Peptide-HLA II interaction prediction for post-translationally modified peptides.

Authors

Alexandru Dumitrescu, Dani Korpela, Adrian M Bebenek, Anna Ju, Griffin M Lawrence, Karl R Clauser, Jennifer G Abelin, Martin Stražar, Harri Lähdesmäki, Daniel B Graham, Ramnik J Xavier

Published in

bioRxiv : the preprint server for biology. Aug 13, 2026. Epub Aug 13, 2026.

Abstract

CD4 ⁺ T cells recognize peptides presented by human leukocyte antigen (HLA) II, implementing a fundamental mediation mechanism of the adaptive immune system. Although post-translational modifications (PTMs) alter immune responses, PTM-peptide-HLA interaction prediction remains challenging due to data scarcity resulting from substoichiometric levels of PTMs. To overcome this, we developed PepChem, a deep learning model utilizing novel, molecular-level peptide representations that enable predictions for sidechain modifications. Using monoallelic datasets that we reanalyze for PTMs of interest, we show accurate predictions on PTMs that were unseen during training. Furthermore, we introduce a novel training protocol that improves PTM-peptide generalization compared to conventional methods. We predict and experimentally validate citrullination-induced binding increase of rheumatoid arthritis (RA)-linked peptides to HLA II risk allele DRB1*04:01. This framework bridges the critical gap in PTM-aware immune recognition prediction, with immediate applications in autoimmunity, cancer, and infectious disease.

PMID:
42845371
Bibliographic data and abstract were imported from PubMed on 08 Oct 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.