Screening of Human Tumor Antigens for CD4+ T Cell Epitopes by Combination of HLA-Transgenic Mice, Recombinant Adenovirus and Antigen Peptide Libraries

BACKGROUND: As tumor antigen-specific CD4+ T cells can mediate strong therapeutic anti-tumor responses in melanoma patients we set out to establish a comprehensive screening strategy for the identification of tumor-specific CD4+ T cell epitopes suitable for detection, isolation and expansion of tumor-reactive T cells from patients. METHODS AND FINDINGS: To scan the human melanoma differentiation antigens TRP-1 and TRP-2 for HLA-DRB1*0301-restricted CD4+ T cell epitopes we applied the following methodology: Splenocytes of HLA-DRB1*0301-transgenic mice immunized with recombinant adenovirus encoding TRP-1 (Ad5.TRP-1) or TRP-2 (Ad5.TRP-2) were tested for their T cell reactivity against combinatorial TRP-1- and TRP-2-specific peptide libraries. CD4+ T cell epitopes thus identified were validated in the human system by stimulation of peripheral blood mononuclear cells (PBMC) from healthy donors and melanoma patients. Using this strategy we observed that recombinant Ad5 induced strong CD4+ T cell responses against the heterologous tumor antigens. In Ad5.TRP-2-immunized mice CD4+ T cell reactivity was detected against the known HLA-DRB1*0301-restricted TRP-2(60-74) epitope and against the new epitope TRP-2(149-163). Importantly, human T cells specifically recognizing target cells loaded with the TRP-2(149-163)-containing library peptide or infected with Ad5.TRP-2 were obtained from healthy individuals, and short term in vitro stimulation of PBMC revealed the presence of epitope-reactive CD4+ T cells in melanoma patients. Similarly, immunization of mice with Ad5.TRP-1 induced CD4+ T cell responses against TRP-1-derived peptides that turned out to be recognized also by human T cells, resulting in the identification of TRP-1(284-298) as a new HLA-DRB1*0301-restricted CD4+ T cell epitope. CONCLUSIONS: Our screening approach identified new HLA-DRB1*0301-restricted CD4+ T cell epitopes derived from melanoma antigens. This strategy is generally applicable to target antigens of other tumor entities and to different HLA class II molecules even without prior characterization of their peptide binding motives

Peptide binding predictions for HLA DR, DP and DQ molecules

<p>Abstract</p> <p>Background</p> <p>MHC class II binding predictions are widely used to identify epitope candidates in infectious agents, allergens, cancer and autoantigens. The vast majority of prediction algorithms for human MHC class II to date have targeted HLA molecules encoded in the DR locus. This reflects a significant gap in knowledge as HLA DP and DQ molecules are presumably equally important, and have only been studied less because they are more difficult to handle experimentally.</p> <p>Results</p> <p>In this study, we aimed to narrow this gap by providing a large scale dataset of over 17,000 HLA-peptide binding affinities for a set of 11 HLA DP and DQ alleles. We also expanded our dataset for HLA DR alleles resulting in a total of 40,000 MHC class II binding affinities covering 26 allelic variants. Utilizing this dataset, we generated prediction tools utilizing several machine learning algorithms and evaluated their performance.</p> <p>Conclusion</p> <p>We found that 1) prediction methodologies developed for HLA DR molecules perform equally well for DP or DQ molecules. 2) Prediction performances were significantly increased compared to previous reports due to the larger amounts of training data available. 3) The presence of homologous peptides between training and testing datasets should be avoided to give real-world estimates of prediction performance metrics, but the relative ranking of different predictors is largely unaffected by the presence of homologous peptides, and predictors intended for end-user applications should include all training data for maximum performance. 4) The recently developed NN-align prediction method significantly outperformed all other algorithms, including a naïve consensus based on all prediction methods. A new consensus method dropping the comparably weak ARB prediction method could outperform the NN-align method, but further research into how to best combine MHC class II binding predictions is required.</p

The rational development of molecularly imprinted polymer-based sensors for protein detection.

The detection of specific proteins as biomarkers of disease, health status, environmental monitoring, food quality, control of fermenters and civil defence purposes means that biosensors for these targets will become increasingly more important. Among the technologies used for building specific recognition properties, molecularly imprinted polymers (MIPs) are attracting much attention. In this critical review we describe many methods used for imprinting recognition for protein targets in polymers and their incorporation with a number of transducer platforms with the aim of identifying the most promising approaches for the preparation of MIP-based protein sensors (277 references)

High Throughput T Epitope Mapping and Vaccine Development

Mapping of antigenic peptide sequences from proteins of relevant pathogens recognized by T helper (Th) and by cytolytic T lymphocytes (CTL) is crucial for vaccine development. In fact, mapping of T-cell epitopes provides useful information for the design of peptide-based vaccines and of peptide libraries to monitor specific cellular immunity in protected individuals, patients and vaccinees. Nevertheless, epitope mapping is a challenging task. In fact, large panels of overlapping peptides need to be tested with lymphocytes to identify the sequences that induce a T-cell response. Since numerous peptide panels from antigenic proteins are to be screened, lymphocytes available from human subjects are a limiting factor. To overcome this limitation, high throughput (HTP) approaches based on miniaturization and automation of T-cell assays are needed. Here we consider the most recent applications of the HTP approach to T epitope mapping. The alternative or complementary use of in silico prediction and experimental epitope definition is discussed in the context of the recent literature. The currently used methods are described with special reference to the possibility of applying the HTP concept to make epitope mapping an easier procedure in terms of time, workload, reagents, cells and overall cost

Epitope mapping using combinatorial phage-display libraries: a graph-based algorithm

A phage-display library of random peptides is a combinatorial experimental technique that can be harnessed for studying antibody–antigen interactions. In this technique, a phage peptide library is scanned against an antibody molecule to obtain a set of peptides that are bound by the antibody with high affinity. This set of peptides is regarded as mimicking the genuine epitope of the antibody's interacting antigen and can be used to define it. Here we present PepSurf, an algorithm for mapping a set of affinity-selected peptides onto the solved structure of the antigen. The problem of epitope mapping is converted into the task of aligning a set of query peptides to a graph representing the surface of the antigen. The best match of each peptide is found by aligning it against virtually all possible paths in the graph. Following a clustering step, which combines the most significant matches, a predicted epitope is inferred. We show that PepSurf accurately predicts the epitope in four cases for which the epitope is known from a solved antibody–antigen co-crystal complex. We further examine the capabilities of PepSurf for predicting other types of protein–protein interfaces. The performance of PepSurf is compared to other available epitope mapping programs