Welcome to WaterNetworkAnalysis’s documentation!

WaterNetworkAnalysis

The WaterNetworkAnalysis (WNA) Python package serves as a set of tools for input preparation for ConservedWaterSearch python package which identifies conserved water molecules from Molecular Dynamics (MD) trajectories.

WNA can also be used to create PyMol or nglview visualisations of conserved water networks for drug discovery or materials science purposes.

Important links

Documentation: hosted on Read The Docs

GitHub repository: source code/contribute code

Issue tracker: Report issues/ request features

Citation

For citations and more infromation see ConservedWaterSearch citation.

Installation

The easiest ways to install WaterNetworkAnalysis is using pip:

pip install WaterNetworkAnalysis

Conda builds will be available soon.

WNA depends on ConservedWaterSearch (CWS). To utilize full visualisation capabilities of CWS, pymol should be installed. Easiest way to install pymol is via conda:

conda install -c conda-forge pymol-open-source

For more information see CWS installation guide.

Example

The following example shows how to use WaterNetworkAnalysis to prepare a MD trajectory and analyse the results for determination of conserved water networks.

from WaterNetworkAnalysis import align_trajectory
from WaterNetworkAnalysis import get_center_of_selection
from WaterNetworkAnalysis import get_selection_string_from_resnums
from WaterNetworkAnalysis import extract_waters_from_trajectory
from ConservedWaterSearch.water_clustering import WaterClustering
from ConservedWaterSearch.utils import get_orientations_from_positions

# MD trajectory filename
trajectory="md.xtc"
# topology filename
topology="md.gro"
# aligned trajectory filename
alignedtrj = "aligned_trj.xtc"
# aligned snapshot filename
aligned_snap = "aligned.pdb"
# distance to select water molecules around
distance = 12.0
# align the trajectory and save the alignment reference configuration
align_trajectory(
    trajectory=trajectory,
    topology=topology,
    align_target_file_name=aligned_snap,
    output_trj_file=alignedtrj,
)
# define active site by aminoacid residue numbers
active_site_resnums = [111, 112, 113, 122, 133, 138, 139, 142, 143, 157, 166, 167, 169, 170, 203, 231, 232, 238]
# find centre of the active site in aligned trajectory
selection_centre = get_center_of_selection(
    get_selection_string_from_resnums(active_site_resnums),
    trajectory=alignedtrj,
    topology=topology,
)
# extract water coordinates of interest around selection centre
coordO, coordH =  extract_waters_from_trajectory(
    trajectory=alignedtrj,
    topology=topology,
    selection_center=selection_centre,
    dist=distance
)
# start the clustering procedure
Nsnaps = 200
WC=WaterClustering(nsnaps= Nsnaps)
# perform multi stage reclustering
WC.multi_stage_reclustering(*get_orientations_from_positions(coordO,coordH))
# visualise results with pymol
WC.visualise_pymol(aligned_snap, active_site_ids=active_site_resnums, dist=distance)

Introduction:

API:

WaterNetworkAnalysis