4.4.2. Leaflet identification — MDAnalysis.analysis.leaflet

This module implements the LeafletFinder algorithm, described in [Michaud-Agrawal2011]. It can identify the lipids in a bilayer of arbitrary shape and topology, including planar and undulating bilayers under periodic boundary conditions or vesicles.

One can use this information to identify

  • the upper and lower leaflet of a planar membrane by comparing the the center_of_geometry() of the leaflet groups, or
  • the outer and inner leaflet of a vesicle by comparing histograms of distances from the centre of geometry (or possibly simply the radius_of_gyration()).

See example scripts in the MDAnalysisCookbook on how to use LeafletFinder. The function optimize_cutoff() implements a (slow) heuristic method to find the best cut off for the LeafletFinder algorithm.

4.4.2.1. Algorithm

  1. build a graph of all phosphate distances < cutoff
  2. identify the largest connected subgraphs
  3. analyse first and second largest graph, which correspond to the leaflets

For further details see [Michaud-Agrawal2011].

4.4.2.2. Classes and Functions

class MDAnalysis.analysis.leaflet.LeafletFinder(universe, selectionstring, cutoff=15.0, pbc=False, sparse=None)[source]

Identify atoms in the same leaflet of a lipid bilayer.

This class implements the LeafletFinder algorithm [Michaud-Agrawal2011].

Parameters:
  • universe (Universe or str) – MDAnalysis.Universe or a file name (e.g., in PDB or GRO format)
  • selection (AtomGroup or str) – A AtomGroup instance or a Universe.select_atoms() selection string for atoms that define the lipid head groups, e.g. universe.atoms.PO4 or “name PO4” or “name P*”
  • cutoff (float (optional)) – head group-defining atoms within a distance of cutoff Angstroms are deemed to be in the same leaflet [15.0]
  • pbc (bool (optional)) – take periodic boundary conditions into account [False]
  • sparse (bool (optional)) – None: use fastest possible routine; True: use slow sparse matrix implementation (for large systems); False: use fast distance_array() implementation [None].

Example

The components of the graph are stored in the list LeafletFinder.components; the atoms in each component are numbered consecutively, starting at 0. To obtain the atoms in the input structure use LeafletFinder.groups():

L = LeafletFinder(PDB, 'name P*')
leaflet0 = L.groups(0)
leaflet1 = L.groups(1)

The residues can be accessed through the standard MDAnalysis mechanism:

leaflet0.residues

provides a ResidueGroup instance. Similarly, all atoms in the first leaflet are then

leaflet0.residues.atoms
group(component_index)[source]

Return a MDAnalysis.core.groups.AtomGroup for component_index.

groups(component_index=None)[source]

Return a MDAnalysis.core.groups.AtomGroup for component_index.

If no argument is supplied, then a list of all leaflet groups is returned.

groups_iter()[source]

Iterator over all leaflet groups()

sizes()[source]

Dict of component index with size of component.

update(cutoff=None)[source]

Update components, possibly with a different cutoff

write_selection(filename, **kwargs)[source]

Write selections for the leaflets to filename.

The format is typically determined by the extension of filename (e.g. “vmd”, “pml”, or “ndx” for VMD, PyMol, or Gromacs).

See MDAnalysis.selections.base.SelectionWriter for all options.

MDAnalysis.analysis.leaflet.optimize_cutoff(universe, selection, dmin=10.0, dmax=20.0, step=0.5, max_imbalance=0.2, **kwargs)[source]

Find cutoff that minimizes number of disconnected groups.

Applies heuristics to find best groups:

  1. at least two groups (assumes that there are at least 2 leaflets)

  2. reject any solutions for which:

    \[\frac{|N_0 - N_1|}{|N_0 + N_1|} > \mathrm{max_imbalance}\]

    with \(N_i\) being the number of lipids in group \(i\). This heuristic picks groups with balanced numbers of lipids.

Parameters:
  • universe (Universe) – MDAnalysis.Universe instance
  • selection (AtomGroup or str) – AtomGroup or selection string as used for LeafletFinder
  • dmin (float (optional)) –
  • dmax (float (optional)) –
  • step (float (optional)) – scan cutoffs from dmin to dmax at stepsize step (in Angstroms)
  • max_imbalance (float (optional)) – tuning parameter for the balancing heuristic [0.2]
  • kwargs (other keyword arguments) – other arguments for LeafletFinder
Returns:

optimum cutoff and number of groups found

Return type:

(cutoff, N)

Note

This function can die in various ways if really no appropriate number of groups can be found; it ought to be made more robust.