PeptideMHC
Peptide-MHC Binding
Task Overview
In the human body, T cells monitor the existing peptides and trigger an immune response if the peptide is foreign. To decide whether or not if the peptide is not foreign, it must bound to a major histocompatibility complex (MHC) molecule. Therefore, predicting peptide-MHC binding affinity is pivotal for determining immunogenicity. There are two classes of MHC molecules: MHC Class I and MHC Class II. They are closely related in overall structure but differ in their subunit composition. This task is to predict the binding affinity between the peptide and the pseudo sequence in contact with the peptide representing MHC molecules.
Impact
Identifying the peptide that can bind to MHC can allow us to engineer peptides-based therapeutics such vaccines and cancer-specific peptides.
Generalization
The models are expected to be generalized to unseen peptide-MHC pairs.
Product
Immunotherapy.
Pipeline Stage
Activity - peptide design.
Available Datasets
Usage Example
You can access these datasets using the PyTDC library:
from tdc_ml.multi_pred import PeptideMHC
# Load a dataset
data = PeptideMHC(name='MHC1_IEDB-IMGT_Nielsen')
# Access the data
df = data.get_data()
print(df.head())
# Get train/val/test splits
split = data.get_split()
print(split)