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.

Machine learning force fields and coarse-grained variables in molecular dynamics: application to materials and biological systems

Gkeka P, Stoltz G, Farimani AB, Belkacemi Z, Ceriotti M, Chodera JD, Dinner AR, Ferguson A, Maillet JB, Minoux H, Peter C, Pietrucci F, Silveira A, Tkatchenko A, Trstanova Z, Wiewiora R, Leliévre T.
Journal of Chemical Theory and Computation 60:6211, 2020. [DOI] [arXiv]

We review the state of the art in applying machine learning to coarse grain force fields in space and time to study mutliscale dynamics.

Ancestral reconstruction reveals mechanisms of ERK regulatory evolution

Dajun Sang, Sudarshan Pinglay, Rafal P Wiewiora, Myvizhi E Selvan, Hua Jane Lou, John D Chodera, Benjamin E Turk, Zeynep H Gümüş, and Liam J Holt.
eLife 2019;8:e38805 [DOI] [eLife] [PDF] [Folding@home data]

To understand how kinase regulation by phosphorylation emerged, we reconstruct the common ancestor of CDKs and MAPKs, using biochemical experiments and massively parallel molecular simulations to study how a few mutations were sufficient to switch ERK-family kinases from high- to low-autophosphorylation.

The dynamic conformational landscapes of the protein methyltransferase SETD8

Rafal P. Wiewiora*, Shi Chen*, Fanwang Meng, Nicolas Babault, Anqi Ma, Wenyu Yu, Kun Qian, Hao Hu, Hua Zou, Junyi Wang, Shijie Fan, Gil Blum, Fabio Pittella-Silva, Kyle A. Beauchamp, Wolfram Tempel, Hualing Jiang, Kaixian Chen, Robert Skene, Y. George Zheng, Peter J. Brown, Jian Jin, John D. Chodera+, and Minkui Luo+.
eLife 8:e45403, 2019. [DOI] [bioRxiv] [GitHub] [OSF] [movies] [MSKCC blog post]
* These authors contributed equally to this work
+ Co-corresponding authors

In this work, we show how targeted X-ray crystallography using covalent inhibitors and depletion of native ligands to reveal structures of low-population hidden conformations can be combined with massively distributed molecular simulation to resolve the functional dynamic landscape of the protein methyltransferase SETD8 in unprecedented atomistic detail. Using an aggregate of six milliseconds of fully atomistic simulation from Folding@home, we use Markov state models to illuminate the conformational dynamics of this important epigenetic protein.

All Folding@home simulation trajectories for this paper are available on the Open Science Framework.

The trajectories generated for this project were used as the source for a unique musical composition 'Metastable' by George Holloway, performed by the Ligeti String Quartet with visual accompaniment from Robert Arbon.

OpenMM 7: Rapid Development of High Performance Algorithms for Molecular Dynamics

Peter Eastman, Jason Swails, John D. Chodera, Robert T. McGibbon, Yutong Zhao, Kyle A. Beauchamp, Lee-Ping Wang, Andrew C. Simmonett, Matthew P. Harrigan, Chaya D. Stern, Rafal P. Wiewiora, Bernard R. Brooks, Vijay S. Pande. PLoS Computational Biology 13:e1005659, 2017. [DOI] [bioRxiv] [website] [GitHub]

We describe the latest version of OpenMM, a GPU-accelerated framework for high performance molecular simulation applications.