MDiGest Public repository.
Best practices made easy for analysis of correlated motions from molecular dynamics simulations.
MDiGest is a comprehensive and user-friendly toolbox designed to facilitate the analysis of molecular dynamics simulations. It contains a wide range of methods ranging from standard to less-standard approaches that allow users to investigate various features extracted from MD trajectories. This includes the correlated dynamics of atomic motions, diherdrals, coupled electrostatic interactions, and more, that can be used to further explore conformational changes of proteins. The tools in the package are organized in a structured way, so that users can easily integrate different metrics into their analysis. Due to the complexity of molecular dynamics analysis, the choice of method can have a major influence on the results. To support this, MDiGest allows users to easily compare multiple approaches, which benefits the user in that it constitutes an all-in one versatile and adaptable platform. Additionally, the package provides a number of visualization tools to further explore the features extracted from the MD trajectories.
pip install git+https://github.com/fmaschietto/mdigest
Pip manages all dependencies but the conda environment file env.yml can be used to
create a new conda environment
conda env create --name <env> --file env.yml
once the environment is created,
conda activate <env>
will activate it. Pymol should be installed in the environment as:
conda install -c schrodinger pymol-bundle
To run in a Jupyter Notebook, you will have to add this new environment to the list of kernels:
python -m ipykernel install --user --name=<env>
Full documentation for the software is available in readthedocs
Load modules
import mdigest
from mdigest.core.parsetrajectory import *
from mdigest.core.correlation import *
from mdigest.core.dcorrelation import *
from mdiges.core.networkcanvas import *
from mdigest.core.auxiliary import *load a trajectory and topology
parent = '/path/to/trajectory/'
topology = parent + 'a_topology.psf'
trajectory = parent + 'a_trajectory.dcd' mds = MDS()
# set number of replicas
mds.set_num_replicas(1) # use 2 if you have 2 replicas.
#load topology and trajectory files into MDS class
mds.load_system(topology, trajectory)
#align trajectory
mds.align_traj(inMemory=True, selection='name CA')
set selections for MDS class
mds.set_selection('protein and name CA', 'protein')
#stride trajectory
mds.stride_trajectory(initial=0, final=-1, step=5) dyncorr = DynCorr(mds)
dyncorr.parse_dynamics(scale=True, normalize=True, LMI='gaussian', MI='None', DCC=True, PCC=True, VERBOSE=True, COV_DISP=True) dihdyncorr = DynCorr(mds)
dihdyncorr.parse_dih_dynamics(mean_center=True, LMI='gaussian', MI='knn_5_2', DCC=True, PCC=True, COV_DISP=True) savedir = '/save/directory'
dyncorr.save_class(file_name_root=savedir + 'dyncorr')
dihdyncorr.save_class(file_name_root=savedir + 'dihdyncorr') dyncorr_load = sd.MDSdata()
dyncorr_load.load_from_file(file_name_root=savedir + 'dyncorr')
dyncorr_load.load_from_file(file_name_root=savedir + 'dihdyncorr') dist = dyncorr_load.distances_allreplicas['rep_0'].copy() load different correlation matrices linearized mutual-information based generalized correlation coefficient ()
viznetdir = '/directory/where/to/save/networks'
gcc = dyncorr_load.gcc_allreplicas['rep_0']['gcc_lmi'].copy()
dgcc = dyncorr_load.dih_gcc_allreplicas['rep_0']['gcc_lmi'].copy()
matrix_dictionary = {'gcc': gcc, 'dgcc':dgcc}
vizcorr = ProcCorr()
vizcorr.source_universe(mds.mda_u)
vizcorr.writePDBforframe(0, viznetdir + 'frame0')
vizcorr.set_outputparams({'outdir': viznetdir })
vizcorr.load_matrix_dictionary(matrix_dictionary.copy())
vizcorr.populate_attributes(matrix_dictionary.copy())
vizcorr.set_thresholds(prune_upon=np.asarray(dist.copy()), lower_thr=0, upper_thr=5.)
vizcorr.filter_by_distance(matrixtype='gccT', distmat=True)
vizcorr.filter_by_distance(matrixtype='dgcc', distmat=True)
df = vizcorr.df
to_pickle(df, output= viznetdir + 'network_filter_d_0_5.pkl'.format(0,5)) cd ./mdigest/visualize_networks/execute pymol locally calling pymol from inside the directory.
load a pdb of one frame of the system. It is best to use one frame extracted from
the trajectory to ensure consistency with residue numbers.
from pymol import cmd, util
import seaborn as sns
cmd.delete('all')
viznetdir = '/directory/where/to/save/networks'
cmd.load(path + 'prot.pdb', '1u2p')
cmd.color('grey80', 'prot')
cmd.remove('!(polymer)')
cmd.run('draw_network_pymol.py')
cmd.hide('lines', '*')visualize short-range correlations from CA displacements on the protein
draw_network_from_df(viznetdir +'network_filter_d_0_5.pkl', which='gcc', color_by='gcc', sns_palette=sns.color_palette("tab20"), label='gcc', edge_norm=1)``interactively compare with short-range correlations computed from dihedrals
draw_network_from_df(viznetdir +'network_filter_d_0_5.pkl', which='dgcc', color_by='dgcc', sns_palette=sns.color_palette("tab20"), label='dgcc', edge_norm=1)easily inspect different different metrics, such as dynamical cross correlation, mutual-information based correlation... at the desired threshold!
Many more examples are illustrated in the mdigest-tutorial-notebook (in the notebooks/ folder) with four case studies to perform analysis of MD trajectories.
Notebooks are best run in google colab.
If run locally, add jupyter-kernel to the environment
conda install -c anaconda ipykernel
python -m ipykernel install --user --name=<env>The molecular trajectories required for the notebook are available for download at the following links
- IGPS: https://drive.google.com/drive/folders/1XK8X18NJQY-dQUrQaeCGZtSyKeaze5mr?usp=sharing
- MptpA: https://drive.google.com/drive/folders/102mgn-bvH3GazRoMTlNqaEN6tilUJqZw?usp=sharing
Federica Maschietto, Brandon Allen, Gregory W. Kyro, Victor S. Batista, Journal of Chemical Physics, (2023), in press; MDiGest: A Python Package for Describing Allostery from Molecular Dynamics Simulations.
preprint to be updated
MDiGest is not the first (nor will be the last) package that allows such analysis, and therefore some of the contents were implemented before in other packages.
Some of the packages such as MDAnalysis, NetworkX, etc are imported directly, others are not directly imported but were used to some extent in building MDiGest.
Among these a notable recently released package antecedent is dynetan, graph-oriented python package to compute and anlalyze mutual-information based generalized correlation correlation from MD trajectories.
Some of the modules of MDiGest, namely processtrajectory.py and savedata.py are riminescent of the structure of modules performing similar tasks in dynetan.
Moreover, as specifically mentioned in the documentation, some accessory functions were adapted from it, the list of which is stated below:
-
core.toolkit.log_progress, generates a log bar showing the progress of the computation -
core.toolkit.get_path, retrieves the minimum path from a source and target node in the calculation of the shortest_path, -
core.toolkit.get_NGLselection_from_node, creates an atom selection for NGLView and an atom-selection object, -
core.toolkit.get_selection_from_node, retrieves a selection string from a node (resname, resid, segid and name), returning an atom-selection object.
Another notable package is correlationplus, which also focuses on analysis of correlated motions from molecular dynamics simulations.
As mentioned in the documentation, the compute_DCC_matrix and compute_DCC functions used to compute dynamical cross-correlation coefficients in MDiGest were adapted from a related function in correlationplus.
dynetan and correlationplus are released under the GPL-v3 and LGPL licenses, hence, MDiGest was released under the GPL-v3 license. In the future, we plan to change such functions, such that we will be able to release the MDiGest under a more permissive license.
Please remember to cite the latter when using these functionalities in MDiGest!
Another package which deserves a mention here is pmdlearn.
Although the main capabilities of the latter are very different from what implemented in MDiGest, it provides a comprehensive module for network analysis, some parts of which we adapted in MDiGest.