Metadata-Version: 2.1
Name: hairpred
Version: 1.0.0
Summary: HAIRPred: A tool for predicting,and designing of antibody binding residues
Home-page: https://github.com/RuchirSahni/HAIRpr
Author: Prof. G.P.S. Raghava
Author-email: raghava@iiitd.ac.in
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: LICENSE.txt
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: scikit-learn
Requires-Dist: argparse
Requires-Dist: tqdm

# 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 : 

```bash
sudo apt-get install git-lfs
git lfs install
```

Then, clone this github repository : 

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

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

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

### Install DSSP

To install DSSP, run the following command:

```bash 
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`

```bash
pip install -r requirements.txt
```
### Using `environment.yml`

```bash
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.



