Perspective: Markov models for long-timescale biomolecular dynamics

Schwantes, C. R.; McGibbon, R. T.

doi:10.1063/1.4895044

Title: Perspective: Markov models for long-timescale biomolecular dynamics

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Abstract

Molecular dynamics simulations have the potential to provide atomic-level detail and insight to important questions in chemical physics that cannot be observed in typical experiments. However, simply generating a long trajectory is insufficient, as researchers must be able to transform the data in a simulation trajectory into specific scientific insights. Although this analysis step has often been taken for granted, it deserves further attention as large-scale simulations become increasingly routine. In this perspective, we discuss the application of Markov models to the analysis of large-scale biomolecular simulations. We draw attention to recent improvements in the construction of these models as well as several important open issues. In addition, we highlight recent theoretical advances that pave the way for a new generation of models of molecular kinetics.

Authors:

Schwantes, C. R.; McGibbon, R. T. ^[1]

Department of Chemistry, Stanford University, Stanford, California 94305 (United States)

Publication Date:: Sun Sep 07 00:00:00 EDT 2014

OSTI Identifier:: 22419847

Resource Type:: Journal Article

Journal Name:: Journal of Chemical Physics

Additional Journal Information:: Journal Volume: 141; Journal Issue: 9; Other Information: (c) 2014 AIP Publishing LLC; Country of input: International Atomic Energy Agency (IAEA); Journal ID: ISSN 0021-9606

Country of Publication:: United States

Language:: English

Subject:: 37 INORGANIC, ORGANIC, PHYSICAL AND ANALYTICAL CHEMISTRY; KINETICS; MARKOV PROCESS; MOLECULAR DYNAMICS METHOD; SIMULATION

Citation Formats


                    Schwantes, C. R., McGibbon, R. T., Pande, V. S., E-mail: pande@stanford.edu, Department of Computer Science, Stanford University, Stanford, California 94305, Department of Structural Biology, Stanford University, Stanford, California 94305, and Biophysics Program, Stanford University, Stanford, California 94305. Perspective: Markov models for long-timescale biomolecular dynamics.  United States: N. p., 2014. 
        Web.  doi:10.1063/1.4895044.

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                    Schwantes, C. R., McGibbon, R. T., Pande, V. S., E-mail: pande@stanford.edu, Department of Computer Science, Stanford University, Stanford, California 94305, Department of Structural Biology, Stanford University, Stanford, California 94305, & Biophysics Program, Stanford University, Stanford, California 94305. Perspective: Markov models for long-timescale biomolecular dynamics.  United States.  https://doi.org/10.1063/1.4895044

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                    Schwantes, C. R., McGibbon, R. T., Pande, V. S., E-mail: pande@stanford.edu, Department of Computer Science, Stanford University, Stanford, California 94305, Department of Structural Biology, Stanford University, Stanford, California 94305, and Biophysics Program, Stanford University, Stanford, California 94305. 2014.  
        "Perspective: Markov models for long-timescale biomolecular dynamics".  United States.  https://doi.org/10.1063/1.4895044.

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@article{osti_22419847,

  title        = {Perspective: Markov models for long-timescale biomolecular dynamics},

  author       = {Schwantes, C. R. and McGibbon, R. T. and Pande, V. S., E-mail: pande@stanford.edu and Department of Computer Science, Stanford University, Stanford, California 94305 and Department of Structural Biology, Stanford University, Stanford, California 94305 and Biophysics Program, Stanford University, Stanford, California 94305},

  abstractNote = {Molecular dynamics simulations have the potential to provide atomic-level detail and insight to important questions in chemical physics that cannot be observed in typical experiments. However, simply generating a long trajectory is insufficient, as researchers must be able to transform the data in a simulation trajectory into specific scientific insights. Although this analysis step has often been taken for granted, it deserves further attention as large-scale simulations become increasingly routine. In this perspective, we discuss the application of Markov models to the analysis of large-scale biomolecular simulations. We draw attention to recent improvements in the construction of these models as well as several important open issues. In addition, we highlight recent theoretical advances that pave the way for a new generation of models of molecular kinetics.},

  doi          = {10.1063/1.4895044},

  url          = {https://www.osti.gov/biblio/22419847},
  journal      = {Journal of Chemical Physics},

  issn         = {0021-9606},

  number       = 9,

  volume       = 141,

  place        = {United States},

  year         = {Sun Sep 07 00:00:00 EDT 2014},

  month        = {Sun Sep 07 00:00:00 EDT 2014}

}

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