tICA-Metadynamics for Identifying Slow Dynamics in Membrane Permeation

• Published in: Journal of chemical theory and computation Vol. 19; no. 23; pp. 8886 - 8900
• Main Authors: Oh, Myongin, da Hora, Gabriel C. A., Swanson, Jessica M. J.
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 12.12.2023
• Subjects: Algorithms; Biomolecular Systems; Convergence; Degrees of freedom; Free energy; Independent component analysis; Machine learning; Membranes; Molecular dynamics; Permeation; System dynamics; Trimethoprim
• ISSN: 1549-9618; 1549-9626; 1549-9626
• DOI: 10.1021/acs.jctc.3c00526

Molecular simulations are commonly used to understand the mechanism of membrane permeation of small molecules, particularly for biomedical and pharmaceutical applications. However, despite significant advances in computing power and algorithms, calculating an accurate permeation free energy profile remains elusive for many drug molecules because it can require identifying the rate-limiting degrees of freedom (i.e., appropriate reaction coordinates). To resolve this issue, researchers have developed machine learning approaches to identify slow system dynamics. In this work, we apply time-lagged independent component analysis (tICA), an unsupervised dimensionality reduction algorithm, to molecular dynamics simulations with well-tempered metadynamics to find the slowest collective degrees of freedom of the permeation process of trimethoprim through a multicomponent membrane. We show that tICA-metadynamics yields translational and orientational collective variables (CVs) that increase convergence efficiency ∼1.5 times. However, crossing the periodic boundary is shown to introduce artifacts in the translational CV that can be corrected by taking absolute values of molecular features. Additionally, we find that the convergence of the tICA CVs is reached with approximately five membrane crossings and that data reweighting is required to avoid deviations in the translational CV.