Welcome to pyMODE-TASK’s documentation!

Introduction

pyMODE-TASK is a PyMOL plugin of MODE-TASK tool. MODE-TASK is a collection of open source tools to perform Principle component analysis (PCA), Kernel PCA, Incremental PCA, Multi dimensional analysis (MDS), t-distributed Stochastic Neighbor Embedding (t-SNE) on a protein MD trajectory and, Normal mode analysis (NMA) on a protein/viral capsid with a novel coarse graining algorithm.

Kindly see the detailed documentation of MODE-TASK here

http://mode-task.readthedocs.io/en/latest/index.html

Cite this project

A publication is currently being prepared

License

The project is licensed under GNU GPL 3.0

Installation

MODE-TASK can be installed by following the instruction given at http://mode-task.readthedocs.io/en/latest/installation.html

For installing the pyMODE-TASK - a pymol plugin.

Requirements:

python
Tkinter
Pmw
numpy==1.13.1
cython==0.26
scikit-learn==0.19.0
scipy==0.19.1
sklearn==0.0
matplotlib==2.0.2
mdtraj==1.8.0

1. Download the project:

git clone https://github.com/RUBi-ZA/pyMODE-TASK.git

OR

Download zip from github page and extract to a directory.

2. Install dependencies:

Run the following command from within pyMODE-TASK directory

sudo pip install -r requirements.txt

pyMODE-TASK requires Tkinter and Pmw.1.3. Tkinter comes prepackaged with most standard python. Pmw could be installed by following the instruction from: http://pmw.sourceforge.net/doc/starting.html

3. Install pyMODE-TASK plugin in pymol:

3.1. Start pymol

3.2. Go to Plugin -> Plugin Manager, and clcik on install new plugin tab. Under install from local file click on ‘choose file…’ button. Browse the pyMODE-TASK.py and follow the on screen installation instructions.

3.3. If everything goes well, you can start the plugin from pymol plugin menu.

Usage

For more detailed documentation on installation and usage of the tool suite please see our [ReadTheDocs](http://pymode-task.readthedocs.io/en/latest/index.html) site

Theory

Please see the following MODE-TASK documentation page for theory.

http://mode-task.readthedocs.io/en/latest/theory.html

pyMODE-TASK usage

pyMODE-TASK is a pymol plugin and GUI for command line based MODE-TASK tool. It include all the functionality of the MODE-TASK.

PCA of a MD trajectory

1. Preparation of trajectory

Prepare the MD trajectory as described in the tutorial section of MODE-TASK. It involves removing waters and periodicity if any.

2. PCA

  1. Start the plugin from pymol, plugin –> pyMODE-TASK.

2. Locate the pyMODE-TASK directory in the configuration tab by clicking on button “pyMODE-TASK directory”. Browse to the directory and click ok.

_images/conf_page.png

Fig 1: Set pyMODE-TASK directory

  1. It should look something like this
_images/conf_page1.png

Fig 2: Set pyMODE-TASK directory

4. Next go to the PCA tab. Read the trajectory, topology and reference PDB by clicking on the respective buttons. Next select the appropriate options and click on Run PCA. The results file will be saved in a folder within the output directory.

_images/pca_page.png

Fig 3: PCA

  1. If the run is successful you will see something like following
_images/pca_result.png

Fig 4: PCA successful

MDS and t-SNE

1. Read the trajectory, topology files by clicking on the respective buttons. Select the appropriate options under MDS options widget and click on Run MDS.

  1. Similarly, run the t-SNE.
_images/mds_page.png

Fig 5: MDS and t-SNE

Normal mode analysis (NMA)

  1. Set the configuration page to locate the pyMODE-TASK directory.
_images/nma1.png

Fig 6: NMA set configuration

  1. Perform the coarse graining for large protein molecules.
_images/nma2.png

Fig 7: Coarse graining

_images/nma3.png

Fig 8: Coarse graining results

3. Perform the coarse graining for second conformation of the protein. This is useful in case if you want to perform “Conformation/ Combination tool analysis” and “Mean square fluctuation”.

_images/nma4.png

Fig 9: Coarse graining 2

  1. Run NMA on each coarse grained PDB using a suitable cutoff.
_images/nma5.png

Fig 10: NMA for first PDB

_images/nma6.png

Fig 11: NMA for second PDB

  1. Run conformation / combination mode analysis. This require two PDB of different conformation.
_images/nma7.png

Fig 12: conformation/combination mode analysis

  1. Run MSF. This requires decomposed ANM matrices for comparing the mean square fluctuation between 2 proteins.
_images/nma8.png

Fig 13: MSF

  1. Get an assembly covariance for a specified chain of an asymmetric unit
_images/nma9.png

Fig 14: Get Eigenvectors

  1. Generate mode animation for visualization. Afterward use the following command in VMD for visualization
vmd VISUALISE/VISUAL_1.pdb -e VISUALISE/VISUAL_ARROWS_1.txt
_images/nma10.png

Fig 15: Mode animation