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Application of Hidden Markov Models in Biomolecular Simulations

  • Saurabh Shukla
  • Zahra Shamsi
  • Alexander S. Moffett
  • Balaji Selvam
  • Diwakar ShuklaEmail author
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1552)

Abstract

Hidden Markov models (HMMs) provide a framework to analyze large trajectories of biomolecular simulation datasets. HMMs decompose the conformational space of a biological molecule into finite number of states that interconvert among each other with certain rates. HMMs simplify long timescale trajectories for human comprehension, and allow comparison of simulations with experimental data. In this chapter, we provide an overview of building HMMs for analyzing bimolecular simulation datasets. We demonstrate the procedure for building a Hidden Markov model for Met-enkephalin peptide simulation dataset and compare the timescales of the process.

Key words

Hidden Markov models Molecular dynamics Protein dynamics Markov state models Protein conformational change 

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Copyright information

© Springer Science+Business Media LLC 2017

Authors and Affiliations

  • Saurabh Shukla
    • 1
  • Zahra Shamsi
    • 1
  • Alexander S. Moffett
    • 2
  • Balaji Selvam
    • 1
  • Diwakar Shukla
    • 1
    • 2
    • 3
    • 4
    Email author
  1. 1.Department of Chemical & Biomolecular EngineeringUniversity of Illinois at Urbana-ChampaignUrbanaUSA
  2. 2.Center for Biophysics and Quantitative BiologyUniversity of Illinois at Urbana-ChampaignUrbanaUSA
  3. 3.National Center for Supercomputing ApplicationsUniversity of Illinois at Urbana-ChampaignUrbanaUSA
  4. 4.Plant BiologyUniversity of Illinois at Urbana-ChampaignUrbanaUSA

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