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Experiment-Guided Refinement of Milestoning Network.

Xiaojun Ji^1,2, Hao Wang³, Wenjian Liu³

¹Research Center for Mathematics and Interdisciplinary Sciences, Shandong University, Qingdao, Shandong 266237, P.R. China.

Journal of Chemical Theory and Computation

|January 23, 2025

Summary

This summary is machine-generated.

This study refines Milestoning simulations using maximum caliber (MaxCal) to improve kinetic predictions. By integrating experimental data, the enhanced method better aligns simulation results with real-world observations for molecular dynamics.

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Area of Science:

Computational Chemistry
Molecular Dynamics
Biophysics

Background:

Milestoning is a computational method for calculating rare event kinetics by building kinetic networks.
The accuracy of Milestoning is limited by force field accuracy, often causing discrepancies with experimental data.
Existing methods effectively control sampling errors but not force field inaccuracies.

Purpose of the Study:

To present a novel refinement approach for Milestoning networks using the maximum caliber (MaxCal) principle.
To integrate experimental thermodynamic and kinetic data into Milestoning simulations.
To improve the quantitative accuracy of Milestoning by minimizing discrepancies with experimental data.

Main Methods:

Developed a refinement approach based on the maximum caliber (MaxCal) variational principle.
Used Kullback-Leibler divergence rate as a loss function to minimize differences between Milestoning networks.
Incorporated experimental equilibrium and rate constants as constraints in the MaxCal framework.
Applied the method to study ligand binding/unbinding dynamics with β-cyclodextrin.

Main Results:

The MaxCal refinement approach successfully integrates experimental data into Milestoning networks.
The refined kinetic networks show improved alignment with experimental thermodynamic and kinetic data.
Demonstrated the method's efficacy using a model system of small molecule ligands and β-cyclodextrin.

Conclusions:

The MaxCal-based refinement offers a robust strategy to enhance the accuracy of Milestoning simulations.
This approach minimizes perturbation from original simulations while satisfying experimental constraints.
The refined Milestoning networks provide more reliable predictions for molecular kinetics and thermodynamics.