Improved prediction of HLA antigen presentation hotspots: applications for immunogenicity risk assessment of therapeutic proteins

Anders Steenholdt Attermann, Carolina Barra, Birkir Reynisson, Heidi Schiøler Schultz, Ulrike Leurs, Kasper Lamberth, Morten Nielsen*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

Immunogenicity risk assessment is a critical element in protein drug development. Currently, the risk assessment is most often performed using MHC-Associated Peptide Proteomics (MAPPs) and/or T cell activation assays. However, this is a highly costly procedure that encompasses limited sensitivity imposed by sample sizes, the MHC repertoire of the tested donor cohort, and the experimental procedures applied. Recent work has suggested that these techniques could be complemented by accurate, high-throughput, and cost-effective prediction in silico models. However, this work covered a very limited set of therapeutic proteins and eluted ligand (EL) data. Here, we resolved these limitations by showcasing, in a broader setting, the versatility of in silico models for assessment of protein drug immunogenicity. A method for prediction of MHC class II antigen presentation was developed on the hereto largest available mass spectrometry (MS) HLA-DR EL dataset. Using independent test sets, the performance of the method for prediction HLA-DR antigen presentation hotspots was benchmarked. In particular, the method was showcased on a set of protein sequences including four therapeutic proteins and demonstrated to accurately predict the experimental MS hotspot regions at a significantly lower false-positive rate compared to other methods. This gain in performance was particularly pronounced when compared to the NetMHCIIpan-3.2 method trained on binding affinity data. These results suggest that in silico methods trained on MS HLA EL data can effectively and accurately be used to complement MAPPs assays for the risk assessment of protein drugs.
Original languageEnglish
JournalImmunology
Volume162
Issue number2
Pages (from-to)208-219
ISSN0019-2805
DOIs
Publication statusPublished - 2021

Keywords

  • HLA antigen presentation
  • Protein immunogenicity
  • Prediction
  • Immunogenicity assessment
  • HLA eluted ligands

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