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.

The Social Network (of protein conformations)

John D. Chodera and Vijay S. Pande.
Proc. Natl. Acad. Sci. USA 108:12969, 2011. [DOI] [PDF]

Commentary: A new methodology for mapping protein conformational spaces is reminiscent of how we use two-dimensional maps to navigate a three-dimensional world.

The protein folding problem: When will it be solved?

Ken A. Dill, S. Banu Ozkan, Thomas R. Weikl, John D. Chodera, and Vincent A. Voelz.
Curr. Opin. Struct. Biol. 17:342, 2007. [DOI] [PDF]

A review of the current state of the protein folding problem.

Protein Folding by Zipping and Assembly

S. Banu Ozkan, G. Albert Wu, John D. Chodera, and Ken A. Dill.
Proc. Natl. Acad. Sci. USA 104:11987, 2007. [DOI] [PDF]

A review of the utility of the proposed zipping and assembly mechanism for the concomitant formation of secondary and tertiary structure in protein folding for predicting folding pathways and native structures.

Long-time protein folding dynamics from short-time molecular dynamics simulations

John D. Chodera, William C. Swope, Jed W. Pitera, and Ken A. Dill.
Multiscale Model. Simul. 5:1214, 2006. [DOI] [PDF]

We show how the long-time dynamics of biomolecular systems can be recapitulated from statistics collected from short molecular simulations sampling transitions between kinetically metastable states.