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.     

Quantitative approaches to computational vaccinology

This article reviews the newly released JenPep database and two new powerful techniques for T-cell epitope prediction: (i) the additive method; and (ii) a 3D-Quantitative Structure Activity Relationships (3D-QSAR) method, based on Comparative Molecular Similarity Indices Analysis (CoMSIA). The JenPep database is a family of relational databases supporting the growing need of immunoinformaticians for quantitative data on peptide binding to major histocompatibility complexes and to the Transporters associated with Antigen Processing (TAP). It also contains an annotated list of T-cell epitopes. The database is available free via the Internet (http://www.jenner.ac.uk/JenPep). The additive prediction method is based on the assumption that the binding affinity of a peptide depends on the contributions from each amino acid as well as on the interactions between the adjacent and every second side-chain. In the 3D-QSAR approach, the influence of five physicochemical properties (steric bulk, electrostatic potential, local hydrophobicity, hydrogen-bond donor and hydrogen-bond acceptor abilities) on the affinity of peptides binding to MHC molecules were considered. Both methods were exemplified through their application to the well-studied problem of peptides binding to the human class I MHC molecule HLA-A*0201.     

A biochemical and structural analysis of genetic diversity within the HLA-A*11 subtype

The HLA-A*11 subtype includes 17 naturally occurring variants (-A*1101 to -A*1117) distributed among different ethnic groups worldwide. At present, only HLA-A*1101 has been characterized at the molecular, structural, and immunological level. Developing similar knowledge on other HLA-A*11 alleles is highly important for bone marrow and graft transplantation. This is also important to better understand disease linkages within the HLA-A*11 subtype given that HLA-A*11 molecules are associated with resistance to acquisition of HIV-1 infection and various autoimmune diseases. To broaden our understanding of HLA-A*11 molecules, we have determined the impact of natural polymorphism on the peptide-binding properties of several HLA-A*11 molecules: -A*1103, -A*1106, -A*1108, -A*1110, -A*1111, and -A*1114. We used an approach that combines data from thermal stability studies of recombinant, soluble forms of these molecules in complex with HIV-1 peptides, together with a detailed structural analysis of the resulting HLA-A*11 molecule/peptide complexes based on crystal and molecular model structures. Our analysis shows that natural polymorphism within the HLA-A*11 subtype is distributed along the 1 and 2 helices of the peptide-binding groove, in marked contrast to the pattern of polymorphism in HLA-A*2 and HLA-B*27 subtypes. Natural polymorphism greatly altered the abilities of individual -A*11 molecules to form stable complexes with HIV-1 peptides. In comparison to -A*1101, natural polymorphism altered the peptide-presenting properties of -A*1103, -A*1108, and -A*1114 and has the potential to affect the peptide-selecting properties of -A*1106, -A*1110, and -A*1111 as well. Overall, our findings suggest that HLA-A*11 molecules may stimulate alloreactive CD8+ cytotoxic T-cell responses.     

Rapid and sensitive identification of major histocompatibility complex class I-associated tumor peptides by Nano-LC MALDI MS/MS

Identification of major histocompatibility complex (MHC)-associated peptides recognized by T-lymphocytes is a crucial prerequisite for the detection and manipulation of specific immune responses in cancer, viral infections, and autoimmune diseases. Unfortunately immunogenic peptides are less abundant species present in highly complex mixtures of MHC-extracted material. Most peptide identification strategies use microcapillary LC coupled to nano-ESI MS/MS in a challenging on-line approach. Alternatively MALDI PSD analysis has been applied for this purpose. We report here on the first off-line combination of nanoscale (nano) LC and MALDI TOF/TOF MS/MS for the identification of naturally processed MHC peptide ligands. These peptides were acid-eluted from human leukocyte antigen (HLA)-A2, HLA-A3, and HLA-B/-C complexes separately isolated from a renal cell carcinoma cell lysate using HLA allele-specific antibodies. After reversed-phase HPLC, peptides were further fractionated via nano-LC. This additional separation step provided a substantial increase in the number of detectable candidate species within the complex peptide pools. MALDI MS/MS analysis on nano-LC-separated material was then sufficiently sensitive to rapidly identify more than 30 novel HLA-presented peptide ligands. Peptide sequences contained perfect anchor amino acid residues described previously for HLA-A2, HLA-A3, and HLA-B7. The most promising candidate for a T-cell epitope is an HLA-B7-binding nonamer peptide derived from the tumor-associated gene NY-BR-16. To demonstrate the sensitivity of our approach we characterized peptides binding to HLA-C molecules that are usually expressed at the cell surface at approximately only 10% the levels of HLA-A or HLA-B. In fact, multiple renal cell carcinoma peptides were identified that contained anchor amino acid residues of HLA-Cw5 and HLA-Cw7. We conclude that the nano-LC MALDI MS/MS approach is a sensitive tool for the rapid and automated identification of MHC-associated tumor peptides.     

Stepwise identification of HLA-A*0201-restricted CD8+ T-cell epitope peptides from herpes simplex virus type 1 genome boosted by a StepRank scheme

Identification of immunodominant epitopes is the first step in the rational design of peptide vaccines aimed at T-cell immunity. To date, however, it is yet a great challenge for accurately predicting the potent epitope peptides from a pool of large-scale candidates with an efficient manner. In this study, a method that we named StepRank has been developed for the reliable and rapid prediction of binding capabilities/affinities between proteins and genome-wide peptides. In this procedure, instead of single strategy used in most traditional epitope identification algorithms, four steps with different purposes and thus different computational demands are employed in turn to screen the large-scale peptide candidates that are normally generated from, for example, pathogenic genome. The steps 1 and 2 aim at qualitative exclusion of typical nonbinders by using empirical rule and linear statistical approach, while the steps 3 and 4 focus on quantitative examination and prediction of the interaction energy profile and binding affinity of peptide to target protein via quantitative structure-activity relationship (QSAR) and structure-based free energy analysis. We exemplify this method through its application to binding predictions of the peptide segments derived from the 76 known open-reading frames (ORFs) of herpes simplex virus type 1 (HSV-1) genome with or without affinity to human major histocompatibility complex class I (MHC I) molecule HLA-A*0201, and find that the predictive results are well compatible with the classical anchor residue theory and perfectly match for the extended motif pattern of MHC I-binding peptides. The putative epitopes are further confirmed by comparisons with 11 experimentally measured HLA-A*0201-restrcited peptides from the HSV-1 glycoproteins D and K. We expect that this well-designed scheme can be applied in the computational screening of other viral genomes as well.     

Additive method for the prediction of protein-peptide binding affinity. Application to the MHC class I molecule HLA-A*0201

A method has been developed for prediction of binding affinities between proteins and peptides. We exemplify the method through its application to binding predictions of peptides with affinity to major histocompatibility complex class I molecule HLA-A*0201. The method is named "additive" because it is based on the assumption that the binding affinity of a peptide could be presented as a sum of the contributions of the amino acids at each position and the interactions between them. The amino acid contributions and the contributions of the interactions between adjacent side chains and every second side chain were derived using a partial least squares (PLS) statistical methodology using a training set of 420 experimental IC50 values. The predictive power of the method was assessed using rigorous cross-validation and using an independent test set of 89 peptides. The mean value of the residuals between the experimental and predicted pIC50 values was 0.508 for this test set. The additive method was implemented in a program for rapid T-cell epitope search. It is universal and can be applied to any peptide-protein interaction where binding data is known.     

Crisscross CTL induction by SYT-SSX junction peptide and its HLA-A*2402 anchor substitute

To investigate the effects of anchor substitutions in SYT-SSX junction peptide, an HLA-A24 anchor residue (position 9) of the SYT-SSX B peptide (GYDQIMPKK) was substituted to more favorable residues according to the HLA-A24-binding motif. Among four substitutes constructed, a substitute with isoleucine (termed K9I peptide) most apparently enhanced the affinity for HLA-A24 molecule. Subsequent in vitro CTL induction analysis using PBMCs of 15 HLA-A24(+) synovial sarcoma patients revealed that the original B peptide allowed to induce synovial sarcoma-specific CTLs from 7 patients (47%), whereas such CTLs were inducible from 12 patients (80%) with K9I peptide. Moreover, the extent of cytotoxicity against HLA-A24(+) synovial sarcoma cell lines was higher in K9I peptide-induced CTLs than B peptide-induced CTLs. Influence of anchor substitution on peptide/TCR interaction was evaluated by cytotoxicity assays against autologous cells and tetramer analysis. CTLs induced from a synovial sarcoma patient using K9I peptide did not lyse autologous PHA blasts or EBV-infected B cells. In vitro stimulations of PBMCs from 5 HLA-A24(+) synovial sarcoma patients with K9I peptide increased the frequency of T cells reacting with both HLA-A24/K9I peptide tetramer and HLA-A24/B peptide tetramer. In contrast, the frequency of T cells reacting with HLA/HIV-derived peptide tetramer remained low. These findings support the validity in design of anchor residue substitution in SYT-SSX fusion gene-derived peptide, and provide a potential clue to the current stagnation in vaccination trials of fusion gene-derived natural junction peptides.     

An HLA-A3-binding prostate acid phosphatase-derived peptide can induce CTLs restricted to HLA-A2 and -A24 alleles

We previously reported peptide vaccine candidates for HLA-A3 supertype (-A3, -A11, -A31, -A33)-positive cancer patients. In the present study, we examined whether those peptides can also induce cytotoxic T lymphocyte (CTL) activity restricted to HLA-A2, HLA-A24, and HLA-A26 alleles. Fourteen peptides were screened for their binding activity to HLA-A*0201, -A*0206, -A*0207, -A*2402, and -A*2601 molecules and then tested for their ability to induce CTL activity in peripheral blood mononuclear cells (PBMCs) from prostate cancer patients. Among these peptides, one from the prostate acid phosphatase protein exhibited binding activity to HLA-A*0201, -A*0206, and -A*2402 molecules. In addition, PBMCs stimulated with this peptide showed that HLA-A2 or HLA-A24 restricted CTL activity. Their cytotoxicity toward cancer cells was ascribed to peptide-specific and CD8⁺ T cells. These results suggest that this peptide could be widely applicable as a peptide vaccine for HLA-A3 supertype-, HLA-A2-, and -A24-positive cancer patients.     

Cross-Allele Cytotoxic T Lymphocyte Responses against 2009 Pandemic H1N1 Influenza A Virus among HLA-A24 and HLA-A3 Supertype-Positive Individuals

Lack of a universal vaccine against all serotypes of influenza A viruses and recent progress on T cell-related vaccines against influenza A virus illuminate the important role of human leukocyte antigen (HLA)-restricted cytotoxic T lymphocytes (CTLs) in anti-influenza virus immunity. However, the diverse HLA alleles among humans complicate virus-specific cellular immunity research, and elucidation of cross-HLA allele T cell responses to influenza virus specificity requires further detailed work. An ideal CTL epitope-based vaccine would cover a broad spectrum of epitope antigens presented by most, if not all, of the HLAs. Here, we evaluated the 2009 pandemic influenza A (H1N1) virus-specific T cell responses among the HLA-A24⁺ population using a rationally designed peptide pool during the 2009 pandemic. Unexpectedly, cross-HLA allele T cell responses against the influenza A virus peptides were detected among both HLA-A11⁺ and HLA-A24⁺ donors. Furthermore, we found cross-responses in the entire HLA-A3 supertype population (including HLA-A11, -A31, -A33, and -A30). The cross-allele antigenic peptides within the peptide pool were identified and characterized, and the crystal structures of the major histocompatibility complex (MHC)-peptide complexes were determined. The subsequent HLA-A24-defined cross-allele peptides recognized by the HLA-A11⁺ population were shown to mildly bind to the HLA-A*1101 molecule. Together with the structural models, these results partially explain the cross-allele responses. Our findings elucidate the promiscuity of the cross-allele T cell responses against influenza A viruses and are beneficial for the development of a T cell epitope-based vaccine applied in a broader population.     

MHCPred: A server for quantitative prediction of peptide-MHC binding

Accurate T-cell epitope prediction is a principal objective of computational vaccinology. As a service to the immunology and vaccinology communities at large, we have implemented, as a server on the World Wide Web, a partial least squares-based multivariate statistical approach to the quantitative prediction of peptide binding to major histocom- patibility complexes (MHC), the key checkpoint on the antigen presentation pathway within adaptive cellular immunity. MHCPred implements robust statistical models for both Class I alleles (HLA-A*0101, HLA-A*0201, HLA-A*0202, HLA-A*0203, HLA-A*0206, HLA-A*0301, HLA-A*1101, HLA-A*3301, HLA-A*6801, HLA-A*6802 and HLA-B*3501) and Class II alleles (HLA-DRB*0401, HLA-DRB*0401 and HLA-DRB*0701). MHCPred is available from the URL: http://www.jenner.ac.uk/MHCPred.     

Energetics and cooperativity of the hydrogen bonding and anchor interactions that bind peptides to MHC class II protein

The complexity of the interaction between major histocompatibility complex class II (MHC II) proteins and peptide ligands has been revealed through structural studies and crystallographic characterization. Peptides bind through side-chain "anchor" interactions with MHC II pockets and an extensive array of genetically conserved hydrogen bonds to the peptide backbone. Here we quantitatively investigate the kinetic hierarchy of these interactions. We present results detailing the impact of single side-chain mutations of peptide anchor residues on dissociation rates, utilizing two I-A(d)-restricted peptides, one of which has a known crystal structure, and 24 natural and non-natural amino acid mutant variants of these peptides. We find that the N-terminal P1, P4 and P6 anchor-pocket interactions can make significant contributions to binding stability. We also investigate the interactions of these peptides with four I-A(d) MHC II proteins, each mutated to disrupt conserved hydrogen bonds to the peptide backbone. These complexes exhibit kinetic behavior suggesting that binding energy is disproportionately invested near the peptide N terminus for backbone hydrogen bonds. We then evaluate the effects of simultaneously modifying both anchor and hydrogen bonding interactions. A quantitative analysis of 71 double mutant cycles reveals that there is little apparent cooperativity between anchor residue interactions and hydrogen bonds, even when they are directly adjacent (<5A).     

Identification of a cyclin B1-derived CTL epitope eliciting spontaneous responses in both cancer patients and healthy donors

With the aim to identify cyclin B1-derived peptides with high affinity for HLA-A2, we used three in silico prediction algorithms to screen the protein sequence for possible HLA-A2 binders. One peptide scored highest in all three algorithms, and the high HLA-A2-binding affinity of this peptide was verified in an HLA stabilization assay. By stimulation with peptide-loaded dendritic cells a CTL clone was established, which was able to kill two breast cancer cell lines in an HLA-A2-dependent and peptide-specific manner, demonstrating presentation of the peptide on the surface of cancer cells. Furthermore, blood from cancer patients and healthy donors was screened for spontaneous T-cell reactivity against the peptide in IFN-γ ELISPOT assays. Patients with breast cancer, malignant melanoma, or renal cell carcinoma hosted powerful and high-frequency T-cell responses against the peptide. In addition, when blood from healthy donors was tested, similar responses were observed. Ultimately, serum from cancer patients and healthy donors was analyzed for anti-cyclin B1 antibodies. Humoral responses against cyclin B1 were frequently detected in both cancer patients and healthy donors. In conclusion, a high-affinity cyclin B1-derived HLA-A2-restricted CTL epitope was identified, which was presented on the cell surface of cancer cells, and elicited spontaneous T-cell responses in cancer patients and healthy donors.     

Prediction of promiscuous peptides that bind HLA class I molecules

Promiscuous T-cell epitopes make ideal targets for vaccine development. We report here a computational system, MULTIPRED, for the prediction of peptide binding to the HLA-A2 supertype. It combines a novel representation of peptide/MHC interactions with a hidden Markov model as the prediction algorithm. MULTIPREDis both sensitive and specific, and demonstrates high accuracy of peptide-binding predictions for HLA-A*0201, *0204, and *0205 alleles, good accuracy for *0206 allele, and marginal accuracy for *0203 allele. MULTIPREDreplaces earlier requirements for individual prediction models for each HLA allelic variant and simplifies computational aspects of peptide-binding prediction. Preliminary testing indicates that MULTIPRED can predict peptide binding to HLA-A2 supertype molecules with high accuracy, including those allelic variants for which no experimental binding data are currently available.     

Coupling in silico and in vitro analysis of peptide-MHC binding: a bioinformatic approach enabling prediction of superbinding peptides and anchorless epitopes

The ability to define and manipulate the interaction of peptides with MHC molecules has immense immunological utility, with applications in epitope identification, vaccine design, and immunomodulation. However, the methods currently available for prediction of peptide-MHC binding are far from ideal. We recently described the application of a bioinformatic prediction method based on quantitative structure-affinity relationship methods to peptide-MHC binding. In this study we demonstrate the predictivity and utility of this approach. We determined the binding affinities of a set of 90 nonamer peptides for the MHC class I allele HLA-A*0201 using an in-house, FACS-based, MHC stabilization assay, and from these data we derived an additive quantitative structure-affinity relationship model for peptide interaction with the HLA-A*0201 molecule. Using this model we then designed a series of high affinity HLA-A2-binding peptides. Experimental analysis revealed that all these peptides showed high binding affinities to the HLA-A*0201 molecule, significantly higher than the highest previously recorded. In addition, by the use of systematic substitution at principal anchor positions 2 and 9, we showed that high binding peptides are tolerant to a wide range of nonpreferred amino acids. Our results support a model in which the affinity of peptide binding to MHC is determined by the interactions of amino acids at multiple positions with the MHC molecule and may be enhanced by enthalpic cooperativity between these component interactions.     

A neural network model approach to the study of human TAP transporter

We used an artificial neural network (ANN) computer model to study peptide binding to the human transporter associated with antigen processing (TAP). After validation, an ANN model of TAP-peptide binding was used to mine a database of HLA-binding peptides to elucidate patterns of TAP binding. The affinity of HLA-binding peptides for TAP was found to differ according to the HLA supertype concerned: HLA-B27, -A3 or -A24 binding peptides had high, whereas HLA-A2, -B7 or -B8 binding peptides had low affinity for TAP. These results support the idea that TAP and particular HLA molecules may have co-evolved for efficient peptide processing and presentation. The strong similarity between the sets of peptides bound by TAP or HLA-B27 suggests functional co-evolution whereas the lack of a relationship between the sets of peptides bound by TAP or HLA-A2 is against these particular molecules having co-evolved. In support of these conclusions, the affinities of HLA-A2 and HLA-B7 binding peptides for TAP show similar distributions to that of randomly generated peptides. On the basis of these results we propose that HLA alleles constitute two separate classes: those that are TAP-efficient for peptide loading (HLA-B27, -A3 and -A24) and those that are TAP-inefficient (HLA-A2, -B7 and -B8). Computer modelling can be used to complement laboratory experiments and thereby speed up knowledge discovery in biology. In particular, we provide evidence that large-scale experiments can be avoided by combining initial experimental data with limited laboratory experiments sufficient to develop and validate appropriate computer models. These models can then be used to perform large-scale simulated experiments the results of which can then be validated by further small-scale laboratory experiments.     

Molecular cloning and DNA sequence analysis of genes encoding cytotoxic T lymphocyte-defined HLA-A3 subtypes: the E1 subtype

Influenza-specific cytotoxic T cells restricted by HLA-A3 and allogeneic CTL specific for HLA-A3 recognize differences between serologically indistinguishable HLA-A3 antigens. Previous biochemical studies have indicated that such differential recognition can be explained by alterations in the primary structure of class I heavy chains. Characterization of these sequence differences may therefore identify portions of the class I molecule that form determinants recognized by CTL. In this study, we describe the cloning and sequencing of an HLA-A3 subtype from donor E1 (E1-A3). Cloning of the gene encoding E1-A3 was simplified by determining that a 15.5-kb BamHI fragment contains the complete gene and is characteristic of HLA-A3 and only one other class I gene (HLA-A11). Comparison of the E1-A3 sequence to that of a previously sequenced HLA-A3 gene for exons encoding extracellular class I domains revealed three nucleotide differences. All of these differences were located within a discrete region of exon 3 (encoding the alpha 2 domain) and result in a change of two amino acids, at positions 152 (Glu----Val) and 156 (Leu----Gln). This finding suggests that these amino acids are crucial for the information of a determinant recognized by CTL. Furthermore, the altered nucleotide sequence of E1-A3 is identical to the sequence of the HLA-Aw24 gene for codons 128 to 161. These observations of multiple clustered changes in the E1-A3 subtype (relative to the prototype sequence) and identity of the altered sequence with the sequence of another class I gene support the concept that gene conversion is a primary mechanism for the generation of class I polymorphism.     

Identification of peptide epitopes of MAGE-1, -2, -3 that demonstrate HLA-A3-specific binding

 The MAGE gene family of tumour antigens are expressed in a wide variety of human cancers. We have identified 43 nonamer peptide sequences, from MAGE-1, -2 and -3 proteins that contain binding motifs for HLA-A3 MHC class I molecules. The T2 cell line, transfected with the cDNA for the HLA-A3 gene, was used in a MHC class I stabilisation assay performed at 37°C and 26°C. At 37°C, 2 peptides were identified that stabilised HLA-A3 with high affinity (fluorescence ratio, FR >1.5), 4 peptides with low affinity (FR 1.11 – 1.49) and 31 peptides that did not stabilise this HLA haplotype (FR <1.1). At 26°C, 12 peptides were identified that stabilised HLA-A3 with high affinity, 8 peptides with low affinity and 17 peptides that did not stabilise this HLA haplotype. Two peptides stabilised HLA-A3 at both temperatures. Small changes in one to three amino acids at positions distinct from the anchor residues altered peptide affinity. Data were compared to a similar study in which a peptide competition assay was used to investigate MAGE-1 peptide binding to several HLA haplotypes. This study demonstrates that anchor residues do not accurately predict peptide binding to specific HLA haplotypes, changes in one to three amino acids at positions distinct from anchor residues influence peptide binding and alternative methods of determining peptide binding yield different results. We are currently investigating the ability of these peptides to induce antitumour cytotoxic T lymphocyte activity as they may be of potential therapeutic value. Received: 4 January 1996 / Accepted: 20 March 1996     

Identification of novel MAGE-A6- and MAGE-A12-derived HLA-A24-restricted cytotoxic T lymphocyte epitopes using an in silico peptide-docking assay

Many cancer-testis antigen genes have been identified; however, few human leukocyte antigen (HLA)-A24-restricted cytotoxic T cell (CTL) epitope peptides are available for clinical immunotherapy. To solve this problem, novel tools increasing the efficacy and accuracy of CTL epitope detection are needed. In the present study, we utilized a highly active dendritic cell (DC)-culture method and an in silico HLA-A24 peptide-docking simulation assay to identify novel CTL epitopes from MAGE-A6 and MAGE-A12 antigens. The highly active DCs, called ??-type-1 DCs, were prepared using a combination of maturation reagents to produce a large amount of interleukin-12. Meanwhile, our HLA-A24 peptide-docking simulation assay was previously demonstrated to have an obvious advantage of accuracy over the conventional prediction tool, bioinformatics and molecular analysis section. For CTL induction assays, peripheral blood mononuclear cells derived from six cases of HLA-A24⁺ melanoma were used. Through CTL induction against melanoma cell lines and peptide-docking simulation assays, two peptides (IFGDPKKLL from MAGE-A6 and IFSKASEYL from MAGE-A12) were identified as novel CTL epitope candidates. Finally, we verified that the combination of the highly active DC-culture method and HLA-A24 peptide-docking simulation assay might be tools for predicting CTL epitopes against cancer antigens.     

Generation of CTL recognizing an HLA-A*0201-restricted epitope shared by MAGE-A1, -A2, -A3, -A4, -A6, -A10, and -A12 tumor antigens: implication in a broad-spectrum tumor immunotherapy

MAGE-A1, -A2, -A3, -A4, -A6, -A10, and -A12 are expressed in a significant proportion of primary and metastatic tumors of various histological types and are targets of tumor Ag-specific CTL. Individual MAGE-A expression varies from one tumor type to the other but, overall, the large majority of tumors expresses at least one MAGE-A Ag. Therefore, targeting epitopes shared by all MAGE-A Ags would be of interest in immunotherapy against a broad spectrum of cancers. In the present study, we describe a heteroclitic MAGE-A peptide (p248V9) that induces CTL in vivo in HLA-A*0201 transgenic HHD mice and in vitro in healthy donors. These CTL are able to recognize two low HLA-A*0201 affinity peptides differing at their C-terminal position and derived from MAGE-A2, -A3, -A4, -A6, -A10, and -A12 (p248G9) and MAGE-A1 (p248D9). Interestingly, p248V9-specific CTL respond to endogenous MAGE-A1, -A2, -A3, -A4, -A6, -A10, and -A12 in an HLA-A*0201-restricted manner and recognize human HLA-A*0201(+)MAGE-A(+) tumor cells of various histological origin. Therefore, this heteroclitic peptide may be considered as a potent candidate for a broad-spectrum tumor vaccination.     

Peptide-specific, allogeneic T cell response in vitro induced by a self-peptide binding to HLA-A2

The role of the bound peptide in alloreactive T-cell recognition is controversial, ranging from peptide-independent to peptide-specific recognition of alloreactive T-cells. The aim of this study is to find the evidence that there exist peptide/MHC complex (pMHC)-specific CTLs among alloreactive T cells generated with long-term mixed lymphocytes culture (LTMLC). A single pMHC was manipulated by loading the TAP-defective, HLA-A2 expressing T2 cells with a viral peptide (LMP2A426-434) or a self-peptide (Tyr369-377). The PBLs samples from 4 HLA-A2 positive (HLA-A2+ve) and 4 HLA-A2 negative (HLA-A2-ve) donors were included in this study. The HLA-A2+ve PBL co-cultured with the LMP2A426-434pulsed T2 (T2/LMP) stands for the nominal T-cell response to a viral antigen, and the HLA-A2-ve PBLs co-cultured with the Tyr369-377 pulsed T2 (T2/Tyr) for alloreactive T-cell response to an allogeneic antigen.The specificity of the expanded CTLs after the LTMLC was detected by their specific cytotoxicity and binding ability to specific pMHC-tetramer. An HLA-A2 restricted, HIV peptide (Gag77-85) was included for control. The cultural bulk of HLA-A2+ve PBLs with the T2/LMP showed an elevated specific cytotoxicity against the T2/LMP compared to that against the T2/HIV (26.52%±3.72% vs 7.01%±0.87%, P＜0.001), and an increased frequency of binding to LMP-tetramer compared to that binding to HIV-tetramer (0.98%±0.33% vs 0.05%±0.01%, P=0.0014). The cultural bulk of HLA-A2-ve PBLs with the T2/Tyr showed a more active cytotoxicity against the T2/Tyr than that against T2/HIV (28.07%±2.58% vs 6.87%±1.01%,P＜0.001), and a higher frequency of binding to the Tyr-tetramer than that binding to the HIV-tetramer (0.88%±0.3% vs 0.06%±0.03%, P=0.0018). Our results indicate that the LTMLC is able to expand the viral antigen-specific CTLs as well as allogeneic antigen-specific CTLs. A relatively large proportion of alloreactive CTLs should be pMHC-specific, i.e., the specificity of the alloreactive lines depends on both the bound peptide and the allotype of MHC. Our observations support the hypothesis that the cumularive effect of T cells specific to each peptide epitope could account for the strength and diversity of the alloresponse. The method using manipulated pMHC and the LTMLC to generate pMHC-specific, alloreactive CTLs is of potential importance for adoptive T-cell immunotherapy.