Dataset Information

POTN: A Human Leukocyte Antigen-A2 Immunogenic Peptides Screening Model and Its Applications in Tumor Antigens Prediction.

ABSTRACT: Whole genome/exome sequencing data for tumors are now abundant, and many tumor antigens, especially mutant antigens (neoantigens), have been identified for cancer immunotherapy. However, only a small fraction of the peptides from these antigens induce cytotoxic T cell responses. Therefore, efficient methods to identify these antigenic peptides are crucial. The current models of major histocompatibility complex (MHC) binding and antigenic prediction are still inaccurate. In this study, 360 9-mer peptides with verified immunological activity were selected to construct a prediction of tumor neoantigen (POTN) model, an immunogenic prediction model specifically for the human leukocyte antigen-A2 allele. Based on the physicochemical properties of amino acids, such as the residue propensity, hydrophobicity, and organic solvent/water, we found that the predictive capability of POTN is superior to that of the prediction programs SYPEITHI, IEDB, and NetMHCpan 4.0. We used POTN to screen peptides for the cancer-testis antigen located on the X chromosome, and we identified several peptides that may trigger immunogenicity. We synthesized and measured the binding affinity and immunogenicity of these peptides and found that the accuracy of POTN is higher than that of NetMHCpan 4.0. Identifying the properties related to the T cell response or immunogenicity paves the way to understanding the MHC/peptide/T cell receptor complex. In conclusion, POTN is an efficient prediction model for screening high-affinity immunogenic peptides from tumor antigens, and thus provides useful information for developing cancer immunotherapy.

SUBMITTER: Meng Q

PROVIDER: S-EPMC7579403 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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POTN: A Human Leukocyte Antigen-A2 Immunogenic Peptides Screening Model and Its Applications in Tumor Antigens Prediction.

Meng Qingqing Q Wu Yahong Y Sui Xinghua X Meng Jingjie J Wang Tingting T Lin Yan Y Wang Zhiwei Z Zhou Xiuman X Qi Yuanming Y Du Jiangfeng J Gao Yanfeng Y

Frontiers in immunology 20201007

Whole genome/exome sequencing data for tumors are now abundant, and many tumor antigens, especially mutant antigens (neoantigens), have been identified for cancer immunotherapy. However, only a small fraction of the peptides from these antigens induce cytotoxic T cell responses. Therefore, efficient methods to identify these antigenic peptides are crucial. The current models of major histocompatibility complex (MHC) binding and antigenic prediction are still inaccurate. In this study, 360 9-mer ...[more]

PMID: 33133063

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Project description:Persistent infection with high-risk human papilloma virus (HPV) is the primary cause of cervical intraepithelial neoplasia (CIN) and cervical carcinoma. HPV58 is the third most common HPV genotype in China after HPV16 and HPV18. HPV E6 and E7 are oncoproteins and are constitutively expressed in HPV-associated cancer cells, therefore they are considered to be ideal target antigens for immunotherapy, including HPV therapeutic vaccine. In the present study, human leukocyte antigen (HLA)-A2-restricted cytotoxic T lymphocyte (CTL) epitope peptides were predicted and screened from HPV58 E7 antigen and their immunogenicity was subsequently determined. A total of 6 HLA-A2-binding peptides derived from HPV58 E7 were predicted and selected using 3 different prediction programs. A negative control peptide and PBS were used as two negative controls. Peripheral blood mononuclear cells (PBMCs) with HLA-A2(+) allele were used to detect the specific cellular immune response among the 6 predicted peptides by enzyme-linked immunospot assay (ELISOPT). Following preliminary screening for the predicted peptides, the antigenicity of the peptide HPV58 E772-80 was further assessed by an immunoassay to a vaccine contained HPV58 E7 antigen. Specific humoral and cellular immunity were detected using the peptide HPV58 E772-80 as the specific antigen. A total of 6 peptides from HPV58 E7 protein were predicted and subsequently named P1 (E77-15: TLREYILDL), P2 (E714-22: DLHPEPTDL), P3 (E769-77: CINSTTTDV), and P4 (E772-80: STTTDVRTL), P5 (E779-87: TLQQLLMGT) and P6 (E783-91: LLMGTCTIV). In the ELISPOT assay on HLA-A2 (+) human PBMCs, interferon (IFN)-?-production was evident in the P2 and P4 groups. The average numbers of IFN-? associated spots in the P2 and P4 groups was 50.61±5.37 spot-forming cells (SFC)/1×105 and 266±34.42 SFC/1×105, respectively. The numbers of spots in the two peptides were significantly increased compared with the other 4 peptides and the control groups (P<0.05). In the further antigenicity verification of P4 (HPV58 E772-80), the peptide only stimulated the humoral immune response of the AD-HPV16/18/58 mE6E7 vaccine containing HPV58 E7 antigen. Compared with the 2 negative control groups (1:400), the antibody titers of the vaccine group (1:25,600) were significantly increased (P<0.05). In cellular immunoassays the average number of IFN-? associated spots was 143.3±32.13 SFC/1×105 in the vaccine group, which was significantly enhanced compared with the PBS group (8±5.29 SFC/1×105; P<0.01) and the AD-NC group (28±5.13 SFC/1×105; P<0.01). The peptide HPV58 E772-80 (STTTDVRTL) displayed sufficient antigenicity to a vaccine contained HPV58 E7 antigen. Therefore, HPV58 E772-80 peptide may be considered as a candidate epitope peptide for the construction of HPV58 peptide vaccines.

Project description:As the major histocompatibility complex (MHC), human leukocyte antigens (HLAs) are one of the most polymorphic genes in humans. Patients carrying certain HLA alleles may develop adverse drug reactions (ADRs) after taking specific drugs. Peptides play an important role in HLA related ADRs as they are the necessary co-binders of HLAs with drugs. Many experimental data have been generated for understanding HLA-peptide binding. However, efficiently utilizing the data for understanding and accurately predicting HLA-peptide binding is challenging. Therefore, we developed a network analysis based method to understand and predict HLA-peptide binding.Qualitative Class I HLA-peptide binding data were harvested and prepared from four major databases. An HLA-peptide binding network was constructed from this dataset and modules were identified by the fast greedy modularity optimization algorithm. To examine the significance of signals in the yielded models, the modularity was compared with the modularity values generated from 1,000 random networks. The peptides and HLAs in the modules were characterized by similarity analysis. The neighbor-edges based and unbiased leverage algorithm (Nebula) was developed for predicting HLA-peptide binding. Leave-one-out (LOO) validations and two-fold cross-validations were conducted to evaluate the performance of Nebula using the constructed HLA-peptide binding network.Nine modules were identified from analyzing the HLA-peptide binding network with a highest modularity compared to all the random networks. Peptide length and functional side chains of amino acids at certain positions of the peptides were different among the modules. HLA sequences were module dependent to some extent. Nebula archived an overall prediction accuracy of 0.816 in the LOO validations and average accuracy of 0.795 in the two-fold cross-validations and outperformed the method reported in the literature.Network analysis is a useful approach for analyzing large and sparse datasets such as the HLA-peptide binding dataset. The modules identified from the network analysis clustered peptides and HLAs with similar sequences and properties of amino acids. Nebula performed well in the predictions of HLA-peptide binding. We demonstrated that network analysis coupled with Nebula is an efficient approach to understand and predict HLA-peptide binding interactions and thus, could further our understanding of ADRs.

Dataset Information

POTN: A Human Leukocyte Antigen-A2 Immunogenic Peptides Screening Model and Its Applications in Tumor Antigens Prediction.

Publications

POTN: A Human Leukocyte Antigen-A2 Immunogenic Peptides Screening Model and Its Applications in Tumor Antigens Prediction.

Similar Datasets

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