Literature DB >> 25194354

Perspective: Markov models for long-timescale biomolecular dynamics.

C R Schwantes1, R T McGibbon1, V S Pande1.   

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.

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Year:  2014        PMID: 25194354      PMCID: PMC4156582          DOI: 10.1063/1.4895044

Source DB:  PubMed          Journal:  J Chem Phys        ISSN: 0021-9606            Impact factor:   3.488


  63 in total

Review 1.  Molecular dynamics simulations of biomolecules.

Authors:  Martin Karplus; J Andrew McCammon
Journal:  Nat Struct Biol       Date:  2002-09

2.  Computing time scales from reaction coordinates by milestoning.

Authors:  Anton K Faradjian; Ron Elber
Journal:  J Chem Phys       Date:  2004-06-15       Impact factor: 3.488

3.  EMMA: A Software Package for Markov Model Building and Analysis.

Authors:  Martin Senne; Benjamin Trendelkamp-Schroer; Antonia S J S Mey; Christof Schütte; Frank Noé
Journal:  J Chem Theory Comput       Date:  2012-06-18       Impact factor: 6.006

4.  Energy landscape of a small peptide revealed by dihedral angle principal component analysis.

Authors:  Yuguang Mu; Phuong H Nguyen; Gerhard Stock
Journal:  Proteins       Date:  2005-01-01

5.  Bayesian comparison of Markov models of molecular dynamics with detailed balance constraint.

Authors:  Sergio Bacallado; John D Chodera; Vijay Pande
Journal:  J Chem Phys       Date:  2009-07-28       Impact factor: 3.488

6.  Constructing multi-resolution Markov State Models (MSMs) to elucidate RNA hairpin folding mechanisms.

Authors:  Xuhui Huang; Yuan Yao; Gregory R Bowman; Jian Sun; Leonidas J Guibas; Gunnar Carlsson; Vijay S Pande
Journal:  Pac Symp Biocomput       Date:  2010

7.  Markov state model reveals folding and functional dynamics in ultra-long MD trajectories.

Authors:  Thomas J Lane; Gregory R Bowman; Kyle Beauchamp; Vincent A Voelz; Vijay S Pande
Journal:  J Am Chem Soc       Date:  2011-10-26       Impact factor: 15.419

Review 8.  Everything you wanted to know about Markov State Models but were afraid to ask.

Authors:  Vijay S Pande; Kyle Beauchamp; Gregory R Bowman
Journal:  Methods       Date:  2010-06-04       Impact factor: 3.608

9.  The Polarizable Atomic Multipole-based AMOEBA Force Field for Proteins.

Authors:  Yue Shi; Zhen Xia; Jiajing Zhang; Robert Best; Chuanjie Wu; Jay W Ponder; Pengyu Ren
Journal:  J Chem Theory Comput       Date:  2013       Impact factor: 6.006

10.  Improved side-chain torsion potentials for the Amber ff99SB protein force field.

Authors:  Kresten Lindorff-Larsen; Stefano Piana; Kim Palmo; Paul Maragakis; John L Klepeis; Ron O Dror; David E Shaw
Journal:  Proteins       Date:  2010-06
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  22 in total

1.  Markov state modeling and dynamical coarse-graining via discrete relaxation path sampling.

Authors:  B Fačkovec; E Vanden-Eijnden; D J Wales
Journal:  J Chem Phys       Date:  2015-07-28       Impact factor: 3.488

2.  Extracting intrinsic dynamic parameters of biomolecular folding from single-molecule force spectroscopy experiments.

Authors:  Gi-Moon Nam; Dmitrii E Makarov
Journal:  Protein Sci       Date:  2015-07-14       Impact factor: 6.725

3.  Galerkin approximation of dynamical quantities using trajectory data.

Authors:  Erik H Thiede; Dimitrios Giannakis; Aaron R Dinner; Jonathan Weare
Journal:  J Chem Phys       Date:  2019-06-28       Impact factor: 3.488

4.  MSMBuilder: Statistical Models for Biomolecular Dynamics.

Authors:  Matthew P Harrigan; Mohammad M Sultan; Carlos X Hernández; Brooke E Husic; Peter Eastman; Christian R Schwantes; Kyle A Beauchamp; Robert T McGibbon; Vijay S Pande
Journal:  Biophys J       Date:  2017-01-10       Impact factor: 4.033

5.  Optimized parameter selection reveals trends in Markov state models for protein folding.

Authors:  Brooke E Husic; Robert T McGibbon; Mohammad M Sultan; Vijay S Pande
Journal:  J Chem Phys       Date:  2016-11-21       Impact factor: 3.488

Review 6.  Molecular Dynamics Simulation for All.

Authors:  Scott A Hollingsworth; Ron O Dror
Journal:  Neuron       Date:  2018-09-19       Impact factor: 17.173

7.  A new class of enhanced kinetic sampling methods for building Markov state models.

Authors:  Arti Bhoutekar; Susmita Ghosh; Swati Bhattacharya; Abhijit Chatterjee
Journal:  J Chem Phys       Date:  2017-10-21       Impact factor: 3.488

8.  Atomistic Insights into Structural Differences between E3 and E4 Isoforms of Apolipoprotein E.

Authors:  Angana Ray; Navjeet Ahalawat; Jagannath Mondal
Journal:  Biophys J       Date:  2017-12-19       Impact factor: 4.033

9.  Integrated Variational Approach to Conformational Dynamics: A Robust Strategy for Identifying Eigenfunctions of Dynamical Operators.

Authors:  Chatipat Lorpaiboon; Erik Henning Thiede; Robert J Webber; Jonathan Weare; Aaron R Dinner
Journal:  J Phys Chem B       Date:  2020-10-09       Impact factor: 2.991

Review 10.  Molecular dynamics simulations of biological membranes and membrane proteins using enhanced conformational sampling algorithms.

Authors:  Takaharu Mori; Naoyuki Miyashita; Wonpil Im; Michael Feig; Yuji Sugita
Journal:  Biochim Biophys Acta       Date:  2016-01-05
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