T cell activity correlates with oligomeric peptide-major histocompatibility complex binding on T cell surface

Coarse-grained molecular dynamics-guided immunoinformatics to explain the binder and non-binder classification of Cytotoxic T-cell epitope for SARS-CoV-2 peptide-based vaccine discovery

Epitope-based peptide vaccine can elicit T-cell immunity against SARS-CoV-2 to clear the infection. However, finding the best epitope from the whole antigen is challenging. A peptide screening using immunoinformatics usually starts from MHC-binding peptide, immunogenicity, cross-reactivity with the human proteome, to toxicity analysis. This pipeline classified the peptides into three categories, i.e., strong-, weak-, and non-binder, without incorporating the structural aspect. For this reason, the molecular detail that discriminates the binders from non-binder is interesting to be investigated. In this study, five CTL epitopes against HLA-A*02:01 were identified from the coarse-grained molecular dynamics-guided immunoinformatics screening. The strong binder showed distinctive activities from the non-binder in terms of structural and energetic properties. Furthermore, the second residue from the nonameric peptide was most important in the interaction with HLA-A*02:01. By understanding the nature of MHC-peptide interaction, we hoped to improve the chance of finding the best epitope for a peptide vaccine candidate.

Structural Prediction of Peptide-MHC Binding Modes

The immune system is constantly protecting its host from the invasion of pathogens and the development of cancer cells. The specific CD8⁺ T-cell immune response against virus-infected cells and tumor cells is based on the T-cell receptor recognition of antigenic peptides bound to class I major histocompatibility complexes (MHC) at the surface of antigen presenting cells. Consequently, the peptide binding specificities of the highly polymorphic MHC have important implications for the design of vaccines, for the treatment of autoimmune diseases, and for personalized cancer immunotherapy. Evidence-based machine-learning approaches have been successfully used for the prediction of peptide binders and are currently being developed for the prediction of peptide immunogenicity. However, understanding and modeling the structural details of peptide/MHC binding is crucial for a better understanding of the molecular mechanisms triggering the immunological processes, estimating peptide/MHC affinity using universal physics-based approaches, and driving the design of novel peptide ligands. Unfortunately, due to the large diversity of MHC allotypes and possible peptides, the growing number of 3D structures of peptide/MHC (pMHC) complexes in the Protein Data Bank only covers a small fraction of the possibilities. Consequently, there is a growing need for rapid and efficient approaches to predict 3D structures of pMHC complexes. Here, we review the key characteristics of the 3D structure of pMHC complexes before listing databases and other sources of information on pMHC structures and MHC specificities. Finally, we discuss some of the most prominent pMHC docking software.

Quantitative T-cell repertoire analysis of peripheral blood mononuclear cells from lung cancer patients following long-term cancer peptide vaccination

Therapeutic cancer peptide vaccination is an immunotherapy designed to elicit cytotoxic T-lymphocyte (CTL) responses in patients. A number of therapeutic vaccination trials have been performed, nevertheless there are only a few reports that have analyzed the T-cell receptors (TCRs) expressed on tumor antigen-specific CTLs. Here, we use next-generation sequencing (NGS) to analyze TCRs of vaccine-induced CTL clones and the TCR repertoire of bulk T cells in peripheral blood mononuclear cells (PBMCs) from two lung cancer patients over the course of long-term vaccine therapy. In both patients, vaccination with two epitope peptides derived from cancer/testis antigens (upregulated lung cancer 10 (URLC10) and cell division associated 1 (CDCA1)) induced specific CTLs expressing various TCRs. All URLC10-specific CTL clones tested showed Ca2+ influx, IFN-γ production, and cytotoxicity when co-cultured with URLC10-pulsed tumor cells. Moreover, in CTL clones that were not stained with the URLC10/MHC-multimer, the CD3 ζ chain was not phosphorylated. NGS of the TCR repertoire of bulk PBMCs demonstrated that the frequency of vaccine peptide-specific CTL clones was near the minimum detectable threshold level. These results demonstrate that vaccination induces antigen-specific CTLs expressing various TCRs at different time points in cancer patients, and that some CTL clones are maintained in PBMCs during long-term treatment, including some with TCRs that do not bind peptide/MHC-multimer.

Rejection of leukemic cells requires antigen-specific T cells with high functional avidity

In a context where injection of antigen (Ag)-specific T cells probably represents the future of leukemia immunotherapy, identification of optimal target Ags is crucial. We therefore sought to discover a reliable marker for selection of the most potent Ags. To this end, (1) we immunized mice against 8 individual Ags: 4 minor histocompatibility Ags (miHAs) and 4 leukemia-associated Ags (LAAs) that were overexpressed on leukemic relative to normal thymocytes; (2) we assessed their ability to reject EL4 leukemic cells; and (3) we correlated the properties of our Ags (and their cognate T cells) with their ability to induce protective antileukemic responses. Overall, individual miHAs instigated more potent antileukemic responses than LAAs. Three features had no influence on the ability of primed T cells to reject leukemic cells: (1) MHC-peptide affinity; (2) the stability of MHC-peptide complexes; and (3) epitope density at the surface of leukemic cells, as assessed using mass spectrometry. The cardinal feature of successful Ags is that they were recognized by high-avidity CD8 T cells that proliferated extensively in vivo. Our work suggests that in vitro evaluation of functional avidity represents the best criterion for selection of Ags, which should be prioritized in clinical trials of leukemia immunotherapy.

Accurate structure prediction of peptide-MHC complexes for identifying highly immunogenic antigens

Designing an optimal HIV-1 vaccine faces the challenge of identifying antigens that induce a broad immune capacity. One factor to control the breadth of T cell responses is the surface morphology of a peptide-MHC complex. Here, we present an in silico protocol for predicting peptide-MHC structure. A robust signature of a conformational transition was identified during all-atom molecular dynamics, which results in a model with high accuracy. A large test set was used in constructing our protocol and we went another step further using a blind test with a wild-type peptide and two highly immunogenic mutants, which predicted substantial conformational changes in both mutants. The center residues at position five of the analogs were configured to be accessible to solvent, forming a prominent surface, while the residue of the wild-type peptide was to point laterally toward the side of the binding cleft. We then experimentally determined the structures of the blind test set, using high resolution of X-ray crystallography, which verified predicted conformational changes. Our observation strongly supports a positive association of the surface morphology of a peptide-MHC complex to its immunogenicity. Our study offers the prospect of enhancing immunogenicity of vaccines by identifying MHC binding immunogens.

Interaction of streptavidin-based peptide-MHC oligomers (tetramers) with cell-surface TCRs

The binding of oligomeric peptide-MHC (pMHC) complexes to cell surface TCR can be considered to approximate TCR-pMHC interactions at cell-cell interfaces. In this study, we analyzed the equilibrium binding of streptavidin-based pMHC oligomers (tetramers) and their dissociation kinetics from CD8(pos) T cells from 2C-TCR transgenic mice and from T cell hybridomas that expressed the 2C TCR or a high-affinity mutant (m33) of this TCR. Our results show that the tetramers did not come close to saturating cell-surface TCR (binding only 10-30% of cell-surface receptors), as is generally assumed in deriving affinity values (K(D)), in part because of dissociative losses from tetramer-stained cells. Guided by a kinetic model, the oligomer dissociation rate and equilibrium constants were seen to depend not only on monovalent association and dissociation rates (k(off) and k(on)), but also on a multivalent association rate (μ) and TCR cell-surface density. Our results suggest that dissociation rates could account for the recently described surprisingly high frequency of tetramer-negative, functionally competent T cells in some T cell responses.

Constraints within major histocompatibility complex class I restricted peptides: presentation and consequences for T-cell recognition

Residues within processed protein fragments bound to major histocompatibility complex class I (MHC-I) glycoproteins have been considered to function as a series of "independent pegs" that either anchor the peptide (p) to the MHC-I and/or interact with the spectrum of alphabeta-T-cell receptors (TCRs) specific for the pMHC-I epitope in question. Mining of the extensive pMHC-I structural database established that many self- and viral peptides show extensive and direct interresidue interactions, an unexpected finding that has led us to the idea of "constrained" peptides. Mutational analysis of two constrained peptides (the HLA B44 restricted self-peptide (B44DPalpha-EEFGRAFSF) and an H2-D(b) restricted influenza peptide (D(b)PA, SSLENFRAYV) demonstrated that the conformation of the prominently exposed arginine in both peptides was governed by interactions with MHC-I-orientated flanking residues from the peptide itself. Using reverse genetics in a murine influenza model, we revealed that mutation of an MHC-I-orientated residue (SSLENFRAYV --> SSLENARAYV) within the constrained PA peptide resulted in a diminished cytotoxic T lymphocyte (CTL) response and the recruitment of a limited pMHC-I specific TCR repertoire. Interactions between individual peptide positions can thus impose fine control on the conformation of pMHC-I epitopes, whereas the perturbation of such constraints can lead to a previously unappreciated mechanism of viral escape.

Ab initio prediction of peptide-MHC binding geometry for diverse class I MHC allotypes

Since determining the crystallographic structure of all peptide-MHC complexes is infeasible, an accurate prediction of the conformation is a critical computational problem. These models can be useful for determining binding energetics, predicting the structures of specific ternary complexes with T-cell receptors, and designing new molecules interacting with these complexes. The main difficulties are (1) adequate sampling of the large number of conformational degrees of freedom for the flexible peptide, (2) predicting subtle changes in the MHC interface geometry upon binding, and (3) building models for numerous MHC allotypes without known structures. Whereas previous studies have approached the sampling problem by dividing the conformational variables into different sets and predicting them separately, we have refined the Biased-Probability Monte Carlo docking protocol in internal coordinates to optimize a physical energy function for all peptide variables simultaneously. We also imitated the induced fit by docking into a more permissive smooth grid representation of the MHC followed by refinement and reranking using an all-atom MHC model. Our method was tested by a comparison of the results of cross-docking 14 peptides into HLA-A*0201 and 9 peptides into H-2K(b) as well as docking peptides into homology models for five different HLA allotypes with a comprehensive set of experimental structures. The surprisingly accurate prediction (0.75 A backbone RMSD) for cross-docking of a highly flexible decapeptide, dissimilar to the original bound peptide, as well as docking predictions using homology models for two allotypes with low average backbone RMSDs of less than 1.0 A illustrate the method's effectiveness. Finally, energy terms calculated using the predicted structures were combined with supervised learning on a large data set to classify peptides as either HLA-A*0201 binders or nonbinders. In contrast with sequence-based prediction methods, this model was also able to predict the binding affinity for peptides to a different MHC allotype (H-2K(b)), not used for training, with comparable prediction accuracy.

Class I T-cell epitope prediction: improvements using a combination of proteasome cleavage, TAP affinity, and MHC binding

Cleavage by the proteasome is responsible for generating the C terminus of T-cell epitopes. Modeling the process of proteasome cleavage as part of a multi-step algorithm for T-cell epitope prediction will reduce the number of non-binders and increase the overall accuracy of the predictive algorithm. Quantitative matrix-based models for prediction of the proteasome cleavage sites in a protein were developed using a training set of 489 naturally processed T-cell epitopes (nonamer peptides) associated with HLA-A and HLA-B molecules. The models were validated using an external test set of 227 T-cell epitopes. The performance of the models was good, identifying 76% of the C-termini correctly. The best model of proteasome cleavage was incorporated as the first step in a three-step algorithm for T-cell epitope prediction, where subsequent steps predicted TAP affinity and MHC binding using previously derived models.

Structural prediction of peptides bound to MHC class I

An ab initio structure prediction approach adapted to the peptide-major histocompatibility complex (MHC) class I system is presented. Based on structure comparisons of a large set of peptide-MHC class I complexes, a molecular dynamics protocol is proposed using simulated annealing (SA) cycles to sample the conformational space of the peptide in its fixed MHC environment. A set of 14 peptide-human leukocyte antigen (HLA) A0201 and 27 peptide-non-HLA A0201 complexes for which X-ray structures are available is used to test the accuracy of the prediction method. For each complex, 1000 peptide conformers are obtained from the SA sampling. A graph theory clustering algorithm based on heavy atom root-mean-square deviation (RMSD) values is applied to the sampled conformers. The clusters are ranked using cluster size, mean effective or conformational free energies, with solvation free energies computed using Generalized Born MV 2 (GB-MV2) and Poisson-Boltzmann (PB) continuum models. The final conformation is chosen as the center of the best-ranked cluster. With conformational free energies, the overall prediction success is 83% using a 1.00 Angstroms crystal RMSD criterion for main-chain atoms, and 76% using a 1.50 Angstroms RMSD criterion for heavy atoms. The prediction success is even higher for the set of 14 peptide-HLA A0201 complexes: 100% of the peptides have main-chain RMSD values < or =1.00 Angstroms and 93% of the peptides have heavy atom RMSD values < or =1.50 Angstroms. This structure prediction method can be applied to complexes of natural or modified antigenic peptides in their MHC environment with the aim to perform rational structure-based optimizations of tumor vaccines.

T cell cross-reactivity and conformational changes during TCR engagement

All thymically selected T cells are inherently cross-reactive, yet many data indicate a fine specificity in antigen recognition, which enables virus escape from immune control by mutation in infections such as the human immunodeficiency virus (HIV). To address this paradox, we analyzed the fine specificity of T cells recognizing a human histocompatibility leukocyte antigen (HLA)-A2–restricted, strongly immunodominant, HIV gag epitope (SLFNTVATL). The majority of 171 variant peptides tested bound HLA-A2, but only one third were recognized. Surprisingly, one recognized variant (SLYNTVATL) showed marked differences in structure when bound to HLA-A2. T cell receptor (TCR) recognition of variants of these two peptides implied that they adopted the same conformation in the TCR–peptide–major histocompatibility complex (MHC) complex. However, the on-rate kinetics of TCR binding were identical, implying that conformational changes at the TCR–peptide–MHC binding interface occur after an initial permissive antigen contact. These findings have implications for the rational design of vaccines targeting viruses with unstable genomes.

A novel MHCp binding prediction model

Many statistical and molecular mechanics models have been developed and tested for major histocompatibility complex peptide (MHCp) binding predictions during the last decade. The statistical model prediction using pooled peptide sequence data and three-dimensional modeling prediction by molecular mechanics calculations have been assessed for efficiency and human leukocyte antigen diversity coverage. We describe a novel predictive model using information gleaned from 29 human MHCp crystal structures. The validation for the new model is performed using four different sets of data: (1) MHCp crystal structures, (2) peptides with known IC(50) binding values, (3) peptides tested positive by tetramer staining, (4) peptides with known binding information at the MHCBN database. The model produces high prediction efficiencies (average 60 %) with good sensitivity (approximately 50%-73%) and specificity (52%-58%) values. The average positive predictive value of the model is 89%, while the average negative predictive value is only 18%. The efficiency is very high in predicting binders and very low in predicting nonbinders. This model is superior to many existing methods because of its potential application to any given MHC allele whose sequence is clearly defined.

High affinity xenoreactive TCR:MHC interaction recruits CD8 in absence of binding to MHC

The TCR from a xenoreactive murine cytotoxic T lymphocyte clone, AHIII 12.2, recognizes murine H-2D(b) complexed with peptide p1058 (FAPGFFPYL) as well as human HLA-A2.1 complexed with human self-peptide p1049 (ALWGFFPVL). To understand more about T cell biology and cross-reactivity, the ectodomains of the AHIII 12.2 TCR have been produced in E. coli as inclusion bodies and the protein folded to its native conformation. Flow cytometric and surface plasmon resonance analyses indicate that human p1049/A2 has a significantly greater affinity for the murine AHIII 12.2 TCR than does murine p1058/D(b). Yet, T cell binding and cytolytic activity are independent of CD8 when stimulated with human p1049/A2 as demonstrated with anti-CD8 Abs that block CD8 association with MHC. Even in the absence of direct CD8 binding, stimulation of AHIII 12.2 T cells with "CD8-independent" p1049/A2 produces p56(lck) activation and calcium flux. Confocal fluorescence microscopy and fluorescence resonance energy transfer flow cytometry demonstrate CD8 is recruited to the site of TCR:peptide MHC binding. Taken together, these results indicate that there exists another mechanism for recruitment of CD8 during high affinity TCR:peptide MHC engagement.