Research Article | Open Access
Shruthi Padavu1, Ballamoole Krishna Kumar1, Anoop Kumar2 and Praveen Rai1
1Nitte (Deemed to be University), Nitte University Centre for Science Education and Research (NUCSER), Division of Infectious Diseases, Deralakatte, Mangaluru, Karnataka, India.
2National Institute of Biologicals (NIB), Ministry of Health & Family Welfare, Government of India A-32, Sector-62, Noida, India.
Article Number: 8109 | © The Author(s). 2023
J Pure Appl Microbiol. 2023;17(1):554-566. https://doi.org/10.22207/JPAM.17.1.53
Received: 17 September 2022 | Accepted: 17 January 2023 | Published online: 03 March 2023
Issue online: March 2023
Abstract

Globally, cervical cancer is the fourth most common cancer among women. After being cloned from a recurring cervical lesion in 1987, Human papillomavirus (HPV) type-45 was identified as a high-risk HPV type. It is the third most common cancer-causing HPV subtype, after HPV-16 and HPV-18. Immunogenic epitopes and structural features provide the most useful information for vaccine development. Computational algorithms provide quick, simple, trustworthy, and cost-efficient methods for predicting immunogenic epitopes. In this study, both B and T cell epitopes have been identified as potential immunogens that can elicit a response from the host system. Three potential B-cell epitopes, i.e., SIAGQYRGQCNTCCDQ, LQEIVLHLEPQNELDP, and DSTVYLPPPSVARVVS, were identified in this study. A potential epitope for E6 (ATLERTEVY) was predicted to 8 MHC-I alleles (HLA-A*30:02, HLA-B*15:01, HLA-A*01:01, HLA-A*26:01, HLA-A*32:01, HLA-B*35:01, HLA-B*58:01, HLA-A*11:01) and for L1 epitope (NVFPIFLQM) was predicted for 4 MHC-I alleles (HLA-A*30:02, HLA-A*32:01, HLA-B*53:01, HLA-B*51:01). To conclude, the epitopes identified here might potentially be useful for developing a cervical cancer vaccine against HPV-45 strains, but in vitro and in vivo trials are needed to validate their safety and efficacy.

Keywords

Human Papillomavirus, Cervical Cancer, Immunogenic Epitopes, Vaccine, In silico

Introduction

Globally, cervical cancer ranks as the fourth most frequently occurring malignancy among women population.1 Over 500,000 women worldwide are diagnosed with cervical cancer each year, with low-income nations bearing the burden of mortality.2 Nearly all cervical malignancies contain oncogenic human papillomavirus (HPV) DNA. With the highest universally attributable percentage ever reported for a particular etiology of a major human malignancy, researchers concluded that HPV is an essential element in the development of cervical cancer.3 A working committee of the International Agency for Research on Cancer (IARC) Monographs categorized 14 types of HPV as “carcinogenic to humans” out of 200 different types. While the majority of HPV infections are asymptomatic and are eventually removed by our immune system, the virus can remain in some situations, thus leading to cancer.4

A persistent cervical lesion seen in a woman in the United States led to the discovery of the high-risk (HR) HPV type HPV-45 in 1987. HPV-45 is more frequent in adenocarcinoma of the cervix. After HPV-16 and HPV-18, HPV-45 has been ranked as the third most oncogenic type, which accounts for around 10% of cervical cancer cases.4 The cellular structure of this virus is made up of 8,000 bp of circular double-stranded DNA that contains early regions (E1, E2, E4, E5, E6, E7, and E8) encoding early viral proteins, late regions (L1 and L2) that codes for the capsid proteins, and a non-coding region known as the long control region (LCR), which plays a key role in replication and transcription.3,5

The oncoproteins of genes E6 and E7 are identified as the key causes of HPV-associated cervical cancer; elevated expression of E6 and E7 is necessary for the onset and maintenance of the malignant phenotype.6 p53 and pRb (retinoblastoma) tumour suppressors are inactivated when E6 and E7 genes are expressed, respectively.7 These oncoproteins are tumour-specific antigens, and hence there is no risk of autoimmunity. They are expressed in all the phases of cervical cancer, making them ideal targets for prophylactic vaccination.8 The icosahedral capsid structure is formed by the major capsid protein L1. Charged residues (K and R) are concentrated near the C-terminus, and there is often >60% L1 amino acid sequence homology between HPV variants that infect the genital epithelia. It indicates that the majority of the L1 protein is conserved among different types of HPV.9,10 The protein can self-assemble into an icosahedral capsid by forming 72 pentameric capsomers. Because of its icosahedral form, L1 protein is equally distributed on the surface of the capsid, making it highly immunogenic.11 This protein is capable of forming virus-like particles (VLPs) by self-assembling spontaneously. VLPs that have been assembled are thought to be potent immunogens that B-cells can recognise quickly.12

The comprehensive cervical cancer control strategy involves HPV vaccination as primary prevention, screening and treating precancerous lesions as secondary prevention. The Food and Drug Administration (FDA) has approved the use of three forms of prophylactic vaccines: Cervarix® (bivalent), Gardasil® (quadrivalent), and Gardasil®9 (nonavalent). These vaccines are efficient in protecting against HPV infection and neoplasms. However, they are prophylactic vaccines that offer no therapeutic benefit and have limited benefits in eradicating pre-existing infections. As a result, therapeutic vaccinations are gaining popularity due to their capacity to trigger cell-mediated immune responses and destroy infected cells rather than neutralising antibodies (nAbs).13 All of the aforementioned studies suggest that E6, E7, and L1 are key proteins that can be used as a potential vaccine candidate against HPV-45. Using several bioinformatics tools and programmes, we attempted to examine the E6, E7, and L1 proteins of HPV-45 as a potential vaccine candidate in this work.

Materials and Methods

Amino acid sequence
E6, E7 and L1 amino acid sequences of HPV-45 having GenBank accession numbers CAA52573.1, CAA52574.1 and CAA52578.1 (Genome ID: X74479), respectively, were retrieved from the NCBI databank.

Sequence analysis
Protparam (http://web.expasy.org/protparam/) is an online web tool used for the analysis of the different physical and chemical characteristics of E6, E7 and L1 protein sequences of HPV-45, including molecular mass, amino acid composition, and atomic composition.14

Prediction of secondary structure
PSIPRED is an internet server (http://bioinf.cs.ucl.ac.uk/psipred) that incorporates two feed-forward neural networks that analyse PSI-BLAST (Position-Specific Iterated-BLAST) output to predict concise and precise secondary structure of E6, E7 and L1 proteins of HPV-45.15

T-cell epitope prediction
Immune Epitope Database (IEDB) is a resource (http://www.iedb.org/) funded by the National Institute of Allergy and Infectious Diseases (NIAID), a division of the National Institutes of Health. This tool was used for the prediction of T-cell epitopes for E6, E7 and L1 protein sequences. The IEDB recommended 2020.09 (NetMHCpan El 4.1) prediction method was used to predict the epitopes for MHC-I alleles, while the IEDB recommended 2.22 prediction method was used for MHC-II alleles. The reference set of HLA alleles were selected for predicting both MHC-I and MHC-II binding in various human populations.16 The antigenicity of the predicted epitopes for MHC-I alleles were calculated using Vaxigen v2.0 (http://www.ddg-pharmfac.net/vaxijen/VaxiJen/VaxiJen.html).17 IFNepitope web server (http://crdd.osdd.net/raghava/ifnepitope/) was used to investigate the ability of epitopes for MHC-II alleles to stimulate interferon-gamma (IFN-g) production. The parameters for this study were set as IFN-g versus non-IFN-g model and Motif and SVM hybrid algorithms.18

B-cell epitope prediction
The antigenic epitope within the oncogenic proteins (E6 and E7) and major capsid (L1) protein molecule of HPV-45 is predicted using ABCpred server (https://webs.iiitd.edu.in/raghava/abcpred/ABC_submission.html), a standard bioinformatics technique. All the parameters were in their default settings, but the epitopes selected had a score of more than 0.7. The ABCpred web server uses an artificial neural network to predict B-cell epitopes. This server is the first to use fixed-length patterns with a recurrent neural network (machine-based approach). This server can anticipate continuous (linear) B-cell epitopes. A linear B-cell epitope is a short peptide that binds to a conformational epitope and cross-reacts with an antibody. This server has a 65.93% accuracy rate in predicting epitopes.19,20

RESULTS

Protein structure analysis
The E6, E7 and L1 proteins of HPV-45 contain 158, 106 and 539 amino acid residues and have a molecular weight of 18.89 kDa, 12.05 kDa and 60.31 kDa, respectively. ProtParam server was used to estimate the amino acid composition of all three proteins (Supplementary Table 1). The most common amino acids in E6 was arginine (R) (20 residues), followed by leucine (L) (15 residues). The secondary structure prediction revealed that 44.3% of the protein is coil (C), 41.8% is helix (H), and 13.9% is strand (E) (Figure 1). The most common amino acids in E7 protein was found to be leucine (L) (15 residues), followed by glutamic acid (E) (14 residues). The secondary structure prediction revealed that 65.1% of the protein is coil (C), 21.7% is helix (H), and 13.2% is strand (E) (Figure 2). The most common amino acids in L1 protein was proline (P), serine (S) and threonine (T) (42 residues), followed by valine (V) and leucine (L) (40 residues). The secondary structure prediction revealed that 63.5% of the protein is coil (C), 17.8% is helix (H), and 18.7% is strand (E). This was found to be the same in both the variant and reference sequences (Figure 3).

Figure 1. Secondary structure prediction of HPV-45 E6 protein through PSIPRED

Figure 2. Secondary structure prediction of HPV-45 E7 protein through PSIPRED

Figure 3. Secondary structure prediction of HPV-45 L1 protein through PSIPRED

Prediction of epitopes for MHC-I alleles
IEDB server was used to predict epitopes for MHC-I alleles. It is crucial to understand the MHC-I and -II alleles that are highly expressed for the development of an efficient immunological response. The HLA allele reference set from the database and most frequently occurring MHC-I alleles were chosen for MHC-I binding. The immunogenicity score (<0.4) and percentile value (<0.5) were used to evaluate the possible epitopes (Table 1) for binding MHC-I alleles. HLA-A*01:01, HLA-A*02:01, HLA-A*02:03, HLA-A*02:06, HLA-B*07:02, HLA-B*15:01, HLA-A*30:02, HLA-A*31:01, HLA-A*33:01, HLA-B*35:01, HLA-B*40:01, HLA-B*44:03, HLA-B*57:01, HLA-B*58:01, HLA-A*68:01 are the highly expressed MHC-I alleles.

Table (1):
Predicted MHC-I epitopes for HPV-45 E6, E7 and L1 proteins through IEDB.

No. Start End Length Peptide Allele Percentile Rank Immunogenicity Score Antigenicity
E6
1 37 45 9 ATLERTEVY HLA-A*30:02, HLA-B*15:01, HLA-A*01:01, HLA-A*26:01, HLA-A*32:01, HLA-B*35:01, HLA-B*58:01, HLA-A*11:01 0.06-0.46 0.29271 0.6093 ( Probable ANTIGEN )
2 90 98 9 LEKITNTEL HLA-B*40:01 0.08 0.18689 0.4303 ( Probable ANTIGEN )
3 99 107 9 YNLLIRCLR HLA-A*33:01 0.34 0.11158 0.5910 ( Probable ANTIGEN )
4 70 78 9 DFYSRIREL HLA-B*08:01 0.05 0.11019 0.5919 ( Probable ANTIGEN )
5 122 130 9 KDKRRFHSI HLA-B*08:01 0.43 0.07332 0.9519 ( Probable ANTIGEN )
6 44 52 9 VYQFAFKDL HLA-A*24:02, HLA-A*23:01 0.2-0.27 0.05946 0.9584 ( Probable ANTIGEN )
7 13 21 9 KLPDLCTEL HLA-A*02:01, HLA-A*02:03, HLA-A*02:06 0.1-0.14 0.04843 0.9489 ( Probable ANTIGEN )
8 48 56 9 AFKDLCIVY HLA-A*30:02 0.15 0.02721 1.8184 ( Probable ANTIGEN )
9 113 121 9 NPAEKRRHL HLA-B*08:01, HLA-B*07:02 0.03-0.04 0.01475 0.9487 ( Probable ANTIGEN )
E7
10 19 27 9 NELDPVDLL HLA-B*40:01, HLA-B*44:03, HLA-B*44:02 0.05-0.26 0.05902 1.7631 ( Probable ANTIGEN )
L1
11 428 436 9 ILENWNFGV HLA-A*30:02 0.25 0.3542 1.1761 ( Probable ANTIGEN )
12 473 481 9 KLKFWTVDL HLA-A*32:01 0.29 0.34784 0.5705 ( Probable ANTIGEN )
13 17 25 9 NVNVFPIFL HLA-A*30:02 0.17 0.32372 0.5018 ( Probable ANTIGEN )
14 426 434 9 SSILENWNF HLA-A*30:02, HLA-A*32:01 0.19-0.4 0.30661 0.7650 ( Probable ANTIGEN )
15 409 417 9 CTITLTAEV HLA-A*30:02 0.08 0.19952 0.6661 ( Probable ANTIGEN )
16 19 27 9 NVFPIFLQM HLA-A*30:02, HLA-A*32:01, HLA-B*53:01, HLA-B*51:01 0.04-0.48 0.1896 0.8028 ( Probable ANTIGEN )
17 157 165 9 ESAHAATAV HLA-A*30:02 0.03 0.1758 0.5028 ( Probable ANTIGEN )
18 359 367 9 FVTVVDTTR HLA-A*30:02-0.42 0.19-0.42 0.17066 0.9193 ( Probable ANTIGEN )
19 271 279 9 DSMFFCLRR HLA-A*30:02 0.06-0.35 0.14493 0.8574 ( Probable ANTIGEN )
20 70 78 9 TVGNPYFRV HLA-A*30:02, HLA-A*02:06 0.13-0.5 0.11925 0.8608 ( Probable ANTIGEN )
21 499 507 9 LVQAGLRRR HLA-A*30:02 0.29 0.09825 1.1916 ( Probable ANTIGEN )
22 69 77 9 LTVGNPYFR HLA-A*68:01, HLA-A*30:02, HLA-A*33:01 0.05-0.21 0.09604 1.2652 ( Probable ANTIGEN )
23 67 75 9 RLLTVGNPY HLA-A*30:02, HLA-A*32:01 0.11-0.24 0.09562 0.5385 ( Probable ANTIGEN )
24 10 18 9 GIIIFLKNV HLA-A*02:03, HLA-A*02:06 0.45-0.5 0.0949 0.6342 ( Probable ANTIGEN )
25 274 282 9 FFCLRREQL HLA-B*08:01 0.22 0.08728 1.8410 ( Probable ANTIGEN )
26 398 406 9 EEYDLQFIF HLA-B*44:03, HLA-A*30:02 0.01-0.14 0.07784 1.7384 ( Probable ANTIGEN )
27 396 404 9 HVEEYDLQF HLA-B*35:01, HLA-B*53:01, HLA-A*26:01, HLA-A*01:01 0.24-0.47 0.07349 1.4499 ( Probable ANTIGEN )
28 132 140 9 MEIGRGQPL HLA-A*30:02, HLA-B*44:03, HLA-B*44:02 0.07-0.36 0.05536 0.9861 ( Probable ANTIGEN )
29 441 449 9 TTSLVDTYR HLA-A*68:01, HLA-A*33:01, HLA-A*31:01 0.02-0.41 0.02682 0.4094 ( Probable ANTIGEN )
30 452 460 9 QSVAVTCQK HLA-A*30:02 0.26-0.32 0.01633 1.3682 ( Probable ANTIGEN )

VaxiJen is the first server that allows antigen classification and predict protective antigens exclusively based on protein physicochemical properties rather than sequence alignment. Based on the high binding affinity score, the epitopes obtained from the IEDB server for MHC-I alleles were submitted to Vaxigen v2.0 for the prediction of probable antigens. The non-antigenic epitopes were removed and probable antigens were retained based on their antigenicity score.

Prediction of epitopes for MHC-II alleles
The epitopes for MHC-II alleles were predicted using IEDB server. The complete HLA reference set was chosen from the database for MHC-II binding. The potential epitopes (Table 2) for binding MHC-II alleles were assessed based on the percentile rank (<2.5). MHC-II alleles HLA-DRB1*01:01, HLA-DRB5*01:01, HLA-DRB3*02:02, HLA-DRB1*07:01, HLA-DRB1*09:01, HLA-DRB1*13:02, HLA-DRB1*15:01, HLA-DPA1*02:01/DPB1*01:01,HLA-DPA1*01:03/DPB1*02:01,HLA-DPA1*03:01/DPB1*04:02, HLA-DQA1*01:01/DQB1*05:01, HLA-DPA1*01:03/DPB1*04:01, HLA-DQA1*01:02/DQB1*06:02, HLA-DPA1*02:01/DPB1*14:01, HLA-DQA1*05:01/DQB1*03:01, HLA-DPA1*02:01/DPB1*05:01 are majorly expressed. IFNepitope is an online prediction tool that seeks to predict and build peptides from protein sequences that can cause CD4+ T cells to release IFN-gamma. The MHC-II alleles retrieved from the IEDB server were further tested for IFN-g production, and those epitopes that were negative for IFN-g release were eliminated.

Table (2):
Predicted MHC-II epitopes for HPV-45 E6, E7 and L1 proteins through IEDB.

No Peptide Length Start End Allele Percentile Rank IFNg Score
E6
1 SRIRELRYYSNSVYG 15 73 87 HLA-DRB1*15:01 0.49 1.000
2 YSRIRELRYYSNSVY 15 72 86 HLA-DRB1*15:01 0.49 1.000
3 IRELRYYSNSVYGET 15 75 89 HLA-DRB1*15:01 0.54 1.000
4 RIRELRYYSNSVYGE 15 74 88 HLA-DRB1*15:01 0.54 1.000
5 RELRYYSNSVYGETL 15 76 90 HLA-DRB1*15:01 1.3 1.000
6 FHSIAGQYRGQCNTC 15 127 141 HLA-DRB5*01:01 1.6 0.151
7 SNSVYGETLEKITNT 15 82 96 HLA-DPA1*03:01/DPB1*04:02 1.7 0.474
8 YSNSVYGETLEKITN 15 81 95 HLA-DPA1*03:01/DPB1*04:02 1.7 0.146
9 YYSNSVYGETLEKIT 15 80 94 HLA-DPA1*03:01/DPB1*04:02 1.7 0.489
15 HLA-DPA1*02:01/DPB1*01:01 2.2 0.489
10 RTEVYQFAFKDLCIV 15 41 55 HLA-DQA1*01:01/DQB1*05:01 1.8 0.541
15 HLA-DPA1*01:03/DPB1*04:01 2.1 0.541
11 TEVYQFAFKDLCIVY 15 42 56 HLA-DQA1*01:01/DQB1*05:01 1.8 0.660
15 HLA-DPA1*01:03/DPB1*04:01 2.5 0.660
12 ELRYYSNSVYGETLE 15 77 91 HLA-DRB1*15:01 2.0 1.000
13 RYYSNSVYGETLEKI 15 79 93 HLA-DPA1*02:01/DPB1*01:01 2.1 0.476
14 ERTEVYQFAFKDLCI 15 40 54 HLA-DQA1*01:01/DQB1*05:01 2.5 0.205
E7
15 RTLQQLFLSTLSFVC 15 85 99 HLA-DPA1*01:03/DPB1*04:01 0.53 0.204
15 HLA-DPA1*02:01/DPB1*01:01 1.5 0.204
15 HLA-DPA1*01:03/DPB1*02:01 2.0 0.204
15 HLA-DPA1*03:01/DPB1*04:02 2.3 0.204
16 LRTLQQLFLSTLSFV 15 84 98 HLA-DPA1*01:03/DPB1*04:01 0.61 0.264
15 HLA-DPA1*02:01/DPB1*01:01 1.2 0.264
15 HLA-DPA1*01:03/DPB1*02:01 1.9 0.264
15 HLA-DPA1*03:01/DPB1*04:02 2.2 0.264
17 DLRTLQQLFLSTLSF 15 83 97 HLA-DPA1*01:03/DPB1*04:01 0.99 0.037
18 QQLFLSTLSFVCPWC 15 88 102 HLA-DPA1*01:03/DPB1*04:01 1.2 0.045
15 HLA-DPA1*01:03/DPB1*02:01 2.2 0.045
19 EDLRTLQQLFLSTLS 15 82 96 HLA-DPA1*01:03/DPB1*04:01 1.7 0.179
15 HLA-DPA1*02:01/DPB1*01:01 2.0 0.179
15 HLA-DPA1*03:01/DPB1*04:02 2.3 0.179
20 AEDLRTLQQLFLSTL 15 81 95 HLA-DPA1*01:03/DPB1*04:01 1.8 0.214
15 HLA-DPA1*02:01/DPB1*01:01 2.0 0.214
15 HLA-DPA1*03:01/DPB1*04:02 2.3 0.214
21 QLFLSTLSFVCPWCA 15 89 103 HLA-DPA1*01:03/DPB1*04:01 2.2 0.088
L1
21 AHNIIYGHGIIIFLK 15 2 16 HLA-DRB1*13:02 2 0.679
22 AYQYRVFRVALPDPN 15 94 108 HLA-DPA1*02:01/DPB1*14:01 1.1 0.748
23 DDTESAHAATAVITQ 15 154 168 HLA-DQA1*05:01/DQB1*03:01 1.5 0.66
24 DTESAHAATAVITQD 15 155 169 HLA-DQA1*05:01/DQB1*03:01 1.5 0.843
15 HLA-DQA1*01:02/DQB1*06:02 2.4 0.843
25 ESAHAATAVITQDVR 15 157 171 HLA-DQA1*01:02/DQB1*06:02 2.3 1.052
26 FLKNVNVFPIFLQMA 15 14 28 HLA-DPA1*01:03/DPB1*04:01 2.5 0.178
27 FLVQAGLRRRPTIGP 15 498 512 HLA-DRB5*01:01 1.1 0.151
28 GRKFLVQAGLRRRPT 15 495 509 HLA-DRB5*01:01 0.13 0.615
15 HLA-DPA1*02:01/DPB1*14:01 0.64 0.615
29 IFLKNVNVFPIFLQM 15 13 27 HLA-DRB3*02:02 1.3 0.137
15 HLA-DPA1*01:03/DPB1*04:01 2.3 0.137
15 HLA-DRB1*13:02 2.3 0.137
30 IFYHAGSSRLLTVGN 15 59 73 HLA-DRB1*09:01 0.09 0.181
15 HLA-DRB1*07:01 0.75 0.181
15 HLA-DRB1*01:01 1.2 0.181
15 HLA-DRB3*02:02 2.2 0.181
31 IIFLKNVNVFPIFLQ 15 12 26 HLA-DRB1*13:02 0.98 0.243
15 HLA-DRB3*02:02 0.99 0.243
15 HLA-DRB1*15:01 2.5 0.243
32 KFLVQAGLRRRPTIG 15 497 511 HLA-DRB5*01:01 0.88 0.387
33 KVSAYQYRVFRVALP 15 91 105 HLA-DPA1*01:03/DPB1*04:01 1.9 0.429
34 LDDTESAHAATAVIT 15 153 167 HLA-DQA1*05:01/DQB1*03:01 2.5 0.58
35 LGRKFLVQAGLRRRP 15 494 508 HLA-DRB5*01:01 0.13 0.386
15 HLA-DPA1*02:01/DPB1*14:01 0.56 0.386
36 MAHNIIYGHGIIIFL 15 1 15 HLA-DRB1*13:02 1.8 0.55
37 MFFCLRREQLFARHF 15 273 287 HLA-DPA1*02:01/DPB1*05:01 2.4 1
38 PKVSAYQYRVFRVAL 15 90 104 HLA-DPA1*01:03/DPB1*04:01 2.2 0.775
39 PLGRKFLVQAGLRRR 15 493 507 HLA-DRB5*01:01 0.13 0.371
15 HLA-DPA1*02:01/DPB1*14:01 0.66 0.371
40 QYRVFRVALPDPNKF 15 96 110 HLA-DPA1*02:01/DPB1*14:01 0.6 0.797
41 RHVEEYDLQFIFQLC 15 395 409 HLA-DQA1*01:01/DQB1*05:01 2 0.115
42 RKFLVQAGLRRRPTI 15 496 510 HLA-DRB5*01:01 0.13 0.547
15 HLA-DPA1*02:01/DPB1*14:01 1.2 0.547
43 RTSIFYHAGSSRLLT 15 56 70 HLA-DRB1*09:01 0.07 0.062
15 HLA-DRB1*07:01 0.28 0.062
15 HLA-DRB1*01:01 0.67 0.062
15 HLA-DRB3*02:02 1.1 0.062
15 HLA-DRB5*01:01 2.2 0.062
44 SAYQYRVFRVALPDP 15 93 107 HLA-DPA1*02:01/DPB1*14:01 1.7 0.441
45 SMFFCLRREQLFARH 15 272 286 HLA-DPA1*02:01/DPB1*05:01 2.5 1
46 TESAHAATAVITQDV 15 156 170 HLA-DQA1*05:01/DQB1*03:01 1.8 0.957
15 HLA-DQA1*01:02/DQB1*06:02 2.1 0.957
47 TSIFYHAGSSRLLTV 15 57 71 HLA-DRB1*09:01 0.07 0.063
15 HLA-DRB1*07:01 0.28 0.063
15 HLA-DRB1*01:01 0.51 0.063
15 HLA-DRB3*02:02 1.1 0.063
15 HLA-DRB5*01:01 2.1 0.063
48 YPLGRKFLVQAGLRR 15 492 506 HLA-DRB5*01:01 0.13 0.368
15 HLA-DPA1*02:01/DPB1*14:01 1.5 0.368
49 YQYRVFRVALPDPNK 15 95 109 HLA-DPA1*02:01/DPB1*14:01 0.66 1.049
50 YRVFRVALPDPNKFG 15 97 111 HLA-DPA1*02:01/DPB1*14:01 1.4 1.122

Potential B-cell epitope prediction
The B-cell epitopes for HPV-45 E6, E7, and L1 proteins were predicted using the default settings of the ABCpred server (Table 3). B-cell epitopes are essential for cancer immunotherapy. In total, 12 potent B-epitopes were predicted for HPV-45 E6 protein. The most prominent epitope was SIAGQYRGQCNTCCDQ, with a binding score of 0.87. For HPV-45 E7 protein sequences, 6 potent B-epitopes were predicted, with the most prominent epitope LQEIVLHLEPQNELDP, with a binding score of 0.92. Whereas, for HPV-45 L1 protein sequences, 53 potent B-epitopes were predicted. The most prominent epitope was DSTVYLPPPSVARVVS, with a binding score of 0.96.

Table (3):
Predicted potential B-cell epitopes in HPV-45 E6, E7 and L1 proteins by ABCPred.

Rank Sequence Start position Score
E6
1 SIAGQYRGQCNTCCDQ 129 0.87
2 YGETLEKITNTELYNL 86 0.85
2 SRIRELRYYSNSVYGE 73 0.85
3 HKCIDFYSRIRELRYY 66 0.83
4 MARFDDPKQRPYKLPD 1 0.82
5 CVYCKATLERTEVYQF 32 0.81
5 PDLCTELNTSLQDVSI 15 0.81
6 RRHLKDKRRFHSIAGQ 118 0.80
7 YNLLIRCLRCQKPLNP 99 0.78
8 QRPYKLPDLCTELNTS 9 0.77
9 YQFAFKDLCIVYRDCI 45 0.74
10 DCIAYAACHKCIDFYS 58 0.72
E7
1 LQEIVLHLEPQNELDP 8 0.92
2 ADGVSHAQLPARRAEP 42 0.89
3 DGRIELTVESSAEDLR 70 0.88
4 AEPQRHKILCVCCKCD 55 0.81
5 ILCVCCKCDGRIELTV 62 0.78
5 SESEEENDEADGVSHA 33 0.78
L1
1 DSTVYLPPPSVARVVS 34 0.96
2 VSAYQYRVFRVALPDP 92 0.92
2 AVTCQKDTTPPEKQDP 455 0.92
2 CQSICKYPDYLQMSAD 252 0.92
3 KFLVQAGLRRRPTIGP 497 0.91
4 RPTIGPRKRPAASTST 507 0.89
4 PPEKQDPYDKLKFWTV 464 0.89
4 DSTIYNPETQRLVWAC 114 0.89
5 LGCVPAIGEHWAKGTL 186 0.88
5 RVALPDPNKFGLPDST 101 0.88
6 RHVEEYDLQFIFQLCT 395 0.87
6 NGICWHNQLFVTVVDT 350 0.87
7 FWTVDLKEKFSSDLDQ 476 0.86
7 AHAATAVITQDVRDNV 159 0.86
7 LGIGLSGHPFYNKLDD 140 0.86
7 QRLVWACVGMEIGRGQ 123 0.86
8 SIITSDSQLFNKPYWL 327 0.85
8 GSCVYSPSPSGSIITS 316 0.85
8 ANMRETPGSCVYSPSP 309 0.85
8 RHFWNRAGVMGDTVPT 285 0.85
8 QMALWRPSDSTVYLPP 26 0.85
8 EHWAKGTLCKPAQLQP 194 0.85
9 TAEVMSYIHSMNSSIL 414 0.84
9 PVPSTYDPTKFKQYSR 380 0.84
10 GVMGDTVPTDLYIKGT 292 0.83
11 LQPGDCPPLELKNTII 207 0.82
11 QDVRDNVSVDYKQTQ 167 0.82
12 YFRVVPNGAGNKQAVP 75 0.81
12 YGHGIIIFLKNVNVFP 7 0.81
12 TSTASTASRPAKRVRI 520 0.81
12 TDLYIKGTSANMRETP 300 0.81
12 KNTIIEDGDMVDTGYG 218 0.81
13 TSLVDTYRFVQSVAVT 442 0.80
14 HSMNSSILENWNFGVP 422 0.79
14 DYLQMSADPYGDSMFF 260 0.79
14 GDMVDTGYGAMDFSTL 225 0.79
14 GMEIGRGQPLGIGLSG 131 0.79
15 LCTITLTAEVMSYIHS 408 0.78
15 TLCASTQNPVPSTYDP 372 0.78
15 SVDYKQTQLCILGCVP 175 0.78
15 PFYNKLDDTESAHAAT 148 0.78
16 SRTSIFYHAGSSRLLT 55 0.77
17 AGNKQAVPKVSAYQYR 83 0.76
17 SSDLDQYPLGRKFLVQ 486 0.76
18 TVVDTTRSTNLTLCAS 361 0.75
18 LHKAQGHNNGICWHNQ 342 0.75
18 STLQDTKCEVPLDICQ 238 0.75
19 VARVVSTDDYVSRTSI 44 0.73
19 YGDSMFFCLRREQLFA 269 0.73
20 RFVQSVAVTCQKDTTP 449 0.72
21 RREQLFARHFWNRAGV 278 0.71
21 YGAMDFSTLQDTKCEV 232 0.71
22 DPTKFKQYSRHVEEYD 386 0.74
DISCUSSION

HPV-related cancers account for approximately 4.5% of all cancers, affecting nearly 600,000 people globally every year. Both E6 and E7 proteins of HPV promote excessive cell proliferation. E6 binds to and degrades p53 and other host cell proteins, whereas E7 binds to and degrades Retinoblastoma (Rb) protein. Both p53 and Rb protein are cellular growth repressors. HPV virions use L1 and L2 proteins to attach to the basal cells after infection. Antibodies bind to the virus, preventing infection by stopping it from infecting epithelial cells.21 An immunoglobulin-coated capsid is formed by high antibody titers, thus preventing the viral particle from attaching to basal cells, which is the initial stage of infection. As a result, neutrophils remove the virus that has been coated with antibodies. The virus particles are partially prevented from adhering to the basal cells in the presence of low antibody titers. The main mechanism of action is triggered by the capsid not binding to the second L1 receptor on the surface of the epithelial cell. As a result, the virus is removed from the tissue.22 E6, E7, and L1 proteins appear to be promising vaccine candidates due to the presence of numerous known neutralising epitopes.23

It is crucial to understand the vaccine candidate’s structural characteristics, such as its secondary structure. Alpha helix and coil-containing proteins and peptides are significant structural antigens because antibodies can identify them.24 The amino acid composition has shown that leucine residues are the most frequently occurring amino acids in all three proteins, i.e., E6, E7, and L1 of HPV-45. The most intriguing fact is that leucine residues have been studied earlier due to their significance in histone deacetylases (HDACs) binding. Histones attached to the MHC-I promoter are physically coupled with HDACs, and act as transcriptional co-repressors. It is likely that these HDACs cause MHC-I down-regulation due to the suppression of chromatin activation.25

In the present study, three potential B-cell epitopes were identified i.e., SIAGQYRGQCNTCCDQ, LQEIVLHLEPQNELDP, DSTVYLPPPSVARVVS, each in E6, E7 and L1 protein of HPV-45, respectively. The KLPDLCTEL epitope was predicted as a potential epitope for MHC class I alleles (HLA-A*02:01, HLA-A*02:03, HLA-A*02:06), similar to the HPV-18 validated epitopes,26,27 the predicted epitope can provide the cross-protection against HPV-18. Another potential epitope for E6 (ATLERTEVY) was predicted to 8 different MHC-I alleles (HLA-A*01:01, HLA-A*11:01, HLA-B*15:01, HLA-A*26:01, HLA-A*30:02, HLA-A*32:01, HLA-B*35:01, HLA-B*58:01) and for L1 epitope (NVFPIFLQM) was predicted for 4 MHC-I alleles (HLA-A*30:02, HLA-A*32:01, HLA-B*51:01, HLA-B*53:01).

As experimental procedures are labor-intensive and time-consuming, numerous in silico techniques for distinguishing protein epitopes are being developed. Computational techniques, on the other hand, provide quick, simple, cost-effective, and reliable methods for the prediction of immunogenic epitopes. Scientists can use bioinformatics tools to extract epitopes from a protein of interest instead of potential binding sites in epitope-based vaccinations. Moreover, enhanced computational model dependability for the prediction of desired epitopes will undoubtedly aid in the pre-experimental stage of vaccine development. Due to several limitations, such as the occurrence of diverse genotypes and vaccine price and accessibility, HPV prevention has remained a major problem. The most significant drawback appears to be the present vaccine’s limited coverage.28

A reference set of HLA alleles has been derived for both MHC I and MHC II binding prediction tools, providing more than 95% global population coverage, a significant characteristic for drug development. These techniques are useful for identifying a class of high-affinity binding peptides that could be produced and tested in the lab. The analysis projected the coverage of B and T cell epitope-based vaccinations in the population, allowing vaccines to be designed to maximise coverage.29

CONCLUSION

In silico approaches were used in this study to develop a vaccine candidate against oncoproteins and the major capsid protein of HPV-45. These proteins are strong candidates for antigenicity and immunogenicity due to their roles in viral replication, oncogenicity, and virus assembly. In this study, the amino acid sequence of the selected proteins was analysed, and their secondary structure was predicted. MHC-I and MHC-II epitopes for all three proteins were predicted and chosen based on their ability to induce antigenicity and produce IFN-γ, respectively. Further, B-cell epitopes were also predicted for the protein sequences. The epitopes identified by various web servers can be further used to create an effective antigenic vaccine capable of eliciting a significant immunological reaction over HPV-45. The discovery of potential epitopes has aided in developing cancer immunotherapy and detecting a wide range of infectious illnesses. Based on its rational design, we predict that the above-mentioned epitopes might be good candidates for vaccines against HPV-45 strains that are responsible for causing cervical cancer. It is possible to conduct additional molecular docking studies, followed by vaccine construct design using the predicted epitopes. It will require experimental confirmation by in vivo and in vitro studies, but it can be validated as a universally derived antigen when computationally analysed.

SUPPLEMENTARY INFORMATION

Additional file: Additional Table S1.

Declarations

ACKNOWLEDGMENTS
The authors would like to thank Nitte (Deemed to be University) for providing a research facility.

CONFLICT OF INTEREST
The authors declare that there is no conflict of interest.


AUTHORS’ CONTRIBUTION
PR and AK conceptualized the study. SP and AK applied methodology; SP wrote the original draft. PR, AK and BKK wrote, reviewed and edited the manuscript. All authors read and approved the final manuscript for publication.

FUNDING
None.

DATA AVAILABILITY
All datasets generated or analyzed during this study are included in the manuscript and/or in the supplementary files.

ETHICS STATEMENT
This article does not contain any studies with human participants or animals performed by any of the authors.

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