scholarly journals What Markov State Models Can and Cannot Do: Correlation versus Path-Based Observables in Protein-Folding Models

2021 ◽  
Author(s):  
Ernesto Suárez ◽  
Rafal P. Wiewiora ◽  
Chris Wehmeyer ◽  
Frank Noé ◽  
John D. Chodera ◽  
...  
Keyword(s):  
Protein Folding ◽  
Markov State ◽  
Markov State Models ◽  
State Models

scholarly journals Optimized parameter selection reveals trends in Markov state models for protein folding

2016 ◽  
Vol 145 (19) ◽  
pp. 194103 ◽  
Author(s):  
Brooke E. Husic ◽  
Robert T. McGibbon ◽  
Mohammad M. Sultan ◽  
Vijay S. Pande
Keyword(s):  
Protein Folding ◽  
Parameter Selection ◽  
Markov State ◽  
Markov State Models ◽  
State Models

scholarly journals Accurate Estimation of Protein Folding and Unfolding Times: Beyond Markov State Models

2016 ◽  
Vol 12 (8) ◽  
pp. 3473-3481 ◽  
Author(s):  
Ernesto Suárez ◽  
Joshua L. Adelman ◽  
Daniel M. Zuckerman
Keyword(s):  
Protein Folding ◽  
Accurate Estimation ◽  
Markov State ◽  
Markov State Models ◽  
State Models

scholarly journals A Molecular Interpretation of 2D IR Protein Folding Experiments with Markov State Models

2014 ◽  
Vol 106 (6) ◽  
pp. 1359-1370 ◽  
Author(s):  
Carlos R. Baiz ◽  
Yu-Shan Lin ◽  
Chunte Sam Peng ◽  
Kyle A. Beauchamp ◽  
Vincent A. Voelz ◽  
...  
Keyword(s):  
Protein Folding ◽  
Markov State ◽  
2D Ir ◽  
Markov State Models ◽  
Molecular Interpretation ◽  
State Models

scholarly journals Emergence of Glass-like Behavior in Markov State Models of Protein Folding Dynamics

2013 ◽  
Vol 135 (15) ◽  
pp. 5501-5504 ◽  
Author(s):  
Jeffrey K. Weber ◽  
Robert L. Jack ◽  
Vijay S. Pande
Keyword(s):  
Protein Folding ◽  
Markov State ◽  
Markov State Models ◽  
Folding Dynamics ◽  
State Models

scholarly journals What Markov state models can and cannot do: Correlation versus path-based observables in protein folding models

2020 ◽  
Author(s):  
Ernesto Suárez ◽  
Rafal P. Wiewiora ◽  
Chris Wehmeyer ◽  
Frank Noé ◽  
John D. Chodera ◽  
...  
Keyword(s):  
Protein Folding ◽  
Correlation Functions ◽  
Conformational Dynamics ◽  
Coarse Graining ◽  
Md Simulations ◽  
Lag Time ◽  
Time Correlation ◽  
Markov State ◽  
Markov State Models ◽  
State Models

AbstractMarkov state models (MSMs) have been widely applied to study the kinetics and pathways of protein conformational dynamics based on statistical analysis of molecular dynamics (MD) simulations. These MSMs coarse-grain both configuration space and time in ways that limit what kinds of observables they can reproduce with high fidelity over different spatial and temporal resolutions. Despite their popularity, there is still limited understanding of which biophysical observables can be computed from these MSMs in a robust and unbiased manner, and which suffer from the space-time coarse-graining intrinsic in the MSM model. Most theoretical arguments and practical validity tests for MSMs rely on long-time equilibrium kinetics, such as the slowest relaxation timescales and experimentally observable time-correlation functions. Here, we perform an extensive assessment of the ability of well-validated protein folding MSMs to accuractely reproduce path-based observable such as mean first-passage times (MFPTs) and transition path mechanisms compared to a direct trajectory analysis. We also assess a recently proposed class of history-augmented MSMs (haMSMs) that exploit additional information not accounted for in standard MSMs. We conclude with some practical guidance on the use of MSMs to study various problems in conformational dynamics of biomolecules. In brief, MSMs can accurately reproduce correlation functions slower than the lag time, but path-based observables can only be reliably reproduced if the lifetimes of states exceed the lag time, which is a much stricter requirement. Even in the presence of short-lived states, we find that haMSMs reproduce path-based observables more reliably.

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