Selection of peptides that bind to the HLA-A2.1 molecule by molecular modelling

Lim, Jong‐Seok; Kim, Seungmoak; Lee, Hee Gu; Lee, Ki-Young; Kwon, Tae-Jong; Kim, Kilhyoun

doi:10.1016/0161-5890(95)00065-8

Cited by 37 publications

(22 citation statements)

References 30 publications

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“…Peptide KLPQLCTEL18- 26 from E6 has a single amino acid variation in the fourth position; the variation glutamine (HPV 16) instead of aspartic acid (HPV 18) has already been reported [8]. Similarly, alanine (HPV 16) instead of valine (HPV 18) was observed in L1-RLVWACAGV158-166…”

Section: Discussionmentioning

confidence: 97%

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Genome-Wide Prediction of Human Papilloma Virus Specific T-Cell Epitopes Using a Combination of Matrix Based Computational Tools

Mohan

Sundar

2017

Int J Pharm Pharm Sci

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Objective: To predict the immunogenic epitopes from human papillomavirus (HPV) virus using matrix based computational tools. Methods:In the present study, three matrix based algorithms, SYFPETHI, BIMAS and RANKPEP were used to predict the cytotoxic T lymphocyte (CTL) epitopes of HPV 16 and 18. The ability of the peptides to bind HLA A_0201, a most common allele, was evaluated using these algorithms. High scoring peptides were considered as potential binders.Results: Evaluation of HPV 16 proteome resulted in the prediction of 249 peptides as potential binders. Out of these only 25 peptides were predicted as binders by all three algorithms. Analysis of HPV 18 predicted 215 peptides, as potential binders. Among the 215 peptides only 20 peptides were predicted as binders by all three algorithms. Conclusion:The efficacy of these peptides in inducing a stronger immune response needs to be tested using in vitro and in vivo assays. The identified epitopes could be used in designing a novel epitope vaccine for HPV.

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Section: Discussionmentioning

confidence: 97%

“…Many computational algorithms have been developed for predicting the binding of peptides to MHC molecules [20,21] including quantitative matrices [22,23], arti ficial neural netw orks [24], hidden-markov models [25] and molecular modelling [26,27]. These approaches could be used for prediction of antigenic epitopes.…”

Section: Introductionmentioning

confidence: 99%

Genome-Wide Prediction of Human Papilloma Virus Specific T-Cell Epitopes Using a Combination of Matrix Based Computational Tools

Mohan

Sundar

2017

Int J Pharm Pharm Sci

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“…To date, many computational methods have been developed to facilitate the identification of MHCbinding peptides [16][17][18]. Moreover at present, we can select peptide that bind to HLA-A*0201 molecule by molecular modeling with accumulation of knowledge on the crystal structures of class I molecules and the sequences of peptides of endogenous origin bound by the class I molecules [19]. A comparative evaluation of prediction serves have been reported [20][21][22].…”

Section: Discussionmentioning

confidence: 99%

“…Ki-67 280-288 (LQGETQLLV) had the strongest affinity for HLA-A*0201 among the 15 candidate peptides (FI 0.41). PCNA [14][15][16][17][18][19][20][21][22] (KVLEALKDL) and PCNA 110-118 (KVSDYEMKL) were capable of up-regulating HLA-A*0201 molecule expression to some extent. The remaining peptides are low affinity binders to HLA-A*0201 molecular.…”

Section: Discussionmentioning

confidence: 99%

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

Liu

et al. 2010

Tumor Biol.

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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.

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“…Synthesized peptides were tested for their HLA-A2-binding ability using the T2-binding assay. 23 In brief, 1 million T2 cells per condition were washed extensively with serum-free IMDM (Invitrogen) and then incubated in serum-free IMDM with or without individual predicted peptides or the influenza M1 peptide at 40M. Cells were harvested at 0 and 24 hours, stained with FITC-labeled mouse anti-HLA-A*0201 (clone BB7.2; BD Pharmingen), and fixed in 2% formalin overnight.…”

Section: T2-binding Assaymentioning

confidence: 99%

Novel myeloma-associated antigens revealed in the context of syngeneic hematopoietic stem cell transplantation

Biernacki

Tai

Zhang³

et al. 2012

Blood

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Selection of peptides that bind to the HLA-A2.1 molecule by molecular modelling

Cited by 37 publications

References 30 publications

Genome-Wide Prediction of Human Papilloma Virus Specific T-Cell Epitopes Using a Combination of Matrix Based Computational Tools

Genome-Wide Prediction of Human Papilloma Virus Specific T-Cell Epitopes Using a Combination of Matrix Based Computational Tools

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

Novel myeloma-associated antigens revealed in the context of syngeneic hematopoietic stem cell transplantation

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