DRBpred

DRBpred: A Sequence-based Machine Learning Method to effectively predict DNA and RNA Binding Residues

Table of Content

• Getting Started
• Datasets
• Prerequisites
• Download and install code
• Authors
• References

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.

Datasets

The dataset can be found in the Dataset/FullDataset directory. The dataset is collected from [1].

Prerequisites

We have tested DRBpred on Ubuntu 20.04. You would need to install the following software before replicating this framework in your local or server machine.

1. pyenv latest version

   curl https://pyenv.run | bash
   exec $SHELL
   

   For more details, visit: https://github.com/pyenv/pyenv-installer

2. Python version 3.9.5

   pyenv install miniconda3-3.9-4.10.3
   pyenv local miniconda3-3.9-4.10.3 
   

   For more details, visit: https://github.com/pyenv/pyenv

   Alternatively, Python version 3.9.5 can be installed using Anaconda.

   conda create -n py39 python=3.9.5 
   conda activate py39
   

3. Poetry version 1.3.2

   curl -sSL https://install.python-poetry.org | python3 - --version 1.3.2
   

   For more details, visit: https://python-poetry.org/docs/

4. Docker

   The feature extraction part depends on many other tools. So we created a docker image to extract features easily without any setup. It might take quite some time to get all the features.

5. Protein Databases

   The tool depends on the nr and uniclust30_2017_04 databases. The database should be placed in the following directory structure. Alternatively, it is possible to pass the database path as parameters.

project
│─── README.md    
│
└─── script
    └─── Databases
           └─── nr
            └───uniclust30_2017_04

Download and install code

• Retrieve the code

git clone https://github.com/wasicse/DRBpred.git

To run the program, first install all required libraries by running the following command:

POETRY_VIRTUALENVS_IN_PROJECT="true"
poetry install

Then execute the following command to run DRBpred from the script directory on the example dataset. You need to change the input of the Dataset/example directory to get prediction for new protein sequences and replace DATABASE_PATH with the absoutue path of the databases e.g, "/home/wasi/DRBpred/script/Databases/"

cd script
poetry run python run_DRBpred.py -f "featuresv2" -o "./output/" -d "DATABASE_PATH"

• Finally, check output folder for results. The output directory contains predicted lebels with probabilities for each residues.

Authors

Md Wasi Ul Kabir, Duaa Mohammad Alawad, Md Tamjidul Hoque. For any issue please contact: Md Tamjidul Hoque, thoque@uno.edu

References

1. Yan, J. and Kurgan, L.J.N.a.r. DRNApred, fast sequence-based method that accurately predicts and discriminates DNA-and RNA-binding residues. 2017;45(10):e84-e84.