Improvements in Markov State Model Construction Reveal Many Non-Native Interactions in the Folding of NTL9

By Christian Schwantes, Vijay S. Pande. Journal of Chemical Theory and Computation, 2013

570 citations recorded by the source corpus.

Markov State Models (MSMs) provide an automated framework to investigate the dynamical properties of high-dimensional molecular simulations. These models can provide a human-comprehensible picture of the underlying process, and have been successfully used to study protein folding, protein aggregation, protein ligand binding, and other biophysical systems. The MSM requires the construction of a discrete state-space such that two points are in the same state if they can interconvert rapidly. In the following, we suggest an improved method, which utilizes second order Independent Components Analysis (also known as time-structure based Independent Components Analysis, or tICA), to construct the state-space. We apply this method to simulations of NTL9 (provided by Lindorff-Larsen et al. Science 2011), and show that the MSM is an improvement over previously built models using conventional distance metrics. Additionally, the resulting model provides insight into the role of non-native contacts by revealing many slow timescales associated with compact, non-native states.

Subfields: Molecular Biology, Signal Processing

View Paper: https://www.semanticscholar.org/paper/2b88e8c79dd233fc5627d720fd427c7901b104c6

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Citations in the corpus

Coarse graining molecular dynamics with graph neural networks. (2020, methodology): “The reaction coordinates are obtained from TICA 57,58 (see Appen-dices D and E).”
VAMPnets for deep learning of molecular kinetics (2017, background): “…much fewer (typically 2-100) slow collective variables (CVs), often based on the variational approach or conformation dynamics [17, 18], time-lagged independent component analysis (TICA) [19, 20], blind source separation [21–23] or dynamic mode decomposition [24–28] – see [29, 30] for an overview.”
Kinetic distance and kinetic maps from molecular dynamics simulation. (2015, methodology): “A number of approaches are available to approximate these reaction coordinates from MD data: Diffusion maps [26], Markov state models [24] and Markov transition models [35], TICA [18, 30] and kernel TICA [31].”
Data-Driven Model Reduction and Transfer Operator Approximation (2017, methodology): “TICA is now a popular dimension reduction technique in the field of molecular dynamics [1, 4].”
Multiensemble Markov models of molecular thermodynamics and kinetics (2016, methodology): “To detect rare events in the unbiased coordinates, time-lagged independent component analysis (TICA) (29, 30) was used with the Cartesian coordinates of residues around the binding site.”
Variational Approach for Learning Markov Processes from Time Series Data (2017, methodology): “ov transition models [53],Ulam’sGalerkinmethod[11,3,16],blind-sourceseparation[33,55],thevariationalapproach of conformation dynamics (VAC) [36, 37], time-lagged independent component analysis (TICA) [39,45],dynamicmodedecomposition(DMD)[42,43,51],extendeddynamicmodedecomposition (EDMD)[52],variatio”