Identification of simple reaction coordinates from complex dynamics

McGibbon, Robert T.; Husic, Brooke E.; Pande, Vijay S.

doi:10.1063/1.4974306

Cited by 91 publications

(107 citation statements)

References 101 publications

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“…In the protein folding example, we see that deflation can obscure a long-timescale process from a kinetic model in order to facilitate further analysis that is desired to be independent of that process. In MD simulations, the dominant processes may not be the same as the processes of interest due to low sampling or force field artifacts 8,9 . Thus, deflation presents a systematic way of removing the effects of these undesired modes on model building.…”

Section: Discussionmentioning

confidence: 99%

Deflation reveals dynamical structure in nondominant reaction coordinates

Husic

Noé

2019

The Journal of Chemical Physics

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The output of molecular dynamics simulations is high-dimensional, and the degrees of freedom among the atoms are related in intricate ways. Therefore, a variety of analysis frameworks have been introduced in order to distill complex motions into lower-dimensional representations that model the system dynamics. These dynamical models have been developed to optimally approximate the system's global kinetics. However, the separate aims of optimizing global kinetics and modeling a process of interest diverge when the process of interest is not the slowest process in the system. Here, we introduce deflation into state-of-the-art methods in molecular kinetics in order to preserve the use of variational optimization tools when the slowest dynamical mode is not the same as the one we seek to model and understand. First, we showcase deflation for a simple toy system and introduce the deflated variational approach to Markov processes (dVAMP). Using dVAMP, we show that nondominant reaction coordinates produced using deflation are more informative than their counterparts generated without deflation. Then, we examine a protein folding system in which the slowest dynamical mode is not folding. Following a dVAMP analysis, we show that deflation can be used to obscure this undesired slow process from a kinetic model, in this case a VAMPnet. The incorporation of deflation into current methods opens the door for enhanced sampling strategies and more flexible, targeted model building.arXiv:1907.04101v1 [q-bio.BM]

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Section: Discussionmentioning

confidence: 99%

Deflation reveals dynamical structure in nondominant reaction coordinates

Husic

Noé

2019

The Journal of Chemical Physics

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“…Markov modeling 28 involves Voronoi partitioning of the accessible phase space into states and counting the transitions between the states. The metastable states are defined using a kinetically relevant distance metric (see Methods) that is learnt via sparse time structure-based independent component analysis (sparse-tICA) [32][33][34][35] . tICA finds linear combinations of input MD features that de-correlate the slowest within the given dataset.…”

Section: Btk's Apo Domain Is Primarily Inactivementioning

confidence: 99%

“…After we determined the optimal model given the current amount of sampling, we retrained the model on the entire set of trajectories. For the reported tICA model, we used a sparse variant of tICA 34 for increased interpretability (Supporting Figure 6-7). The Markov transition matrix was fit via maximum likelihood estimation (MLE) with reversibility and ergodicity constraints.…”

Section: Markov State Modelmentioning

confidence: 99%

Millisecond dynamics of BTK reveal kinome-wide conformational plasticity within the apo kinase domain

Sultan

Denny

Unwalla

et al. 2017

Preprint

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“…In this example, we reduce the dimensionality of our kinase data from 518 dihedrals to 5 tICA coordinates. MSMBuilder includes support for similar algorithms (SparseTICA [22]) as well as general manifold learning algorithms like principal components analysis (PCA), SparsePCA, or MiniBatchSparsePCA. Prior to 2013, this step was not available for model construction.…”

Section: A Constructing An Msmmentioning

confidence: 99%

MSMBuilder: Statistical Models for Biomolecular Dynamics

Harrigan

Sultan

Hernández

et al. 2016

Preprint

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MSMBuilder is a software package for building statistical models of high-dimensional time-series data. It is designed with a particular focus on the analysis of atomistic simulations of biomolecular dynamics such as protein folding and conformational change. MSMBuilder is named for its ability to construct Markov state models (MSMs), a class of models that has gained favor among computational biophysicists. In addition to both well-established and newer MSM methods, the package includes complementary algorithms for understanding time-series data such as hidden Markov models and time-structure based independent component analysis. MSMBuilder boasts an easy to use command-line interface, as well as clear and consistent abstractions through its Python application programming interface. MSMBuilder was developed with careful consideration for compatibility with the broader machine learning community by following the design of scikit-learn. The package is used primarily by practitioners of molecular dynamics, but is just as applicable to other computational or experimental time-series measurements.

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Identification of simple reaction coordinates from complex dynamics

Cited by 91 publications

References 101 publications

Deflation reveals dynamical structure in nondominant reaction coordinates

Deflation reveals dynamical structure in nondominant reaction coordinates

Millisecond dynamics of BTK reveal kinome-wide conformational plasticity within the apo kinase domain

MSMBuilder: Statistical Models for Biomolecular Dynamics

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