Introduction to Markov state modeling with the PyEMMA software [Article v1.0]

Wehmeyer, Christoph; Scherer, Martin K.; Hempel, Tim; Husic, Brooke E.; Olsson, Simon; Noé, Frank

doi:10.33011/livecoms.1.1.5965

Cited by 65 publications

(104 citation statements)

References 62 publications

(112 reference statements)

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“…It is possible to use certain number of eigenvectors with highest eigenvalues instead of all eigenvectors. This selection may be driven by relaxation time decays (see Wehmeyer et al, 2019) but this is out of scope of this article. t-SNE can be applied on distances between simulation snapshots calculated in the space of Z as:…”

Section: Methodsmentioning

confidence: 99%

Time-Lagged t-Distributed Stochastic Neighbor Embedding (t-SNE) of Molecular Simulation Trajectories

Spiwok

Kříž

2020

Front. Mol. Biosci.

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Molecular simulation trajectories represent high-dimensional data. Such data can be visualized by methods of dimensionality reduction. Non-linear dimensionality reduction methods are likely to be more efficient than linear ones due to the fact that motions of atoms are non-linear. Here we test a popular non-linear t-distributed Stochastic Neighbor Embedding (t-SNE) method on analysis of trajectories of 200 ns alanine dipeptide dynamics and 208 µs Trp-cage folding and unfolding. Furthermore, we introduce a time-lagged variant of t-SNE in order to focus on rarely occurring transitions in the molecular system. This time-lagged t-SNE efficiently separates states according to distance in time. Using this method it is possible to visualize key states of studied systems (e.g., unfolded and folded protein) as well as possible kinetic traps using a two-dimensional plot. Time-lagged t-SNE is a visualization method and other applications, such as clustering and free energy modeling, must be done with caution.

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Section: Methodsmentioning

confidence: 99%

Time-Lagged t-Distributed Stochastic Neighbor Embedding (t-SNE) of Molecular Simulation Trajectories

Spiwok

Kříž

2020

Front. Mol. Biosci.

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“…The 200 MD short simulation trajectories for both the proteins GB1 and Trp-cage eventually used separately to build a Markov state model (MSM) 60 for statistically mapping the complete protein folding process using Pyemma software. 61,62 The stationary populations of the discrete microstates, calculated from MSM, were subsequently used to reweigh the free energy surfaces derived from these short trajectories.…”

Section: Methodsmentioning

confidence: 99%

Unifying the Ambivalent Mechanisms of Protein-Stabilising Osmolytes

Mukherjee

Mondal

2020

Preprint

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The mechanism of protein stabilization by zwiterionic osmolytes has remained a long-standing puzzle. While the prevalent mechanistic hypothesis suggests an 'osmophobic' model in which osmolytes are assumed to stabilize proteins by preferentially excluding themselves from the protein surface, emerging evidences of preferential binding of popular osmolyte trimethyl amine N-oxide (TMAO) with hydrophobic macromolecules contradict this view. Here we address these contrasting perspectives by investigating the folding mechanism of a set of mini proteins in aqueous solutions of two di↵erent osmolytes glycine and TMAO, via free energy simulations. Our results demonstrate that, while both osmolytes are found to stabilize the folded conformation of the mini proteins, their mechanism of actions are mutually diverse: Specifically, glycine always depletes from the surface of all mini proteins, thereby conforming to the osmophobic model; but TMAO is found to display ambivalent signatures of proteinspecific preferential binding and exclusion to/from the protein surface. At molecular level, the presence of an extended hydrophobic patch in protein topology is found to be recurrent motif in proteins leading to favorable binding with TMAO. Finally, an analysis based upon the preferential interaction theory and folding free energetics reveals that irrespective of preferential binding vs exclusion of osmolytes, it is the relative preferential depletion of osmolytes on transition from folded to unfolded conformation of proteins, which drives the overall conformational equilibrium towards the folded state in presence of osmolytes. Taken together, moving beyond the model system and hypothesis, this work brings out ambivalent mechanism of osmolytes on proteins and provides an unifying justification.

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“…The emergence of Markov State Models (MSMs) as a formalism for analysing and interpreting equilibrium molecular dynamics (MD) simulation data in a statistically optimal manner has proven to be one of the most important recent developments in the field of computational chemistry (for details of MSM methods, see recent reviews [6][7][8][9] and applications [1][2][3][4][5]10 ).…”

Section: Introductionmentioning

confidence: 99%