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

TY - JOUR

T1 - NetMHCIIphosPan

T2 - A Machine Learning Tool for Predicting HLA Class II Antigen Presentation of Phosphorylated Peptides

AU - Garcia Alvarez, Heli M.

AU - Kaabinejadian, Saghar

AU - Yari, Hooman

AU - Shepherd, Chloe M.

AU - Hildebrand, William H.

AU - Sette, Alessandro

AU - Peters, Bjoern

AU - Parker, Robert

AU - Ternette, Nicola

AU - Nielsen, Morten

N1 - Publisher Copyright: © 2026 American Chemical Society

PY - 2026/5/1

Y1 - 2026/5/1

N2 - 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.

AB - 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.

KW - antigen presentation

KW - HLA class II

KW - immunoinformatics

KW - immunopeptidomics

KW - machine learning

KW - neural networks

KW - phosphorylation

KW - post-translational modification

KW - predictive methods

UR - https://www.scopus.com/pages/publications/105037764369

U2 - 10.1021/acs.jproteome.5c01284

DO - 10.1021/acs.jproteome.5c01284

M3 - Article

C2 - 41994856

AN - SCOPUS:105037764369

SN - 1535-3893

VL - 25

SP - 2529

EP - 2545

JO - Journal of Proteome Research

JF - Journal of Proteome Research

IS - 5

ER -