lipyphilic is a set of tools for analysing MD simulations of lipid bilayers. It is an object-oriented Python package built directly on top of MDAnalysis, and makes use of NumPy and SciPy for efficient computation.
The analysis tools are designed with the same interface as those of MDAnalysis - so if you know how to use analysis modules in MDAnalysis then learning lipyphilic will be a breeze.
Assign lipids to leaflets: Illustrates basic usage of LiPyphilic, including how to store results for later usage. Also shows how to assign lipids to leaflets, which is required for many other analyses.
Flip-flop rate: Shows how to use LiPyphilic to calculate the rate of cholesterol flip-flop, as well as identify the frames at which each flip-flop event begins and ends.
Local lipid environments: Illustrates how to determine the local lipid environment of each lipid over time, as well as the enrichment/depletion index.
Lipid domains: Shows how to calculate the largest cluster of specific lipids over time. Examples include finding the largest ganglioside cluster in a neuronal plasma membrane and identifying the largest domain of $L_o$ lipids in a phase separated membrane.
Interleaflet registration: This notebook shows how to calculate the interleaflet registration over time. The example shows how to calculate the registration of $L_o$ lipids across leaflets.
Lateral diffusion: Illustrates how to perform "nojump" trajectory unwrapping with LiPyphilic, then use the unwrapped coordinates to calculate the mean-squared displacement and lateral diffusion coefficient of lipids in a membrane.
Coarse-grained lipid order parameter: Shows how to calculate the coarse-grained order parameter, and how to create a two-dimensional proection of these values onto the membrane plane.
Projection plots: Shows how to create two-dimensional projections of arbitrary lipid properties onto the membrane plane. Examples include projecting local membrane thicknesses calculated using FATSLiM onto the membrane plane, and projecting the ordered state ($\rm L_o$ and $\rm L_d$) of lipids onto the membrane plane.
Potential of mean force (PMF): This notebook illustrates how to use LiPyphilic to calculate the height and orientation of sterols in a membrane, and subsequently plot the two-dimensional PMF of sterol height and orientation.
Hidden Markov Models: Learn how to use the output of LiPyphilic to construct Hidden Markov Models (HMM) with HMMLearn. Here we will create a HMM based on lipid thicknesses to detect $\rm L_o$ and $\rm L_d$ lipids in a phase separated membrane. The output from this is can be used as input to other analyses in LiPyphilic, such as calculating interleaflet registration or local lipid environments.