LiPyphilic: A Python Toolkit for the Analysis of Lipid Membrane Simulations

Abstract

Molecular dynamics simulations are now widely used to study emergent phenomena in lipid membranes with complex compositions. Here, we present LiPyphilica fast, fully tested, and easy-to-install Python package for analyzing such simulations. Analysis tools in LiPyphilic include the identification of cholesterol flipflop events, the classification of local lipid environments, and the degree of interleaflet registration. LiPyphilic is both force fieldand resolution-agnostic, and by using the powerful atom selection language of MDAnalysis, it can handle membranes with highly complex compositions. LiPyphilic also offers two on-the-fly trajectory transformations to (i) fix membranes split across periodic boundaries and (ii) perform nojump coordinate unwrapping. Our implementation of nojump unwrapping accounts for fluctuations in the box volume under the NPT ensemblean issue that most current implementations have overlooked. The full documentation of LiPyphilic, including installation instructions and links to interactive online tutorials, is available at https://lipyphilic.readthedocs.io/en/latest.

Unraveling membrane properties at the organelle-level with LipidDyn

“…For the all-atom trajectories, the class includes a reimplementation of the algorithm in NMRlipids [ https://github.com/NMRLipids ] to calculate the carbon-hydrogen order parameter ( S CH ) of the acyl chains. For the coarse-grained trajectories, the class includes the module SCC from the LiPyphilic package [40] to calculate the carbon–carbon order parameter ( S CC ) of the acyl chains. For each lipid species, consecutive carbon atom pairs composing the sn-1 and sn-2 acyl chains are defined inside the configuration file.…”

“…We compared the analyses provided by LipidDyn with other available tools ( Fig. 2 ) [40] , [41] , [42] . Each tool focuses on a group of analyses, with some classical ones in common, such as order parameter, thickness, and APL.…”

“…Similar applications will easily result in massive simulation data to analyze. Various tools calculate properties from MD simulations that can be compared to experimental observables from biophysical spectroscopies [36] , [37] , [38] , [39] , [40] , [41] , [42] . A bottleneck is making reproducible and simplifying the steps for analysis when several simulations should be analyzed in parallel.…”

Specific interactions of peripheral membrane proteins with lipids: what can molecular simulations show us?

“…These might not necessarily be optimal for looking at specific protein–lipid interactions but might offer useful tools for looking at the interaction of the PMP with the membrane more generally, including how the membrane changes upon PMP binding. Of particular note are FATSLiM [ 111 ], MemSurfer [ 112 ], LOOS [ 113 , 114 ] and LiPyphilic [ 115 ]. This range of programs, along with ProLint and PyLipID mentioned above, showcase the increasing desire for more rigorous and detailed analyses of membrane simulations, and the impressive commitment of the academic community to fulfil this demand.…”

Impact of Surface Polarity on Lipid Assembly under Spatial Confinement

“…Velocity profiles were calculated using the MDAnalysis streamlines package, and lipid-order parameters were created using the lipyphilic python package in python, and plots were generated using matplotlib. 41 , 42 …”