EpiDope for the newest Version.

EpiDope

Prediction of B-cell epitopes from amino acid sequences using deep neural networks. Supported on Linux and Mac.

System-requirements

8 GB RAM should be available. With 8GB even processing protein sequences longer than 6000 amino acids and/or multiple hundreds of sequences shouldn't be problematic.

Installation

Download and install Conda.
Create a Conda environment with Python 3.7
```
conda create -n epidope python=3.7
```
Activate the Conda environment. You will need to activate the Conda environment in each terminal in which you want to use epidope.
```
conda activate epidope
```
Install epidope via conda
```
conda install -c flomock -c conda-forge -c pytorch epidope h5py=2.10 jsonnet 
```
Note: While installation with conda, the loading bar of epidope is not working. So depending on your internet connection, it can take from a few seconds too minutes until you see any progress.
Install other dependencies
```
pip install allennlp==0.9.0
```

Usage

Example

epidope -i /path_to/multifasta.fa -o ./results/ -e /known/epitopes.txt

Options:

command	what it does
-i, --infile	Multi- or Singe- Fasta file with protein sequences. [required]
-o, --outdir	Specifies output directory. Default = .
--delim	Delimiter char for fasta header. Default = White space
--idpos	Position of gene ID in fasta header. Zero based. Default = 0
-t, --threshold	Threshold for epitope score. Default = 0.818
-l, --slicelen	Length of the sliced predicted epitopes. Default = 15
-s, --slice_shiftsize	Shiftsize of the slices on predited epitopes. Default = 5
-p, --processes	Number of processes used for predictions. Default = #CPU-cores
-e, --epitopes	File containing a list of known epitope sequences for plotting
-n, --nonepitopes	File containing a list of non epitope sequences for plotting
-h, --help	show this message and exit

Docker

We also provide a Docker image for EpiDope.
Simply pull and run a ready-to-use image from Dockerhub:

docker run -t --rm -v /path/to/input/files:/in -v /path/to/output:/out \
flomock/epidope:v0.2 -i /in/proteins.fasta -o /out/epidope_results

(you need to mount files/folders that you want to access in the Docker via -v)

Or if you want you can build the image yourself locally from the Dockerfile in this repo:

docker build -t epidope .

Note:

Run as non-root user under linux:

docker run -t --rm -v /path/to/input/files:/in -v /path/to/output:/out -u `id -u $USER`:`id -g $USER` \
flomock/epidope:v0.2 -i /in/proteins.fasta -o /out/epidope_results

Run docker with a different memory allocation see System requirements (default is 2GB for linux and mac):
(e.g. 8GB)

docker run -t --rm -v -m=8g /path/to/input/files:/in -v /path/to/output:/out \
flomock/epidope:v0.2 -i /in/proteins.fasta -o /out/epidope_results

Further

If you are interested, you find most of the code which was used to create this tool under:

https://github.com/flomock/epitop_pred

Citation

Maximilian Collatz, Florian Mock, Emanuel Barth, Martin Hölzer, Konrad Sachse, Manja Marz, EpiDope: A Deep Neural Network for linear B-cell epitope prediction, Bioinformatics, , btaa773, https://doi.org/10.1093/bioinformatics/btaa773

mcollatz / EpiDope

readme