ExoProPred

A computational approach to predict the subcellular localisation of exosomal proteins using the sequence information of the proteins.

Introduction

ExoProPred is a webserver to predict exosomal proteins based on hybrid model that combines machine learning model with motif-search approach. The models are trained on a dataset comprising of 2831 exosomal proteins and 2831 non-exosomal proteins. The performance of the models were evaluated using 5-fold cross-validation. The models were trained on top 70 best features comprising of composition-based and evolutionary information based features as well as on hybrid features(Top 70 features + Motif-search) by implementing random-forest classifier from the scikit library of python. In the standalone version, random-forerst classifier based model is implemented alongwith the motif-search usinf MERCI tool, named it as hybrid approach. ExoProPred is also available as web-server at https://webs.iiitd.edu.in/raghava/exopropred. Please read/cite the content about the ExoProPred for complete information including algorithm behind the approach.

Reference

Arora A, Patiyal S, Sharma N, Devi NL, Kaur D, Raghava GPS. A random forest model for predicting exosomal proteins using evolutionary information and motifs. Proteomics. 2023 Jul 31:e2300231. doi: 10.1002/pmic.202300231. Epub ahead of print. PMID: 37525341.

PIP Installation

PIP version is also available for easy installation and usage of this tool. The following command is required to install the package

pip install exopropred

To know about the available option for the pip package, type the following command:

exopropred -h

Standalone

The Standalone version of exopropred is written in python3 and following libraries are necessary for the successful run:

• scikit-learn
• Pandas
• Numpy

For the successful run of the standalone, please download and install the latest version for BLAST from https://ftp.ncbi.nlm.nih.gov/blast/executables/blast+/LATEST/.

Minimum USAGE

To know about the available option for the stanadlone, type the following command:

python3 exopropred.py -h

To run the example, type the following command:

python3 exopropred.py -i example_input.fa

This will predict if the submitted sequences are exososomal proteins or non-exososomal proteins. It will use other parameters by default. It will save the output in "outfile.csv" in CSV (comma seperated variables).

Full Usage

usage: exopropred.py [-h] -i INPUT [-o OUTPUT] [-m {1,2}] [-t THRESHOLD]
                     [-d {1,2}]

Please provide following arguments

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
  -m {1,2}, --model {1,2}
                        Model Type: 1: Composition based model, 2: Hybrid
                        Model, by default 1
  -t THRESHOLD, --threshold THRESHOLD
                        Threshold: Value between 0 to 1 by default 0.51
  -d {1,2}, --display {1,2}
                        Display: 1:Exosomal Proteins only, 2: All Proteins, by
                        default 1

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 0.51.

Model: User is allowed to choose between two different models, such as, 1 for composition-based model, 2 for hybrid model, by default its 1.

Display type: This option allow users to fetch either only exososomal proteins by choosing option 1 or prediction against all proteins by choosing option 2.

ExoProPred 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

model.zip : This zipped file contains the compressed version of model

envfile : This file compeises of paths for the PSI-BLAST, MERCI_motif_locator.pl, Motifs, and Swiss-Prot database.

exopropred.py : Main python program

MERCI_motif_locator.pl : Perl script for locating motifs using MERCI

swissprot : Swiss-Prot database for calculating PSSM profile

motifs : Folder containing the motif files

src : Folder containing the python scripts for PSSM based composition features

Data : Folder containing the files to calculate the features using Pfeature

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

example_composition_model_output.csv : Example output file for composition-based model

example_hybrid_model_output.csv : Example output file for hybrid model