PPTStab: Designing of thermostable proteins with a desired melting temperature
Project description
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}]
[-m {EMB,AAC,SER}]
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
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.