NetMHCIIphosPan: A Machine Learning Tool for Predicting HLA Class II Antigen Presentation of Phosphorylated Peptides

Phosphorylated peptides presented by human leukocyte
antigen (HLA)
class II molecules play pivotal roles in immune regulation, yet their
characterization and prediction remain challenging due to data noise
and limited HLA coverage. Here, we introduce NetMHCIIphosPan, a prediction
method for HLA-II antigen presentation of phosphorylated peptides,
developed using mass spectrometry (MS)-based immunopeptidomics data
sets. Employing a refined peptide identification workflow, we reanalyzed
earlier HLA-II phospholigand data sets and trained predictive models,
achieving superior performance compared to models trained on the original
data. Binding motif analysis revealed that HLA-specific preferences
for phospholigands closely aligned with those of unmodified ligands.
Incorporating unmodified ligands into training further enhanced predictive
accuracy, particularly for HLA-DP and HLA-DQ molecules. NetMHCIIphosPan
outperformed existing tools, such as NetMHCIIpan-4.3 and MixMHC2pred-1.3,
for prediction of HLA antigen presentation of phosphorylated peptides,
demonstrating robustness and utility. This work establishes NetMHCIIphosPan
as a state-of-the-art tool for understanding the HLA-II phospholigandome,
with potential applications in immunotherapy and vaccine design.