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Installing MGLTools2 package (ADFR/AGFR/AutoSite) on Ubuntu

Dr. Muniba Faiza

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Installing mgltools2 package on ubuntu

MGLTools2 package comes with three different tools for docking: ADFR, AGFR, and AutoSite [1-3]. In this article, we will install this package on Ubuntu.

Getting started

Let’s update and upgrade the system first.

$ sudo apt-get update

$ sudo apt-get upgrade

Downloading MGLTools2

Change to the directory where you want to download and install the software. Let’s say, Downloads. Open a terminal (Ctrl+Alt+T) and type the following commands. Don’t forget to check the architecture of your system first whether it is 32-bit or 64-bit and then download accordingly. Click here to select an appropriate package.

$ cd Downloads/

$ wget http://adfr.scripps.edu/AutoDockFR/installers/mgltools2_x86_64Linux2_1.0.tar.gz

Installing MGLTools2

We are in the same directory (i.e., Downloads). Now extract the downloaded package and install using the following commands.

$ tar xvzf mgltools2_x86_64Linux2_1.0.tar.gz

$ cd mgltools2_x86_64Linux2_1.0/

$ ./install.sh

Adding in the path

Now add this in your path as shown below.

$ export PATH=/home/muniba/Downloads/billi/mgltools2_x86_64Linux2_1.0/bin:$PATH

Creating aliases

Open the bashrc file and add the following at the end of the file.

$ sudo gedit ~/.bashrc

It will open your bashrc file in gedit. Scroll to the end of the file and add the following commands:

$ alias adfr='sudo /home/user/Downloads/mgltools2_x86_64Linux2_1.0/bin/adfr'

$ alias agfr=' sudo /home/user/Downloads/mgltools2_x86_64Linux2_1.0/bin/agfr'

$ alias autosite=’sudo /home/user/Downloads/mgltools2_x86_64Linux2_1.0/bin/autosite’

Don’t forget to replace the user in the above commands with your username.

$ source ~/.bashrc

Now you can execute these three tools by typing their aliases in the terminal. For example,

$ adfr

$ agfr

$ autosite


References

  1. Ravindranath, P. A., Forli, S., Goodsell, D. S., Olson, A. J., & Sanner, M. F. (2015). AutoDockFR: advances in protein-ligand docking with explicitly specified binding site flexibility. PLoS computational biology11(12), e1004586.
  2. Zhao, Y., Stoffler, D., & Sanner, M. (2006). Hierarchical and multi-resolution representation of protein flexibility. Bioinformatics22(22), 2768-2774.
  3. Ravindranath, P. A., & Sanner, M. F. (2016). AutoSite: an automated approach for pseudo-ligands prediction—from ligand-binding sites identification to predicting key ligand atoms. Bioinformatics32(20), 3142-3149.

Dr. Muniba is a Bioinformatician based in New Delhi, India. She has completed her PhD in Bioinformatics from South China University of Technology, Guangzhou, China. She has cutting edge knowledge of bioinformatics tools, algorithms, and drug designing. When she is not reading she is found enjoying with the family. Know more about Muniba

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