Biology subjects

Nelde, A.

Publications and source records attributed to Nelde, A..

6 recordsLinked to original sources

bioRxiv2026

Deep learning enables direct HLA typing from immunopeptidomics data

The immune system eliminates malignant and infected cells through T-cell-mediated recognition of peptides presented by human leukocyte antigen molecules. Mass spectrometry-based immunopeptidomics enables unbiased identification of naturally presented HLA-restricted peptides and has become central to the development of T-cell-based immunotherapies. However, immunopeptidomics data reflects the combined peptide presentation of multiple HLA alleles, and determining which allotypes are represented in this multi-allelic complexity remains an unmet computational challenge. Here, we introduce immunotype, a deep learning-based ensemble predictor for HLA class I allotyping directly from immunopeptidomics data. Immunotype integrates peptide and HLA protein sequence information through transformer encoders and a graph neural network, complemented by a curated mono-allelic reference of known peptide-HLA binding preferences. Immunotype achieves an overall accuracy of 87.2% at protein-level resolution across diverse tissues and thereby enables rapid, cost-effective HLA typing of large-scale immunopeptidomics datasets.

bioinformatics↗

bioRxiv2026

t2pmhc: A Structure-Informed Graph Neural Network to predict TCR-pMHC Binding

Mapping of T cell receptors (TCRs) to their cognate MHC-presented peptides (pMHC) is central for the development of precision immunotherapies and vaccine design. However, accurate prediction of TCR affinity to peptide antigens remains an open challenge. Most approaches rely solely on sequence information, although increasing evidence suggests that TCR-pMHC binding is primarily determined by three-dimensional structural interactions within the entire TCR-pMHC complex. Consequently, sequence-based methods often fail to generalize to peptides not included in the training data (unseen peptides). Here we introduce t2pmhc, a structure-based graph neural network framework for predicting TCR-pMHC binding using predicted structures of the entire TCR-pMHC complex. We evaluated a Graph Convolutional Network (GCN) and a Graph Attention Network, both demonstrating improved generalization to unseen peptides compared to state-of-the-art models across a variety of public datasets. Evaluation with crystallographic structures yields high-confidence predictions, indicating that current limitations of structure-based models are largely driven by the accuracy of structure prediction. Analysis of node attention patterns in t2pmhc-GCN reveals biologically consistent patterns, assigning high attention to the peptide and the CDR3 regions. Within the peptide sequence, canonical MHC anchor residues are consistently downweighted, whereas potential TCR-binding residues are upweighted. These findings establish t2pmhc as a structure-informed framework for robust TCR-pMHC binding prediction, enabling improved generalization to unseen antigens and providing a foundation for integrating TCR repertoire sequencing into vaccine design and immunotherapy.

bioinformatics↗

bioRxiv2024

Unveiling novel conserved HIV-1 open reading frames encoding T cell antigens using ribosome profiling.

The development of ribosomal profiling (Riboseq) revealed the immense coding capacity of human and viral genomes. Here, we used Riboseq to delineate the first translatome of HIV-1 in infected CD4+ T cells. In addition to canonical viral protein coding sequences (CDSs), we identify 98 alternative open reading frames (ARFs), corresponding to small Open Reading Frames (sORFs) that are distributed across the HIV genome including the UTR regions. Using a database of HIV genomes, we observed that most ARF amino-acid sequences are likely conserved among clade B and C of HIV-1, with 8 ARF-encoded amino-acid sequences being more conserved than the overlapping CDSs. Using T cell-based assays and mass spectrometry-based immunopeptidomics, we demonstrate that ARFs encode viral polypeptides. In the blood of HIV-infected individuals, ARF-derived peptides elicit potent poly-functional T cell responses mediated by both CD4+ and CD8+ T cells. Our discovery expands the list of conserved viral polypeptides that are targets for vaccination strategies and might reveal the existence of viral microproteins or pseudogenes.

immunology↗

bioRxiv2020

NeutrobodyPlex - Nanobodies to monitor a SARS-CoV-2 neutralizing immune response

As the COVID-19 pandemic escalates, the need for effective vaccination programs, therapeutic intervention, and diagnostic tools increases. Here, we identified 11 unique nanobodies (Nbs) specific for the SARS-CoV-2 spike receptor-binding domain (RBD) of which 8 Nbs potently inhibit the interaction of RBD with angiotensin-converting enzyme 2 (ACE2) as the major viral docking site. Following a detailed epitope determination and characterization of the binding mode by structural analysis, we constructed a hetero-bivalent Nb targeting two different epitopes within the RBD:ACE2 interface. This resulted in a high-affinity binder with a viral neutralization efficacy in the picomolar range. Using the bivalent Nb as a surrogate, we established a competitive multiplex binding assay ("NeutrobodyPlex") for detailed analysis of the presence and performance of neutralizing RBD-binding antibodies in the serum of convalescent or vaccinated patients. As demonstrated, the NeutrobodyPlex enables high-throughput screening and detailed analysis of neutralizing immune responses in infected or vaccinated individuals, helping to monitor immune status or guide vaccine design. This approach is easily transferrable to diagnostic laboratories worldwide.

immunology↗

bioRxiv2020

An innovative approach for HLA typing, molecular tumor testing and the validation of tumor exclusive antigens

DisclaimerThis manuscript has been withdrawn by the corresponding author, as it was submitted and made public in bioRxiv without knowledge and the full consent of all the authors listed. The co-authors are therefore not responsible for the contents of this manuscript. For this reason, the submitting author has chosen to withdraw this preprint as a precautionary measure and would like to state that this work should not be cited as a reference. For any related questions that may arise, please contact the corresponding author.Competing Interest StatementThe authors have declared no competing interest.

immunology↗

bioRxiv2019

Validation of a high-performance liquid chromatography-tandem mass spectrometry immunopeptidomics assay for the identification of HLA class I ligands suitable for pharmaceutical therapies

For more than two decades naturally presented, human leukocyte antigen (HLA)-restricted peptides (immunopeptidome) have been eluted and sequenced using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Since, identified disease-associated HLA ligands have been characterized and evaluated as potential active substances. Treatments based on HLA-presented peptides have shown promising results in clinical application as personalized T cell-based immunotherapy. Peptide vaccination cocktails are produced as investigational medicinal products under GMP conditions. In order to support clinical trials based on HLA-presented tumor-associated antigens, in this study the sensitive LC-MS/MS HLA class I antigen identification pipeline was fully validated for our technical equipment according to the current US Food and Drug Administration (FDA) and European Medicines Agency (EMA) guidelines.\n\nThe immunopeptidomes of JY cells with or without spiked-in, isotope labeled peptides, of peripheral blood mononuclear cells of healthy volunteers as well as a chronic lymphocytic leukemia and a bladder cancer sample were reliably identified using a data-dependent acquisition method. As the LC-MS/MS pipeline is used for identification purposes, the validation parameters include accuracy, precision, specificity, limit of detection and robustness.

immunology↗