|Year : 2022 | Volume
| Issue : 4 | Page : 327-336
In silico design of a multi-epitope Chimera from Aedes aegypti salivary proteins OBP 22 and OBP 10: A promising candidate vaccine
Department of Microbiology, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Chennai, India
|Date of Submission||18-Mar-2022|
|Date of Acceptance||24-Jul-2022|
|Date of Web Publication||07-Feb-2023|
Dr Sathish Sankar
Department of Microbiology, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Poonamallee High Road, Chennai-600 077, Tamil Nadu
Source of Support: None, Conflict of Interest: None
Background & objectives: The emergence and re-emergence of arboviruses such as dengue, Chikungunya and Zika viruses causing morbidity and mortality around the globe are of serious concern. A safe and effective vaccine is essential to control viral transmission. The salivary proteins of the mosquito that aid in blood probing, feeding and development are immunogenic. We aimed to report a multi-epitope candidate vaccine chimera from Aedes aegyptii mosquito salivary proteins OBP 22 and OBP 10 that could confer protection against all pathogens transmitted by the vector.
Methods: Linear and conformation B-cell epitopes and MHC class-I and class-II binding T- cell epitopes were predicted using bioinformatic tools. Selected B- and T-cell epitopes were chosen for designing a multiepitope vaccine construct. The chimeric construct was analyzed for its immunogenicity, TAP and proteasomal cleavage, allergenicity, and structural validation for its suitability to be used as a candidate vaccine. Molecular docking was carried out to analyze the binding interactions with TLRs molecules.
Results: A chimeric multiepitope vaccine was designed with the best-selected combination of immunogenic B-cell epitope, cytotoxic and helper T-cell and gamma interferon inducing epitopes with suitable adjuvant and linkers. The interacting residues between the candidate vaccine and the TLR molecules have been identified.
Interpretation & conclusion: The proposed multiepitope candidate vaccine was designed from the mosquito salivary protein OBP 22 and OBP 10. The candidate vaccine was found promising for the protection against arboviruses. Further clinical validation is warranted to prove its efficacy, safety and immunogenicity for its potential use.
|How to cite this article:|
Sankar S. In silico design of a multi-epitope Chimera from Aedes aegypti salivary proteins OBP 22 and OBP 10: A promising candidate vaccine. J Vector Borne Dis 2022;59:327-36
|How to cite this URL:|
Sankar S. In silico design of a multi-epitope Chimera from Aedes aegypti salivary proteins OBP 22 and OBP 10: A promising candidate vaccine. J Vector Borne Dis [serial online] 2022 [cited 2023 Mar 28];59:327-36. Available from: http://www.jvbd.org//text.asp?2022/59/4/327/353271
| Introduction|| |
Arboviruses including Flaviviridae, Togaviridae and Bunyaviridae families are spread by arthropod vectors such as mosquitoes, ticks and sand flies. Diseases caused by these viruses especially dengue, chikungunya, yellow fever, and Zika contribute to global morbidity and mortality. The main reasons include changes in the climate and demography, migration and distribution of insect vectors, and increased air travel. There are several limitations in the serological diagnosis which either suffer from specificity or availability, inadequacy in regular arbovirus surveillance in many countries and failed vector control measures.
According to the Centre for Disease Control and Prevention (CDC), dengue fever is common in more than 100 countries around the world and about 3 billion people (nearly 40% of the world’s population) lives in areas with a risk of dengue. Infection with the Zika virus during pregnancy can cause birth defects including miscarriage and microcephaly. Chikungunya disease mostly presented with acute onset of fever and severe polyarthralgia occurs as major outbreaks globally. Mosquito-borne diseases constitute a major public health burden in many parts of the world. However, no approved vaccine is available so far for the protection against these diseases.
Dengue, Chikungunya and Zika viruses are transmitted by Aedes mosquitoes. Upon feeding, the mosquitoes release their salivary proteins along with the viral pathogens. The mosquito salivary gland has anti-hemostatic and immunomodulatory functions and helps in vasodilation, anticoagulation and plasma activation during its blood meal. Aedes aegypti saliva contains more than 100 unique proteins including different odorant-binding proteins (OBPs) and many protease enzymes and each plays a different function. The contribution of both humoral and cell-mediated immunity (cytotoxic and helper T cells) is important for the effective immune response against viral infections.
OBPs are a relatively large class of proteins involved in the hormonal regulation of its blood-feeding behavior and are involved in the first step of molecular recognition. Increased expression of the OBP 22 and 10 within the mosquito salivary gland have been reported during dengue fever. These salivary proteins have been reported to induce favourable B- and T-cell immune responses against many pathogenic infections. The study also indicates the probable immunomodulatory and immunosuppressive effects of certain salivary gland extracts. In our study, specific OBPs were chosen that have been documented to induce an immune response. This further blocks the pathogen transmission as it acts very early at the site of the mosquito bite on the skin. It is hypothesized that the salivary components will act as suitable vaccine targets and offer protection to the host against viral infections spread by the vector. Such vaccines could provide panviral protection against diseases transmitted by vectors like Ae. aegypti,.
There is now a steady understanding of disease pathogenesis with the availability of vast genomic information and tools for analysing genomic data. Peptide-based vaccines offer several advantages over whole-cell or subunit vaccines in eliciting immune response only to relevant epitopes. The reverse vaccinology approach provides a safe and economical methodology through computational prediction tools for the identification of highly immunogenic and relevant epitopes for the elicitation of B- and T-cell immune responses.
In this study, we designed to develop a multiepitope chimeric vaccine based on Ae. aegypti mosquito salivary odorant-binding proteins 22 and 10. The study, however, warrants experimental evaluation for validation purposes.
| Material & Methods|| |
Linear B-cell epitope
The amino acid sequence of Ae. aegypti OBP 22 (UniProtKB ID: Q1HRL7) and OBP 10 (UniProtKB ID: Q8WPC2) was obtained from the UniProtKB database (www.uniprot.org). Linear B-cell epitopes were predicted using Bepipred Linear Epitope Prediction 2.0 (www.cbs.dtu.dk/services/BepiPred/) program with the amino acid sequences as input and with a set threshold value of 0.5. Percentage of exposed residues was calculated for the epitopes based on the predicted relative surface accessibility.
3D protein models of OBP 22 and OBP 10
The three-dimensional (3D) protein models of OBP 22 and OBP 10 were designed using I-TASSER program using the amino acid sequences. Ramachandran plot was derived to visualize the distribution of dihedral torsion angles (φ against ψ) of amino acid residues in the protein models. Other validations of the protein structures were carried out using ERRAT and VERIFY3D programs in SAVES v6.0 online server program. The ERRAT program analyzes the non-bonded interactions between different atom types and plots the value ofthe error function versus position of a 9-residue sliding window. VERIFY 3D program analyses the compatibility of the 3D model with its 1D model by assigning a structural class based on its amino acid properties.
Conformational B-cell epitope
Conformational B-cell epitopes were predicted for OBP 22 and OBP 10 using the three-dimensional protein model (.pdb) as the input. Ellipro program was used for the prediction with a set threshold value of 0.5 and a maximum distance of 6Å. The predicted first model with the lowest root mean square deviation (RMSD) value and highest C-score was chosen and used for the prediction of discontinuous B-cell epitope. The resulted epitopes were visualized in Pymol.
CD4 T-cell epitope
Amino acid sequences of OBP 22 and OBP 10 were used to predict class II MHC binding epitopes using NetMHCpan program (www.tools.immuneepitope.org/mhcii/). IEDB recommended 2.22 prediction method with selected 7-allele reference set epitope length of 12 to 18 were set. The epitopes were selected based on the percentile rank and adjusted rank as suggested by the program.
CD8 T-cell epitope
MHC-I binding prediction was carried out using the IEDB analysis resource program (www.tools.immuneepitope.org/mhci/) using the IEDB recommended 2020.09 (NetMHCpan EL 4.1) prediction method. HLA allele reference set consisting of a reference panel of 27 alleles was used as MHC source species for binding predictions. The peptides were sorted and selected based on the score and percentile rank as suggested by the program.
TAP binding and proteasomal cleavage
Class I MHC binding epitopes generated were tested for their ability to be transported by Transporter-associated protein. The binding affinity of peptides was predicted using TAPred tool (www.webs.iiitd.edu.in/raghava/tappred/info.html), an SVM based predictor that uses the sequence and properties of the amino acids. High and intermediate affinity binders were selected for further analysis. Peptides ofsize 9 and 10 with cleavage sites only at C-terminus were predicted using Proteasome cleavage prediction server (www.imed.med.ucm.es/Tools/pcps/) with a set threshold limit of 0.5. The peptides that had no internal cleavage sites within the predicted epitopes were selected.
Interferon-gamma inducing epitope
Interferon-gamma-inducing epitopes/regions from OBP 22 and OBP 10 protein sequences were identified using the scan option in the IFNepitope server program (www.crdd.osdd.net/raghava/ifnepitope/scan.php). Window length of 15 and motif-SVM hybrid approach were used as set parameters. The program was used to identify IFN-gamma-inducing peptides. The peptides with positive prediction and high scores were selected for further analysis.
Multiepitope vaccine construct
A multiepitope chimeric vaccine construct was designed by fusing selected B-cell and MHC class-I and II and IFN-inducing T-cell epitopes with linkers. The selected B-cell epitope s were linked together with GPGPG linkers and T-cell epitopes with AAY linkers. To improve the immunogenicity of this multiepitope vaccine, an adjuvant 50S ribosomal protein L7/L12 Mycobacterium tuberculosis complex was fused to the N-terminal with the aid of an EAAAK linker.
Different physiochemical parameters of the chimeric candidate vaccine including amino acid composition, molecular weight, theoretical pI, atomic composition, extinction coefficients, estimated half-life, instability index, aliphatic index and grand average ofhydropathicity were assessed usingthe ProtParamtool (www.web.expasy.org/protparam/). The secondary structure of the protein sequence was analyzed using PSIpred workbench (www.bioinf.cs.ucl.ac.uk/psipred/) and the sequence analysis including the molecular weight, charge clusters and charge segments were analyzed using SAPS program (www.www.ebi.ac.uk/Tools/seqstats/saps/). The allergenicity of the protein was analyzed using AllerTop v.2 program (www.www.ddg-pharmfac.net/AllerTOP/). The program uses an auto-cross-covariance (ACC) mining method that converts protein sequences to uniform equal-length vectors. The 3-D structure of the protein was modelled using I-TASSER program. The model with the lowest RMSD and highest C-score was selected.
Molecular docking with TLRs
The multi-epitope chimeric vaccine construct model was docked independently against Toll-like receptors (TLR-1, −2, −3, −5 and −8) in Cluspro Docking online server (www.cluspro.bu.edu/home.php). The 3-dimensional protein structures (.pdb) of TLRs as receptor protein and the 3-dimensional protein structure of the vaccine construct were submitted with default parameters using balanced coefficient option. The docked protein-peptide complex with active interactions and the highest number of cluster sizes was selected for each TLR protein. Key interactive residues were identified and labelled using the Pymol software program.
Ethical statement: Not applicable
| Results|| |
The amino acid sequence of OBP 22 and OBP 10 retrieved from the UniProtKB database were 138 and 140 residues in length respectively. Linear and discontinuous B-cell epitopes and Class-I and Class-II MHC binding epitopes were identified from the sequences.
Linear B-cell epitope
A total of four linear B-cell epitopes from OBP 22 and three linear B-cell epitopes from OBP 10 were identified by the program and qualified based on the length. The predicted epitopes are listed in [Table 1]. The 3D protein models of OBP 22 and OBP 10 were designed using I-TASSER software program. The OBP 22 protein model had a C-score of −0.74, an estimated TM-score of 0.62±0.14 and an estimated RMSD = 6.1±3.8Å. The overall quality factor in the ERRAT was 90.77 and the structure validation passed VERIFY score as at least 80% of the amino acids had values ≥ 0.2 in the 3D/1D profile. The Ramachandran plot showed 75% of residues in the most favoured region, 22% in additional allowed regions, and 1.5% in disallowed regions. The OBP 10 protein model had a C-score of -1.26 and estimated TM-score and RMSD score of 0.56±0.15 and 7.3±4.2Å respectively. The overall quality factor in the ERRAT was 91.67 and the structure validation passed VERIFY score as at least 80% of the amino acids had values ≥ 0.2 in the 3D/1D profile. The Ramachandran plot showed 78% of residues in the most favoured region, 16% in additional allowed regions, and less than 2% in disallowed regions.
Conformational B-cell epitope
Conformational B-cell epitopes were predicted using Ellipro server program for OBP 22 and OBP 10. Four epitopes for OBP 22 were identified with lengths ranging from 6 to 23 amino acid residues and 6 epitopes were identified for OBP 10. The resulted peptides for OBP 10 are listed in [Table 2].
CD4 T-cell epitope
Class-II MHC binding T-cell epitope prediction for OBP 22 resulted in more than 20000 peptide epitopes in the size range of 12 to 18 amino acid residues. Of which 37 had an adjusted rank of less than one. Among these, epitope FKCFQKNNLSLIKA with the least adjusted rank of 0.37 and an affinity towards more than 200 HLA alleles. Class-II MHC binding T-cell epitope prediction for OBP 10 resulted in more than 23000 peptide epitopes size ranging from 12 to 18 amino acid residues. Among these, 64 epitopes had an adjusted rank of less than one. Epitope KCYVQCFFSKLRLM had the least adjusted rank of 0.38 with an affinity towards more than 200 HLA alleles.
Epitopes that could induce gamma interferon were predicted using IFNepitope server program. A total of115 epitopes were generated, of which 36 resulted positive. Two epitopes (HWAFRGFKC and LIKASIKKD) with high prediction score were selected.
CD8 T-cell epitope
MHC-I binding prediction was carried out with the HLA allele reference set for OBP 22. A total of 6184 peptides were identified. The TAP prediction resulted in a total of 115 peptides, of which 8 that had high prediction score and 35 that had the intermediate score were selected; 72 others that had low or undetectable score were excluded. Proteasomal cleavage prediction resulted in a total of 53 peptides each for 9- and -10 mer length, of which 24 and 23 peptides were predicted to be survived from proteasomal internal cleavage respectively and were selected. The prediction of IFN-inducing epitopes resulted in 115 peptides, of which 36 turned positive, the peptide HWAFRGFKC that had a high score (0.81) was selected.
A total of 7102 peptides were identified for OBP10 on MHC-I binding prediction with the HLA allele reference set. The TAP prediction resulted in a total of 55 peptides, (high = 8 and intermediate = 47) and were selected; 77 others that had low or undetectable score were excluded. Proteasomal cleavage prediction resulted in a total of 70 and 69 peptides for 9- and -10 mer length, of which 24 and 21 peptides were predicted to be survived from proteasomal internal cleavage respectively and were selected.
Among the MHC class-II binding epitopes, the peptides that had adjusted rank of less than 0.5 were selected. MHC class-I peptides that had at least a score and percentile rank of 0.5 IC50 nM in binding prediction, efficiently predicted to be transported by TAP protein and that survives proteasomal cleavage were selected and are listed in [Table 3].
|Table 3: List of B- and T- cell epitopes identified from OBP 22 and OBP 10|
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Multiepitope chimeric candidate vaccine
A multi-epitope vaccine chimera construct was designed consisting of six linear B-cell epitopes (four from OBP 22 and two from OBP 10), four class I MHC binding epitopes (two from each protein), two class II MHC binding epitopes (one from each protein) and two IFN gamma inducing epitopes (one from each protein). The total length of the construct was 337 residues. The designed chimeric candidate vaccine is shown in [Figure 1]. The chimeric vaccine construct was modeled using I-TASSER program and is shown in [Figure 2]. The predicted model had a C-score, TM-Score and RMSD score of -4.5, 0.24 and 18.2 ±2.3Å respectively.
|Figure 1: Multi-epitope vaccine chimera construct.|
Black: Adjuvant; Red: Linkers (EAAAK, AAY, GPGPG); Green: B-cell epitopes (OBP 22); Purple: B-cell epitopes (OBP 10); Pink: MHC class I (OBP 22 and OBP 10); Brown: MHC class II (OBP 22 and OBP10); Yellow: IFN inducing (OBP 22 and OBP10)
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|Figure 2: A representative image of docked complex indicating interactions between the TLR-3 and vaccine construct.|
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The molecular weight of the vaccine construct was 35kDa and the theoretical pI was 5.30. In the amino acid residue composition, alanine residues were higher with 14.2% followed by glycine (11.6%) and lysine (10.4%). The total number of positive (Asp + Glu) and negatively charged residues (Arg + Lys) were almost equal with 48 and 42 residues respectively. The extinction coefficient was 27305 M-1 cm-1, at 280 nm measured in water and the estimated half-life was 30 hours for mammalian reticulocytes. The instability index was 16.98 indicating that the protein is stable. The aliphatic index was 69.94 and the grand average of hydropathicity (GRAVY) score was −0.322. The secondary structure prediction indicated. number of coils (n= 279), helix (n= 31) and strands (n= 27). Positive/negative/mixed charge clusters or charge segments and high scoring hydrophobic segments were not seen. The construct was reported as probable nonallergen as predicted by AllerTop program.
Molecular docking with TLRs
Cluspro protein-protein docking was carried out to analyze the binding interactions with different TLR molecules. The server, generated four sets ofmodels using different scoring schemes, of which a balanced coefficient was used as recommended by the program. Each docking resulted in the top 10 models based on cluster size. The model that had the highest cluster size (members) irrespective of the weighted score was considered. Interactive residues between the receptor and the ligand, number and length of H-bonds were analyzed using Pymol Program. The interaction with immune cells (TLR3) was docked with 3D protein model of chimeric vaccine construct. The docking analysis showed interactions with multiple residues at the higher level of protein structure. Molecular docking analysis of the vaccine-construct with TLR1 resulted in 33 cluster members, 39 cluster members each with TLR2 and TLR5, 30 with TLR3, and 28 members with TLR8. Interacting residues from both the receptors and the ligand were identified through Pymol program. A representative image of interactions between TLR3 and the vaccine construct is shown in Fig. 3. The list of interactive residues and bonds for each TLR molecule is listed in [Table 4A], [Table 4B], [Table 4C], [Table 4D], [Table 4E].
|Table 4A: List of interactive residues between vaccine construct and TLR-1. Length of H bond in Å is indicated in brackets|
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|Table 4B: List of interactive residues between vaccine construct and TLR-2. Length of H bond in Å is indicated in brackets|
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|Table 4C: List of interactive residues between vaccine construct and TLR-3. Length of H bond in Å is indicated in brackets|
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|Table 4D: List of interactive residues between vaccine construct and TLR-5. Length of H bond in Å is indicated in brackets|
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|Table 4E: List of interactive residues between vaccine construct and TLR-8. Length of H bond in Å is indicated in brackets|
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| Discussion|| |
Mosquito salivary proteins are now an important focus of vaccine research for pan-viral protection against arboviruses. Mosquito salivary transcriptomics and comprehensive genomic information of each protein available in public databases provide key information for designing novel vaccines. OBPs of insects are small, globular, water-soluble proteins expressed in the olfactory sensilla support cells. OBPs such as OBP 22 and OBP 10 constitute an important secreted component of the salivary gland in Aedes mosquitoes. In the present study, a multiepitope chimeric vaccine was designed with suitable B- and T-cell epitopes predicted from OBP 22 and OBP 10 protein of Ae. aegypti using bioinformatics tools.
Ae. aegypti OBP 22 protein is 138 amino acid in length carrying an N-terminal 16-mers signal peptide. The protein has been reported to have multiple functions including host-seeking, feeding and reproduction. The protein is expressed not only in sensory organs, such as the antennae and proboscis but also in the male reproductive apparatus and transferred to the females.
A total of 34 Classic OBPs, 17 Plus-C OBPs (that possess six conserved cysteine residues and a conserved spacing between the cysteines) and 15 atypical OBPs have been identified in Ae. aegypti. The classic OBPs such as OBP 22 in Ae. aegypti have a homolog in Anopheles gambiae with amino acid sequence similarity ranging from 16% to 63%,. The vaccine construct is prepared from the predominant OBPs of Aedes mosquitoes that is highly immunogenic. The chimera includes carefully selected immunogenic B- and T-cell epitopes along with IFN-γ inducing epitopes, specific to the proteins. The construct could also be modified by adding immunogenic epitopes from other mosquito salivary proteins or other vectors such as Anopheles stephensi for protection against other infectious diseases like malaria.
The genomic analysis of OBP gene families in different arthropod species indicates the high diversity and variability of the proteins among species, but are conserved within the same species and structurally distinct,. The immunogenic peptides identified from the OBPs are highly conserved and immune responses mounted against these peptides might not cross-react with other proteins.
The OBPs are abundantly produced in the biological glands and secretory fluids of the mosquitoes. OBPs induce immune response and knockdown of OBPs in mosquitoes result in abnormality in behavioral response and olfactory function. OBP 10 and OBP 22 do not directly interact with flavivirus replication. However, interruption ofthese proteins has reduced the transmission of flaviviruses through disruption of blood-feeding and the virus secretion in its saliva. Immunogenic OBPs have been identified from the Aedes mosquito salivary gland extracts that reacted with patient serum infected with dengue fever. These cardinal findings provide a perspective on the use of these proteins as a vaccine candidate that efficiently interrupts virus transmission.
OBP 22 is therefore considered a novel target to potentially disrupt the transmission of Ae. aegypti borne infections and also control of mosquito populations. The protein could also serve as a key antigen for specific immune response in the host during mosquito feeding as it is expressed in both sensory organs and salivary glands potentially disrupting viral infectivity. OBP 10 encodes a 140 amino acid protein including a predicted 25 amino acid signal peptide at the N-terminal. Genomic sequences of OBP 10 has been well characterized and compared with other insects OBPs. The homolog of these proteins in other insects such as Anopheles gambiae has been characterized for its potential use towards developing effective control measures against this vector.
Genome-wide microarray analysis of Ae. aegypti salivary gland transcriptomes showed regulation of 147 transcripts in response to dengue virus infection. Of which, OBP 22 and OBP 10 were enriched with high expression levels in the salivary gland both in the naïve gland as well as during dengue viral infection. Silencing of these two genes resulted in increased time for blood probing leading to delayed and inefficient bloodmeal. These protein have also been found to be involved in immunity-related pathways in salivary glands. This led to the hypothesis that these two salivary gland proteins would become suitable vaccine targets for eliciting B- and T-cell immune response. The antigen-primed B and T cells confer protection against viral inoculation by the mosquitoes.
Conventional vaccine approaches use whole-cell or subunit of the pathogen. The immune system responds to unwanted regions more than the required relatively short antigenic peptide. Synthetic peptide-based vaccines carry only specific epitope region for which immune response is warranted. This approach can evade unnecessary immune response and risk of mutation or reversion and carryover contamination of toxic agents. Antigenic epitopes that have more exposed residues, binding affinity to MHC molecules and ability to induce both humoral and cell-mediated immunity are designed through bioinformatic approaches.
Linear B-cell epitopes are predicted from the amino acid sequence of the protein based on propensity score and physicochemical properties of each residue such as beta-turn, surface accessibility, flexibility, polarity and antigenicity. The program used in our study uses Random Forest Algorithm along with the propensity scale. All the predicted epitopes had higher solvent-accessible areas (>80%). Therefore, all the four epitopes of OBP 22 and two epitopes of OBP 10 were selected for the multiepitope vaccine construct.
Conformational or discontinuous B-cell epitopes consist of patches of solvent-accessible residues located in different regions of the primary structure but are brought into proximity during the tertiary folding of the protein in vivo. While the majority of the B-cell epitopes (90%) are discontinuous, prediction of conformational epitopes were also carried out from the modelled 3D protein structures. Ellipro program was used to predict the conformational B-cell epitopes. The program uses Thornton’s method with a residue clustering algorithm.
I-TASSER program implements local multiple threading server, with full-length atomic models constructed by iterative template-based fragment assembly simulations. The protein structure prediction utilizes structural templates from the PDB structure library. The accuracy of the predicted models was estimated in terms of RMSD, C-score and TM-score. The confidence score (C-score) is a measure of threading template alignments and structure assembly simulations. The value ranges from −5 to 2, high values indicating high confidence. TM-score signifies a measure of structural similarity and a score of >0.5 indicates a model of correct topology,. The 3D protein model generated in our study had a C-score of −0.74 and TM-score of 0.62 indicating acceptable protein structure prediction.
The 3D protein model generated in our study was evaluated for structural validation using the Ramachandran plot. The statistical distribution of the combinations of the backbone dihedral angles φ and ψ in allowed and forbidden regions. The protein model had about 95% in the allowed region indicating it as a good quality homology for both the models. For poor-quality homology models, many dihedral angles are found in the forbidden regions of the Ramachandran plot. The overall quality factor in the ERRAT and VERIFY score indicated the acceptability of the protein structure.
Antigen processing involves intracellular antigens with MHC-1 proteins and extracellular antigens with MHC-II proteins. Development of effective T-cell inducing vaccines is warranted for acquired protective immune response. Our study used prediction programs that screen and identify the immunogenic peptide with affinity to MHC class I genes (HLA-A, -B, and -C) and MHC class II genes (HLA-DR, -DP, and -DQ).
T-helper cells are particularly essential for antibody-mediated immunity and for activating B-cells for antibody production. Epitope prediction for MHC II binding was carried out using IEDB analysis resource software program. The server uses a consensus approach integrating different prediction methods such as NN-align, SMM-align, CombLib and Sturniolo or NetMHCIIpan. The MHC class-II binding T-cell epitopes are predicted and ranked based on the percentile score or binding affinity. The program calculates an adjusted percentile rank is calculated based on the frequency of peptide lengths. A small numbered percentile rank indicates high affinity. is recommended by the program. In our study, of several thousands of peptides generated by the program, the one that had the least adjusted rank score were chosen for both proteins. A reference panel of 7 alleles has been selected for predicting peptides with the promiscuous binding of multiple HLA alleles.
Prediction of MHC class-I binding T-cell epitopes was carried out using IEDB analysis resource program using the recommended method of NetMHCPan EL 4.1. HLA allele reference set that is based on allele frequency database, HLA population coverage, shared binding affinity (promiscuous binding) was selected. A small numbered percentile rank indicated high affinity and therefore, peptides that had the least percentile rank were selected for both proteins. The epitopes were also scrutinized for their ability to be transported by the TAP protein and survival from the proteasomal cleavage using appropriate prediction tools. This further increase the probability of the vaccine to elicit an immune response against the target antigen.
A multiepitope vaccine comprising immunogenic epitopes from different regions is considered ideal for its effective use. Design of multiepitope vaccine through bioinformatics approaches has been reported for several diseases,,. Multiple epitopes increase the probability of antibody development and binding to the native conformation of the protein. Moreover, the synthetic recombinant vaccine construct would induce both B- and T-cell immune responses effectively for enhanced protection. The chimeric vaccine construct designed in our study was also tested for allergenicity and physiochemical parameters to be suitable for use as a vaccine.
Human TLRs have been classified into 12 types, each with different functions. These reside either in the cell surface or within the cell, triggering the release of cytokines for innate immune response. TLRs have now been identified as essential for eliciting vaccine-specific responses. We selected TLR1, TLR2, TLR3 and TLR5 that are localized to the cell surface and TLR8 that resides in the intracellular compartments. The binding of ligands to the TLR receptors was assessed by molecular docking experiments. The results indicated that TLR1 had the highest number of interactive residues followed by TLR3, TLR5 and TLR8 with the ligand.
A vaccine must induce both humoral and cell-mediated immunity for effective and long-term protection. Our study reports a multiepitope chimeric vaccine construct comprising immunogenic B- and T-cell epitopes against mosquito salivary protein OBP 22 and OBP 10. While vaccines for many arboviral infections are still unavailable, the hope for universal pan-vaccine for protection against all arboviruses is highly anticipated. The salivary protein-based vaccines might hold a key in near future. While animal experiments and initial human clinical trials have given encouraging data, more human clinical trials have to be accelerated towards arboviruses.
Conflict of interest: None
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[Figure 1], [Figure 2]
[Table 1], [Table 2], [Table 3], [Table 4A], [Table 4B], [Table 4C], [Table 4D], [Table 4E]