hairpred 1.0.0

HAIRPred: A tool for predicting,and designing of antibody binding residues

HAIRpred

This repository contains the standalone Python script HAIRpred.py.

Installation (For Linux Users)

This Github repository has files which need Git-LFS for installation. To install Git-LFS :

sudo apt-get install git-lfs
git lfs install

Then, clone this github repository :

git clone https://github.com/Ruchir3003/HAIRpr.git

Next, we need to untar the tar.gz files :

tar -xvJf models.tar.xz
tar -xvfz pssm.tar.gz

Install DSSP

To install DSSP, run the following command:

apt-get install dssp

You can set up the environment using either requirements.txt (for pip users) or environment.yml (for Conda users).

Using requirements.txt

pip install -r requirements.txt

Using environment.yml

conda env create -f env.yml

Installation (For Other Users)

You would need to install ncbi psi-blast files in the pssm and dssp for your system.

Install DSSP

You can install dssp by following https://github.com/cmbi/dssp

Install PSSM

You can install system-specific ncbi psi-blast files from https://ftp.ncbi.nlm.nih.gov/blast/executables/blast+/LATEST/

Usage

usage: python3 HAIRpred.py [-h]
                       [-i INPUT
                       [-o OUTPUT]
                       [-j {1,2}] 
                       [-m {1,2}] (Only for Predict Module)
                       [-t THRESHOLD]

Please provide following arguments for successful run

optional arguments:
  -h, --help            show this help message and exit
  -i INPUT, --input INPUT
                        Input: protein or peptide sequence(s) in FASTA format
                        or single sequence per line in single letter code
  -o OUTPUT, --output OUTPUT
                        Output: File for saving results by default outfile.csv
  -j {1,2}, --job {1,2}
                        Job Type: 1:Predict, 2: Design, by default 1
  -t THRESHOLD, --threshold THRESHOLD
                        Threshold: Value between 0 to 1 by default 0.5
  -m {1,2}, --model {1,2}
                        Model Type: (Only for Predict Module) Model Type: 1: RSA based RF, 2: RSA + PSSM ensemble model (Best Model). Default : 2


**Input File:** It allow users to provide input in the FASTA format.

**Output File:** Program will save the results in the CSV format, in case user do not provide output file name, it will be stored in "outfile.csv".

**Threshold:** User should provide threshold between 0 and 1, by default its 0.5.

**Job:** User is allowed to choose between two different modules, such as, 1 for prediction and 2 for Designing , by default its 1.

**Model **: User is allowed to choose between two different models, such as, 1 for RSA based RF and 2 for RSA + PSSM ensemble RF, by default its 2.