Biology subjects

Gfeller, D.

Publications and source records attributed to Gfeller, D..

At least 19 recordsLinked to original sources

bioRxiv2026

Capabilities, specificity gaps and training-data dependence of AlphaFold3 across diverse application areas

Structure prediction models have moved from single proteins to assemblies that include diverse biomolecules and their modifications. AlphaFold3 (AF3) and related models extended structural modelling via an all-atom framework, opening many new potential applications in structural biology. We evaluate how well the new capabilities of AF3 translate into application tasks in diverse areas: prediction of ubiquitinated protein structures, T-cell receptor (TCR)-epitope recognition, antibody-antigen complexes, protein-RNA and protein-lipid interactions. We find that, while AF3 can perform well in favourable settings, this performance is uneven across applications. In RNA-target predictions, the model confidence fails to separate genuine from decoy interaction partners and in several tasks accuracy depends on the presence of related complexes in the training set. Taken together, our assessment is more cautious than for AF2, whose gains in modelling monomers and complexes were clear and broadly generalisable. AF3s extension to new biomolecule types shows less consistent performance and generalisation. AF3 can be a powerful tool for hypothesis generation and prioritisation, but its predictions and use of confidence metrics will depend strongly on the specific application area and must be interpreted with respect to training-set overlap. We expect that the benchmarks provided here will serve for testing of future developments in the structure prediction field.

bioinformatics↗

bioRxiv2026

Autophagy shapes the peptide repertoire of rheumatoid arthritis-associated HLA class II alleles

Alternative pathways of antigen presentation are crucial in different immunological contexts such as autoimmunity and anti-microbial defense. Among these pathways, autophagy has a central role in delivering cytosolic substrates to the MHC class II compartment. However, its contribution to endogenous MHC class II presentation was only demonstrated for a few antigens. Here we focused our study on the contribution of autophagy to the peptidome of one major allele of the HLA-DR shared epitope, HLA DRB1*04:01 conferring the greatest risk factor for the development of rheumatoid arthritis (RA). We provide an extensive qualitative and quantitative mass spectrometry analysis of the autophagy related MHC class II peptide repertoire of the human DRB1*04:01 allele. A fraction of peptides representing 30% of the repertoire differ profoundly between autophagy sufficient and deficient cells. Our analysis demonstrates that autophagy contributes to MHC class II presentation of peptides from seven described RA autoantigens, the majority of them being related to the ER folding and stress response (calreticulin, calnexin, the 78 kDa glucose-regulated protein (GRP78)-also known as binding immunoglobulin protein (BiP) and several protein from the heat-shock-protein 70 family). Our results correlate with an increased activation of autophagy, in situ, in synovial biopsies and synovial fibroblast (SF) of RA patients. We could further show that SF upregulate MHC class II and present peptides from autophagy related auto-antigens to CD4 T cells from RA patients. Our finding identifies autophagy as a potential process that could contribute to the break of peripheral tolerance during RA.

immunology↗

bioRxiv2026

Unpaired TCRα + TCRβ sequencing is sufficient for training machine learning TCR-epitope recognition predictors

T-cell recognition of infected and malignant cells is elicited by the binding of heterodimeric T-Cell Receptors (TCRs) to epitopes and both the TCR and the TCR{beta} chains play a key role in these interactions. Machine learning tools trained on databases of TCRs recognizing diverse epitopes are useful for identifying epitope-specific TCRs in large TCR repertoire datasets. However, collecting paired TCR{beta} sequences to train such tools is associated with significant sequencing costs. Here we demonstrate that unpaired TCR + TCR{beta} sequencing of epitope-specific T cells can be used for training TCR-epitope recognition predictors at a much reduced cost compared to standard single-cell TCR sequencing protocols and with no impact on prediction accuracy. Applying this approach to some unseen epitopes used in the IMMREP community benchmark demonstrates improved accuracy compared to both existing machine learning models and AlphaFold3-based predictions.

immunology↗

bioRxiv2026

SuperCell2.0 enables semi-supervised construction of multimodal metacell atlases

Multimodal single-cell atlases comprising hundreds of thousands of cells provide unique resources for exploring complex biological tissues and generating testable hypotheses. To streamline the analysis of such large datasets, we introduce SuperCell2.0, a robust workflow to build (semi-)supervised multimodal metacells. We demonstrate that multimodal metacells outperform metacells built with a single modality, improve inter-modality consistency, and facilitate integration of multiomic single-cell datasets. SuperCell2.0 can further leverage full or partial cell type annotations to improve metacell quality. This workflow enables us to construct multimodal metacell atlases from blood and tumor samples and identifies interferon-primed monocytes and macrophages in the circulation and in the tumor microenvironment. Markers derived from the metacell analysis enable us to sort and phenotypically characterize this population in healthy donors. Overall, our work demonstrates how SuperCell2.0 facilitates the analysis of large multimodal single-cell atlases.

bioinformatics↗

bioRxiv2025

Statistical modelling of CDR3 sequences provides robust quality control for TCR repertoire datasets

T-Cell Receptors (TCRs) show extensive sequence diversity across T cells. This diversity arises from different choices of V and J genes and from insertions and deletions at the V(D)J junction within the Complementary-Determining Region 3 (CDR3) loop. Here, we quantify how V and J gene usage shapes CDR3 length and amino acid composition. In repertoires of TCRs with either unknown or known specificity, we show that CDR3 length is strongly influenced by the number of germline-encoded CDR3 residues in V and J genes, and that, on average, 80% of CDR3 and 65% of CDR3{beta} residues are determined by V and J gene usage. We further show that inconsistencies between V and J gene annotations and CDR3 sequences can be leveraged to identify potential issues in multiple TCR repertoire datasets. Overall, our study quantifies the impact of V and J gene usage on CDR3 length and amino acid composition and provides a robust framework for quality control of TCR repertoire sequencing data.

immunology↗

bioRxiv2025

Key determinants of T cell epitope recognition revealed by TCR specificity profiles

Interactions between T-Cell Receptors (TCRs) and antigenic peptides presented on Major Histocompatibility Complex (MHC) molecules are central to the immune recognition of infected and malignant cells. The complexity of the TCR sequence space, the flexibility of the TCR-epitope interface and the lack of a standardized framework to visualize TCR specificity have hindered a comprehensive understanding of the principles governing TCR-epitope recognition across a broad range of epitopes. Here, we introduce a fully interpretable probabilistic framework, termed TCR specificity profiles (TSPs), which captures fundamental properties distinguishing epitope-specific from baseline TCR repertoires. We demonstrate that TSPs unravel key determinants of TCR-epitope recognition specificity. By identifying and analyzing TCRs recognizing dozens of epitope variants, we show that TSPs accurately predict cross-reactivity and reveal how TCR specificity evolves with epitope sequence, binding mode and MHC restriction. TSPs further enable interpretation of machine learning tools and reveal how AlphaFold3 can be used to decipher key determinants of TCR-epitope recognition specificity.

immunology↗

bioRxiv2024

Phage display profiling of CDR3β loops enables machine learning predictions of NY-ESO-1 specific TCRs

T cells targeting epitopes in infectious diseases or cancer play a central role in spontaneous and therapy-induced immune responses. T-cell epitope recognition is mediated by the binding of the T-Cell Receptor (TCR) and TCRs recognizing clinically relevant epitopes are promising for T-cell based therapies. Starting from one of the few known TCRs targeting the cancer-testis antigen NY-ESO-1157-165 epitope, we built large phage display libraries of TCRs with randomized Complementary Determining Region 3 of the {beta} chain. The TCR libraries were panned against the NY-ESO-1 epitope, which enabled us to collect thousands of epitope-specific TCR sequences. We then trained a machine learning TCR-epitope interaction predictor with this data and could identify several epitope-specific TCRs directly from TCR repertoires. Cellular binding and functional assays revealed that the predicted TCRs displayed activity towards the NY-ESO-1 epitope and no detectable cross-reactivity with self-peptides. Overall, our work demonstrates how display technologies combined with machine learning models of TCR-epitope recognition can effectively leverage large TCR repertoires for TCR discovery.

bioinformatics↗

bioRxiv2024

SuperSpot: Coarse Graining Spatial Transcriptomic Data into Metaspots

SummarySpatial Transcriptomics is revolutionizing our ability to phenotypically characterize complex biological tissues and decipher cellular niches. As of today, thousands of genes can be detected across hundreds of thousands of spots. Akin to standard single-cell RNA-Seq data, spatial transcriptomic data are very sparse due to the limited amount of RNA within each spot. Building upon the metacell concept, we present a workflow, called SuperSpot, to combine adjacent and transcriptionally similar spots into "metaspots". The process involves representing spots as nodes in a graph with edges connecting spots in spatial proximity and edge weights representing transcriptional similarity. Hierarchical clustering is used to aggregate spots into metaspots at a user-defined resolution. We demonstrate that metaspots can be used to reduce the size of spatial transcriptomic data and remove some of the dropout noise. Availability and implementationSuperSpot is an R package available at https://github.com/GfellerLab/SuperSpot.

bioinformatics↗

bioRxiv2024

Predicting MHC-I ligands across alleles and species: How far can we go?

CD8+ T-cell activation is initiated by the recognition of epitopes presented on class I major histocompatibility complex (MHC-I) molecules. Identifying such epitopes is useful for molecular understanding of cellular immune responses and can guide the development of personalized vaccines for various diseases including cancer. Here, we capitalize on high-quality MHC-I peptidomics data available from different species and an expanded architecture of our MHC-I ligand predictor (MixMHCpred) to carefully explore how much predictions can be extrapolated to MHC-I alleles without known ligands. Our results reveal high prediction accuracy for most MHC-I alleles in human and in laboratory mouse strains, but significantly lower accuracy in other species. Our work further outlines some of the molecular determinants of MHC-I ligand predictions accuracy across alleles and species. Robust benchmarking on external data shows that the pan-allele version of MixMHCpred (v3.0) outperforms other state-of-the-art MHC-I ligand predictors and can be used for CD8+ T-cell epitope predictions.

bioinformatics↗

bioRxiv2024

Building and analyzing metacells in single-cell genomics data

The advent of high-throughput single-cell genomics technologies has fundamentally transformed biological sciences. Currently, millions of cells from complex biological tissues can be phenotypically profiled across multiple modalities. The scaling of computational methods to analyze such data is a constant challenge and tools need to be regularly updated, if not redesigned, to cope with ever-growing numbers of cells. Over the last few years, metacells have been introduced to reduce the size and complexity of single-cell genomics data while preserving biologically relevant information. Here, we review recent studies that capitalize on the concept of metacells - and the many variants in nomenclature that have been used. We further outline how and when metacells should (or should not) be used to study single-cell genomics data and what should be considered when analyzing such data at the metacell level. To facilitate the exploration of metacells, we provide a comprehensive tutorial on construction and analysis of metacells from single-cell RNA-seq data (https://github.com/GfellerLab/MetacellAnalysisTutorial) as well as a fully integrated pipeline to rapidly build, visualize and evaluate metacells with different methods (https://github.com/GfellerLab/MetacellAnalysisToolkit).

bioinformatics↗

bioRxiv2023

How to predict binding specificity and ligands for new MHC-II alleles with MixMHC2pred

MHC-II molecules are key mediators of antigen presentation in vertebrate species and bind to their ligands with high specificity. The very high polymorphism of MHC-II genes within species and the fast-evolving nature of these genes across species has resulted in tens of thousands of different alleles, with hundreds of new alleles being discovered yearly through large sequencing projects in different species. Here we describe how to use MixMHC2pred to predict the binding specificity of any MHC-II allele directly from its amino acid sequence. We then show how both MHC-II ligands and CD4+ T-cell epitopes can be predicted in different species with our approach. MixMHC2pred is available at http://mixmhc2pred.gfellerlab.org/.

immunology↗

bioRxiv2023

Robust estimation of cancer and immune cell-type proportions from bulk tumor ATAC-Seq data.

Assay for Transposase-Accessible Chromatin sequencing (ATAC-Seq) is a widely used technique to explore gene regulatory mechanisms. For most ATAC-Seq data from healthy and diseased tissues such as tumors, chromatin accessibility measurement represents a mixed signal from multiple cell types. In this work, we derive reliable chromatin accessibility marker peaks and reference profiles for most non-malignant cell types frequently observed in the micro-environment of human tumors. We then integrate these data into the EPIC deconvolution framework (Racle et al., 2017) to quantify cell-type heterogeneity in bulk ATAC-Seq data. Our EPIC-ATAC tool accurately predicts non-malignant and malignant cell fractions in tumor samples. When applied to a human breast cancer cohort, EPIC-ATAC accurately infers the immune contexture of the main breast cancer subtypes.

bioinformatics↗

bioRxiv2023

Deep learning predictions of TCR-epitope interactions reveal epitope-specific chains in dual alpha T cells

T cells have the ability to eliminate infected and cancer cells and play an essential role in cancer immunotherapy. T-cell activation is elicited by the binding of the T-cell receptor (TCR) to epitopes displayed on MHC molecules, and the TCR specificity is determined by the sequence of its and {beta} chains. Here, we collected and curated a dataset of 17,715 {beta}TCRs interacting with dozens of class I and class II epitopes. We used this curated data to develop MixTCRpred, a deep learning TCR-epitope interaction predictor. MixTCRpred accurately predicts TCRs recognizing several viral and cancer epitopes. MixTCRpred further provides a useful quality control tool for multiplexed single-cell TCR sequencing assays of epitope-specific T cells and pinpoints a substantial fraction of putative contaminants in public databases. Analysis of epitope-specific dual T cells demonstrates that MixTCRpred can identify chains mediating epitope recognition. Applying MixTCRpred to TCR repertoires from COVID-19 patients reveals enrichment of clonotypes predicted to bind an immunodominant SARS-CoV-2 epitope. Overall, MixTCRpred provides a robust tool to predict TCRs interacting with specific epitopes and interpret TCR-sequencing data from both bulk and epitope-specific T cells.

bioinformatics↗

bioRxiv2023

Semi-supervised integration of single-cell transcriptomics data

Single-cell sequencing technologies offer unprecedented opportunities to characterize the complexity of biological samples with high resolution. At the same time, variations in sample processing and experimental protocols introduce technical variability - or "batch effects" - in the molecular readouts, hindering comparative analyses across samples and individuals. Although batch effect correction methods are routinely applied in single-cell omics analyses, data integration often leads to overcorrection, resulting in the loss of true biological variability. In this study, we present STACAS v2, a semi-supervised scRNA-seq data integration method that leverages prior knowledge in the form of cell type annotations to preserve biological variance. Through an open and reproducible benchmarking pipeline, we show that semi-supervised STACAS outperforms popular unsupervised methods such as Harmony, FastMNN, Seurat v4, scVI, and Scanorama, as well as supervised methods such as scANVI and scGen. Notably, STACAS is robust to incomplete and imprecise cell type annotations, which are commonly encountered in real-life integration tasks. Highlighting its scalability, we successfully applied semi-supervised STACAS to construct a high-resolution map of tumor-infiltrating CD8 T cells encompassing over 500,000 cells from 265 individuals. Based on our findings, we argue that the incorporation of prior cell type information should be a common practice in single-cell data integration, and we provide a flexible framework for semi-supervised batch effect correction. STACAS seamlessly integrates with Seurat pipelines and can be run with one command: Run.STACAS(seurat.list, cell.labels).

bioinformatics↗

bioRxiv2022

Rapid BCMA downmodulation on myeloma cells upon CAR T cell contact is mediated by trogocytosis and BCMA internalization

BackgroundChimeric antigen receptor (CAR) T cell therapy targeting B cell maturation antigen (BCMA) on multiple myeloma (MM) produces fast but not long-lasting responses. Reasons for treatment failure are poorly understood. CARs simultaneously targeting two antigens may represent an alternative. Here, we (1) designed and characterized novel A proliferation inducing ligand (APRIL) based dual-antigen targeting CARs, and (2) investigated mechanisms of resistance to CAR T cells with three different BCMA-binding moieties (APRIL, single-chain-variable-fragment, heavy-chain-only). MethodsThree new APRIL-CARs were designed and characterized. Human APRIL-CAR T cells were evaluated for their cytotoxic function in vitro and in vivo, for their polyfunctionality, immune synapse formation, memory, exhaustion phenotype and tonic signaling activity. To investigate resistance mechanisms, we analyzed BCMA levels and cellular localization and quantified CAR T cell - target cell interactions by live microscopy. Impact on pathway activation and tumor cell proliferation was assessed in vitro and in vivo. ResultsAPRIL-BB{zeta} CAR T cells in a trimeric ligand binding conformation conferred the best polyfunctionality, immune synapse formation and fast anti-tumor function in vivo in two different mouse xenograft models. Upon CAR T cell - myeloma cell contact, we found rapid BCMA downmodulation on target cells with all three evaluated binding moieties. CAR T cells acquired BCMA on their cell surface by trogocytosis, and BCMA on MM cells was rapidly internalized. Since trogocytosis can lead to CAR T cell exhaustion and presence of CAR T cells does not protect patients from relapse, we investigated whether non-functional CAR T cells play a role in tumor progression. While CAR T cell - MM cell interactions activated BCMA pathway, we did not find enhanced tumor growth in vitro or in vivo. ConclusionWe designed and characterized distinct APRIL-CAR T cells for dual-antigen targeting of MM. Rapid BCMA downmodulation occurred upon CAR T cell - tumor cell encounter independently of whether an APRIL or antibody-based binding moiety was used. BCMA internalization mostly contributed to this effect, but trogocytosis by CAR T cells was also observed. Our study sheds light on the mechanisms underlying CAR T cell failure in MM and can inform the development of improved treatment strategies.

immunology↗

bioRxiv2022

Machine learning predictions of MHC-II specificities reveal alternative binding mode of class II epitopes

CD4+ T cells orchestrate the adaptive immune response against pathogens and cancer by recognizing epitopes presented on MHC-II molecules. The high polymorphism of MHC-II genes represents an important hurdle towards accurate prediction and identification of CD4+ T-cell epitopes in different individuals and different species. Here we collected and curated a dataset of 627,013 unique MHC-II ligands identified by mass spectrometry. This enabled us to precisely determine the binding motifs of 88 MHC-II alleles across human, mouse, cattle and chicken. Analysis of these binding specificities combined with X-ray crystallography refined our understanding of the molecular determinants of MHC-II motifs and revealed a widespread reverse binding mode in MHC-II ligands. We then developed a machine learning framework to accurately predict binding specificities and ligands of any MHC-II allele. This tool improves and expands predictions of CD4+ T-cell epitopes, and enabled us to discover and characterize several viral and bacterial epitopes following the aforementioned reverse binding mode.

immunology↗

bioRxiv2022

Predictions of immunogenicity reveal potent SARS-CoV-2 CD8+ T-cell epitopes

The recognition of pathogen or cancer-specific epitopes by CD8+ T cells is crucial for the clearance of infections and the response to cancer immunotherapy. This process requires epitopes to be presented on class I Human Leukocyte Antigen (HLA-I) molecules and recognized by the T-Cell Receptor (TCR). Machine learning models capturing these two aspects of immune recognition are key to improve epitope predictions. Here we assembled a high-quality dataset of naturally presented HLA-I ligands and experimentally verified neo-epitopes. We then integrated these data with new algorithmic developments to improve predictions of both antigen presentation and TCR recognition. Applying our tool to SARS-CoV-2 proteins enabled us to uncover several epitopes. TCR sequencing identified a monoclonal response in effector/memory CD8+ T cells against one of these epitopes and cross-reactivity with the homologous SARS-CoV-1 peptide.

bioinformatics↗

bioRxiv2021

Deciphering the landscape of phosphorylated HLA-II ligands

CD4+ T-cell activation in infectious diseases and cancer is governed by the recognition of peptides presented on class II human leukocyte antigen (HLA-II) molecules. Therefore, HLA-II ligands represent promising targets for vaccine design and personalized cancer immunotherapy. Much work has been done to identify and predict unmodified peptides presented on HLA-II molecules. However, little is known about the presentation of phosphorylated HLA-II ligands. Here, we analyzed Mass Spectrometry HLA-II peptidomics data and identified 1,113 unique phosphorylated HLA-II ligands. This enabled us to precisely define phosphorylated binding motifs for more than 30 common HLA-II alleles and to explore various molecular properties of phosphorylated peptides. Our data were further used to develop the first predictor of phosphorylated peptide presentation on HLA-II molecules.

immunology↗