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Kinetic distance and kinetic maps from molecular dynamics simulation.

Frank Noé1, Cecilia Clementi2

  • 1FU Berlin , Department of Mathematics, Computer Science and Bioinformatics, Arnimallee 6, 14195 Berlin, Germany.

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This study introduces a kinetic distance metric for molecular dynamics simulations to quantify conformational interconversion. This metric, derived from time-lagged independent component analysis (TICA), simplifies kinetic model construction and improves analysis of macromolecular dynamics.

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Area of Science:

  • Computational chemistry and biophysics
  • Statistical mechanics
  • Machine learning for molecular modeling

Background:

  • Characterizing macromolecular kinetics from molecular dynamics (MD) simulations is crucial for understanding biological processes.
  • Existing methods often require complex distance metrics to distinguish slowly interconverting states.
  • A robust and interpretable metric is needed to accurately model conformational dynamics.

Purpose of the Study:

  • To develop a novel kinetic distance metric based on diffusion map theory for irreducible Markov processes.
  • To quantify the rate of interconversion between molecular conformations.
  • To simplify the construction and parameterization of kinetic models derived from MD simulations.

Main Methods:

  • Utilized diffusion map theory to define a kinetic distance metric.
  • Employed time-lagged independent component analysis (TICA) to approximate Markov operator eigenvalues and eigenvectors (reaction coordinates).
  • Scaled TICA components to create a kinetic map where Euclidean distance equals kinetic distance.

Main Results:

  • Demonstrated the kinetic distance metric using TICA and Markov state model (MSM) analyses on illustrative models and protein systems.
  • Showcased that the number of TICA dimensions to retain becomes irrelevant, reducing model parameters.
  • Identified Total Kinetic Variance (TKV) as a reliable indicator for assessing model quality and ranking feature sets.

Conclusions:

  • The proposed kinetic distance metric effectively quantifies macromolecular conformational interconversion rates.
  • TICA-based kinetic mapping simplifies kinetic model construction by eliminating the need to pre-specify the number of dimensions.
  • Total Kinetic Variance (TKV) serves as a valuable metric for evaluating the quality of kinetic models and input data.