Sequential Voxel-Based Leaflet Segmentation of Complex Lipid Morphologies

Abstract

As molecular dynamics simulations increase in complexity, new analysis tools are necessary to facilitate interpreting the results. Lipids, for instance, are known to form many complicated morphologies, because of their amphipathic nature, becoming more intricate as the particle count increases. A few lipids might form a micelle, where aggregation of tens of thousands could lead to vesicle formation. Millions of lipids comprise a cell and its organelle membranes, and are involved in processes such as neurotransmission and transfection. To study such phenomena, it is useful to have analysis tools that understand what is meant by emerging entities such as micelles and vesicles. Studying such systems at the particle level only becomes extremely tedious, counterintuitive, and computationally expensive. To address this issue, we developed a method to track all the individual lipid leaflets, allowing for easy and quick detection of topological changes at the mesoscale. By using a voxel-based approach and focusing on locality, we forego costly geometrical operations without losing important details and chronologically identify the lipid segments using the Jaccard index. Thus, we achieve a consistent sequential segmentation on a wide variety of (lipid) systems, including monolayers, bilayers, vesicles, inverted hexagonal phases, up to the membranes of a full mitochondrion. It also discriminates between adhesion and fusion of leaflets. We show that our method produces consistent results without the need for prefitting parameters, and segmentation of millions of particles can be achieved on a desktop machine.

Molecular dynamics simulation of an entire cell

“…One possibility is storing only centers-of-mass movement of the non-aqueous components, which would facilitate the analysis of diffusional behavior, for instance. Another approach would be using a voxel-based method ( Bruininks et al, 2021 ) to dynamically segment the whole-cell model into similarity regions, e.g., membrane periphery or chromosomal region. The system segmentation would allow for efficient quantitative comparison of the cytosolic properties within and between distinct regions of the cell.…”

Two decades of Martini: Better beads, broader scope

“…Dedicated analysis tools include Prolint 105 and PyLipID 106 for identifying and analyzing protein–lipid interactions, MemSurfer 107 to define and analyze membrane surfaces, MDVoxelSegmentation 108 for automated lipid leaflet detection, cgHeliParm 109 to analyze CG DNA, a tool based on machine learning to identify membrane domains called NMFk, 110 a tool to directly calculate SAXS spectra from Martini simulations, 111 a tool to analyze the aggregation state of self‐assembling systems (MorphoScanner 112 ), and a tool to analyze diffusion on curved surfaces (CurD 113 ).…”

Unbreaking Assemblies in Molecular Simulations with Periodic Boundaries

“…The selection of interest is voxelized using the previously proposed voxelization scheme . For voxelization, the resolution must be defined; it can be set using the - resolution flag (Figure .2).…”