NetCleave is a retrainable method for predicting C-terminal peptide processing of MHC-I and MHC-II pathways.
In brief, NetCleave maps reported IEDB peptides to protein sequences in UniProt/UniParc. After the identification of the C-terminal cleavage site, amino acid sequences are coded using QSAR descriptors, including steric, electrostatic and hydrophobic properties. Finally, a neural network architecture is used to generate the predictive model.
If you use NetCleave, please cite us:
Amengual-Rigo, P., Guallar, V. NetCleave: an open-source algorithm for predicting C-terminal antigen processing for MHC-I and MHC-II. Sci Rep 11, 13126 (2021). https://doi.org/10.1038/s41598-021-92632-y
Install NetCleave dependencies by:
pip install -r requirements.txt
NetCleave is easy to use. It can be run using a python command-line (which can be stored in a bash script).
netcleave_path="NetCleave.py" # path to NetCleave.py
mhc_class="I" # MHC class to be modelled
technique="mass_spectrometry" # Technique to be modelled
mhc_family="HLA" # MHC family to be modelled
fasta="example/example.fasta" # Input fasta file
python $netcleave_path \
--generate \
--train \
--mhc_class $mhc_class \
--technique $technique \
--mhc_family $mhc_family \
--score_fasta $fastaThe arguments --generate and --train are needed for creating the model for the first time.
NetCleave was specificaly designed to be easily retrained. In order to do so, user needs:
- Decompress or obtain databases
IEDB - data/databases/iedb folder. New versions may be found at: http://www.iedb.org/database_export_v3.php
UniProt/UniParc - data/databases/uniprot and data/databases/uniparc folders. New versions may be found at: https://www.uniprot.org/downloads
We recomend to use the last version of IEDB while keeping the same UniProt/UniParc version that we provided in this repository
- Define IEDB, UniProt and UniParc file paths on NetCleave main function