Identification Of T-cell Epitopes Research Articles

A bioinformatic analysis of T-cell epitope diversity in SARS-CoV-2 variants: association with COVID-19 clinical severity in the United States population.

Long-term immunity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) requires the identification of T-cell epitopes affecting host immunogenicity. In this computational study, we explored the CD8+ epitope diversity estimated in 27 of the most common HLA-A and HLA-B alleles, representing most of the United States population. Analysis of 16 SARS-CoV-2 variants [B.1, Alpha (B.1.1.7), five Delta (AY.100, AY.25, AY.3, AY.3.1, AY.44), and nine Omicron (BA.1, BA.1.1, BA.2, BA.4, BA.5, BQ.1, BQ.1.1, XBB.1, XBB.1.5)] in analyzed MHC class I alleles revealed that SARS-CoV-2 CD8+ epitope conservation was estimated at 87.6%-96.5% in spike (S), 92.5%-99.6% in membrane (M), and 94.6%-99% in nucleocapsid (N). As the virus mutated, an increasing proportion of S epitopes experienced reduced predicted binding affinity: 70% of Omicron BQ.1-XBB.1.5 S epitopes experienced decreased predicted binding, as compared with ~3% and ~15% in the earlier strains Delta AY.100-AY.44 and Omicron BA.1-BA.5, respectively. Additionally, we identified several novel candidate HLA alleles that may be more susceptible to severe disease, notably HLA-A*32:01, HLA-A*26:01, and HLA-B*53:01, and relatively protected from disease, such as HLA-A*31:01, HLA-B*40:01, HLA-B*44:03, and HLA-B*57:01. Our findings support the hypothesis that viral genetic variation affecting CD8 T-cell epitope immunogenicity contributes to determining the clinical severity of acute COVID-19. Achieving long-term COVID-19 immunity will require an understanding of the relationship between T cells, SARS-CoV-2 variants, and host MHC class I genetics. This project is one of the first to explore the SARS-CoV-2 CD8+ epitope diversity that putatively impacts much of the United States population.

Identification of T-Cell Epitopes Using a Combined In-Silico and Experimental Approach in a Mouse Model for SARS-CoV-2.

Following viral infection, T-cells are crucial for an effective immune response to intracellular pathogens, including respiratory viruses. During the COVID-19 pandemic, diverse assays were required in pre-clinical trials to evaluate the immune response following vaccination against SARS-CoV-2 and assess the response following exposure to the virus. To assess the nature and potency of the cellular response to infection or vaccination, a reliable and specific activity assay was needed. A cellular activity assay based on the presentation of short peptides (epitopes) allows the identification of T cell epitopes displayed on different alleles of the MHC, shedding light on the strength of the immune response towards antigens and aiding in antigen design for vaccination. In this report, we describe two approaches for scanning T cell epitopes on the surface glycoprotein of the SARS-CoV-2 (spike), which is utilized for attachment and entry and serves as an antigen in many vaccine candidates. We demonstrate that epitope scanning is feasible using peptide libraries or computational scanning combined with a cellular activity assay. Our scans identified four CD8 T cell epitopes, including one novel undescribed epitope. These epitopes enabled us to establish a reliable T-cell response assay, which was examined and used in various experimental mouse models for SARS-CoV-2 infection and vaccination. These approaches could potentially aid in future antigen design for vaccination and establish cellular activity assays against uncharacterized antigens of emerging pathogens.

Characterization of T-Cell Epitopes in Food Allergens by Bioinformatic Tools.

The identification of T-cell epitopes is a critical step in the understanding of theimmunologic mechanisms such as food allergy. Epitope screening in silico by bioinformatic tools can be used to identify T-cell epitopes, which can save time and resources. In this chapter, a multiparametric approach to predict and assess major histocompatibility complex (MHC) class II binding T-cell epitopes using bioinformatics was introduced for food allergens. Furthermore, the ability of predicted T-cell epitopes to induce interleukin (IL)-4, as well as the allergenicity potential based on the sequence analysis and population coverage of epitopes were also determined. The molecular docking approach was further used to explore the binding ability between epitopes and human leukocyte antigen (HLA) class II molecules. The amino acids that might be responsible for binding to HLA class II molecules and their binding interactions were analyzed.

Decision tree based ensemble machine learning model for the prediction of Zika virus T-cell epitopes as potential vaccine candidates

Zika fever is an infectious disease caused by the Zika virus (ZIKV). The disease is claiming millions of lives worldwide, primarily in developing countries. In addition to vector control strategies, the most effective way to prevent the spread of ZIKV infection is vaccination. There is no clinically approved vaccine to combat ZIKV infection and curb its pandemic. An epitope-based peptide vaccine (EBPV) is seen as a powerful alternative to conventional vaccinations because of its low production cost and short production time. Nonetheless, EBPVs have gotten less attention, despite the fact that they have a significant untapped potential for enhancing vaccine safety, immunogenicity, and cross-reactivity. Such a vaccine technology is based on target pathogen’s selected antigenic peptides called T-cell epitopes (TCE), which are synthesized chemically based on their amino acid sequences. The identification of TCEs using wet-lab experimental approach is challenging, expensive, and time-consuming. Therefore in this study, we present computational model for the prediction of ZIKV TCEs. The model proposed is an ensemble of decision trees that utilizes the physicochemical properties of amino acids. In this way a large amount of time and efforts would be saved for quick vaccine development. The peptide sequences dataset for model training was retrieved from Virus Pathogen Database and Analysis Resource (ViPR) database. The sequences dataset consist of experimentally verified T-cell epitopes (TCEs) and non-TCEs. The model demonstrated promising results when evaluated on test dataset. The evaluation metrics namely, accuracy, AUC, sensitivity, specificity, Gini and Mathew’s correlation coefficient (MCC) recorded values of 0.9789, 0.984, 0.981, 0.987, 0.974 and 0.948 respectively. The consistency and reliability of the model was assessed by carrying out the five (05)-fold cross-validation technique, and the mean accuracy of 0.97864 was reported. Finally, model was compared with standard machine learning (ML) algorithms and the proposed model outperformed all of them. The proposed model will aid in predicting novel and immunodominant TCEs of ZIKV. The predicted TCEs may have a high possibility of acting as prospective vaccine targets subjected to in-vivo and in-vitro scientific assessments, thereby saving lives worldwide, preventing future epidemic-scale outbreaks, and lowering the possibility of mutation escape.

Large-Scale Identification of T-Cell Epitopes Derived From Severe Acute Respiratory Syndrome Coronavirus 2 for the Development of Peptide Vaccines Against Coronavirus Disease 2019.

Coronavirus disease 2019 (COVID-19) continues to be a major public health challenge globally. The identification of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-derived T-cell epitopes is of critical importance for peptide vaccines or diagnostic tools of COVID-19. In this study, several SARS-CoV-2-derived human leukocyte antigen (HLA)-I binding peptides were predicted by NetMHCpan-4.1 and selected by Popcover to achieve pancoverage of the Chinese population. The top 5 ranked peptides derived from each protein of SARS-CoV-2 were then evaluated using peripheral blood mononuclear cells from unexposed individuals (negative for SARS-CoV-2 immunoglobulin G). Seven epitopes derived from 4 SARS-CoV-2 proteins were identified. It is interesting to note that most (5 of 7) of the SARS-CoV-2-derived peptides with predicted affinities for HLA-I molecules were identified as HLA-II-restricted epitopes and induced CD4+ T cell-dependent responses. These results complete missing pieces of pre-existing SARS-CoV-2-specific T cells and suggest that pre-existing T cells targeting all SARS-CoV-2-encoded proteins can be discovered in unexposed populations. In summary, in the current study, we present an alternative and effective strategy for the identification of T-cell epitopes of SARS-CoV-2 in healthy subjects, which may indicate an important role in the development of peptide vaccines for COVID-19.

Using bioinformatic protein sequence similarity to investigate if SARS CoV-2 infection could cause an ocular autoimmune inflammatory reactions?

Although severe acute respiratory syndrome coronavirus 2 (SARS CoV-2) infection have emerged globally, findings related to ocular involvement and reported cases are quite limited. Immune reactions against viral infections are closely related to viral and host proteins sequence similarity. Molecular Mimicry has been described for many different viruses; sequence similarities of viral and human tissue proteins may trigger autoimmune reactions after viral infections due to similarities between viral and human structures. With this study, we aimed to investigate the protein sequence similarity of SARS CoV-2 with retinal proteins and retinal pigment epithelium (RPE) surface proteins. Retinal proteins involved in autoimmune retinopathy and retinal pigment epithelium surface transport proteins were analyzed in order to infer their structural similarity to surface glycoprotein (S), nucleocapsid phosphoprotein (N), membrane glycoprotein (M), envelope protein (E), ORF1ab polyprotein (orf1ab) proteins of SARS CoV-2. Protein similarity comparisons, 3D protein structure prediction, T cell epitopes-MHC binding prediction, B cell epitopes-MHC binding prediction and the evaluation of the antigenicity of peptides assessments were performed. The protein sequence analysis was made using the Pairwise Sequence Alignment and the LALIGN program. 3D protein structure estimates were made using Swiss Model with default settings and analyzed with TM-align web server. T-cell epitope identification was performed using the Immune Epitope Database and Analysis (IEDB) resource Tepitool. B cell epitopes based on sequence characteristics of the antigen was performed using amino acid scales and HMMs with the BepiPred 2.0 web server. The predicted peptides/epitopes in terms of antigenicity were examined using the default settings with the VaxiJen v2.0 server. Analyses showed that, there is a meaningful similarities between 6 retinal pigment epithelium surface transport proteins (MRP-4, MRP-5, RFC1, SNAT7, TAUT and MATE) and the SARS CoV-2 E protein. Immunoreactive epitopic sites of these proteins which are similar to protein E epitope can create an immune stimulation on T cytotoxic and T helper cells and 6 of these 9 epitopic sites are also vaxiJen. These result imply that autoimmune cross-reaction is likely between the studied RPE proteins and SARS CoV-2 E protein. The structure of SARS CoV-2, its proteins and immunologic reactions against these proteins remain largely unknown. Understanding the structure of SARS CoV-2 proteins and demonstration of similarity with human proteins are crucial to predict an autoimmune response associated with immunity against host proteins and its clinical manifestations as well as possible adverse effects of vaccination.

Identification of T-cell epitopes in African swine fever virus CD2v and C-type lectin proteins.

African swine fever (ASF) is an emerging disease threat for the swine industry worldwide. No ASF vaccine is available, and progress is hindered by lack of knowledge concerning the extent of ASF virus (ASFV) strain diversity and the viral antigens conferring type-specific protective immunity in pigs. Previously, we demonstrated that ASFV serotype-specific proteins CD2v (EP402R) and/or C-type lectin (EP153R) are important for protection against homologous ASF infection. Here, we identified six discrete T-cell epitope regions present on CD2v and C-type lectin using IFN-γ ELISpot assay and PBMCs from ASF immune animals, indicating cellular reactivity to these proteins in the context of ASFV infection and protective immunity. Notably, three of the epitope regions map to previously described serotype-specific signature regions of these proteins. Improved understanding of ASFV protective antigens, relevant epitopes and their diversity in nature will facilitate ASFV subunit vaccine design and development.

B-cell and T-cell epitope identification with stability analysis of AI-2 import ATP-binding cassette LsrA from S. typhi[sbnd]In silico approach

Typhoid fever is a severe illness in humans, caused by Salmonella typhi, a Gram-negative bacterium. Membrane proteins of S. typhi have strong potential for its use in development of subunit vaccine against typhoid. In current study, peptide-based subunit vaccine constructed from AI-2 import ATP-binding cassette transporter protein (LsrA) against S. typhi. B-cell and T-cell epitopes were identified at fold level with validated 3-D theoretical modelled structure. T-cell epitope from LsrA (LELPGSRPQ) has binds to maximum number (82.93%) of MHC class I and class II alleles. LsrA epitope was docked with HLA-DR4 and contact map were constructed to analyze molecular interaction (docking) studies. Simulation search for the binding site for full flexibility of the peptide from CABS-dock shows the stable interactions. MD simulation analysis reveals that LsrA epitope was binding and interacting firmly with the HLA-DR4. Hence, we are proposing that LsrA epitope would be a prominent epitope vaccine for human specific pathogen of S. typhi, which requires further steps to be elevated as a vaccine drug in near future.

GradDock: rapid simulation and tailored ranking functions for peptide-MHC Class I docking.

The identification of T-cell epitopes has many profound translational applications in the areas of transplantation, disease diagnosis, vaccine/therapeutic protein development and personalized immunotherapy. While data-driven methods have been widely used for the prediction of peptide binders with notable successes, the structural modeling of peptide binding to MHC molecules is crucial for understanding the underlying molecular mechanism of the immunological processes. We developed GradDock, a structure-based method for the rapid and accurate modeling of peptide binding to MHC Class I (pMHC-I). GradDock explicitly models diverse unbound peptides in vacuo and inserts them into the MHC-I groove through a steered gradient descent with a topological correction process. The simulation process yields diverse structural conformations including native-like peptides. We completely revised the Rosetta score terms and developed a new ranking function specifically for pMHC-I. Using the diverse peptides, a linear programming approach is applied to find the optimal weights for the individual Rosetta score terms. Our examination revealed that a refinement of the dihedral angles and a modification of the repulsion can dramatically improve the modeling quality. GradDock is five-times faster than a Rosetta-based docking approach for pMHC-I. We also demonstrate that the predictive capability of GradDock with the re-weighted Rosetta ranking function is consistently more accurate than the Rosetta-based method with the standard Rosetta score (approximately three-times better for a cross-docking set). GradDock is freely available for academic purposes. The program and the ranking score weights for Rosetta are available at http://bel.kaist.ac.kr/research/GradDock. hskim76@kaist.ac.kr. Supplementary data are available at Bioinformatics online.

In silico and cell-based analyses reveal strong divergence between prediction and observation of T-cell–recognized tumor antigen T-cell epitopes

Tumor exomes provide comprehensive information on mutated, overexpressed genes and aberrant splicing, which can be exploited for personalized cancer immunotherapy. Of particular interest are mutated tumor antigen T-cell epitopes, because neoepitope-specific T cells often are tumoricidal. However, identifying tumor-specific T-cell epitopes is a major challenge. A widely used strategy relies on initial prediction of human leukocyte antigen-binding peptides by in silico algorithms, but the predictive power of this approach is unclear. Here, we used the human tumor antigen NY-ESO-1 (ESO) and the human leukocyte antigen variant HLA-A*0201 (A2) as a model and predicted in silico the 41 highest-affinity, A2-binding 8-11-mer peptides and assessed their binding, kinetic complex stability, and immunogenicity in A2-transgenic mice and on peripheral blood mononuclear cells from ESO-vaccinated melanoma patients. We found that 19 of the peptides strongly bound to A2, 10 of which formed stable A2-peptide complexes and induced CD8+ T cells in A2-transgenic mice. However, only 5 of the peptides induced cognate T cells in humans; these peptides exhibited strong binding and complex stability and contained multiple large hydrophobic and aromatic amino acids. These results were not predicted by in silico algorithms and provide new clues to improving T-cell epitope identification. In conclusion, our findings indicate that only a small fraction of in silico-predicted A2-binding ESO peptides are immunogenic in humans, namely those that have high peptide-binding strength and complex stability. This observation highlights the need for improving in silico predictions of peptide immunogenicity.

In silico analysis of Shiga toxins (Stxs) to identify new potential vaccine targets for Shiga toxin-producing Escherichia coli

Shiga toxins belong to a family of structurally and functionally related toxins serving as the main virulence factors for pathogenicity of the Shiga toxin-producing Escherichia coli (STEC) associating with Hemolytic uremic syndrome (HUS). At present, there is no effective treatment or prevention for HUS. The aim of the present study was to find conserved regions within the amino acid sequences of Stx1, Stx2 (Shiga toxin) and their variants. In this regard, In-silico identification of conformational continuous B cell and T-cell epitopes was performed in order to introduce new potential vaccine candidates. 93–100% Homology was observed in Stx1 and its variants. In Stx2 and its variants, 69–100% homology was shown. By sequence alignment with Stx1 and Stx2, 54% homology was detected. T-cell epitope identification in Stx1A and Stx2A epitopes with highest binding affinity for each HLA (human leukocyte antigen) was demonstrated with 100% identity among all Stxs. B-cell epitope prediction was resulted in finding of four common epitopes between Stxs. In silico analysis of Stxs was resulted to identification of new peptide targets that could be used in development of new epitope vaccine candidates or in immunodiagnostic tests.

HLaffy: estimating peptide affinities for Class-1 HLA molecules by learning position-specific pair potentials.

T-cell epitopes serve as molecular keys to initiate adaptive immune responses. Identification of T-cell epitopes is also a key step in rational vaccine design. Most available methods are driven by informatics and are critically dependent on experimentally obtained training data. Analysis of a training set from Immune Epitope Database (IEDB) for several alleles indicates that the sampling of the peptide space is extremely sparse covering a tiny fraction of the possible nonamer space, and also heavily skewed, thus restricting the range of epitope prediction. We present a new epitope prediction method that has four distinct computational modules: (i) structural modelling, estimating statistical pair-potentials and constraint derivation, (ii) implicit modelling and interaction profiling, (iii) feature representation and binding affinity prediction and (iv) use of graphical models to extract peptide sequence signatures to predict epitopes for HLA class I alleles. HLaffy is a novel and efficient epitope prediction method that predicts epitopes for any Class-1 HLA allele, by estimating the binding strengths of peptide-HLA complexes which is achieved through learning pair-potentials important for peptide binding. It relies on the strength of the mechanistic understanding of peptide-HLA recognition and provides an estimate of the total ligand space for each allele. The performance of HLaffy is seen to be superior to the currently available methods. The method is made accessible through a webserver http://proline.biochem.iisc.ernet.in/HLaffy : nchandra@biochem.iisc.ernet.in Supplementary data are available at Bioinformatics online.

Analysis of Major Histocompatibility Complex (MHC) Immunopeptidomes Using Mass Spectrometry

Analysis of Major Histocompatibility Complex (MHC) Immunopeptidomes Using Mass Spectrometry

Poor correlation between T-cell activation assays and HLA-DR binding prediction algorithms in an immunogenic fragment of Pseudomonas exotoxin A

The ability to identify immunogenic determinants that activate T-cells is important for the development of new vaccines, allergy therapy and protein therapeutics. In silico MHC-II binding prediction algorithms are often used for T-cell epitope identification. To understand how well those programs predict immunogenicity, we computed HLA binding to peptides spanning the sequence of PE38, a fragment of an anti-cancer immunotoxin, and compared the predicted and experimentally identified T-cell epitopes. We found that the prediction for individual donors did not correlate well with the experimental data. Furthermore, prediction of T-cell epitopes in an HLA heterogenic population revealed that the two strongest epitopes were predicted at multiple cutoffs but the third epitope was predicted negative at all cutoffs and overall 4/9 epitopes were missed at several cutoffs. We conclude that MHC class-II binding predictions are not sufficient to predict the T-cell epitopes in PE38 and should be supplemented by experimental work.

Building MHC class II epitope predictor using machine learning approaches.

Identification of T-cell epitopes binding to MHC class II molecules is an important step in epitope-based vaccine development. This process has since been accelerated with the use of bioinformatics tools to aid in the prediction of peptide binding to MHC class II molecules and also to systematically scan for candidate peptides in antigenic proteins. There have been many prediction software developed over the years using various methods and algorithms and they are becoming increasingly sophisticated. Here, we illustrate the use of machine learning algorithms to train on MHC class II peptide data represented by feature vectors describing their amino acid physicochemical properties. The developed prediction model can then be used to predict new peptide data.

NetMHCstab – predicting stability of peptide–MHC‐I complexes; impacts for cytotoxic T lymphocyte epitope discovery

Major histocompatibility complex class I (MHC-I) molecules play an essential role in the cellular immune response, presenting peptides to cytotoxic T lymphocytes (CTLs) allowing the immune system to scrutinize ongoing intracellular production of proteins. In the early 1990s, immunogenicity and stability of the peptide-MHC-I (pMHC-I) complex were shown to be correlated. At that time, measuring stability was cumbersome and time consuming and only small data sets were analysed. Here, we investigate this fairly unexplored area on a large scale compared with earlier studies. A recent small-scale study demonstrated that pMHC-I complex stability was a better correlate of CTL immunogenicity than peptide-MHC-I affinity. We here extended this study and analysed a total of 5509 distinct peptide stability measurements covering 10 different HLA class I molecules. Artificial neural networks were used to construct stability predictors capable of predicting the half-life of the pMHC-I complex. These predictors were shown to predict T-cell epitopes and MHC ligands from SYFPEITHI and IEDB to form significantly more stable MHC-I complexes compared with affinity-matched non-epitopes. Combining the stability predictions with a state-of-the-art affinity predictions NetMHCcons significantly improved the performance for identification of T-cell epitopes and ligands. For the HLA alleles included in the study, we could identify distinct sub-motifs that differentiate between stable and unstable peptide binders and demonstrate that anchor positions in the N-terminal of the binding motif (primarily P2 and P3) play a critical role for the formation of stable pMHC-I complexes. A webserver implementing the method is available at www.cbs.dtu.dk/services/NetMHCstab.

A combination of epitope prediction and molecular docking allows for good identification of MHC class I restricted T-cell epitopes

In silico identification of T-cell epitopes is emerging as a new methodology for the study of epitope-based vaccines against viruses and cancer. In order to improve accuracy of prediction, we designed a novel approach, using epitope prediction methods in combination with molecular docking techniques, to identify MHC class I restricted T-cell epitopes. Analysis of the HIV-1 p24 protein and influenza virus matrix protein revealed that the present approach is effective, yielding prediction accuracy of over 80% with respect to experimental data. Subsequently, we applied such a method for prediction of T-cell epitopes in SARS coronavirus (SARS-CoV) S, N and M proteins. Based on available experimental data, the prediction accuracy is up to 90% for S protein. We suggest the use of epitope prediction methods in combination with 3D structural modelling of peptide-MHC-TCR complex to identify MHC class I restricted T-cell epitopes for use in epitope based vaccines like HIV and human cancers, which should provide a valuable step forward for the design of better vaccines and may provide in depth understanding about activation of T-cell epitopes by MHC binding peptides.

Prediction of HLA class I-restricted T-cell epitopes of islet autoantigen combined with binding and dissociation assays

Identification of cognate peptides recognized by human leucocyte antigen (HLA)/T cell receptor (TCR) complex provides insight into the pathogenic process of type 1 diabetes (T1D). We hypothesize that HLA-binding assays alone are inadequate metrics for the affinity of peptides. Zinc transporter-8 (ZnT8) has emerged in recent years as a novel, major, human autoantigen. Therefore, we aim to identify the HLA-A2-restricted ZnT8 epitopes using both binding and dissociation assays. HLA class I peptide affinity algorithms were used to predict candidate ZnT8 peptides that bind to HLA-A2. We analyzed 15 reported epitopes of seven β-cell candidate autoantigens and eight predicted candidate ZnT8 peptides using binding and dissociation assays. Using IFN-γ ELISpot assay, we tested peripheral blood mononuclear cells (PBMCs) from recent-onset T1D patients and healthy controls for reactivity to seven reported epitopes and eight candidate ZnT8 peptides directly ex vivo. We found five of seven recently reported epitopes in Chinese T1D patients. Of the eight predicted ZnT8 peptides, ZnT8153–161 had a strong binding affinity and the lowest dissociation rate to HLA-A*0201. We identified it as a novel HLA-A*0201-restricted T-cell epitope in three of eight T1D patients. We conclude that ZnT8153–161 is a novel HLA-A*0201-restricted T-cell epitope. We did not observe a significant correlation (P = 0.3, R = − 0.5) between cytotoxic T cell (CTL) response and peptide/HLA*0201 complex stability. However, selection of peptides based on affinity and their dissociation rate may be helpful for the identification of candidate CTL epitopes. Thus, we can minimize the number of experiments for the identification of T-cell epitopes from interesting antigens.

Prediction of MHC class I binding peptides with a new feature encoding technique

The recognition of specific peptides, bound to major histocompatibility complex (MHC) class I molecules, is of particular importance to the robust identification of T-cell epitopes and thus the successful design of protein-based vaccines. Here, we present a new feature amino acid encoding technique termed OEDICHO to predict MHC class I/peptide complexes. In the proposed method, we have combined orthonormal encoding (OE) and the binary representation of selected 10 best physicochemical properties of amino acids derived from Amino Acid Index Database (AAindex). We also have compared our method to current feature encoding techniques. The tests have been carried out on comparatively large Human Leukocyte Antigen (HLA)-A and HLA-B allele peptide binding datasets. Empirical results show that our amino acid encoding scheme leads to better classification performance on a standalone classifier.

Identification of T-cell Epitopes by a Novel mRNA PCR-based Epitope Chase Technique

Identification of T-cell Epitopes by a Novel mRNA PCR-based Epitope Chase Technique