Install#

System Requirements#

  • A 64-bit operating system (We have tested the installation on Ubuntu 20.04, Windows 10, and Windows 11, but any modern operating system should work.)

  • An Anaconda or Miniconda Python distribution (3.9 or greater)

  • An internet connection

Installation Instructions#

Step 1: Download and install Anaconda (https://www.anaconda.com/products/individual) or Miniconda (https://docs.conda.io/en/latest/miniconda.html) Python distribution.

_images/Anaconda.png

Step 2: Create the conda environment with MD DaVis dependencies. Open a Terminal or Anaconda prompt (on Windows) and issue the following command:

conda env create djmaity/md-davis

This creates a conda environment called md-davis with all required dependencies.

Step 3: Activate the md-davis environment with:

conda activate md-davis

Step 4: Install MD DaVis in this environment with pip:

pip install md-davis

Step 5: Obtain the external dependencies as stated below.

Installation for Development#

Step 1: Download and install Anaconda (https://www.anaconda.com/products/individual) or Miniconda (https://docs.conda.io/en/latest/miniconda.html) Python distribution.

Step 2: Create the conda environment with MD DaVis dependencies. In this case, you should use:

conda env create djmaity/md-davis-dev

This would create a conda environment called md-davis-env with the core dependencies and install additional libraries for packaging, documentation, and linting. You can also change the name of the conda environment by modifying the command above:

conda env create djmaity/md-davis-dev --name my_env

This will name the conda environment to my_env. Make sure that there are no other conda environments with the same name. The djmaity/md-davis-dev in the above commands uses the environment files uploaded to https://anaconda.org/djmaity/environments. These are the same files environment.yml and dev_environment.yml in the source code, which may also be used to create the conda environment and install the dependencies:

conda env create --file dev_environment.yml

See https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html to learn more about conda environments.

Note

We recommend using the environment files over installing the dependencies individually because installing all the dependencies together allows conda to resolve the appropriate version of packages to install and avoid package conflicts.

Step 3: Activate the conda environment with:

conda activate md-davis-dev

Remember to change md-davis-dev to the appropriate environment name if you modify it.

Step 4: Obtain a copy of the source code by cloning the public repository:

git clone git@github.com:djmaity/md-davis.git

Or, download the tarball:

curl -OL https://github.com/djmaity/md-davis/tarball/master

Step 5: Once you have a copy of the source, it can be installed with:

pip install path/to/source/code

The path to the directory containing the setup.py file has to be provided in the command above. You may want to install it as an editable package with:

pip install -e path/to/source/code

This will allow changes to the source code to be immediately reflected in the installed package.

Or, go to the directory containing the setup.py file and use:

python setup.py install

You can also install the development version directly with:

pip install https://github.com/djmaity/md-davis/archive/master.zip

Step 6: Obtain the external dependencies as stated below.

External Dependencies#

GROMACS#

Currently, most analyses have to be performed with GROMACS, and the output is provided to MD DaVis for visualization. We have successfully used MD DaVis on simulations and analysis performed with various GROMACS versions from 5.1.4 to 2021.3. Other analysis tools may be used as long as the input to MD DaVis can be appropriately formatted. See Interfacing MD DaVis to Other Analysis Tools.

DSSP#

The secondary structure of a trajectory is calculated by the GROMACS tool do_dssp, which requires DSSP. The latest and only available version of DSSP is 2.3.0. The executable is called mkdssp now. Help GROMACS find it by any of the following means:

  • Rename mkdssp to dssp

  • Make a symlink called dssp to mkdssp

  • Set the DSSP environment

PyMOL#

You do not need to obtain PyMOL separately if you use the installation methods outlined above using conda. Unfortunately, PyMOL is not available in the python package index. Therefore, it cannot be automatically installed with pip. However, Open-Source PyMOL and Commercial/Educational PyMOL are available in conda channels conda-forge and schrodinger, respectively.

Open-Source PyMOL#

The command in Step 2 automatically installs Open-Source PyMOL available from conda-forge. It can also be installed with:

conda install -c conda-forge pymol-open-source=2.5.0

Warning

Open-Source PyMOL 2.4.0 has a bug where it cannot open Gaussian cube files. DelPhi to output phimap volumetric data is in this format, which is used in the electrostatics calculations.

Alternatively, on Linux, PyMOL can be installed with the system package manager, e.g., apt in Ubuntu or dnf in Fedora. However, it is not possible to install PyMOL into a virtual environment using this method. Therefore, MD DaVis must be installed in the system python, which may create conflicts with existing python packages on which many system programs may depend.

On Windows, if you are not using conda, then pre-built Open-Source PyMOL can be downloaded from Christoph Gohlke’s page distributing unofficial windows binaries for python extension packages. However, we have faced issues with using this package with other dependencies of MD DaVis.

Commercial/Educational PyMOL#

The Commercial/Educational PyMOL can be used instead of Open-Source PyMOL by installing the md-davis-pymol or md-davis-pymol-dev environment in Step 2 of the above-mentioned installation methods:

conda env create djmaity/md-davis-pymol

Alternatively, it can be installed with:

conda install -c schrodinger pymol-bundle

DelPhi and MSMS#

Python dependencies are automatically installed. However, the electrostatics calculation requires the following two programs, which must be obtained separately.

Uninstall#

MD DaVis can be easily uninstalled like any other python package, with:

pip uninstall md-davis

As with any python package, this does not remove the dependencies installed by MD DaVis. That is why installing MD DaVis in a conda environment is recommended. Then, the whole conda environment may be entirely removed without affecting other python packages on the system.

conda env remove --name md-davis

where md-davis is the name of the conda environment. Modify the command to provide the appropriate name for the conda environment if you change it.

Note

On Linux, if MD DaVis was installed as root or with sudo (highly discouraged), the uninstall command should also be run with sudo.