Journal: Biophysical Journal

Article Title: Structural Disorder of Folded Proteins: Isotope-Edited 2D IR Spectroscopy and Markov State Modeling

doi: 10.1016/j.bpj.2014.12.061

Figure Lengend Snippet: (Left) Network plot of the Markov state model for the native ensemble of NTL91-39. The states are color-coded by RMSD to the crystal structure. The node size encodes the degree, or number of connections to other states, of each state. (Dashed circle) Registry-shifted states (34). (Center) Structure overlay of the four highest populated states. The main differences are observed in the β-turn region. (Right) Nodes color-coded by the pseudo free energy of native states F = −ln(P), where P denotes the respective populations in the full MSM. To see this figure in color, go online.

Article Snippet: As a basis for characterizing structural heterogeneity, we make use of a Markov state model (MSM) that groups conformers from a molecular dynamics (MD) simulation based on exchange kinetics, forming a convenient basis for comparison between MD simulations and experiments ( 28–30 ).

Techniques:

Journal: Biophysical Journal

Article Title: Structural Disorder of Folded Proteins: Isotope-Edited 2D IR Spectroscopy and Markov State Modeling

doi: 10.1016/j.bpj.2014.12.061

Figure Lengend Snippet: (a) Representative structures of the folded β-turn and disordered turn conformations represented in the MSM of NTL91-39 overlaid onto a cartoon structure of the Markov state with lowest RMSD to the crystal structure. (Spheres) Carbonyls corresponding to the V9 and G13 residues. (Yellow dashes) The two folded hydrogen bonds. (Orange double-headed arrow) The V9-M12 hydrogen bond used as the order parameter to distinguish between folded and disordered turn structures. (b) V9-, G13-, and V9G13-label spectra calculated for folded (solid) and disordered (dashed) structures, respectively. (c) Scatter plot of the V9-G13 coupling constant in wavenumbers as a function of rVM distance for the 140 states in the MSM. (Circles are color-coded by the overall RMSD of each state to the crystal structure.) To see this figure in color, go online.

Article Snippet: As a basis for characterizing structural heterogeneity, we make use of a Markov state model (MSM) that groups conformers from a molecular dynamics (MD) simulation based on exchange kinetics, forming a convenient basis for comparison between MD simulations and experiments ( 28–30 ).

Techniques:

Journal: Bioinformatics and Biology Insights

Article Title: CellTrans: An R Package to Quantify Stochastic Cell State Transitions

doi: 10.1177/1177932217712241

Figure Lengend Snippet: Transitions in the model and regularization of the matrix root. (A) CellTrans estimates the transition matrix of the underlying discrete-time Markov model. This matrix contains the transition probabilities between all cell states. Here, these transitions are illustrated for 3 fictive cell states. (B) to (D) We simulated cell state proportion data for an experiment with 3 fictive cell states in which regularization of the matrix roots is necessary to obtain a stochastic matrix. The dots represent simulated data points, and the solid curves represent the prediction of CellTrans. For details on the required matrix root regularization, see the main text.

Article Snippet: This case study demonstrates that the Markov model underlying CellTrans is potentially able to make better predictions than more complex ODE models with parameter calibration.

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