Biology subjects

Racle, J.

Publications and source records attributed to Racle, J..

14 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↗

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

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↗

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↗

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↗

bioRxiv2019

Mass spectrometry based immunopeptidomics leads to robust predictions of phosphorylated HLA class I ligands

The presentation of peptides on class I human leukocyte antigen (HLA-I) molecules plays a central role in immune recognition of infected or malignant cells. In cancer, non-self HLA-I ligands can arise from many different alterations, including non-synonymous mutations, gene fusion, cancer-specific alternative mRNA splicing or aberrant post-translational modifications. Identifying HLA-I ligands remains a challenging task that requires either heavy experimental work for in-vivo identification or optimized bioinformatics tools for accurate predictions. To date, no HLA-I ligand predictor includes post-translational modifications. To fill this gap, we curated phosphorylated HLA-I ligands from several immunopeptidomics studies (including six newly measured samples) covering 72 HLA-I alleles, and retrieved a total of 2,066 unique phosphorylated peptides. We then expanded our motif deconvolution tool to identify precise binding motifs of phosphorylated HLA-I ligands. Our results reveal a clear enrichment of phosphorylated peptides among HLA-C ligands and demonstrate a prevalent role of both HLA-I motifs and kinase motifs on the presentation of phosphorylated peptides. This data further enabled us to develop and validate the first predictor of interactions between HLA-I molecules and phosphorylated peptides.

immunology↗

bioRxiv2019

Deep motif deconvolution of HLA-II peptidomes for robust class II epitope predictions

CD4 T cells are key for priming and regulating immune recognition of infected and cancer cells, but predictions of class II epitopes have limited accuracy. We combined unbiased Mass Spectrometry-based HLA-II peptidomics with a novel motif deconvolution algorithm to profile and analyze a total of 99265 unique HLA-II ligands. Our work demonstrates substantial improvement in the definition of HLA-II binding motifs and enhanced accuracy in class II epitope predictions.

immunology↗

bioRxiv2018

Peptide length distribution and multiple specificity in naturally presented HLA-I ligands

HLA-I molecules bind short peptides and present them for recognition by CD8+ T cells. The length of HLA-I ligands typically ranges from 8 to 12 amino acids, but variability is observed across different HLA-I alleles. Here we collected recent in-depth HLA peptidomics data, including 12 newly generated HLA peptidomes (31,896 unique peptides) from human meningioma samples, to analyze the peptide length distribution and multiple specificity across 84 different HLA-I alleles. We observed a clear clustering of HLA-I alleles with distinct peptide length distributions, which enabled us to study the structural basis of peptide length distributions and predict peptide length distributions from HLA-I sequences. We further identified multiple specificity in several HLA-I molecules and validated these observations with binding assays. Explicitly modeling peptide length distribution and multiple specificity improved predictions of naturally presented HLA-I ligands, as demonstrated in an independent benchmarking based on the new human meningioma samples.

immunology↗

bioRxiv2017

Simultaneous Enumeration Of Cancer And Immune Cell Types From Bulk Tumor Gene Expression Data

Immune cells infiltrating tumors can have important impact on tumor progression and response to therapy. We present an efficient algorithm to simultaneously estimate the fraction of cancer and immune cell types from bulk tumor gene expression data. Our method integrates novel gene expression profiles from each major non-malignant cell type found in tumors, renormalization based on cell-type specific mRNA content, and the ability to consider uncharacterized and possibly highly variable cell types. Feasibility is demonstrated by validation with flow cytometry, immunohistochemistry and single-cell RNA-Seq analyses of human melanoma and colorectal tumor specimens. Altogether, our work not only improves accuracy but also broadens the scope of absolute cell fraction predictions from tumor gene expression data, and provides a unique novel experimental benchmark for immunogenomics analyses in cancer research.

cancer biology↗