Designing of CD8(+) and CD8(+)-overlapped CD4(+) epitope vaccine by targeting late and early proteins of human papillomavirus

Kaliamurthi, Satyavani; Selvaraj, Gurudeeban; Kaushik, Aman Chandra; Gu, Ke-Ren; Wei, Dong-Qing

PatchDock tool was employed for shape representation, surface patch matching, and filtering and scoring. 55 Based on the ranking, the elite peptide models were selected for docking with targets. Further, the prepared HLA molecules were used for docking with the predicted epitopes. Then, the results were refined by FireDock server according to the energy function. In the final refinement, a full interface side chain was optimized with an atomic radius of 0.85 to facilitate decreasing the number of conflicts. 56 The binding interactions of the docked structures were evaluated using UCSF Chimera 1.11.2, a highly extensible program for interactive visualization (data not shown). 57 Prediction and identification of CD8 + epitopes overlapping cD4 + and B-cell epitopes Prediction of interferon-gamma (iFn-γ)-producing cD4 + T-cell epitopes IEDB consensus method was used to predict CD4 + T-cell epitopes by combining the following: neural network alignment (Net MHCII 2.2), stabilization matrix method alignment, and CombLib or Sturniolo method. IEDB is a comprehensive dataset consisting of over 10,000 unpublished MHC peptide binding affinities, 29 crystal structures, and 664 experimentally proven CD4 + peptides. The major population in the tool is HLA-DRB locus, which consists of 51 alleles. HLA-DRB1, HLA-DRB3, HLA-DRB4, and HLA-DRB5 subtypes were selected for the analysis. As a result, the lowest percentile rank peptides exhibited higher binding affinity. 58 The antigen was presented by the APCs to the CD4 + receptor of Th cells, and then Th cells producing IFN-γ were activated. Moreover, the IFN-γ-producing CD4 + MHC class II-restricted epitopes were analyzed using IFN epitope tool. The prediction program was used with the motif-based model or support vector machine or hybrid algorithms. It showed an accuracy of 81.39% for the prediction of IFN-γ-producing epitopes. 59 The dataset consists of 3,705 IFN-γ-inducing and 6,728 non-IFN-γ-inducing MHC class II binders.