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RRM-RNA-dock

About The Project

This tool is developed to perform anchor-driven fragment-based docking for a single-stranded RNA that is bound to the amino acid(s) of the RNA recognition motif (RRM) throught the stacking interactions. Current version 0.1 is limited to docking one ssRNA trinucleotide (further: fragment), two nucleotides of which are bound to the different amino acids of RRM.

Getting Started

Prerequisites

  • Current version of the tool is developed under Linux (it will not work on Windows or Mac unless a virtual Linux machine is installed)
  • Conda should be installed.
  • In order to use RRM-RNA-pip, one needs to know a little bit about interacting with a command line.

Installation

In order to use RRM-RNA-dock one needs to :

1. Install RRM-RNA-dock and set up its environment

  1. Download or clone scipts for the pipeline: git clone https://github.com/AnnaKravchenko/RRM-RNA-dock.git
  2. Create conda environment: conda create --name rrdock --file <your-path>/RRM-RNA-dock/pip-requitements.txt
  3. Define the RRDOCK environment variable for the current terminal session with export RRDOCK=<your path>/RRM-RNA-dock/ or define it permanently by adding that line in your /home/.bashrc
  4. Edit ~/RRM-RNA-dock/mdir/config.ini:
    • replase line "mdir = ~/RRM-RNA-dock/git/mdir/" with "mdir = /RRM-RNA-dock/git/mdir/"
    • if needed, change the number of CPUs to use during the docking by replasing line "docking_cpu = 8" with "docking_cpu = "

2. Install ATTRACT docking engine and set up its environment

  1. Install ATTRACT from here: https://github.com/sjdv1982/attract
  2. Define the ATTRACTDIR environment variable for the current terminal session with export ATTRACTDIR=<your path>/attract/tools/ or define it permanently by adding that line in your /home/.bashrc
  3. Define the ATTRACTTOOLS environment variable for the current terminal session with export ATTRACTTOOLS=<your path>/attract/bin/ or define it permanently by adding that line in your /home/.bashrc

3. Download fragments library and set up path to it

  1. Download library from here: https://zenodo.org/record/6483823#.YrQttNJByXJ, you are looking for the file trinucl_clust1A_ATTRACT.tar
  2. Follow directions in README to decompress library files from motif.npy to motif.pdb
  3. Define the LIBRARY environment variable for the current terminal session with export LIBRARY=<your path>/fraglib/ or define it permanently by adding that line in your /home/.bashrc

Usage

  1. Activate required environment by conda activate rrdock
    • To run pipeline for the protiein Q61474_RRM1 and ssRNA UUU,
    • where first nucleoide is expected to stack to RNP2,
    • and second nucleotide is to stack to RNP1,
    • and to save the results to the folder home/test/, run the next command line: python3 $RRDOCK/pip.py -id Q61474 -rrm 1 -seq UUU -ancNucB1 1 -ancNucB3 2 -wdir home/test/

Contributing

Contributions are what makes the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

If you have a suggestion that would make this better, please fork the repo and create a pull request.

  1. Fork the Project
  2. Create your Feature Branch (git checkout -b feature/YourFeature)
  3. Commit your Changes (git commit -m 'Add some YourFeature')
  4. Push to the Branch (git push origin feature/YourFeature)
  5. Open a Pull Request

License

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/.

Contacts

Anna Kravchenko - [email protected]

Hrishikesh Dhondge - [email protected]

Project Link: http://rnact.eu/

Acknowledgments

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 813239.