Cancer Immunoinformatics: A Promising Era in the Development of Peptide Vaccines for Human Papillomavirus-induced Cervical Cancer

Abstract

Cancer immunoinformatics have led to new directions towards vaccine design from predicted potential epitope candidates, which are able to stimulate the correct cellular or humoral immune responses. They were employed to accomplish an advanced vaccine design through reverse vaccinology by replacing the whole organisms. In this review, computational tools play an essential role in evaluating multiple proteomes to identify and select the potential targeted epitopes or combinations of distinct epitopes, so that candidates may afford a rationale design competent for obtaining suitable cytotoxic T lymphocytes (CTL) or B cell-mediated immune responses. This review is a complete collection of the most beneficial online and user-friendly immunological tools, servers, and databases for the design and development of the peptide vaccine. The mechanism of major histocompatability (MHC)-restricted peptide presentation and how these tools support the vaccine development are also presented. The human papillomavirus (HPV) is taken as a model microbial strain for peptide vaccine design and its sensitization against HPV-induced cervical cancer is discussed.

In silico/In vivo analysis of high-risk papillomavirus L1 and L2 conserved sequences for development of cross-subtype prophylactic vaccine

“…In each protein, peptides with the highest binding affinity scores were determined as high-potential CTL epitope candidates (Tables 4 and 5). The analysis showed that L1 [12][13][14][15][16][17][18][19][20][21] (YLPPVPVSKV-type 16 and YLPPPSVARV-type 18), L1 460-470 (DQFPLGRKFLL-type 16), L1 461-471 (DQYPLGRKFLV-type 18), L2 [11][12][13][14][15][16][17][18][19][20] (KRASATQLYK-type 16 and KRASVTDLYK-type 18), L2 280-291 (DPDFLDIVALHR-type 16) and L2 273-284 (DSDFMDIIRLHR-type 18) epitopes had the highest binding affinity among their own protein sequences. In general, the results of three different algorithms confirmed each other.…”

“…Since a suitable T-cell epitope should be predicted to bind to different HLA alleles, epitopes with the maximum number of binding HLA-DR alleles were selected as high-potential helper T-cell epitope candidates. Among predicted epitopes, L1 [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22] (EATVYLPPVPVSKVV-type 16), L1 95-111 (TQRLVWACVGVEVGRGQ-type 16 and TQRLVWACAGVEIGRGQ-type 18), L1 416-430 (DTYRFVTSQAIACQK-type 16), L1 417-431 (DTYRFVQSVAITCQK-type 18), L2 [100][101][102][103][104][105][106][107][108][109][110][111][112][113][114][115][116][117][118] (DPSIVTLIEDSSVVTSGAP-type 16), L2 281-297 (PDFLDIVALHRPALTSR-type 16) and L2 [274][275][276][277][278][279][280][281][282][283][284][285][286][287][288][289][290] (SDFMDIIRLHRPALTSR-type 18) had the highest scores of binding affinity. Also, the sequence of all the epitopes were well ...…”

“…For CTL epitopes, the highest population coverage of world's population was calculated for L1 [12][13][14][15][16][17][18][19][20][21] (84.71%), L1 411-421 (90.87%), L2 [11][12][13][14][15][16][17][18][19][20] (73.89%) and L2 280-291 (67.72%). For helper T-cell epitopes, the highest population coverage was calculated 86.18% for L1 [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22] , 91.18% for L1 327-342 , 98.90% for L1 416-430 , 83.47% for L2 100-118 and 97.68% for L2 281-297 . Overall, these results indicated that high-potential helper T-cell epitopes and CTL epitopes can specifically bind to the prevalent HLA molecules in the target populations where the vaccine will be employed.…”

“…To overcome the intrinsically low immunogenicity of the recombinant L2 protein, its potency could be increased by various formulations such as the multivalent L2 epitopes (peptide vaccine) 10-12 , fusion with L1 and other immunogenic proteins 13-15 and multiepitope DNA-based vaccines 16,17 .As a major field of science, bioinformatics has brought together the concepts of in silico analyses of biological queries, mathematics and statistics 18 . Immunoinformatics tools could help researchers to screen multiple HPV genome and predict high immunogenic epitopes, which provide a T or B cell response against HPV infection [19][20][21] . In this study, the combination of in silico/in vivo approaches was used to evaluate L1 and L2 proteins of high-risk HPV types (16, 18, 31, 33, 35, 39, 45, 51, 52, 56, 58, 59, and 68), and to design a pan genotype L1 and L2 constructs for development of DNA-based vaccines.…”

“…At first step, the conserved region sequences were analyzed by BepiPred-2 server to predict potential B-cell epitopes ( Table 3). In L1 protein, L1 [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22] (EATVYLPPVPVSKVV-type16), L1 408-421 (PPPGGTLEDTYRFV-type16) and L1 404-417 (NFGVPPPPTTSLVD-type 18) epitopes had the best B cell epitope identification scores. For L2 protein, L2 [22][23][24][25][26][27][28][29][30][31][32][33][34][35] (KQSGTCPPDVVPKV-type18), L2 100-113 (PSDPSIVSLVEETS-type16), L2 94-107 (EPVGPTDPSIVTLI-type18) and L2 [57][58][59][60][61][62][63][64][65][66][67][68][69][70] (GLGIGTGSGTGGRT-type16) epitopes showed the highest epitope identification score between their own protein sequences.…”