Cell Epitope Identification Research Articles

Ad5-nCoV Vaccination Could Induce HLA-E Restricted CD8+ T Cell Responses Specific for Epitopes on Severe Acute Respiratory Syndrome Coronavirus 2 Spike Protein.

We evaluated cellular immune responses induced by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccines in an immunized population based on HLA-E-restricted CD8+ T cell epitope identification. HLA-E-restricted SARS-CoV-2 CD8+ T cell nonamer peptides were predicted with software. An HLA-E-transfected K562 cell binding assay was used to screen for high-affinity peptides. IFN-γ enzyme-linked immunospot assays were used to identify HLA-E-restricted epitopes. An HLA-E/epitope tetramer was employed to detect the frequencies of epitope-specific CD8+ T cells. Four CD8+ T cell epitopes on the spike protein of SARS-CoV-2 restricted by both HLA-E*0101 and E*0103 were identified. HLA-E-restricted epitope-specific IFN-γ-secreting CD8+ T cell responses could be detected in individuals vaccinated with SARS-CoV-2 vaccines. Importantly, the frequencies of epitope-specific CD8+ T cells in Ad5-nCoV vaccinated individuals were higher than in individuals vaccinated with recombinant protein or inactivated vaccines. Moreover, the frequencies of epitope-specific CD8+ T cells could be maintained for at least 120 days after only one dose of Ad5-nCoV vaccine, while the frequencies of epitope-specific CD8+ T cells decreased in individuals after two doses of Ad5-nCoV vaccine. These findings may contribute to a more comprehensive evaluation of the protective effects of vaccines for SARS-CoV-2; meanwhile, they may provide information to characterize HLA-E-restricted CD8+ T cell immunity against SARS-CoV-2 infection.

HLA-PopSeq: High throughput, multiplexed six-locus Human Leukocyte Antigen typing for population-scale T cell immune profiling using rapid long-read nanopore sequencing

Abstract A major technical hurdle for T cell immune profiling is the time and cost to accurately genotype the Human Leukocyte Antigen (HLA) loci from peripheral blood. Here, we developed a rapid, highly multiplexed approach for HLA typing using RNA from <100,000 peripheral blood mononuclear cells with the Oxford Nanopore Technology (ONT) Minion sequencer. This method uses selective reverse transcription of mRNA of six HLA loci (A,B,C, DRB1, DQB1, DPB1), followed by PCR amplification. The individual amplified HLA cDNA was multiplexed in a single sequencing pool using primers with unique molecular identifiers, designed to permit sequencing errors for enhanced data capture. Pooled HLA amplicons were sequenced using the ONT Minion MK1B and R10.4 flowcells, with sequence Q scores> 20. Total RNA was extracted from PBMC samples from 12 individuals, reverse transcribed and amplified using the designed HLA loci specific primers. The pooled, amplified cDNA was then sequenced for 16 hours on the ONT Minion sequencer. The resulting sequencing data was analyzed and an average depth of coverage of 6000x was observed per sample. An average per loci depth of coverage of 1000x was observed. This method is designed to permit rapid (<24h), low-cost, portable HLA sequencing for T cell immune monitoring and epitope identification for immunologic studies. Arizona Piper Foundation

CD4+ T Cell Epitope Identification from Complex Parasite Antigen Mixtures.

Antigen complexity represents a major challenge for scoring CD4+ T cell immunogenicity, a key hallmark of immunity and with great potential to improve vaccine development. In this chapter, we provide a comprehensive picture of a pipeline that can be applied to virtually any complex antigen to overcome different limitations. Antigens are characterized by Mass Spectrometry to determine the available protein sources and their abundances. A reconstituted in vitro antigen processing system is applied along with bioinformatics tools to prioritize the list of candidates. Finally, the immunogenicity of candidate peptides is validated ex vivo using PBMCs from HLA-typed individuals. This protocol compiles the essential information for executing the whole pipeline while focusing on the candidate epitope prioritizing scheme.

A T-Cell Epitope-Based Multi-Epitope Vaccine Designed Using Human HLA Specific T Cell Epitopes Induces a Near-Sterile Immunity against Experimental Visceral Leishmaniasis in Hamsters.

Visceral leishmaniasis is a neglected tropical disease affecting 12 million people annually. Even in the second decade of the 21st century, it has remained without an effective vaccine for human use. In the current study, we designed three multiepitope vaccine candidates by the selection of multiple IFN-γ inducing MHC-I and MHC-II binder T-cell specific epitopes from three previously identified antigen genes of Leishmania donovani from our lab by an immuno-informatic approach using IFNepitope, the Immune Epitope Database (IEDB) T cell epitope identification tools, NET-MHC-1, and NET MHC-2 webservers. We tested the protective potential of these three multiepitope proteins as a vaccine in a hamster model of visceral leishmaniasis. The immunization data revealed that the vaccine candidates induced a very high level of Th1 biased protective immune response in-vivo in a hamster model of experimental visceral leishmaniasis, with one of the candidates inducing a near-sterile immunity. The vaccinated animals displayed highly activated monocyte macrophages with the capability of clearing intracellular parasites due to increased respiratory burst. Additionally, these proteins induced activation of polyfunctional T cells secreting INF-γ, TNF-α, and IL-2 in an ex-vivo stimulation of human peripheral blood mononuclear cells, further supporting the protective nature of the designed candidates.

In silico T cell epitope identification for SARS-CoV-2: Progress and perspectives.

Growing evidence suggests that T cells may play a critical role in combating severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Hence, COVID-19 vaccines that can elicit a robust T cell response may be particularly important. The design, development and experimental evaluation of such vaccines is aided by an understanding of the landscape of T cell epitopes of SARS-CoV-2, which is largely unknown. Due to the challenges of identifying epitopes experimentally, many studies have proposed the use of in silico methods. Here, we present a review of the in silico methods that have been used for the prediction of SARS-CoV-2 T cell epitopes. These methods employ a diverse set of technical approaches, often rooted in machine learning. A performance comparison is provided based on the ability to identify a specific set of immunogenic epitopes that have been determined experimentally to be targeted by T cells in convalescent COVID-19 patients, shedding light on the relative performance merits of the different approaches adopted by the in silico studies. The review also puts forward perspectives for future research directions.

In Silico T Cell Epitope Identification for SARS-CoV-2: Progress and Perspectives

Growing evidence suggests that T cells may play a critical role in combating severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Hence, COVID-19 vaccines that can elicit a robust T cell response may be particularly important. The design, development and experimental evaluation of such vaccines is aided by an understanding of the landscape of T cell epitopes of SARS-CoV-2, which is largely unknown. Due to the challenges of identifying epitopes experimentally, many studies have proposed the use of in silico methods. Here, we present a review of the in silico methods that have been used for the prediction of SARS-CoV-2 T cell epitopes. These methods employ a diverse set of technical approaches, often rooted in machine learning. A performance comparison is provided based on the ability to identify a specific set of immunogenic epitopes that have been determined experimentally to be targeted by T cells in convalescent COVID-19 patients, shedding light on the relative performance merits of the different approaches adopted by the in silico studies. The review also puts forward perspectives for future research directions.

New Immunoinformatics Tools for Swine: Designing Epitope-Driven Vaccines, Predicting Vaccine Efficacy, and Making Vaccines on Demand.

Novel computational tools for swine vaccine development can expand the range of immunization approaches available to prevent economically devastating swine diseases and spillover events between pigs and humans. PigMatrix and EpiCC are two new tools for swine T cell epitope identification and vaccine efficacy analysis that have been integrated into an existing computational vaccine design platform named iVAX. The iVAX platform is already in use for the development of human vaccines, thus integration of these tools into iVAX improves and expands the utility of the platform overall by making previously validated immunoinformatics tools, developed for humans, available for use in the design and analysis of swine vaccines. PigMatrix predicts T cell epitopes for a broad array of class I and class II swine leukocyte antigen (SLA) using matrices that enable the scoring of sequences for likelihood of binding to SLA. PigMatrix facilitates the prospective selection of T cell epitopes from the sequences of swine pathogens for vaccines and permits the comparison of those predicted epitopes with “self” (the swine proteome) and with sequences from other strains. Use of PigMatrix with additional tools in the iVAX toolkit also enables the computational design of vaccines in silico, for testing in vivo. EpiCC uses PigMatrix to analyze existing or proposed vaccines for their potential to protect, based on a comparison between T cell epitopes in the vaccine and circulating strains of the same pathogen. Performing an analysis of T cell epitope relatedness analysis using EpiCC may facilitate vaccine selection when a novel strain emerges in a herd and also permits analysis of evolutionary drift as a means of immune escape. This review of novel computational immunology tools for swine describes the application of PigMatrix and EpiCC in case studies, such as the design of cross-conserved T cell epitopes for swine influenza vaccine or for African Swine Fever. We also describe the application of EpiCC for determination of the best vaccine strains to use against circulating viral variants of swine influenza, swine rotavirus, and porcine circovirus type 2. The availability of these computational tools accelerates infectious disease research for swine and enable swine vaccine developers to strategically advance their vaccines to market.

Construction and Screening of anAntigen-Derived Peptide Library Displayed on Yeast Cell Surface for CD4+ T Cell Epitope Identification.

Antigenic peptides (termed T cell epitopes) are assembled with major histocompatibility complex (MHC) molecules and presented on the surface of antigen-presenting cells (APCs) for T cell recognition. T cells engage these peptide-MHCs using T cell receptors (TCRs). Because T cell epitopes determine the specificity of a T cell immune response, their prediction and identification are important steps in developing peptide-based vaccines and immunotherapies. In recent years, a number of computational methods have been developed to predict T cell epitopes by evaluating peptide-MHC binding; however, the success of these methods has been limited for MHC class II (MHCII) due to the structural complexity of MHCII antigen presentation. Moreover, while peptide-MHC binding is a prerequisite for a T cell epitope, it alone is not sufficient. Therefore, T cell epitope identification requires further functional verification of the MHC-binding peptide using professional APCs, which are difficult to isolate, expand, and maintain. To address these issues, we have developed a facile, accurate, and high-throughput method for T cell epitope mapping by screening antigen-derived peptide libraries in complex with MHC protein displayed on yeast cell surface. Here, we use hemagglutinin and influenza A virus X31/A/Aichi/68 as examples to describe the key steps in identification of CD4+ T cell epitopes from a single antigenic protein and the entire genome of a pathogen, respectively. Methods for single-chain peptide MHC vector design, yeast surface display, peptide library generation in Escherichia coli, and functional screening in Saccharomyces cerevisiae are discussed.

Abstract 5311: Integrated approaches for design of precision cancer immunotherapies: Selection of Class I and Class II T cell neo-epitopes and removal of Treg epitopes

Abstract Next-generation sequencing has opened the door to precision cancer therapies targeting mutations expressed by tumor cells. However, most neo-epitopes selected by traditional T cell epitope prediction algorithms prove to be non-immunogenic. Poor predictive performance may partially be due to inclusion of mutated epitopes cross-conserved with self-epitopes recognized by the T cell receptor of regulatory (Treg), anergic or deleted T cells. Vaccination with self-epitopes can lead to weak effector responses, active immune suppression, and toxicity due to immune-mediated adverse effects. We have developed Ancer, an advanced cancer T cell epitope identification and characterization tool, that streamlines the selection of Class I and Class II T cell neo-epitopes. Ancer leverages EpiMatrix and JanusMatrix, state-of-the-art predictive algorithms that have been extensively validated in prospective vaccine studies for infectious diseases [Moise et al., Hum. Vaccines Immunother 2015; Wada et al., Sci. Rep. 2017]. Distinctive features of Ancer are its ability to accurately predict Class II HLA ligands with EpiMatrix and its 82% positive predictive value, as estimated in previous prospective studies. Additionally, the application of JanusMatrix allows for the prioritization of neo-epitopes with reduced potential for Treg induction, that is responsible for diminished efficacy of current cancer therapies. We validated Ancer's predictive accuracy using datasets of HLA-bound peptides detected by mass spectrometry, which are independent of training sequence data used in model development. Analysis of sequences from Abelin et al., Immunity 2017 shows a 96% agreement between Ancer predictions and peptides eluted from common Class I HLAs, while only 86% of these sequences are accurately predicted by NetMHC or NetMHCpan. An additional retrospective analysis of a cancer immunogenicity study [Strønen et al., Science 2016] demonstrates that Ancer selects immunogenic neo-epitopes with 72% accuracy, as compared to 21% accuracy when using public prediction tools. These results demonstrate that Ancer may focus epitope candidate selection on higher value sequences than conventional algorithms. Class I and Class II neo-epitopes with low Treg activation potential may then be used to support the development of safer and more effective vaccines. Citation Format: Guilhem Richard, Lenny Moise, Matthew Ardito, Frances Terry, Gad Berdugo, William Martin, Anne De Groot. Integrated approaches for design of precision cancer immunotherapies: Selection of Class I and Class II T cell neo-epitopes and removal of Treg epitopes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 5311.

Role of peptide processing predictions in T cell epitope identification: contribution of different prediction programs.

Proteolysis is the general term to describe the process of protein degradation into peptides. Proteasomes are the main actors in cellular proteolysis, and their activity can be measured in in vitro digestion experiments. However, in vivo proteolysis can be different than what is measured in these experiments if other proteases participate or if proteasomal activity is different in vivo. The in vivo proteolysis can be measured only indirectly, by the analysis of peptides presented on MHC-I molecules. MHC-I presented peptides are protected from further degradation, thus enabling an indirect view on the underlying in vivo proteolysis. The ligands presented on different MHC-I molecules enable different views on this process; in combination, they might give a complete picture. Based on in vitro proteasome-only digestions and MHC-I ligand data, different proteolysis predictors have been developed. With new in vitro digestion and MHC-I ligand data sets, we benchmarked how well these predictors capture in vitro proteasome-only activity and in vivo whole-cell proteolysis, respectively. Even though the in vitro proteasome digestion patterns were best captured by methods trained on such data (ProteaSMM and NetChop 20S), the in vivo whole-cell proteolysis was best predicted by a method trained on MHC-I ligand data (NetChop Cterm). Follow-up analysis showed that the likely source of this difference is the activity from proteases other than the proteasome, such as TPPII. This non-proteasomal in vivo activity is captured by NetChop Cterm and should be taken into account in MHC-I ligand predictions. Electronic supplementary material The online version of this article (doi:10.1007/s00251-014-0815-0) contains supplementary material, which is available to authorized users.

Testing for HLA/peptide tetramer-binding to the T cell receptor complex on human T lymphocytes

HLA/peptide tetramers are frequently used for ex vivo monitoring of disease- or vaccine-induced T cell immune responses and for T cell epitope identification. However, when low-levels HLA/peptide tetramer-positive T cell populations are encountered, it is difficult to ascertain whether this represents a true T cell receptor (TCR)-mediated interaction or background signal. To address this issue, we have developed a method for both HLA class I and class II tetramer assays to confirm tetramer-binding to the TCR/CD3 complex. Preincubation of T cells with anti-CD3 mAb SPV-T3b and subsequent crosslinking interferes with the binding of HLA/peptide tetramers to the TCR/CD3 complex and thereby indicates to what extent HLA/peptide tetramer binds through interaction with TCR/CD3 complex. SPV-T3b pretreatment results in a 2- to 10-fold decrease in tetramer-binding intensity to antigen-specific CD8+ or CD4+ T cells, whereas background reactivity of HLA/peptide tetramers containing HIV-derived peptide in HIV-negative donors remained unchanged. SPV-T3b pretreatment forms a valuable tool to verify tetramer-based detection of antigen-specific T cells during the monitoring of immune responses in clinical studies.

A broadly applicable approach to T cell epitope identification: Application to improving tumor associated epitopes and identifying epitopes in complex pathogens

Epitopes are a hallmark of the antigen specific immune response. The identification and characterization of epitopes is essential for modern immunologic studies, from investigating cellular responses against tumors to understanding host/pathogen interactions especially in the case of bacteria with intracellular residence. Here, we have utilized a novel approach to identify T cell epitopes exploiting the exquisite ability of particulate antigens, in the form of beads, to deliver exogenous antigen to both MHC class I and class II pathways for presentation to T cell hybridomas. In the current study, we coupled this functional assay with two distinct protein expression libraries to develop a methodology for the characterization of T cell epitopes. One set of expression libraries containing single amino acid substitutions in a defined epitope sequence was interrogated to identify epitopes with enhanced T cell stimulation for a MHC class I epitope. The second expression library is comprised of the majority of open reading frames from the intracellular pathogen and potential biowarfare agent, Francisella tularensis. By automating aspects of this technology, we have been able to functionally screen and identify novel T cell epitopes within F. tularensis. We have also expanded upon these studies to generate a novel expression vector that enables immunization of recombinant protein into mice, which has been utilized to facilitate T cell epitope discovery for proteins that are critically linked to Francisella pathogenicity. This methodology should be applicable to a variety of systems and other pathogens.

A broadly applicable approach to T cell epitope identification: Application to improving tumor associated epitopes and identifying epitopes in complex pathogens

A broadly applicable approach to T cell epitope identification: Application to improving tumor associated epitopes and identifying epitopes in complex pathogens

Identification of HLA-A*0201-restricted cytotoxic T lymphocyte epitope from proliferating cell nuclear antigen

Peptide-based immunotherapy strategies appear promising as an approach to successfully induce an antitumor immune response and prolong survival in patients with various cancers. Protein antigens and their specific epitopes are formulation targets for anti-tumor vaccines. Bioinformatical approaches to predict major histocompatibility complex binding peptides can facilitate the resource-consuming effort of T cell epitope identification. Proliferating cell nuclear antigen including Ki-67 and PCNA, associated with the proliferation process of the cell, seems to be an attractive new target for tumor-specific immunotherapy. In this study, we predicted seven HLA-A*0201-restricted CTL candidate epitope of Ki-67 and eight epitope of PCNA by computer algorithm SYFPEITHI, BIMAS, and IEDB_ANN. Subsequently, biological functions of these peptides were tested by experiments in vitro. We found Ki-67((280-288)) (LQGETQLLV) had the strongest binding-affinity with HLA-A*0201. Further study revealed that Ki-67((280-288)) increased the frequency of IFN-γ-producing T cells compared to a negative peptide. Because Ki-67 was broadly expressed in most advanced malignant tumors, indicating a potential anti-tumor application in the future.

MHC-based detection of antigen-specific CD8+ T cell responses.

The hallmark of adaptive immunity is its ability to recognise a wide range of antigens and technologies that capture this diversity are therefore of substantial interest. New methods have recently been developed that allow the parallel analysis of T cell reactivity against vast numbers of different epitopes in limited biological material. These technologies are based on the joint binding of differentially labelled MHC multimers on the T cell surface, thereby providing each antigen-specific T cell population with a unique multicolour code. This strategy of ‘combinatorial encoding’ enables detection of many (at least 25) different T cell populations per sample and should be of broad value for both T cell epitope identification and immunomonitoring.

Integrating In Silico and In Vitro Analysis of Peptide Binding Affinity to HLA-Cw*0102: A Bioinformatic Approach to the Prediction of New Epitopes

BackgroundPredictive models of peptide-Major Histocompatibility Complex (MHC) binding affinity are important components of modern computational immunovaccinology. Here, we describe the development and deployment of a reliable peptide-binding prediction method for a previously poorly-characterized human MHC class I allele, HLA-Cw*0102.Methodology/FindingsUsing an in-house, flow cytometry-based MHC stabilization assay we generated novel peptide binding data, from which we derived a precise two-dimensional quantitative structure-activity relationship (2D-QSAR) binding model. This allowed us to explore the peptide specificity of HLA-Cw*0102 molecule in detail. We used this model to design peptides optimized for HLA-Cw*0102-binding. Experimental analysis showed these peptides to have high binding affinities for the HLA-Cw*0102 molecule. As a functional validation of our approach, we also predicted HLA-Cw*0102-binding peptides within the HIV-1 genome, identifying a set of potent binding peptides. The most affine of these binding peptides was subsequently determined to be an epitope recognized in a subset of HLA-Cw*0102-positive individuals chronically infected with HIV-1.Conclusions/SignificanceA functionally-validated in silico-in vitro approach to the reliable and efficient prediction of peptide binding to a previously uncharacterized human MHC allele HLA-Cw*0102 was developed. This technique is generally applicable to all T cell epitope identification problems in immunology and vaccinology.

Prediction of MHC-Peptide Binding: A Systematic and Comprehensive Overview

T cell immune responses are driven by the recognition of peptide antigens (T cell epitopes) that are bound to major histocompatibility complex (MHC) molecules. T cell epitope immunogenicity is thus contingent on several events, including appropriate and effective processing of the peptide from its protein source, stable peptide binding to the MHC molecule, and recognition of the MHC-bound peptide by the T cell receptor. Of these three hallmarks, MHC-peptide binding is the most selective event that determines T cell epitopes. Therefore, prediction of MHC-peptide binding constitutes the principal basis for anticipating potential T cell epitopes. The tremendous relevance of epitope identification in vaccine design and in the monitoring of T cell responses has spurred the development of many computational methods for predicting MHC-peptide binding that improve the efficiency and economics of T cell epitope identification. In this report, we will systematically examine the available methods for predicting MHC-peptide binding and discuss their most relevant advantages and drawbacks.

EpiJen: a server for multistep T cell epitope prediction.

BackgroundThe main processing pathway for MHC class I ligands involves degradation of proteins by the proteasome, followed by transport of products by the transporter associated with antigen processing (TAP) to the endoplasmic reticulum (ER), where peptides are bound by MHC class I molecules, and then presented on the cell surface by MHCs. The whole process is modeled here using an integrated approach, which we call EpiJen. EpiJen is based on quantitative matrices, derived by the additive method, and applied successively to select epitopes. EpiJen is available free online.ResultsTo identify epitopes, a source protein is passed through four steps: proteasome cleavage, TAP transport, MHC binding and epitope selection. At each stage, different proportions of non-epitopes are eliminated. The final set of peptides represents no more than 5% of the whole protein sequence and will contain 85% of the true epitopes, as indicated by external validation. Compared to other integrated methods (NetCTL, WAPP and SMM), EpiJen performs best, predicting 61 of the 99 HIV epitopes used in this study.ConclusionEpiJen is a reliable multi-step algorithm for T cell epitope prediction, which belongs to the next generation of in silico T cell epitope identification methods. These methods aim to reduce subsequent experimental work by improving the success rate of epitope prediction.

Candidate epitope identification using peptide property models: application to cancer immunotherapy

Peptides derived from pathogens or tumors are selectively presented by the major histocompatibility complex proteins (MHC) to the T lymphocytes. Antigenic peptide–MHC complexes on the cell surface are specifically recognized by T cells and, in conjunction with co-factor interactions, can activate the T cells to initiate the necessary immune response against the target cells. Peptides that are capable of binding to multiple MHC molecules are potential T cell epitopes for diverse human populations that may be useful in vaccine design. Bioinformatical approaches to predict MHC binding peptides can facilitate the resource-consuming effort of T cell epitope identification. We describe a new method for predicting MHC binding based on peptide property models constructed using biophysical parameters of the constituent amino acids and a training set of known binders. The models can be applied to development of anti-tumor vaccines by scanning proteins over-expressed in cancer cells for peptides that bind to a variety of MHC molecules. The complete algorithm is described and illustrated in the context of identifying candidate T cell epitopes for melanomas and breast cancers. We analyzed MART-1, S-100, MBP, and CD63 for melanoma and p53, MUC1, cyclin B1, HER-2/neu, and CEA for breast cancer. In general, proteins over-expressed in cancer cells may be identified using DNA microarray expression profiling. Comparisons of model predictions with available experimental data were assessed. The candidate epitopes identified by such a computational approach must be evaluated experimentally but the approach can provide an efficient and focused strategy for anti-cancer immunotherapy development.

Identification of seventeen new simian immunodeficiency virus-derived CD8+ T cell epitopes restricted by the high frequency molecule, Mamu-A*02, and potential escape from CTL recognition.

MHC class I-restricted CD8+ T cells play an important role in controlling HIV and SIV replication. In SIV-infected Indian rhesus macaques (Macaca mulatta), comprehensive CD8+ T cell epitope identification has only been undertaken for two alleles, Mamu-A*01 and Mamu-B*17. As a result, these two molecules account for virtually all known MHC class I-restricted SIV-derived CD8+ T cell epitopes. SIV pathogenesis research and vaccine testing have intensified the demand for epitopes restricted by additional MHC class I alleles due to the shortage of Mamu-A*01+ animals. Mamu-A*02 is a high frequency allele present in over 20% of macaques. In this study, we characterized the peptide binding of Mamu-A*02 using a panel of single amino acid substitution analogues and a library of 497 unrelated peptides. Of 230 SIVmac239 peptides that fit the Mamu-A*02 peptide-binding motif, 75 peptides bound Mamu-A*02 with IC50 values of < or = 500 nM. We assessed the antigenicity of these 75 peptides using an IFN-gamma ELISPOT assay with freshly isolated PBMC from eight Mamu-A*02+ SIV-infected macaques and identified 17 new epitopes for Mamu-A*02. The synthesis of five Mamu-A*02 tetramers demonstrated the discrepancy between tetramer binding and IFN-gamma secretion by SIV-specific CD8+ T cells during chronic SIV infection. Bulk sequencing determined that 2 of the 17 epitopes accumulated amino acid replacements in SIV-infected macaques by the chronic phase of infection, suggestive of CD8+ T cell escape in vivo. This work enhances the use of the SIV-infected macaque model for HIV and increases our understanding of the breadth of CD8+ T cell responses in SIV infection.