Kinetic distance and kinetic maps from molecular dynamics simulation.

By Frank No'e, Cecilia Clementi. 2015

205 citations recorded by the source corpus.

Characterizing macromolecular kinetics from molecular dynamics (MD) simulations requires a distance metric that can distinguish slowly interconverting states. Here, we build upon diffusion map theory and define a kinetic distance metric for irreducible Markov processes that quantifies how slowly molecular conformations interconvert. The kinetic distance can be computed given a model that approximates the eigenvalues and eigenvectors (reaction coordinates) of the MD Markov operator. Here, we employ the time-lagged independent component analysis (TICA). The TICA components can be scaled to provide a kinetic map in which the Euclidean distance corresponds to the kinetic distance. As a result, the question of how many TICA dimensions should be kept in a dimensionality reduction approach becomes obsolete, and one parameter less needs to be specified in the kinetic model construction. We demonstrate the approach using TICA and Markov state model (MSM) analyses for illustrative models, protein conformation dynamics in bovine pancreatic trypsin inhibitor and protein-inhibitor association in trypsin and benzamidine. We find that the total kinetic variance (TKV) is an excellent indicator of model quality and can be used to rank different input feature sets.

Subfields: Atomic and Molecular Physics, and Optics, Electrochemistry, Molecular Biology

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Citations in the corpus

Deep Generative Markov State Models (2018, methodology): “For comparison with deep MSMs, we build two standard MSMs following a state of the art protocol: we transform input configurations with a kinetic map preserving 95% of the cumulative kinetic variance [17], followed by k-means clustering, where k = 6 and k = 100 are used.”
Variational Approach for Learning Markov Processes from Time Series Data (2017, background): “the sum of the estimated eigenvalues [41, 35], and VAMP-2 maximizes the kinetic variance introduced in [40].”
VAMPnets for deep learning of molecular kinetics (2017, methodology): “The resulting coordinates may be scaled, in order to embed them in a metric space whose distances correspond to some form of dynamical distance [31, 32].”
Variational Encoding of Complex Dynamics (2017, methodology): “In this model, a tICA lag time of 44 ns, 4 tICA components, and kinetic mapping 47 were selected according to hyperparameter optimization 36 .”
Multiensemble Markov models of molecular thermodynamics and kinetics (2016, unknown): “[31]”
Data-Driven Model Reduction and Transfer Operator Approximation (2017, background): “The number M is selected by the criterion of total kinetic variance, that is, M is the smallest number such that the cumulative sum of the first M squared eigenvalues exceeds 95 per cent of the total sum of squared eigenvalues [45].”