Back

Coarse-Grained Simulations of Mycobacterial Outer Membranes Reveal Fluidity-Dependent PDIM Redistribution Across Different Lipid Environments

Acharya, B.; Lammichane, S.; Brown, T. P.; Chavant, M.; Im, W.

2026-02-19 biophysics
10.64898/2026.02.18.706594 bioRxiv
Show abstract

The mycobacterial outer membrane (MOM) constitutes an asymmetric permeability barrier that influences lipid organization and transport in Mycobacterium tuberculosis. In this study, we have developed MARTINI 3 coarse-grained (CG) lipid models of the MOM, incorporating -mycolic acids, 5 different trehalose-based lipids, and PDIM (phthiocerol dimycocerosate). The CG models were parameterized and validated using all-atom simulations of symmetric inner- and outer-leaflet membranes, as well as fully asymmetric MOM models. Bonded parameters were optimized through an iterative refinement procedure targeting atomistic bonded distributions. The CG simulations show good agreement with the all-atom simulation data and available experimental measurements in terms of the membrane thickness, solvent accessible surface area, lipid density profiles, and the outer-leaflet-induced lipid disorder in -mycolic acids at the inner leaflet. The model reproduces the temperature-dependent phase behavior of all-atom -mycolic acid membranes. Using this model, we demonstrate that PDIM localization, diffusion, and aggregation are strongly modulated by membrane fluidity and lipid composition, with enhanced translocation and clustering in liquid disordered environments. Our CG MOM lipid models provide a validated platform for large-scale simulations of mycobacterial membranes and enable mechanistic studies of lipid organization, membrane dynamics, and protein-membrane and membrane-drug interactions.

Published in Biomacromolecules · training set

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.