#!/usr/bin/env python3
"""Module containing the FATSLiM Membranes class and the command line interface."""
from biobb_common.generic.biobb_object import BiobbObject
from biobb_common.tools.file_utils import launchlogger
from biobb_mem.fatslim.common import ignore_no_box, move_output_file
import MDAnalysis as mda
import numpy as np
[docs]
class FatslimMembranes(BiobbObject):
"""
| biobb_mem FatslimMembranes
| Wrapper of the `FATSLiM membranes <https://pythonhosted.org/fatslim/documentation/leaflets.html>`_ module for leaflet and membrane identification.
| FATSLiM is designed to provide efficient and robust analysis of physical parameters from MD trajectories, with a focus on processing large trajectory files quickly.
Args:
input_top_path (str): Path to the input topology file. File type: input. `Sample file <https://github.com/bioexcel/biobb_mem/raw/main/biobb_mem/test/data/A01JD/A01JD.pdb>`_. Accepted formats: tpr (edam:format_2333), gro (edam:format_2033), g96 (edam:format_2033), pdb (edam:format_1476), brk (edam:format_2033), ent (edam:format_1476).
input_traj_path (str) (Optional): Path to the GROMACS trajectory file. File type: input. `Sample file <https://github.com/bioexcel/biobb_mem/raw/main/biobb_mem/test/data/A01JD/A01JD.xtc>`_. Accepted formats: xtc (edam:format_3875), trr (edam:format_3910), cpt (edam:format_2333), gro (edam:format_2033), g96 (edam:format_2033), pdb (edam:format_1476), tng (edam:format_3876).
input_ndx_path (str) (Optional): Path to the input lipid headgroups index NDX file. File type: input. `Sample file <https://github.com/bioexcel/biobb_mem/raw/main/biobb_mem/test/data/A01JD/A01JD.ndx>`_. Accepted formats: ndx (edam:format_2033).
output_ndx_path (str): Path to the output index NDX file. File type: output. `Sample file <https://github.com/bioexcel/biobb_mem/raw/main/biobb_mem/test/reference/fatslim/leaflets.ndx>`_. Accepted formats: ndx (edam:format_2033).
properties (dic - Python dictionary object containing the tool parameters, not input/output files):
* **selection** (*str*) - ("not protein and element P") Alternative ot the NDX file for choosing the Headgroups used in the identification using MDAnalysis `selection language <https://docs.mdanalysis.org/stable/documentation_pages/selections.html>`_.
* **cutoff** (*float*) - (2) Cutoff distance (in nm) to be used when leaflet identification is performed.
* **begin_frame** (*int*) - (-1) First frame index to be used for analysis.
* **end_frame** (*int*) - (-1) Last frame index to be used for analysis.
* **ignore_no_box** (*bool*) - (False) Ignore the absence of box information in the topology. If the topology does not contain box information, the box will be set to the minimum and maximum positions of the atoms.
* **return_hydrogen** (*bool*) - (False) Include hydrogen atoms in the output index file.
* **binary_path** (*str*) - ("fatslim") Path to the fatslim executable binary.
* **remove_tmp** (*bool*) - (True) [WF property] Remove temporal files.
* **restart** (*bool*) - (False) [WF property] Do not execute if output files exist.
* **sandbox_path** (*str*) - ("./") [WF property] Parent path to the sandbox directory.
Examples:
This is a use example of how to use the building block from Python::
from biobb_mem.fatslim.fatslim_membranes import fatslim_membranes
prop = {
'selection': '(resname DPPC and name P8)',
'cutoff': 2.2
}
fatslim_membranes(input_top_path='/path/to/myTopology.tpr',
input_traj_path='/path/to/myTrajectory.xtc',
output_ndx_path='/path/to/newIndex.ndx',
properties=prop)
Info:
* wrapped_software:
* name: FATSLiM
* version: 0.2.2
* license: GNU
* ontology:
* name: EDAM
* schema: http://edamontology.org/EDAM.owl
"""
def __init__(self, input_top_path, output_ndx_path, input_traj_path=None,
input_ndx_path=None, properties=None, **kwargs) -> None:
properties = properties or {}
# Call parent class constructor
super().__init__(properties)
self.locals_var_dict = locals().copy()
# Input/Output files
self.io_dict = {
"in": {"input_top_path": input_top_path,
"input_traj_path": input_traj_path,
"input_ndx_path": input_ndx_path
},
"out": {"output_ndx_path": output_ndx_path}
}
# Properties specific for BB
self.selection = properties.get('selection', "not protein and element P")
self.cutoff = properties.get('cutoff', 2)
self.begin_frame = properties.get('begin_frame', -1)
self.end_frame = properties.get('end_frame', -1)
self.ignore_no_box = properties.get('ignore_no_box', False)
self.return_hydrogen = properties.get('return_hydrogen', False)
self.binary_path = properties.get('binary_path', 'fatslim')
self.properties = properties
# Check the properties
self.check_properties(properties)
self.check_arguments()
[docs]
@launchlogger
def launch(self) -> int:
"""Execute the :class:`FatslimMembranes <fatslim.fatslim_membranes.FatslimMembranes>` fatslim.fatslim_membranes.FatslimMembranes object."""
# Setup Biobb
if self.check_restart():
return 0
self.stage_files()
# Create index file using MDAnalysis
u = mda.Universe(topology=self.stage_io_dict["in"]["input_top_path"],
coordinates=self.stage_io_dict["in"].get("input_traj_path"))
ignore_no_box(u, self.ignore_no_box, self.out_log, self.global_log)
# Build the index to select the atoms from the membrane
if self.stage_io_dict["in"].get('input_ndx_path', None):
tmp_ndx = self.stage_io_dict["in"]["input_ndx_path"]
else:
tmp_ndx = self.create_tmp_file('_headgroups.ndx')
with mda.selections.gromacs.SelectionWriter(tmp_ndx, mode='w') as ndx:
ndx.write(u.select_atoms(self.selection), name='headgroups')
if self.stage_io_dict["in"]["input_top_path"].endswith('gro'):
cfg = self.stage_io_dict["in"]["input_top_path"]
else:
# Convert topology .gro and add box dimensions if not available in the topology
cfg = self.create_tmp_file('_output.gro')
# Save as GRO file with box information
u.atoms.write(cfg)
tmp_out = self.create_tmp_file('_output.ndx')
# Build command
self.cmd = [
self.binary_path, "membranes",
"-n", tmp_ndx,
"-c", cfg,
"--output-index", tmp_out,
"--cutoff", str(self.cutoff),
"--begin-frame", str(self.begin_frame),
"--end-frame", str(self.end_frame)
]
# Run Biobb block
self.run_biobb()
# Fatslim ignore H atoms so we add them manually
if self.return_hydrogen:
# Parse the atoms indices of the membrane without Hs
leaflet_groups = parse_index(tmp_out[:-4]+'_0000.ndx')
with mda.selections.gromacs.SelectionWriter(self.stage_io_dict["out"]["output_ndx_path"], mode='w') as ndx:
for key, value in leaflet_groups.items():
# Select the residues using atom indexes
res_sele = set(u.atoms[np.array(value)-1].residues.resindices)
# Use the rexindex to select all the atoms of the residue
sele = f"resindex {' '.join(map(str, res_sele))}"
ndx.write(u.select_atoms(sele), name=key)
else:
move_output_file(tmp_out, self.stage_io_dict["out"]["output_ndx_path"], self.out_log, self.global_log)
# Copy files to host
self.copy_to_host()
# Remove temporary files
self.remove_tmp_files()
self.check_arguments(output_files_created=True, raise_exception=False)
return self.return_code
[docs]
def fatslim_membranes(input_top_path: str, output_ndx_path: str, input_traj_path: str = None, input_ndx_path: str = None, properties: dict = None, **kwargs) -> int:
"""Execute the :class:`FatslimMembranes <fatslim.fatslim_membranes.FatslimMembranes>` class and
execute the :meth:`launch() <fatslim.fatslim_membranes.FatslimMembranes.launch>` method."""
return FatslimMembranes(**dict(locals())).launch()
fatslim_membranes.__doc__ = FatslimMembranes.__doc__
main = FatslimMembranes.get_main(fatslim_membranes, "Calculates the density along an axis of a given cpptraj compatible trajectory.")
[docs]
def parse_index(ndx):
"""
Parses a GROMACS index file (.ndx) to extract leaflet groups.
Args:
ndx (str): Path to the GROMACS index file (.ndx).
Returns:
dict: A dictionary where keys are group names for each leaflet in format "membrane_1_leaflet_1" and values are lists of integers representing atom indices starting from 1.
"""
# Read the leaflet.ndx file
with open(ndx, 'r') as file:
leaflet_data = file.readlines()
# Initialize dictionaries to store leaflet groups
leaflet_groups = {}
current_group = None
# Parse the leaflet.ndx file
for line in leaflet_data:
line = line.strip()
if line.startswith('[') and line.endswith(']'):
current_group = line[1:-1].strip()
leaflet_groups[current_group] = []
elif current_group is not None:
leaflet_groups[current_group].extend(map(int, line.split()))
return leaflet_groups
[docs]
def display_fatslim(input_top_path: str, lipid_sel: str, input_traj_path: str = None, output_ndx_path="leaflets.ndx", leaflets=True,
colors=['blue', 'cyan', 'yellow', 'orange', 'purple', 'magenta'], non_mem_color='red'):
"""
Visualize the leaflets of a membrane using NGLView. The lipids in the membrane are colored according to their leaflet. The ones not in the membrane are colored in red.
Args:
input_top_path (str): Path to the input topology file.
input_traj_path (str, optional): Path to the input trajectory file. Default is None.
output_ndx_path (str, optional): Path to the output index file containing leaflet information. Default is "leaflets.ndx".
leaflets (bool, optional): If True, visualize individual leaflets. If False, visualize entire membranes. Default is True.
colors (list of str, optional): List of colors to use for visualizing the leaflets or membranes. Default is ['blue', 'cyan', 'yellow', 'orange', 'purple', 'magenta'].
non_mem_color (str, optional): Color to use for visualizing lipids not in the membrane. Default is 'red'.
Returns:
nglview.NGLWidget: An NGLView widget displaying the membrane leaflets.
"""
try:
import nglview as nv
except ImportError:
raise ImportError('Please install the nglview package to visualize the membrane/s.')
u = mda.Universe(topology=input_top_path,
coordinates=input_traj_path)
# Visualize the system with NGLView
view = nv.show_mdanalysis(u)
view.clear_representations()
leaflet_groups = parse_index(output_ndx_path)
n_mems = len(leaflet_groups.keys())//2
non_mem_resn = set(u.select_atoms(lipid_sel).residues.resnums)
for n in range(n_mems):
# Convert atoms list to resnums (nglview uses cannot use resindex)
top_resn = u.atoms[np.array(leaflet_groups[f'membrane_{n+1}_leaflet_1'])-1].residues.resnums
bot_resn = u.atoms[np.array(leaflet_groups[f'membrane_{n+1}_leaflet_2'])-1].residues.resnums
non_mem_resn -= set(top_resn)
non_mem_resn -= set(bot_resn)
if leaflets:
view.add_point(selection=", ".join(map(str, top_resn)), color=colors[n*2]) # lipids in top leaflet
view.add_point(selection=", ".join(map(str, bot_resn)), color=colors[n*2+1]) # lipids in bot leaflet
else:
mem_resn = np.concatenate((top_resn, bot_resn))
view.add_point(selection=", ".join(map(str, mem_resn)), color=colors[n*2]) # lipids in membrane
if len(non_mem_resn) > 0:
view.add_point(selection=", ".join(map(str, non_mem_resn)), color=non_mem_color) # lipids without membrane
return view
if __name__ == '__main__':
main()