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Dynamic Clamp Methods to Investigate Impaired Neuronal Excitability Associated with Autism

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Directed kinetic transition network model.

Hongyu Zhou¹, Feng Wang¹, Doran I G Bennett¹

¹Department of Chemistry, Center for Scientific Computation, Center for Drug Discovery, Design, and Delivery (CD4), Southern Methodist University, Dallas, Texas 75275, USA.

The Journal of Chemical Physics

|October 17, 2019

Summary

This summary is machine-generated.

We developed a Directed Kinetic Transition Network (DKTN) to analyze protein conformational changes from molecular dynamics simulations. This method models nonequilibrium kinetics without assuming the Markovian property, revealing critical transitions.

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Area of Science:

Computational chemistry
Biophysics
Chemical kinetics

Background:

Molecular dynamics simulations offer rich kinetic data on protein function but are limited by high-dimensional configuration space.
Markov state models and transition networks are common for extracting kinetics from equilibrium simulations.
Existing models often assume equilibrium or Markovian properties, limiting their applicability to nonequilibrium processes.

Purpose of the Study:

To develop a novel graph representation, the Directed Kinetic Transition Network (DKTN), for analyzing nonequilibrium kinetics in molecular systems.
To model protein conformational changes using a master equation framework suitable for non-equilibrium conditions.
To identify critical state transitions in proteins using a mixing time criterion.

Main Methods:

Developed the Directed Kinetic Transition Network (DKTN) as a graph representation of a master equation.
Modeled transition rate matrices among states under detailed balance.
Utilized half mixing time from Markov chains to identify critical protein conformational transitions.
Reformulated DKTN into a continuous-time Markov chain model.

Main Results:

The DKTN effectively models nonequilibrium kinetics by representing the master equation.
The half mixing time criterion successfully identified critical transitions in protein conformational changes.
The DKTN can be reformulated as a continuous-time Markov chain, offering a more general framework.
Demonstrated the DKTN's utility using the photo-sensitive protein vivid as a model system.

Conclusions:

The DKTN provides a powerful graph-based approach to model protein conformational changes using chemical kinetics.
This method overcomes the limitation of the Markovian assumption inherent in many existing models.
DKTN is suitable for analyzing nonequilibrium processes in molecular dynamics simulations.