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

Savol AJ, Burger VM, Agarwal PK, Ramanathan A, Chennubhotla CS - Bioinformatics (2011)

Bottom Line: Molecular dynamics (MD) simulations have dramatically improved the atomistic understanding of protein motions, energetics and function.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.We show the learned model can be extrapolated to synthesize trajectories of arbitrary length. ramanathana@ornl.gov; chakracs@pitt.edu.

View Article: PubMed Central - PubMed

Affiliation: 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.

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 0.5 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.

Show MeSH
Overview of QAARM: We use MD simulations as input to QAA. The output of QAA is a reduced dimensional space, in which conformers clustered together represent micro-states. This reduced-dimensional space is then input into a Markov diffusion framework to identify clusters of conformations that are kinetically accessible. These clusters represent metastable macro-states. We then build second order AR models for each substate to identify pathways between metastable states.
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Figure 1: Overview of QAARM: We use MD simulations as input to QAA. The output of QAA is a reduced dimensional space, in which conformers clustered together represent micro-states. This reduced-dimensional space is then input into a Markov diffusion framework to identify clusters of conformations that are kinetically accessible. These clusters represent metastable macro-states. We then build second order AR models for each substate to identify pathways between metastable states.

Mentions: An overview of QAARM is shown in Figure 1. MD simulation data is first processed to remove rotational and translational degrees of freedom. QAA is then applied (Section 5) which outputs a reduced dimensional representation of the original MD data. Motivated to detect biophysically relevant energy wells, or highly populated regions, in the low-dimensional QAA space, we next use a simple Markov diffusion model to cluster the conformations into meta-stable substates (Section 6). Local dynamics within each substate are then captured by a linear, second-order AR model (Section 7) which explicitly models spatial fluctuations. The AR model thus extends the time-insensitive QAA model by considering temporal relationships between successive MD frames.Fig. 1.


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

Savol AJ, Burger VM, Agarwal PK, Ramanathan A, Chennubhotla CS - Bioinformatics (2011)

Overview of QAARM: We use MD simulations as input to QAA. The output of QAA is a reduced dimensional space, in which conformers clustered together represent micro-states. This reduced-dimensional space is then input into a Markov diffusion framework to identify clusters of conformations that are kinetically accessible. These clusters represent metastable macro-states. We then build second order AR models for each substate to identify pathways between metastable states.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3117343&req=5

Figure 1: Overview of QAARM: We use MD simulations as input to QAA. The output of QAA is a reduced dimensional space, in which conformers clustered together represent micro-states. This reduced-dimensional space is then input into a Markov diffusion framework to identify clusters of conformations that are kinetically accessible. These clusters represent metastable macro-states. We then build second order AR models for each substate to identify pathways between metastable states.
Mentions: An overview of QAARM is shown in Figure 1. MD simulation data is first processed to remove rotational and translational degrees of freedom. QAA is then applied (Section 5) which outputs a reduced dimensional representation of the original MD data. Motivated to detect biophysically relevant energy wells, or highly populated regions, in the low-dimensional QAA space, we next use a simple Markov diffusion model to cluster the conformations into meta-stable substates (Section 6). Local dynamics within each substate are then captured by a linear, second-order AR model (Section 7) which explicitly models spatial fluctuations. The AR model thus extends the time-insensitive QAA model by considering temporal relationships between successive MD frames.Fig. 1.

Bottom Line: Molecular dynamics (MD) simulations have dramatically improved the atomistic understanding of protein motions, energetics and function.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.We show the learned model can be extrapolated to synthesize trajectories of arbitrary length. ramanathana@ornl.gov; chakracs@pitt.edu.

View Article: PubMed Central - PubMed

Affiliation: 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.

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 0.5 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.

Show MeSH