Accurate prediction of antigen presentation for HLA class II across loci using tailored data acquisition and refined machine learning

ABSTRACT: Accurate prediction of antigen presentation by Human Leukocyte Antigen (HLA) class II molecules is crucial for rational development of immunotherapies and vaccines targeting CD4 T cell activation. So far, most prediction methods for HLA class II antigen presentation have focused on HLA-DR due to limited availability of immunopeptidomics data for HLA-DQ and HLA-DP, while not taking into account alternative peptide binding modes. Here, we present an update to the NetMHCIIpan prediction method which closes the performance gap between all three HLA class II loci. We accomplish this by first integrating large immunopeptidomics datasets describing the HLA class II specificity space across loci using a refined machine learning framework that accommodates inverted peptide binders. Next, we apply targeted immunopeptidomics assays to generate novel data that covers additional HLA-DP specificities. The final method, NetMHCIIpan-4.3, achieves high accuracy and molecular coverage across all HLA class II allotypes.

INSTRUMENT(S): TripleTOF 5600

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Cell Suspension Culture

SUBMITTER: Hooman Yari  

LAB HEAD: William Hildebrand

PROVIDER: PXD044810 | Pride | 2025-07-21

REPOSITORIES: Pride