Biology subjects

Scheid, J.

Publications and source records attributed to Scheid, J..

5 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↗

bioRxiv2026

quantms-rescoring enables deep proteome coverage across protein quantification, immunopeptidomics, and post-translational modifications experiments.

The growing volume of public proteomics datasets and the advent of novel machine learning (ML)-based methods create unprecedented opportunities for discovery through large-scale reanalysis. However, traditional desktop tools are increasingly insufficient for processing and integrating data at this scale. To address this challenge, we present a novel package, quantms-rescoring, that extends the cloud-native quantms workflow with a machine learning-based rescoring module. Unlike prior tools that rescore single-engine outputs, quantms-rescoring seamlessly integrates multiple search engines (SAGE, COMET, and MSGF+), performs automatic model selection, model fine-tuning, and scales reproducibly on cloud infrastructures. In quantms-rescoring, we rely on multiple fragment-ion intensity (AlphaPeptDeep and MS2PIP) and retention-time prediction (DeepLC) methods to improve results from multiple peptide database search engines. It features automatic model selection, fine-tuning, and retraining for MS/MS intensity and retention time prediction to select the best model for a given dataset. We applied the novel workflow to five representative datasets spanning DDA label-free quantification, TMT 10-plex isobaric labelling of tumor proteomics data, immunopeptidomics, phospho-proteomics, and unseen lysine malonylation experiments. We achieved a 16-22.8% increase in identified spectra, along with the quantification of 2191 additional phosphorylated peptides and 1337 phosphosites. In the tandem mass tag (TMT)-labeled clear cell renal cell carcinoma dataset, 76 novel differentially expressed multiple search engines identified proteins with quantms-rescoring. Additionally, novel 11,688 HLA-II potential binders were detected in the immunopeptidomics dataset by multiple search engines with quantms-rescoring. For unseen malonylation data, we reported more than 58.8% malonylation PSMs and 30.5% modification sites than COMET alone. Together, these results show that integrating multi-engine searches with machine learning-derived features can be combined in a scalable workflow that enhances identification, PTM localization, and quantification performance.

bioinformatics↗

bioRxiv2024

TIMS2Rescore: A DDA-PASEF optimized data-driven rescoring pipeline based on MS2Rescore

The high throughput analysis of proteins with mass spectrometry (MS) is highly valuable for understanding human biology, discovering disease biomarkers, identifying therapeutic targets, and exploring pathogen interactions. To achieve these goals, specialized proteomics subfields - such as plasma proteomics, immunopeptidomics, and metaproteomics - must tackle specific analytical challenges, such as an increased identification ambiguity compared to routine proteomics experiments. Technical advancements in MS instrumentation can counter these issues by acquiring more discerning information at higher sensitivity levels, as is exemplified by the incorporation of ion mobility and parallel accumulation - serial fragmentation (PASEF) technologies in timsTOF instruments. In addition, AI-based bioinformatics solutions can help overcome ambiguity issues by integrating more data into the identification workflow. Here, we introduce TIMS2Rescore, a data-driven rescoring workflow optimized for DDA-PASEF data from timsTOF instruments. This platform includes new timsTOF MS2PIP spectrum prediction models and IM2Deep, a new deep learning-based peptide ion mobility predictor. Furthermore, to fully streamline data throughput, TIMS2Rescore directly accepts Bruker raw mass spectrometry data, and search results from ProteoScape and many other search engines, including MS Amanda and PEAKS. We showcase TIMS2Rescore performance on plasma proteomics, immunopeptidomics (HLA class I and II), and metaproteomics data sets. TIMS2Rescore is open-source and freely available at https://github.com/compomics/tims2rescore.

bioinformatics↗

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↗