BepiPred - 3.0

Prediction of potential B-cell epitopes from protein sequences

Created by: DTU Health Tech
Available at: https://services.healthtech.dtu.dk/services/BepiPred-3.0/

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Overview

BepiPred 3.0 is a computational tool designed to predict potential B-cell epitopes from protein sequences. It is developed by DTU Health Tech and widely used in immunoinformatics studies to identify regions in proteins that may elicit antibody responses.

This repository provides a Dockerized version of BepiPred 3.0, allowing easy installation and execution without manually handling dependencies.

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Docker Installation

To build the Docker image from scratch:

git clone https://github.com/labioinfoufsc/BepiPred3.git
cd BepiPred3
docker build -t bioinfoufsc/bepipred3:latest .

This will create a Docker image named bioinfoufsc/bepipred3:latest with all necessary dependencies.

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Usage

Run BepiPred 3.0 using Docker:

docker run -it --rm -v ${PWD}:/data bioinfoufsc/bepipred3:latest -i /data/example.fasta -o /data/results -pred vt_pred

• -i /data/example.fasta : path to your input FASTA file
• -o /data/results : path to the output directory
• -pred vt_pred : prediction mode (default BepiPred 3.0 mode)

Your results will be saved in the results folder inside your current working directory.

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Input

• Standard FASTA protein sequences
• Multiple sequences can be included in one FASTA file

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Output

• Tab-delimited or CSV files containing predicted B-cell epitopes and scores
• Output files are stored in the folder specified by -o (e.g., /data/results)
• Each row represents a residue with its predicted epitope score

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Example

Assuming you have a FASTA file named example.fasta in your current directory:

docker run -it --rm -v ${PWD}:/data bioinfoufsc/bepipred3:latest -i /data/example.fasta -o /data/results -pred vt_pred

After execution, you will find prediction results in the results folder in your current directory.

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License and Citation

This repository is provided for academic and research purposes.
Please cite the original BepiPred 3.0 publication when using this tool in your research:

Clifford JN, Høie MH, Deleuran S, Peters B, Nielsen M, Marcatili P. BepiPred-3.0: Improved B-cell epitope prediction using protein language models. Protein Sci. 2022 Dec;31(12):e4497. doi: 10.1002/pro.4497. PMID: 36366745; PMCID: PMC9679979.

For commercial use, please contact DTU Health Tech.