A deep learning framework for linear B-cell epitope prediction and antibody type-specific epitope classification using Transformer and LSTM encoders.
After installed, run command like below. It takes a few seconds to predict 10000 peptides.
python cli.py -i input.fasta -o output.csvTo show help, run python cli.py -h. The input is a FASTA file of peptides. The output is a table with following columns:
- identifier: FASTA header.
- sequence: FASTA sequence.
- score: Probability of being epitope.
- epitope: {0, 1}. 1 for epitope (score > 0.5).
- Ig: {A, E, M}. The antibody most probably binds to in these three types.
Without installation, navigate to Streamlit.
Linux is preferred. GPU is not required.
-
Clone this repo and navigate to the repo folder.
-
Install with pip, preferably in a virtual environment:
pip install -r requirements.txt
-
Alternatively, to be more specific, use mamba in Linux:
mamba env create -p ./envs -f environment.yml mamba activate ./envs
Follow the notebook data/dataset.py to generate datasets, in which redundancy and false negatives are reduced. The raw data is on figshare.
The code is designed to be reusable and extensible. It may be adopted in other peptide classification tasks. Some useful components are:
- Loss functions: logit-adjusted, focal; sigmoid, softmax.
- LSTM (packed variable length input), Transformer encoder, attention.
- Amino acid encoder.