What Markov State Models can and cannot do: Correlation versus path-based observables in protein-folding models

Ernesto Suárez, Rafal P Wiewiora, Chris Wehmeyer, Frank Noé, John D Chodera, Daniel M Zuckerman
Journal of Chemical Theory and Computation 17:3119, 2021
[DOI] [PDF] [bioRxiv] [GitHub]

Markov state models are now well-established for describing the long-time conformational dynamics of proteins. Here, we take a critical look of what properties can reliably be extracted from these coarse-grained models.

OpenPathSampling: A Python framework for path sampling simulations. II. Building and customizing path ensembles and sample schemes

David W.H. Swenson, Jan-Hendrik Prinz, Frank Noé, John D. Chodera, Peter G. Bolhuis
Journal of Chemical Theory and Computation 15:837, 2019. [DOI] [bioRxiv] [PDF] [GitHub] [openpathsampling.org]

To make powerful path sampling techniques broadly accessible and efficient, we have produced a new Python framework for easily implementing path sampling strategies (such as transition path and interface sampling) in Python. This second publication describes advanced aspects of the theory and details of how to customize path ensembles.

OpenPathSampling: A Python framework for path sampling simulations. I. Basics

David W.H. Swenson, Jan-Hendrik Prinz, Frank Noé, John D. Chodera, Peter G. Bolhuis
Journal of Chemical Theory and Computation 15:813, 2019 [DOI] [bioRxiv] [PDF] [GitHub] [openpathsampling.org]

To make powerful path sampling techniques broadly accessible and efficient, we have produced a new Python framework for easily implementing path sampling strategies (such as transition path and interface sampling) in Python. This first publication describes some of the theory and capabilities behind the approach.

A robust approach to estimating rates from time-correlation functions

John D. Chodera, Phillip J. Elms, William C. Swope, Jan-Hendrik Prinz, Susan Marqusee, Carlos Bustamante, Frank Noé, Vijay S. Pande
Preprint ahead of submission: [arXiv] [PDF] [SI]

The estimation of rates from experimental single-molecule data is fraught with peril. We describe some of the failures of existing methods and suggest a robust way to estimate rates from time-correlation functions.