PT  - JOURNAL ARTICLE
AU  - Hesham ElAbd
AU  - Mareike Wendorff
AU  - Tomas Koudelka
AU  - Christian Hentschker
AU  - Ann-Kristin Kamps
AU  - Christoph Prieß
AU  - Lars Wienbrandt
AU  - Frauke Degenhardt
AU  - Tim A. Steiert
AU  - Petra Bacher
AU  - Piyush Mathur
AU  - David Ellinghaus
AU  - Uwe Völker
AU  - Andreas Tholey
AU  - Tobias L. Lenz
AU  - Andre Franke
TI  - Predicting Peptide HLA-II Presentation Using Immunopeptidomics, Transcriptomics and Deep Multimodal Learning
AID  - 10.1101/2022.09.20.508681
DP  - 2022 Jan 01
TA  - bioRxiv
PG  - 2022.09.20.508681
4099  - http://biorxiv.org/content/early/2022/09/22/2022.09.20.508681.short
4100  - http://biorxiv.org/content/early/2022/09/22/2022.09.20.508681.full
AB  - The human leukocyte antigen (HLA) class II proteins present peptides to CD4+ T cells through an interaction with T cell receptors (TCRs). Thus, HLA proteins are key players in shaping immunogenicity and immunodominance. Nevertheless, factors governing peptide presentation by HLA-II proteins are still poorly understood. To address this problem, we profiled the blood transcriptome and immunopeptidome of 20 healthy individuals and integrated the profiles with publicly available immunopeptidomics datasets. In depth multi-omics analysis identified expression levels and subcellular locations as import sequence-independent features governing presentation. Levering this knowledge, we developed the Peptide Immune Annotator Multimodal (PIA-M) tool, as a novel pan multimodal transformer-based framework that utilises sequence-dependent along with sequence-independent features to model presentation by HLA-II proteins. PIA-M illustrated a consistently superior performance relative to existing tools across two independent test datasets (area under the curve: 0.93 vs. 0.84 and 0.95 vs. 0.86), respectively. Besides achieving a higher predictive accuracy, PIA-M with its Rust-based pre-processing engine, had significantly shorter runtimes. PIA-M is freely available with a permissive licence as a standalone pipeline and as a webserver (https://hybridcomputing.ikmb.uni-kiel.de/pia). In conclusion, PIA-M enables a new state-of-the-art accuracy in predicting peptide presentation by HLA-II proteins in vivo.Competing Interest StatementThe authors have declared no competing interest.