Development of HPV16,18,31,45 E5 and E7 peptides-based vaccines predicted by immunoinformatics tools

Abstract

Objectives Viral oncoproteins are ideal targets in therapeutic vaccines for functional inhibition of human papillomaviruses (HPVs). Herein, we designed the peptide constructs derived from E5 and E7 oncoproteins of high-risk HPV types 16, 18, 31 and 45 using the bioinformatics tools and investigated their potency in mice. Results The framework of the combined in silico/ in vivo analysis included (1) to determine physicochemical properties of the designed constructs, (2) to identify potential IFN-c-inducing epitopes, (3) to assess allergenicity, (4) to recognize linear and discontinuous B cell epitopes using modeling and validation of 3D structure of the designed constructs, and (5) to evaluate immune responses and tumor growth in vivo. Our in silico data determined high potency of the HPV 16,18,31,45 E5 and HPV 16,18,31,45 E7 peptides for trigger B-and T-cell responses, and IFN-c secretion. In vivo study indicated that the mixture of E5 and E7 immunodominant peptides from four types of high-risk HPV could induce Th1 immune response, and protect completely mice against TC-1 tumor cells. Conclusion Generally, the combined in silico/ in vivo approaches showed the ability of the designed E5 and E7 peptide constructs from four major highrisk HPV types for development of therapeutic vaccines.

Design of a multi-epitope vaccine against cervical cancer using immunoinformatics approaches

“…and Mahmoudvand et al 44 used L1 protein to predict the epitope. In another study, Namvar et al selected the E5 and E7 proteins from HPV16/18/31/45 as target proteins for epitope prediction 45 . The E6 and E7 proteins are key targets in the development of therapeutic vaccines against cervical cancer 46 for a variety of reasons.…”

Design of novel multiepitope constructs-based peptide vaccine against the structural S, N and M proteins of human COVID-19 using immunoinformatics analysis

“…Since now we know enough information about SARS-CoV-2's genomics and proteomics, we can design peptide vaccines based on a neutralizing epitope. These immunoinformatics methods have made a significant impact on the immunology researches and we can see many examples of in silico design of epitope-based vaccines against many viruses including human immunodeficiency virus (HIV) [16], human papillomavirus (HPV) [38,39], SARS-CoV [40], rhinovirus [41]. SARS-CoV-2 is an RNA virus tending to mutate more frequently [42].…”

“…For CTL epitopes, N 103-113 , S 868-876 , M [36][37][38][39][40][41][42][43][44][45][46] , S 1094-1102 , M 102-111 , S 1051-1061 , S 360-368 , S 191-199 and S 686-696 had the highest average of interaction similarity score, respectively and For HTL epitopes, M 107-128 , N [48][49][50][51][52][53][54][55][56][57][58][59][60][61][62][63] , S 689-704 , S 1057-1074 , S 196-231 , N 328-349 , S , N 126-143 , M 163-181 and S 114-130 had the highest average of interaction similarity score, respectively. Overall, CTL epitopes showed better quality of docking in comparison with HTL epitopes.…”

In silico design and in vitro expression of novel multiepitope DNA constructs based on HIV-1 proteins and Hsp70 T-cell epitopes

“…For 3D modeling of the Gag-Pol-Env-Nef-Rev and Hsp70-Gag-Pol-Env-Nef-Rev constructs, I-TASSER server was used. The C-score for evaluation of 3D structure accuracy normally is in the range of -5 to 2 and the greater value of C-score shows better quality of prediction (Namvar et al 2020). Our data indicated that the accuracy of Hsp70-Gag-Pol-Env-Nef-Rev was greater than Gag-Pol-Env-Nef-Rev, and the quality of the predicted 3D structures was improved after refinement.…”