pptstab 1.0.1
PPTStab: Designing of thermostable proteins with a desired melting temperature
Requires Python: >=3.6

# **PPTStab**
Designing of thermostable proteins with a desired melting temperature
## Introduction
PPTStab is developed to predict the thermostability of proteins and design the thermostable proteins. In the standalone version, ANN+MLP ensemble regressor based model.
PPTStab is also available as web-server at https://webs.iiitd.edu.in/raghava/pptstab. Please read/cite the content about the PPTStab for complete information including algorithm behind the approach.

## Standalone
The Standalone version of PPTStab is written in python3 and following libraries are necessary for the successful run:
- scikit-learn==1.0.2
- transformers==4.44.2
- tensorflow==2.13.0
- Pandas==2.0.3
- Numpy==1.22.4
- torch==2.4.1

## Minimum USAGE
To know about the available option for the stanadlone, type the following command:
```
python pptstab.py -h
```
To run the example, type the following command:
```
python3 pptstab.py -i example_input.fa -f 1
```
This will predict if the submitted sequences can cause diabetes or not. It will use other parameters by default. It will save the output in "outfile.csv" in CSV (comma seperated variables).

## Full Usage
```
usage: pptstab.py [-h] 
                  [-i INPUT]
                  [-o OUTPUT]
		          [-j {1,2}]
		          [-d {1,2}]
		          [-f {0,1}]

```
```
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
  -f FLAG, --flag FLAG {0,1} 
                        Cell Flag: Value between 0 or 1 by default 1. For 1, 'cell' is selected; for 0, 'lysate' is selected.
  -d {1,2}, --display {1,2}
                        Display: 1:Thermophilic proteins only, 2: All peptides, by default 1
  -m {EMB,AAC,SER}, --method {EMB,AAC,SER}
                        Display: EMB for using the embedding model (ProtBert), SER for Shannon entropy for all residues model, AAC for amino acid composition model, by default SER

```

**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".

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

**flag**: User can set the ‘lysate’ or ‘cell’ flag.

**method:** This option allow users to select the model based on composition and embeddings, by default SER.

EIPPred Package Files
=======================
It contantain following files, brief descript of these files given below

INSTALLATION                    : Installations instructions

LICENSE                         : License information

README.md                       : This file provide information about this package

eippred.py                      : Main python program

example_input.fa                : Example file contain peptide sequenaces in FASTA format

example_predict_output.csv      : Example output file for predict module

example_design_output.csv       : Example output file for design module