Improved T cell receptor antigen pairing through data-driven filtering of sequencing information from single cells

Structure-Directed Pan-Specific T-Cell Receptor-Peptide-Major Histocompatibility Complex Interaction Prediction

T-cell receptors (TCRs) play a pivotal role in the adaptive immune system, and understanding their antigen recognition mechanism remains a critical area of research. With the increasing availability of binding and interaction data between TCRs and peptide-major histocompatibility complexes (pMHCs), data-driven computational methods are emerging as powerful tools with significant potential for advancement. In this study, we collected and curated comprehensive sequence and structure data sets of TCRs from human CD8+ T-cells and cognate epitopes presented by MHC class I molecules. We developed two innovative computational frameworks: SG-TPMI, a lightweight, extensible, and structure-guided model for predicting TCR-pMHC binding specificity, and Seq/Struct-TCS, a pair of models (sequence-based and structure-based) for predicting contact sites within TCR-pMHC complexes. Notably, we directly integrated MHC-I alpha helices (or pseudosequences) and structural information on the protein complex into the prediction models. Our comprehensive modeling approach enabled quantitative investigations of TCR-pMHC interaction mechanisms, empowering SG-TPMI and Struct-TCS to achieve performances comparable to those of state-of-the-art methods. Furthermore, our results highlight the necessity of CDR1 and CDR2 loops as well as MHC restriction in pan-specific TCR-pMHC interaction prediction, providing new insights into TCR recognition. In summary, we not only propose SG-TPMI as an effective computational method for predicting TCR-pMHC binary interactions but also introduce the Seq/Struct-TCS design for predicting TCR interacting sites with peptide or MHC alpha helices.

TSpred: a robust prediction framework for TCR-epitope interactions using paired chain TCR sequence data

MOTIVATION

Prediction of T-cell receptor (TCR)-epitope interactions is important for many applications in biomedical research, such as cancer immunotherapy and vaccine design. The prediction of TCR-epitope interactions remains challenging especially for novel epitopes, due to the scarcity of available data.

RESULTS

We propose TSpred, a new deep learning approach for the pan-specific prediction of TCR binding specificity based on paired chain TCR data. We develop a robust model that generalizes well to unseen epitopes by combining the predictive power of CNN and the attention mechanism. In particular, we design a reciprocal attention mechanism which focuses on extracting the patterns underlying TCR-epitope interactions. Upon a comprehensive evaluation of our model, we find that TSpred achieves state-of-the-art performances in both seen and unseen epitope specificity prediction tasks. Also, compared to other predictors, TSpred is more robust to bias related to peptide imbalance in the dataset. In addition, the reciprocal attention component of our model allows for model interpretability by capturing structurally important binding regions. Results indicate that TSpred is a robust and reliable method for the task of TCR-epitope binding prediction.

AVAILABILITY AND IMPLEMENTATION

Source code is available at https://github.com/ha01994/TSpred.

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 α and β chains. Here, we collect and curate a dataset of 17,715 αβTCRs interacting with dozens of class I and class II epitopes. We use this curated data to develop MixTCRpred, an epitope-specific 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 α T cells demonstrates that MixTCRpred can identify α 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.

Simultaneous analysis of pMHC binding and reactivity unveils virus-specific CD8 T cell immunity to a concise epitope set

CD8 T cells provide immunity to virus infection through recognition of epitopes presented by peptide major histocompatibility complexes (pMHCs). To establish a concise panel of widely recognized T cell epitopes from common viruses, we combined analysis of TCR down-regulation upon stimulation with epitope-specific enumeration based on barcode-labeled pMHC multimers. We assess CD8 T cell binding and reactivity for 929 previously reported epitopes in the context of 1 of 25 HLA alleles representing 29 viruses. The prevalence and magnitude of CD8 T cell responses were evaluated in 48 donors and reported along with 137 frequently recognized virus epitopes, many of which were underrepresented in the public domain. Eighty-four percent of epitope-specific CD8 T cell populations demonstrated reactivity to peptide stimulation, which was associated with effector and long-term memory phenotypes. Conversely, nonreactive T cell populations were associated primarily with naive phenotypes. Our analysis provides a reference map of epitopes for characterizing CD8 T cell responses toward common human virus infections.

Enhancing TCR specificity predictions by combined pan- and peptide-specific training, loss-scaling, and sequence similarity integration

Predicting the interaction between Major Histocompatibility Complex (MHC) class I-presented peptides and T-cell receptors (TCR) holds significant implications for vaccine development, cancer treatment, and autoimmune disease therapies. However, limited paired-chain TCR data, skewed towards well-studied epitopes, hampers the development of pan-specific machine-learning (ML) models. Leveraging a larger peptide-TCR dataset, we explore various alterations to the ML architectures and training strategies to address data imbalance. This leads to an overall improved performance, particularly for peptides with scant TCR data. However, challenges persist for unseen peptides, especially those distant from training examples. We demonstrate that such ML models can be used to detect potential outliers, which when removed from training, leads to augmented performance. Integrating pan-specific and peptide-specific models alongside with similarity-based predictions, further improves the overall performance, especially when a low false positive rate is desirable. In the context of the IMMREP22 benchmark, this modeling framework attained state-of-the-art performance. Moreover, combining these strategies results in acceptable predictive accuracy for peptides characterized with as little as 15 positive TCRs. This observation places great promise on rapidly expanding the peptide covering of the current models for predicting TCR specificity. The NetTCR 2.2 model incorporating these advances is available on GitHub (https://github.com/mnielLab/NetTCR-2.2) and as a web server at https://services.healthtech.dtu.dk/services/NetTCR-2.2/.

Herpes Virus Infections in Kidney Transplant Patients (HINT) - a prospective observational cohort study

BACKGROUND

Kidney transplant recipients receive maintenance immunosuppressive therapy to avoid allograft rejection resulting in increased risk of infections and infection-related morbidity and mortality. Approximately 98% of adults are infected with varicella zoster virus, which upon reactivation causes herpes zoster. The incidence of herpes zoster is higher in kidney transplant recipients than in immunocompetent individuals, and kidney transplant recipients are at increased risk of severe herpes zoster-associated disease. Vaccination with adjuvanted recombinant glycoprotein E subunit herpes zoster vaccine (RZV) prevents herpes zoster in older adults with excellent efficacy (90%), and vaccination of kidney transplant candidates is recommended in Danish and international guidelines. However, the robustness and duration of immune responses after RZV vaccination, as well as the optimal timing of vaccination in relation to transplantation remain unanswered questions. Thus, the aim of this study is to characterize the immune response to RZV vaccination in kidney transplant candidates and recipients at different timepoints before and after transplantation.

METHODS

The Herpes Virus Infections in Kidney Transplant Patients (HINT) study is a prospective observational cohort study. The study will include kidney transplant candidates on the waiting list for transplantation (n = 375) and kidney transplant recipients transplanted since January 1, 2019 (n = 500) from all Danish kidney transplant centers who are offered a RZV vaccine as routine care. Participants are followed with repeated blood sampling until 12 months after inclusion. In the case of transplantation or herpes zoster disease, additional blood samples will be collected until 12 months after transplantation. The immune response will be characterized by immunophenotyping and functional characterization of varicella zoster virus-specific T cells, by detection of anti-glycoprotein E antibodies, and by measuring cytokine profiles.

DISCUSSION

The study will provide new knowledge on the immune response to RZV vaccination in kidney transplant candidates and recipients and the robustness and duration of the response, potentially enhancing preventive strategies against herpes zoster in a population at increased risk.

TRIAL REGISTRATION

ClinicalTrials.gov (NCT05604911).