CrossDome: an interactive R package to predict cross-reactivity risk using immunopeptidomics databases

Strengths and limitations of web servers for the modeling of TCRpMHC complexes

Cellular immunity relies on the ability of a T-cell receptor (TCR) to recognize a peptide (p) presented by a class I major histocompatibility complex (MHC) receptor on the surface of a cell. The TCR-peptide-MHC (TCRpMHC) interaction is a crucial step in activating T-cells, and the structural characteristics of these molecules play a significant role in determining the specificity and affinity of this interaction. Hence, obtaining 3D structures of TCRpMHC complexes offers valuable insights into various aspects of cellular immunity and can facilitate the development of T-cell-based immunotherapies. Here, we aimed to compare three popular web servers for modeling the structures of TCRpMHC complexes, namely ImmuneScape (IS), TCRpMHCmodels, and TCRmodel2, to examine their strengths and limitations. Each method employs a different modeling strategy, including docking, homology modeling, and deep learning. The accuracy of each method was evaluated by reproducing the 3D structures of a dataset of 87 TCRpMHC complexes with experimentally determined crystal structures available on the Protein Data Bank (PDB). All selected structures were limited to human MHC alleles, presenting a diverse set of peptide ligands. A detailed analysis of produced models was conducted using multiple metrics, including Root Mean Square Deviation (RMSD) and standardized assessments from CAPRI and DockQ. Special attention was given to the complementarity-determining region (CDR) loops of the TCRs and to the peptide ligands, which define most of the unique features and specificity of a given TCRpMHC interaction. Our study provides an optimistic view of the current state-of-the-art for TCRpMHC modeling but highlights some remaining challenges that must be addressed in order to support the future application of these tools for TCR engineering and computer-aided design of TCR-based immunotherapies.

MORITS: An improved method to predict peptides from heterologous proteins that are recognized by the same T-cell receptor

Antigen-specific priming of T cells results in the activation of T cells that exert effector functions by interaction of their T-cell receptor (TCR) with the corresponding self-MHC molecule presenting a peptide on the surface of a target cell. Such antigen-specific T cells potentially can also interact with peptide-MHC complexes that contain peptides from unrelated antigens, a phenomenon that often is referred to as heterologous immunity. For example, some individuals that were pre-immunized against an allergen, could subsequently mount better anti-viral T-cell responses than non-allergic individuals. So far only few peptide pairs that experimentally have been shown to provoke heterologous immunity were  identified, and available prediction tools that can identify potential candidates are imprecise. We developed the MORITS algorithm to rapidly screen large lists of peptides for sequence similarities, while giving enhanced consideration to peptide residues presented by MHC that are particularly relevant for TCR interactions. In combination with established peptide-MHC binding prediction tools, the MORITS algorithm revealed peptide similarities between the SARS-CoV-2 proteome and certain allergens. The method outperformed previously published workflows and may help to identify novel pairs of peptides that mediate heterologous immune responses.

Novel insights into TCR-T cell therapy in solid neoplasms: optimizing adoptive immunotherapy

Adoptive immunotherapy in the T cell landscape exhibits efficacy in cancer treatment. Over the past few decades, genetically modified T cells, particularly chimeric antigen receptor T cells, have enabled remarkable strides in the treatment of hematological malignancies. Besides, extensive exploration of multiple antigens for the treatment of solid tumors has led to clinical interest in the potential of T cells expressing the engineered T cell receptor (TCR). TCR-T cells possess the capacity to recognize intracellular antigen families and maintain the intrinsic properties of TCRs in terms of affinity to target epitopes and signal transduction. Recent research has provided critical insight into their capability and therapeutic targets for multiple refractory solid tumors, but also exposes some challenges for durable efficacy. In this review, we describe the screening and identification of available tumor antigens, and the acquisition and optimization of TCRs for TCR-T cell therapy. Furthermore, we summarize the complete flow from  laboratory to clinical applications of TCR-T cells. Last, we emerge future prospects for improving therapeutic efficacy in cancer world with combination therapies or TCR-T derived products. In conclusion, this review depicts our current understanding of TCR-T cell therapy in solid neoplasms, and provides new perspectives for expanding its clinical applications and improving therapeutic efficacy.

Structure-aware deep model for MHC-II peptide binding affinity prediction

The prediction of major histocompatibility complex (MHC)-peptide binding affinity is an important branch in immune bioinformatics, especially helpful in accelerating the design of disease vaccines and immunity therapy. Although deep learning-based solutions have yielded promising results on MHC-II molecules in recent years, these methods ignored structure knowledge from each peptide when employing the deep neural network models. Each peptide sequence has its specific combination order, so it is worth considering adding the structural information of the peptide sequence to the deep model training. In this work, we use positional encoding to represent the structural information of peptide sequences and validly combine the positional encoding with existing models by different strategies. Experiments on three datasets show that the introduction of position-coding information can further improve the performance built upon the existing model. The idea of introducing positional encoding to this field can provide important reference significance for the optimization of the deep network structure in the future.

Vaccines against extraintestinal pathogenic Escherichia coli (ExPEC): progress and challenges

The emergence of antimicrobial resistance (AMR) is a principal global health crisis projected to cause 10 million deaths annually worldwide by 2050. While the Gram-negative bacteria Escherichia coli is commonly found as a commensal microbe in the human gut, some strains are dangerously pathogenic, contributing to the highest AMR-associated mortality. Strains of E. coli that can translocate from the gastrointestinal tract to distal sites, called extraintestinal E. coli (ExPEC), are particularly problematic and predominantly afflict women, the elderly, and immunocompromised populations. Despite nearly 40 years of clinical trials, there is still no vaccine against ExPEC. One reason for this is the remarkable diversity in the ExPEC pangenome across pathotypes, clades, and strains, with hundreds of genes associated with pathogenesis including toxins, adhesins, and nutrient acquisition systems. Further, ExPEC is intimately associated with human mucosal surfaces and has evolved creative strategies to avoid the immune system. This review summarizes previous and ongoing preclinical and clinical ExPEC vaccine research efforts to help identify key gaps in knowledge and remaining challenges.

Quantitative approaches for decoding the specificity of the human T cell repertoire

T cell receptor (TCR)-peptide-major histocompatibility complex (pMHC) interactions play a vital role in initiating immune responses against pathogens, and the specificity of TCRpMHC interactions is crucial for developing optimized therapeutic strategies. The advent of high-throughput immunological and structural evaluation of TCR and pMHC has provided an abundance of data for computational approaches that aim to predict favorable TCR-pMHC interactions. Current models are constructed using information on protein sequence, structures, or a combination of both, and utilize a variety of statistical learning-based approaches for identifying the rules governing specificity. This review examines the current theoretical, computational, and deep learning approaches for identifying TCR-pMHC recognition pairs, placing emphasis on each method's mathematical approach, predictive performance, and limitations.