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.

人类白细胞抗原 (human leukocyte antigen, HLA) II类分子呈递的磷酸化肽段在免疫调控中发挥关键作用，但受限于数据噪声与HLA覆盖度不足，这类肽段的鉴定与预测仍存在诸多挑战。本研究推出NetMHCIIphosPan——一款用于预测磷酸化肽段HLA-II类抗原呈递能力的工具，其开发依托基于质谱 (mass spectrometry, MS) 的免疫肽组学 (immunopeptidomics) 数据集。本研究采用优化后的肽段鉴定流程，重新分析了既往的HLA-II类磷酸化配体数据集并训练预测模型，相较于基于原始数据训练的模型，其性能更为优异。结合基序分析显示，HLA对磷酸化配体的特异性偏好与未修饰配体高度一致。将未修饰配体纳入训练集后，预测准确率进一步提升，尤其针对HLA-DP与HLA-DQ分子。在磷酸化肽段HLA抗原呈递预测任务中，NetMHCIIphosPan的性能优于现有工具（如NetMHCIIpan-4.3与MixMHC2pred-1.3），展现出良好的稳健性与应用价值。本研究确立了NetMHCIIphosPan作为解析HLA-II类磷酸化配体组的前沿工具，其在免疫治疗与疫苗设计领域具备潜在应用前景。