tICA-Metadynamics for Identifying Slow Dynamics in Membrane Permeation

Area of Science:

• Computational chemistry
• Biophysics
• Pharmacology

Background:

• Molecular simulations are vital for understanding drug permeation across membranes in biomedical and pharmaceutical research.
• Accurate calculation of permeation free energy profiles is challenging due to the difficulty in identifying rate-limiting molecular degrees of freedom (reaction coordinates).
• Machine learning methods offer a promising avenue for identifying slow system dynamics crucial for accurate simulations.

Purpose of the Study:

• To apply time-lagged independent component analysis (tICA) combined with well-tempered metadynamics to identify slow collective variables for molecular permeation.
• To enhance the efficiency and accuracy of molecular dynamics simulations for drug permeation processes.
• To investigate the behavior of trimethoprim permeation through a multicomponent membrane.

Main Methods:

• Utilized molecular dynamics simulations coupled with well-tempered metadynamics.
• Applied time-lagged independent component analysis (tICA), an unsupervised dimensionality reduction technique.
• Analyzed the permeation of trimethoprim through a multicomponent membrane model.

Main Results:

• tICA-metadynamics successfully identified translational and orientational collective variables (CVs), improving simulation convergence by approximately 1.5 times.
• Artifacts in translational CVs due to periodic boundary crossing were identified and a correction method using absolute values of molecular features was proposed.
• tICA CV convergence was achieved with around five membrane crossings, and data reweighting was found necessary to prevent deviations in the translational CV.

Conclusions:

• The integration of tICA with metadynamics provides an effective strategy for identifying relevant slow dynamics in membrane permeation simulations.
• The developed methods offer a pathway to more efficient and accurate free energy calculations for drug permeation.
• Addressing artifacts like periodic boundary effects is crucial for reliable molecular simulation results in drug discovery.