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

Author:

Nilsson Jonas B.1ORCID,Kaabinejadian Saghar23ORCID,Yari Hooman3ORCID,Kester Michel G. D.4ORCID,van Balen Peter4ORCID,Hildebrand William H.3ORCID,Nielsen Morten1ORCID

Affiliation:

1. Department of Health Technology, Technical University of Denmark, DK-2800 Lyngby, Denmark.

2. Pure MHC LLC, Oklahoma City, OK, USA.

3. Department of Microbiology and Immunology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA.

4. Department of Hematology, Leiden University Medical Center, Leiden, Netherlands.

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 because of limited availability of immunopeptidomics data for HLA-DQ and HLA-DP while not taking into account alternative peptide binding modes. 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 all loci using a refined machine learning framework that accommodates inverted peptide binders. Next, we apply targeted immunopeptidomics assays to generate 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 .

Publisher

American Association for the Advancement of Science (AAAS)

Subject

Multidisciplinary

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