Biology subjects

Moreno, D. L.

Publications and source records attributed to Moreno, D. L..

3 recordsLinked to original sources

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

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↗

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↗