Biology subjects

Pereira Pinho, M.

Publications and source records attributed to Pereira Pinho, M..

3 recordsLinked to original sources

bioRxiv2026

ClonoDeconv: reference-based α - β paired recurrent clonotype proportion estimation from bulk-TCR sequencing

T cell receptor (TCR) sequencing is crucial when studying antigen-specific T cell responses and clonal dynamics. A key objective of TCR repertoire analysis is to identify recurring clonotypes across timepoints or different disease states, providing insight into the evolution of immune response. Bulk-TCR sequencing enables high-throughput profiling of TCR and {beta} sequences but lacks pairing information, while single cell TCR sequencing (scTCR-seq) retains pairing information but is limited by sensitivity, throughput, and costs. We present ClonoDeconv, a computational framework to deconvolute paired TCR {beta} ; clonotype frequency from bulk TCR sequencing experiments using a paired scTCR-seq reference. ClonoDeconv robustly estimated shared clonotype abundances from 5' RACE bulk-TCR datasets using a scTCR-seq reference from the same donor. Applied to longitudinal samples, ClonoDeonv successfully captured the expansion and contraction of rare virus-specific clones across timepoints. Taken together, ClonoDeconv provides a cost-effective and scalable approach for studying the longitudinal T-cell clonal dynamics.

bioinformatics↗

bioRxiv2022

A robust deep learning platform to predict CD8+ T-cell epitopes

T-cells play a crucial role in the adaptive immune system by inducing an anti-tumour response, defending against pathogens, and maintaining tolerance against self-antigens, which has sparked interest in the development of T-cell-based vaccines and immunotherapies. Because screening antigens driving the T-cell response is currently low-throughput and laborious, computational methods for predicting CD8+ T-cell epitopes have emerged. However, most immunogenicity algorithms struggle to learn features of peptide immunogenicity from small datasets, suffer from HLA bias and are unable to reliably predict pathology-specific CD8+ T-cell epitopes. Therefore, we developed TRAP (T-cell recognition potential of HLA-I presented peptides), a robust deep learning platform for predicting CD8+ T-cell epitopes from MHC-I presented pathogenic and self-peptides. TRAP uses transfer learning, deep learning architecture and MHC binding information to make context-specific predictions of CD8+ T-cell epitopes. TRAP also detects low-confidence predictions for peptides that differ significantly from those in the training datasets to abstain from making incorrect predictions. To estimate the immunogenicity of pathogenic peptides with low-confidence predictions, we further developed a novel metric, RSAT (relative similarity to autoantigens and tumour-associated antigens), as a complementary to dissimilarity to self from cancer studies. We used TRAP to identify epitopes from glioblastoma patients as well as SARS-CoV-2 peptides, and it outperformed other algorithms in both cancer and pathogenic settings. Thus, this study presents a novel computational platform for accurately predicting CD8+ T-cell epitopes to foster a better understanding of antigen-specific T-cell response and the development of effective clinical therapeutics. HighlightsO_LIHLA bias and out-of-distribution problem are causes of poor performance of current state-of-the-art algorithms C_LIO_LITransfer learning, deep learning architecture, context-specific and HLA-generalised approaches improve CD8+ T-cell epitope prediction C_LIO_LITRAP reports degree of correctness to improve reliability of the prediction C_LIO_LIA novel metric termed RSAT estimates immunogenicity of pathogenic peptides, as a complementary to dissimilarity to self from cancer studies C_LI

bioinformatics↗

bioRxiv2020

CD8+ T cell cross-reactivity against SARS-CoV-2 conferred by other coronavirus strains and influenza virus

While individuals infected with coronavirus disease 2019 (COVID-19) manifested a broad range in susceptibility and severity to the disease, the pre-existing immune memory of related pathogens can influence the disease outcome. Here, we investigated the potential extent of T cell cross-reactivity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that can be conferred by other coronaviruses and influenza virus, and generated a map of public and private predicted CD8+ T cell epitopes between coronaviruses. Moreover, to assess the potential risk of self-reactivity and/or diminished T cell response for peptides identical or highly similar to the host, we identified predicted epitopes with high sequence similarity with human proteome. Lastly, we compared predicted epitopes from coronaviruses with epitopes from influenza virus deposited in IEDB to support vaccine development against different virus strains. We believe the comprehensive in silico profile of private and public predicted epitopes across coronaviruses and influenza viruses will facilitate design of vaccines capable of protecting against various viral infections.

immunology↗