical & Computational Biology; Mathematics

Citation Formats

Savol, Andrej J., Burger, Virginia M., Agarwal, P. K., Ramanathan, A., and Chennubhotla, C. S. QAARM: quasi-anharmonic autoregressive model reveals molecular recognition pathways in ubiquitin. United States: N. p., 2011. Web. doi:10.1093/bioinformatics/btr248.
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Savol, Andrej J., Burger, Virginia M., Agarwal, P. K., Ramanathan, A., & Chennubhotla, C. S. QAARM: quasi-anharmonic autoregressive model reveals molecular recognition pathways in ubiquitin. United States. https://doi.org/10.1093/bioinformatics/btr248
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Savol, Andrej J., Burger, Virginia M., Agarwal, P. K., Ramanathan, A., and Chennubhotla, C. S. Tue . "QAARM: quasi-anharmonic autoregressive model reveals molecular recognition pathways in ubiquitin". United States. https://doi.org/10.1093/bioinformatics/btr248. https://www.osti.gov/servlets/purl/1625275.
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@article{osti_1625275,
title = {QAARM: quasi-anharmonic autoregressive model reveals molecular recognition pathways in ubiquitin},
author = {Savol, Andrej J. and Burger, Virginia M. and Agarwal, P. K. and Ramanathan, A. and Chennubhotla, C. S.},
abstractNote = {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 0.5µs 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.},
doi = {10.1093/bioinformatics/btr248},
journal = {Bioinformatics},
number = 13,
volume = 27,
place = {United States},
year = {Tue Jun 14 00:00:00 EDT 2011},
month = {Tue Jun 14 00:00:00 EDT 2011}
}
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