Dispredict3.0

Dispredict3.0: Prediction of Intrinsically Disordered Proteins with Protein Language Model

Table of Content

• Setup
• Dataset
• Run with local OS
• Prerequisites
• Download and install code
• Run with Docker
• Run with Singularity
• References

Getting Started

These instructions will get you a copy of the project up and running on your local machine or docker container for disorder prediction.

Dataset

The dataset can be found in the dataset directory. The train, test, and validation set is collected from [1].

Run with local OS

Download the code

• Retrieve the code

git clone https://github.com/wasicse/Dispredict3.0.git

Install Dependencies in local OS

We have tested Dispredict3.0 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 3.7.4

   pyenv install miniconda3-4.7.12
   pyenv local miniconda3-4.7.12 
   

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

3. Poetry version 1.1.13

   curl -sSL https://install.python-poetry.org | python3 - --version 1.1.13
   

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

4. tcsh shell version 6.21.00-1

   git clone https://github.com/tcsh-org/tcsh
   cd tcsh
   ./configure
   make
   

   For more details, visit: https://github.com/tcsh-org/tcsh

5. Setup fldpnn tool

   Copy the tcsh executable file into the corresponding directory.

    ```
    cp tcsh ../Dispredict3.0/tools/fldpnn/programs/fMoRFpred/
    cp tcsh ../Dispredict3.0/tools/fldpnn/programs/DisoRDPbind/psipred  
    ```
   

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

cd Dispredict3.0
../.venv/bin/poetry install
../.venv/bin/poetry shell

(Alternatively) Install Dependencies in local OS with SHELL script

You can install all dependencies by running the following script:

./install_dependencies.sh

Run Dispredict3.0 in local OS

Execute the following command to run Dispredict3.0 from the script directory.

cd script
../.venv/bin/poetry run python Dispredict3.0.py -f "../example/sample.fasta" -o "../output/"

• The following instructions show how to run dispredict3.0 with docker.

Run with Docker

• To run the Dispredict3.0 tool with docker, you can either build the docker image using dockerfile or pull the docker image from the registry.

Build Docker image

docker build -t wasicse/dispredict3.0 https://github.com/wasicse/Dispredict3.0.git#main    

(Alternatively) Pull image from Docker registry.

• Pull the image from the registry.

docker pull wasicse/dispredict3.0

Run Dispredict3.0 using Docker image

• Create the dispredict3.0 container and mount the current (Dispredict3.0) directory (downlaoded from GitHub) into the docker container.

docker run -ti --name dispredict3.0  wasicse/dispredict3.0:latest

• Then, run following python commands inside the docker container to have the disordered prediction.

export PATH="/opt/poetry/bin:${PATH}"
source /opt/Dispredict3.0/.venv/bin/activate
python /opt/Dispredict3.0/script/Dispredict3.0.py -f "/opt/Dispredict3.0/example/sample.fasta" -o "/opt/Dispredict3.0/output/"

• Check output folder for results. The output should be available only inside the docker container.

• You can also copy the output to the host computer using the following command:

docker cp dispredict3.0:/opt/Dispredict3.0/output/ .

Run with Singularity

• You can also run using Singularity using the following command.

singularity pull dispredict3.sif docker://wasicse/dispredict3.0
singularity run --writable-tmpfs dispredict3.sif

• Then, run following python commands inside the Singularity container to have the disordered prediction.

export PATH="/opt/poetry/bin:${PATH}"
source /opt/Dispredict3.0/.venv/bin/activate
python /opt/Dispredict3.0/script/Dispredict3.0.py -f "/opt/Dispredict3.0/example/sample.fasta" -o "/opt/Dispredict3.0/output/"

• The output folder should contain the results. The output directory contains the disorder probabilities with labels for each residue in sample_disPred.txt file. The fully disorder prediction for each protein sequence is stored in sample_fullydisPred.txt file.

Run Parallel Dispredict3.0 with Docker container

To run the parallel version of Dispredict3.0 execute the following commands:

cd ParallelDispredict3.0
./parallelDispredict.sh

Authors

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

References

1. Hu, Gang, Akila Katuwawala, Kui Wang, Zhonghua Wu, Sina Ghadermarzi, Jianzhao Gao, and Lukasz Kurgan. “FlDPnn: Accurate Intrinsic Disorder Prediction with Putative Propensities of Disorder Functions.” Nature Communications 12, no. 1 (December 2021): 4438. https://doi.org/10.1038/s41467-021-24773-7.