Predicting TCR sequences for unseen antigen epitopes using structural and sequence features.

T-cell receptor (TCR) recognition of antigens is fundamental to the adaptive immune response. With the expansion of experimental techniques, a substantial database of matched TCR-antigen pairs has emerged, presenting opportunities for computational prediction models. However, accurately forecasting the binding affinities of unseen antigen-TCR pairs remains a major challenge. Here, we present convolutional-self-attention TCR (CATCR), a novel framework tailored to enhance the prediction of epitope and TCR interactions. Our approach utilizes convolutional neural networks to extract peptide features from residue contact matrices, as generated by OpenFold, and a transformer to encode segment-based coded sequences. We introduce CATCR-D, a discriminator that can assess binding by analyzing the structural and sequence features of epitopes and CDR3-ß regions. Additionally, the framework comprises CATCR-G, a generative module designed for CDR3-ß sequences, which applies the pretrained encoder to deduce epitope characteristics and a transformer decoder for predicting matching CDR3-ß sequences. CATCR-D achieved an AUROC of 0.89 on previously unseen epitope-TCR pairs and outperformed four benchmark models by a margin of 17.4%. CATCR-G has demonstrated high precision, recall and F1 scores, surpassing 95% in bidirectional encoder representations from transformers score assessments. Our results indicate that CATCR is an effective tool for predicting unseen epitope-TCR interactions. Incorporating structural insights enhances our understanding of the general rules governing TCR-epitope recognition significantly. The ability to predict TCRs for novel epitopes using structural and sequence information is promising, and broadening the repository of experimental TCR-epitope data could further improve the precision of epitope-TCR binding predictions.

Can AlphaFold's breakthrough in protein structure help decode the fundamental principles of adaptive cellular immunity?

T cells are essential immune cells responsible for identifying and eliminating pathogens. Through interactions between their T-cell antigen receptors (TCRs) and antigens presented by major histocompatibility complex molecules (MHCs) or MHC-like molecules, T cells discriminate foreign and self peptides. Determining the fundamental principles that govern these interactions has important implications in numerous medical contexts. However, reconstructing a map between T cells and their antagonist antigens remains an open challenge for the field of immunology, and success of in silico reconstructions of this relationship has remained incremental. In this Perspective, we discuss the role that new state-of-the-art deep-learning models for predicting protein structure may play in resolving some of the unanswered questions the field faces linking TCR and peptide-MHC properties to T-cell specificity. We provide a comprehensive overview of structural databases and the evolution of predictive models, and highlight the breakthrough AlphaFold provided the field.

Structure-Based Rational and General Strategy for Stabilizing Single-Chain T-Cell Receptors to Enhance Affinity.

The T-cell receptor (TCR) is a crucial molecule in cellular immunity. The single-chain T-cell receptor (scTCR) is a potential format in TCR therapeutics because it eliminates the possibility of αß-TCR mispairing. However, its poor stability and solubility impede the in vitro study and manufacturing of therapeutic applications. In this study, some conserved structural motifs are identified in variable domains regardless of germlines and species. Theoretical analysis helps to identify those unfavored factors and leads to a general strategy for stabilizing scTCRs by substituting residues at exact IMGT positions with beneficial propensities on the consensus sequence of germlines. Several representative scTCRs are displayed to achieve stability optimization and retain comparable binding affinities with the corresponding αß-TCRs in the range of µM to pM. These results demonstrate that our strategies for scTCR engineering are capable of providing the affinity-enhanced and specificity-retained format, which are of great value in facilitating the development of TCR-related therapeutics.

Chaperone-mediated MHC-I peptide exchange in antigen presentation.

This work focuses on molecules that are encoded by the major histocompatibility complex (MHC) and that bind self-, foreign- or tumor-derived peptides and display these at the cell surface for recognition by receptors on T lymphocytes (T cell receptors, TCR) and natural killer (NK) cells. The past few decades have accumulated a vast knowledge base of the structures of MHC molecules and the complexes of MHC/TCR with specificity for many different peptides. In recent years, the structures of MHC-I molecules complexed with chaperones that assist in peptide loading have been revealed by X-ray crystallography and cryogenic electron microscopy. These structures have been further studied using mutagenesis, molecular dynamics and NMR approaches. This review summarizes the current structures and dynamic principles that govern peptide exchange as these relate to the process of antigen presentation.

EPIC-TRACE: predicting TCR binding to unseen epitopes using attention and contextualized embeddings.

MOTIVATION: T cells play an essential role in adaptive immune system to fight pathogens and cancer but may also give rise to autoimmune diseases. The recognition of a peptide-MHC (pMHC) complex by a T cell receptor (TCR) is required to elicit an immune response. Many machine learning models have been developed to predict the binding, but generalizing predictions to pMHCs outside the training data remains challenging. RESULTS: We have developed a new machine learning model that utilizes information about the TCR from both α and ß chains, epitope sequence, and MHC. Our method uses ProtBERT embeddings for the amino acid sequences of both chains and the epitope, as well as convolution and multi-head attention architectures. We show the importance of each input feature as well as the benefit of including epitopes with only a few TCRs to the training data. We evaluate our model on existing databases and show that it compares favorably against other state-of-the-art models. AVAILABILITY AND IMPLEMENTATION: https://github.com/DaniTheOrange/EPIC-TRACE.

GGNpTCR: A Generative Graph Structure Neural Network for Predicting Immunogenic Peptides for T-cell Immune Response.

Identifying the interactions between T-cell receptor (TCRs) and human antigens is a crucial step in developing new vaccines, diagnostics, and immunotherapy. Current methods primarily focus on learning binding patterns from known TCR binding repertoires by using sequence information alone without considering the binding specificity of new antigens or exogenous peptides that have not appeared in the training set. Furthermore, the spatial structure of antigens plays a critical role in immune studies and immunotherapy, which should be addressed properly in the identification of interacting TCR-antigen pairs. In this study, we introduced a novel deep learning framework based on generative graph structures, GGNpTCR, for predicting interactions between TCR and peptides from sequence information. Results of real data analysis indicate that our model achieved excellent prediction for new antigens unseen in the training data set, making significant improvements compared to existing methods. We also applied the model to a large COVID-19 data set with no antigens in the training data set, and the improvement was also significant. Furthermore, through incorporation of additional supervised mechanisms, GGNpTCR demonstrated the ability to precisely forecast the locations of peptide-TCR interactions within 3D configurations. This enhancement substantially improved the model's interpretability. In summary, based on the performance on multiple data sets, GGNpTCR has made significant progress in terms of performance, universality, and interpretability.

Decoding the deactivation mechanism of R192W mutation of ZAP-70 using molecular dynamics simulations and binding free energy calculations.

CONTEXT: ZAP-70 (zeta-chain-associated protein of 70 kDa), serving as a critical regulator for T cell antigen receptor signaling, represents an attractive therapeutic target for autoimmunity disease. How the mechanistical mechanism of ZAP-70 to a human autoimmune syndrome-associated R192W mutation remains unclear. The results indicated that the R192W mutation of ZAP-70 clearly affected the conformational flexibility of the N-terminal ITAM-Y2P. Structural analysis unveiled that the R192W mutation of ZAP-70 caused the exposure of the N-terminal ITAM-Y2P to the solvent. MM-GBSA binding free energy calculations exhibited that the R192W mutation decreased the binding affinity of ITAM-Y2P to the ZAP-70 mutant. Residue-based free energy decomposition further revealed that the protein-peptide interaction networks involving electrostatic interactions provide significant contributions for complex formation. The energy unfavorable residues include Arg43, Arg192, Tyr240, and Lys244 from ZAP-70 and Asn301, Leu303, pY304, and pY315 from ITAM-Y2P in the R192W mutant. Our obtained results may help the understanding of the deactivation mechanism of ZAP-70 induced by the R192W mutation. METHODS: In the work, multiple replica molecular dynamics simulations and molecular mechanics-generalized Born surface area (MM-GBSA) method were performed to reveal the doubly phosphorylated ITAMs (ITAM-Y2P)-mediated deactivation mechanism of ZAP-70 induced by the R192W mutation.

TCRmodel2: high-resolution modeling of T cell receptor recognition using deep learning.

The cellular immune system, which is a critical component of human immunity, uses T cell receptors (TCRs) to recognize antigenic proteins in the form of peptides presented by major histocompatibility complex (MHC) proteins. Accurate definition of the structural basis of TCRs and their engagement of peptide-MHCs can provide major insights into normal and aberrant immunity, and can help guide the design of vaccines and immunotherapeutics. Given the limited amount of experimentally determined TCR-peptide-MHC structures and the vast amount of TCRs within each individual as well as antigenic targets, accurate computational modeling approaches are needed. Here, we report a major update to our web server, TCRmodel, which was originally developed to model unbound TCRs from sequence, to now model TCR-peptide-MHC complexes from sequence, utilizing several adaptations of AlphaFold. This method, named TCRmodel2, allows users to submit sequences through an easy-to-use interface and shows similar or greater accuracy than AlphaFold and other methods to model TCR-peptide-MHC complexes based on benchmarking. It can generate models of complexes in 15 minutes, and output models are provided with confidence scores and an integrated molecular viewer. TCRmodel2 is available at https://tcrmodel.ibbr.umd.edu.

Specific Genetic Polymorphisms Contributing in Differential Binding of Gliadin Peptides to HLA-DQ and TCR to Elicit Immunogenicity in Celiac Disease.

Immunogenicity of gliadin peptides in celiac disease (CD) is majorly determined by the pattern of molecular interactions with HLA-DQ and T-cell receptors (TCR). Investigation of the interactions between immune-dominant gliadin peptides, DQ protein, and TCR are warranted to unravel the basis of immunogenicity and variability contributed by the genetic polymorphisms. Homology modeling of HLA and TCR done using Swiss Model and iTASSER, respectively. Molecular interactions of eight common deamidated immune-dominant gliadin with HLA-DQ allotypes and specific TCR gene pairs were evaluated. Docking of the three structures was performed with ClusPro2.0 and ProDiGY was used to predict binding energies. Effects of known allelic polymorphisms and reported susceptibility SNPs were predicted on protein-protein interactions. CD susceptible allele, HLA-DQ2.5 was shown to have considerable binding affinity to 33-mer gliadin (ΔG = - 13.9; Kd = 1.5E - 10) in the presence of TRAV26/TRBV7. Higher binding affinity was predicted (ΔG = - 14.3, Kd = 8.9E - 11) when TRBV28 was replaced with TRBV20 paired with TRAV4 suggesting its role in CD predisposition. SNP rs12722069 at HLA-DQ8 that codes Arg76α forms three H-bonds with Glu12 and two H-bonds with Asn13 of DQ2 restricted gliadin in the presence of TRAV8-3/TRBV6. None of the HLA-DQ polymorphisms was found to be in linkage disequilibrium with reported CD susceptibility markers. Haplotypic presentations of rs12722069-G, rs1130392-C, rs3188043-C and rs4193-A with CD reported SNPs were observed in sub-ethnic groups. Highly polymorphic sites of HLA alleles and TCR variable regions could be utilized for better risk prediction models in CD. Therapeutic strategies by identifying inhibitors or blockers targeting specific gliadin:HLA-DQ:TCR binding sites could be investigated.

epiTCR: a highly sensitive predictor for TCR-peptide binding.

MOTIVATION: Predicting the binding between T-cell receptor (TCR) and peptide presented by human leucocyte antigen molecule is a highly challenging task and a key bottleneck in the development of immunotherapy. Existing prediction tools, despite exhibiting good performance on the datasets they were built with, suffer from low true positive rates when used to predict epitopes capable of eliciting T-cell responses in patients. Therefore, an improved tool for TCR-peptide prediction built upon a large dataset combining existing publicly available data is still needed. RESULTS: We collected data from five public databases (IEDB, TBAdb, VDJdb, McPAS-TCR, and 10X) to form a dataset of >3 million TCR-peptide pairs, 3.27% of which were binding interactions. We proposed epiTCR, a Random Forest-based method dedicated to predicting the TCR-peptide interactions. epiTCR used simple input of TCR CDR3ß sequences and antigen sequences, which are encoded by flattened BLOSUM62. epiTCR performed with area under the curve (0.98) and higher sensitivity (0.94) than other existing tools (NetTCR, Imrex, ATM-TCR, and pMTnet), while maintaining comparable prediction specificity (0.9). We identified seven epitopes that contributed to 98.67% of false positives predicted by epiTCR and exerted similar effects on other tools. We also demonstrated a considerable influence of peptide sequences on prediction, highlighting the need for more diverse peptides in a more balanced dataset. In conclusion, epiTCR is among the most well-performing tools, thanks to the use of combined data from public sources and its use will contribute to the quest in identifying neoantigens for precision cancer immunotherapy. AVAILABILITY AND IMPLEMENTATION: epiTCR is available on GitHub (https://github.com/ddiem-ri-4D/epiTCR).

Theoretical quantification of the polyvalent binding of nanoparticles coated with peptide-major histocompatibility complex to T cell receptor-nanoclusters.

Nanoparticles (NPs) coated with peptide-major histocompatibility complexes (pMHCs) can be used as a therapy to treat autoimmune diseases. They do so by inducing the differentiation and expansion of disease-suppressing T regulatory type 1 (Tr1) cells by binding to their T cell receptors (TCRs) expressed as TCR-nanoclusters (TCRnc). Their efficacy can be controlled by adjusting NP size and number of pMHCs coated on them (referred to as valence). The binding of these NPs to TCRnc on T cells is thus polyvalent and occurs at three levels: the TCR-pMHC, NP-TCRnc and T cell levels. In this study, we explore how this polyvalent interaction is manifested and examine if it can facilitate T cell activation downstream. This is done by developing a multiscale biophysical model that takes into account the three levels of interactions and the geometrical complexity of the binding. Using the model, we quantify several key parameters associated with this interaction analytically and numerically, including the insertion probability that specifies the number of remaining pMHC binding sites in the contact area between T cells and NPs, the dwell time of interaction between NPs and TCRnc, carrying capacity of TCRnc, the distribution of covered and bound TCRs, and cooperativity in the binding of pMHCs within the contact area. The model was fit to previously published dose-response curves of interferon-γ obtained experimentally by stimulating a population of T cells with increasing concentrations of NPs at various valences and NP sizes. Exploring the parameter space of the model revealed that for an appropriate choice of the contact area angle, the model can produce moderate jumps between dose-response curves at low valences. This suggests that the geometry and kinetics of NP binding to TCRnc can act in synergy to facilitate T cell activation.

A molecular dynamics investigation of N-glycosylation effects on T-cell receptor kinetics.

The binding interaction between the T-cell receptor (TCR) and peptide-major histocompatibility complex (pMHC) is modulated by several factors (known and unknown), however, investigations into effects of glycosylation are limited. A fully glycosylated computational model of the TCR bound to the pMHC is developed to investigate the effects of glycosylation on dissociation kinetics from the pMHC. Here, we examine the effects of N-glycosylation on TCR-pMHC bond strength using steered molecular dynamic simulations. N-glycosylation is a post-translational modification that adds sugar moieties to molecules and can modulate the activity of several immune molecules. Using a TCR-pMHC pair found in melanoma as a case study, our study demonstrates that N-glycosylation of the TCR-pMHC alters the proteins' conformation; increases the bond lifetime; and increases the number of hydrogen bonds and Lennard-Jones Contacts involved in the TCR-pMHC bond. We find that weak glycan-protein or glycan-glycan interactions impact the equilibrated structure of the TCR and pMHC leading to an increase in the overall bond strength of the TCR-pMHC complex including the duration and energetic strength under constant load. These results indicate that N-glycosylation plays an important role in the TCR-pMHC bond and should be considered in future computational and experimental studies.Communicated by Ramaswamy H. Sarma.

Engagement with the TCR induces plasticity in antigenic ligands bound to MHC class I and CD1 molecules.

Complementarity-determining regions (CDRs) of αß T-cell receptors (TCRs) sense peptide-bound MHC (pMHC) complexes via chemical interactions, thereby mediating antigen specificity and MHC restriction. Flexible finger-like movement of CDR loops contributes to the establishment of optimal interactions with pMHCs. In contrast, peptide ligands captured in MHC molecules are considered more static because of the rigid hydrogen-bond network that stabilizes peptide ligands in the antigen-binding groove of MHC molecules. An array of crystal structures delineating pMHC complexes in TCR-docked and TCR-undocked forms is now available, which enables us to assess TCR engagement-induced conformational changes in peptide ligands. In this short review, we overview conformational changes in MHC class I-bound peptide ligands upon TCR docking, followed by those for CD1-bound glycolipid ligands. Finally, we analyze the co-crystal structure of the TCR:lipopeptide-bound MHC class I complex that we recently reported. We argue that TCR engagement-induced conformational changes markedly occur in lipopeptide ligands, which are essential for exposure of a primary T-cell epitope to TCRs. These conformational changes are affected by amino acid residues, such as glycine, that do not interact directly with TCRs. Thus, ligand recognition by specific TCRs involves not only T-cell epitopes but also non-epitopic amino acid residues. In light of their critical function, we propose to refer to these residues as non-epitopic residues affecting ligand plasticity and antigenicity (NR-PA).

Autoimmunity-associated T cell receptors recognize HLA-B*27-bound peptides.

Human leucocyte antigen B*27 (HLA-B*27) is strongly associated with inflammatory diseases of the spine and pelvis (for example, ankylosing spondylitis (AS)) and the eye (that is, acute anterior uveitis (AAU))1. How HLA-B*27 facilitates disease remains unknown, but one possible mechanism could involve presentation of pathogenic peptides to CD8+ T cells. Here we isolated orphan T cell receptors (TCRs) expressing a disease-associated public ß-chain variable region-complementary-determining region 3ß (BV9-CDR3ß) motif2-4 from blood and synovial fluid T cells from individuals with AS and from the eye in individuals with AAU. These TCRs showed consistent α-chain variable region (AV21) chain pairing and were clonally expanded in the joint and eye. We used HLA-B*27:05 yeast display peptide libraries to identify shared self-peptides and microbial peptides that activated the AS- and AAU-derived TCRs. Structural analysis revealed that TCR cross-reactivity for peptide-MHC was rooted in a shared binding motif present in both self-antigens and microbial antigens that engages the BV9-CDR3ß TCRs. These findings support the hypothesis that microbial antigens and self-antigens could play a pathogenic role in HLA-B*27-associated disease.

Structural plasticity in I-Ag7 links autoreactivity to hybrid insulin peptides in type I diabetes.

We recently provided evidence for promiscuous recognition of several different hybrid insulin peptides (HIPs) by the highly diabetogenic, I-Ag7-restricted 4.1-T cell receptor (TCR). To understand the structural determinants of this phenomenon, we solved the structure of an agonistic HIP/I-Ag7 complex, both in isolation as well as bound to the 4.1-TCR. We find that HIP promiscuity of the 4.1-TCR is dictated, on the one hand, by an amino acid sequence pattern that ensures I-Ag7 binding and, on the other hand, by the presence of three acidic residues at positions P5, P7 and P8 that favor an optimal engagement by the 4.1-TCR's complementary determining regions. Surprisingly, comparison of the TCR-bound and unbound HIP/I-Ag7 structures reveals that 4.1-TCR binding triggers several novel and unique structural motions in both the I-Ag7 molecule and the peptide that are essential for docking. This observation indicates that the type 1 diabetes-associated I-Ag7 molecule is structurally malleable and that this plasticity allows the recognition of multiple peptides by individual TCRs that would otherwise be unable to do so.

Binding Affinity Calculations of Gluten Peptides to HLA Risk Modifiers: DQ2.5 versus DQ7.5.

Free energy perturbation (FEP) calculations can predict relative binding affinities of an antigen and its point mutants to the same human leukocyte antigen (HLA) with high accuracy (e.g., within 1.0 kcal/mol to experiment); however, a more challenging task is to compare binding affinities of wholly different antigens binding to completely different HLAs using FEP. Researchers have used a variety of different FEP schemes to compute and compare absolute binding affinities, with varied success. Here, we propose and assess a unifying scheme to compute the relative binding affinities of different antigens binding to completely different HLAs using absolute binding affinity FEP calculations. We apply our affinity calculation technique to HLA-antigen-T-cell receptor (TCR) systems relevant to celiac disease (CeD) by investigating binding affinity differences between HLA-DQ2.5 (enhanced CeD risk) and HLA-DQ7.5 (CeD protective) in the binary (HLA-gliadin) and ternary (HLA-gliadin-TCR) binding complexes for three gliadin derived epitopes: glia-α1, glia-α2, and glia-ω1. Based on FEP calculations with our carefully designed thermodynamic cycles, we demonstrate that HLA-DQ2.5 has higher binding affinity than HLA-DQ7.5 for gliadin and enhanced binding affinity with a common TCR, agreeing with known results that the HLA-DQ2.5 serotype exhibits increased risk for CeD. Our findings reveal that our proposed absolute binding affinity FEP method is appropriate for predicting HLA binding for disparate antigens with different genotypes. We also discuss atomic-level details of HLA genotypes interacting with gluten peptides and TCRs in regard to the pathogenesis of CeD.

WAT3R: recovery of T-cell receptor variable regions from 3' single-cell RNA-sequencing.

SUMMARY: Diversity of the T-cell receptor (TCR) repertoire is central to adaptive immunity. The TCR is composed of α and ß chains, encoded by the TRA and TRB genes, of which the variable regions determine antigen specificity. To generate novel biological insights into the complex functioning of immune cells, combined capture of variable regions and single-cell transcriptomes provides a compelling approach. Recent developments enable the enrichment of TRA and TRB variable regions from widely used technologies for 3'-based single-cell RNA-sequencing (scRNA-seq). However, a comprehensive computational pipeline to process TCR-enriched data from 3' scRNA-seq is not available. Here, we present an analysis pipeline to process TCR variable regions enriched from 3' scRNA-seq cDNA. The tool reports TRA and TRB nucleotide and amino acid sequences linked to cell barcodes, enabling the reconstruction of T-cell clonotypes with associated transcriptomes. We demonstrate the software using peripheral blood mononuclear cells from a healthy donor and detect TCR sequences in a high proportion of single T cells. Detection of TCR sequences is low in non-T-cell populations, demonstrating specificity. Finally, we show that TCR clones are larger in CD8 Memory T cells than in other T-cell types, indicating an association between T-cell clonotypes and differentiation states. AVAILABILITY AND IMPLEMENTATION: The Workflow for Association of T-cell receptors from 3' single-cell RNA-seq (WAT3R), including test data, is available on GitHub (https://github.com/mainciburu/WAT3R), Docker Hub (https://hub.docker.com/r/mainciburu/wat3r) and a workflow on the Terra platform (https://app.terra.bio). The test dataset is available on GEO (accession number GSE195956). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

DECODE: a computational pipeline to discover T cell receptor binding rules.

MOTIVATION: Understanding the mechanisms underlying T cell receptor (TCR) binding is of fundamental importance to understanding adaptive immune responses. A better understanding of the biochemical rules governing TCR binding can be used, e.g. to guide the design of more powerful and safer T cell-based therapies. Advances in repertoire sequencing technologies have made available millions of TCR sequences. Data abundance has, in turn, fueled the development of many computational models to predict the binding properties of TCRs from their sequences. Unfortunately, while many of these works have made great strides toward predicting TCR specificity using machine learning, the black-box nature of these models has resulted in a limited understanding of the rules that govern the binding of a TCR and an epitope. RESULTS: We present an easy-to-use and customizable computational pipeline, DECODE, to extract the binding rules from any black-box model designed to predict the TCR-epitope binding. DECODE offers a range of analytical and visualization tools to guide the user in the extraction of such rules. We demonstrate our pipeline on a recently published TCR-binding prediction model, TITAN, and show how to use the provided metrics to assess the quality of the computed rules. In conclusion, DECODE can lead to a better understanding of the sequence motifs that underlie TCR binding. Our pipeline can facilitate the investigation of current immunotherapeutic challenges, such as cross-reactive events due to off-target TCR binding. AVAILABILITY AND IMPLEMENTATION: Code is available publicly at https://github.com/phineasng/DECODE. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

IMGT® Immunoinformatics Tools for Standardized V-DOMAIN Analysis.

The variable domains (V-DOMAIN) of the antigen receptors, immunoglobulins (IG) or antibodies and T cell receptors (TR), which specifically recognize the antigens show a huge diversity in their sequences. This diversity results from the complex mechanisms involved in the synthesis of these domains at the DNA level (rearrangements of the variable (V), diversity (D), and joining (J) genes; N-diversity; and, for the IG, somatic hypermutations). The recognition of V, D, and J as "genes" and their entry in databases mark the creation of IMGT by Marie-Paule Lefranc, and the origin of immunoinformatics in 1989. For 30 years, IMGT®, the international ImMunoGeneTics information system® http://www.imgt.org , has implemented databases and developed tools for IG and TR immunoinformatics, based on the IMGT Scientific chart rules and IMGT-ONTOLOGY concepts and axioms, and more particularly, the princeps ones: IMGT genes and alleles (CLASSIFICATION axiom) and the IMGT unique numbering and IMGT Collier de Perles (NUMEROTATION axiom). This chapter describes the online tools for the characterization and annotation of the expressed V-DOMAIN sequences: (a) IMGT/V-QUEST analyzes in detail IG and TR rearranged nucleotide sequences, (b) IMGT/HighV-QUEST is its high throughput version, which includes a module for the identification of IMGT clonotypes and generates immunoprofiles of expressed V, D, and J genes and alleles, (c) IMGT/StatClonotype performs the pairwise comparison of IMGT/HighV-QUEST immunoprofiles, (d) IMGT/DomainGapAlign analyzes amino acid sequences and is frequently used in antibody engineering and humanization, and (e) IMGT/Collier-de-Perles provides two-dimensional (2D) graphical representations of V-DOMAIN, bridging the gap between sequences and 3D structures. These IMGT® tools are widely used in repertoire analyses of the adaptive immune responses in normal and pathological situations and in the design of engineered IG and TR for therapeutic applications.

IMGT/3Dstructure-DB: T-Cell Receptor TR Paratope and Peptide/Major Histocompatibility pMH Contact Sites and Epitope.

T-cell receptors (TR), the antigen receptors of T cells, specifically recognize peptides presented by the major histocompatibility (MH) proteins, as peptide/MH (pMH), on the cell surface. The structure characterization of the trimolecular TR/pMH complexes is crucial to the fields of immunology, vaccination, and immunotherapy. IMGT/3Dstructure-DB is the three-dimensional (3-D) structure database of IMGT®, the international ImMunoGenetics information system®. By its creation, IMGT® marks the advent of immunoinformatics, which emerged at the interface between immunogenetics and bioinformatics. The IMGT® immunoglobulin (IG) and TR gene and allele nomenclature (CLASSIFICATION axiom) and the IMGT unique numbering and IMGT/Collier-de-Perles (NUMEROTATION axiom) are the two founding breakthroughs of immunoinformatics. IMGT-ONTOLOGY concepts and IMGT Scientific chart rules generated from these axioms allowed IMGT® bridging genes, structures, and functions. IMGT/3Dstructure-DB contains 3-D structures of IG or antibodies, TR and MH proteins of the adaptive immune responses of jawed vertebrates (gnathostomata), IG or TR complexes with antigens (IG/Ag, TR/pMH), related proteins of the immune system of any species belonging to the IG and MH superfamilies, and fusion proteins for immune applications. The focus of this chapter is on the TR V domains and MH G domains and the contact analysis comparison in TR/pMH interactions. Standardized molecular characterization includes "IMGT pMH contact sites" for peptide and MH groove interactions and "IMGT paratopes and epitopes" for TR/pMH complexes. Data are available in the IMGT/3Dstructure database, at the IMGT Home page http://www.imgt.org .