Immunoinformatics-Driven Multiepitope Vaccine Targeting Streptococcus mutans for Dental Caries Prevention

doi:10.21203/rs.3.rs-3214083/v1

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Immunoinformatics-Driven Multiepitope Vaccine Targeting Streptococcus mutans for Dental Caries Prevention

https://doi.org/10.21203/rs.3.rs-3214083/v1

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Streptococcus mutans is a major causative agent of dental caries and associated infections, posing significant challenges in oral healthcare. To develop an effective vaccine against S. mutans, we employed an immunoinformatics approach to identify potential vaccine candidates and design a multiepitope vaccine. The genomes of S. mutans were analyzed, and five core-proteins (PBP2X, PBP2b, MurG, ATP-F, and AGPAT) were selected as vaccine candidates based on antigenicity and conservation. Linear B-cell and T-cell epitopes were predicted from these proteins, and four B-cell epitopes and ten T-cell epitopes were chosen for vaccine construction. The multiepitope vaccine comprised 10 CTL epitopes, 5 HTL epitopes, and 5 linear B-cell epitopes linked with appropriate linkers. A 50S ribosomal L7/L12 protein was used as an adjuvant at the N-terminal. The final vaccine construct exhibited high antigenicity, solubility, and stability. The tertiary structure was modeled using AlphaFold v.2 and refined with GalaxyRefine. The refined 3D model had improved quality scores and structural stability. Molecular docking simulations revealed strong interactions between the vaccine and Toll-Like Receptors (TLRs). In silico immune simulation demonstrated the vaccine's ability to trigger both humoral and cell-mediated immune responses. The vaccine design strategy presented here provides a promising platform for developing a potential multiepitope vaccine against S. mutans, offering a novel approach to combat dental caries and associated infections. Further, experimental evaluations should be performed to validate its efficacy and safety as a potential vaccine candidate for dental health.

Streptococcus mutans

dental caries

multiepitope vaccine

toll-like receptors

molecular docking simulation

immune responses

Streptococcus mutans has been identified as a key etiologic bacterium for dental caries, cerebral microbleeds, IgA nephropathy, atherosclerosis, and subacute infective endocarditis [1]. Dental caries has long been regarded as a childhood condition that persists into adulthood. The prevalence of dental caries ranges from 60–90% in children and 26–85% in adults [2]. Dental caries, one of the most predominant oral diseases, affects more than 4 billion people globally and imposes serious health and economic burdens [3, 4].

S. mutans has the ability to adhere to tooth surfaces and produce biofilm, which is one of the principal inhabitants of cariogenic plaque biofilm [5]. This bacterium used sucrose as a carbohydrate source to make acidic metabolites that caused the tooth enamel to erode and become demineralized, eliminating the mineral components, and resulting in dental caries [6]. Dental caries progression is associated with biofilm (dental plaque) formation by producing multiple surface-associated proteins including glycosyltransferases (Gtfs), glucan-binding proteins (Gbps), functional amyloids, and envelope-associated proteins, protein antigen c, and collagen-binding protein [7, 8]. The biofilm formation results in the bacteria tolerating oral cavity environmental changes including acidic pH changes, nutrient availability, and transition from aerobic to anaerobic conditions [6, 9]. The production of multiple glucan proteins improves resistance to mechanical host-clearance pressures and certain antibiotics [10].

At present, several antimicrobial agents such as chlorhexidine, fluoride, quaternary ammonium salts, and iodine have been directed to treat dental caries [11]. Additionally, antibiotics were also administrated to prevent the growth of numerous oral pathogens. However, prolonged administration of these agents could lead to various side effects including antimicrobial resistance, and cytotoxic effects on oral mucosal cells, keratinocytes, osteoblasts, osteoclasts, and blood cells [10] which is considered a serious health issue at present-day (Rezoqe et al., 2012). Therefore, it is imperative to explore alternative antibacterial therapy against dental caries.

Over the past decade, several experiments have been conducted to develop vaccines for preventing and treating dental caries with immunotherapy [13, 14]. Caries vaccination has emerged as a highly promising method for preventing dental caries, presenting the advantage of offering long-term defense against this dental condition [15]. However, the development of conventional anti-caries vaccines has been hindered by several challenges, including the need to identify antigens that are both immunogenic and specific to S. mutans, as well as the difficulty of inducing a strong and long-lasting immune response against the bacteria in the oral environment (Srivastava et al., 2023). Recently, with the advancement of modern reverse vaccinology and immunoinformatic, the introduction of multiepitope vaccines has received a lot of attention, since these vaccines have higher immunogenicity, lower allergenicity, reduced probability of dental caries, cost-effectiveness, time-saving and low risk of side effects [17]. Additionally, multiepitope vaccines have the advantage of simultaneously inducing diverse immune responses such as humoral, innate, and cellular responses than monovalent vaccines [18]. Moreover, a multiepitope vaccine developed using immunoinformatic has been proven to have in-vivo prophylactic properties, and many of these vaccines have advanced to the clinical trial stage [19]. Currently, the multiepitope vaccine approach has been shown to be promising against-SARS-CoV-2 (Muhammad Shehzad Khan et al., 2022), Monkeypox virus [20], Serratia marcescens [21], group B Streptococcus [22], Mycobacterium tuberculosis [23], Streptococcus suis [17], and Staphylococcus aureus [24]. Several studies have been reported on the multiepitope vaccines of several bacterial pathogens, however, literature was scantly available for the multiepitope vaccine against S. mutans. Hence, this study is aimed to develop a universal multiepitope vaccine candidate against S. mutans based on multiple conserved proteins using immunoinformatic approaches. This study identified five membrane proteins that were highly conserved in S. mutans, which were analyzed for antigenicity, B and T cell epitopes. The top-ranking epitopes were assembled to construct the multiepitope vaccine which was further analyzed for its binding affinity toward the human Toll-like-receptors, and MHC molecules, and antigenicity (immune-stimulation) through bioinformatics tools. Moreover, the stability of multiepitope vaccine was also validated by molecular dynamics simulation. In conclusion, the multiepitope vaccine construct could provide a novel outcome to prevent and control against S. mutans infection and dental caries. Therefore, multiepitope vaccines have the potential to revolutionize oral healthcare worldwide by offering a long-term, practical solution to dental caries.

Retrieval of Streptococcus mutans protein sequences

In this study, the genome sequences of eighteen S. mutans strains associated with dental infections were retrieved from the NCBI database (http://www.ncbi.nlm.nih.gov/genbank/; accessed on 02 April 2023) (Table S1). The core genome was generated using EDGAR v.2.0 software framework [25]. The protein sequences that were shared in all the strains were chosen as the core-proteome. The paralog sequences from the core-proteome were filtered using a CD-HIT suit with a cutoff value of 0.7 (70%) [26]. The similar protein sequences of Homo sapiens present in the core-proteome were removed using BLASTp [27] using default parameters. The non-homologous protein sequences with no hit were chosen for subsequent analysis.

The protein sequences were used for the identification of subcellular location using CELLO [28] and PSORTb 3.0 [29] servers. The proteins localized in the non-cytoplasmic regions were selected to predict the major histocompatibility complex (MHC class I and II) binding affinity, and the number of transmembrane helices using Vaxign v2.0 tool [30].

The non-cytoplasmic proteins with MHC adhesion probability more than 0.5 value were selected for prediction of antigenicity, and allergenicity using ANTIGENpro (http://scratch.proteomics.ics.uci.edu/), and AllerTOP v.2.0 server (http://www.ddg-pharm fac.net/AllerTOP), respectively [31].

Prediction of B-cell lymphocyte epitopes

The identified vaccine candidate proteins were analyzed for 20-mer linear B-cell lymphocyte epitopes using SVMTrip (http://sysbio.unl.edu/SVMTriP/prediction.php). The 20 mer linear B-cell lymphocyte epitopes with greater than 0.8 score were selected. The SVMTrip employed the latest IEDB (Immune-Epitope Database and Analysis-Resource) database and computed using SVM (Support Vector Machine) model to improve the prediction of B-cell linear epitope locations in provided protein sequence [23, 32]. The toxicity of B-cell epitopes was identified by the ToxinPred server (https://webs.iiitd.edu.in/raghava/toxinpred/design.php) using the SVM (Support Vector Machine) based method. The B-cell epitope antigenicity was determined using VaxiJen v. 2.0 server at a threshold value of 0.4 [30]. The allergenicity of B-cell epitopes was evaluated using the AllerTOP server (https://www.ddg-pharmfac.net/AllerTOP) [33]. The AllerTOP works by transforming protein sequences into uniform, equal-length vectors using auto cross-covariance (ACC). The Epitope Conservancy Analysis tool was used to determine the degree of the conservancy of anticipated epitopes using the Immune Epitope Database (IEDB) (http://tools.iedb.org/conservancy/) [34].

Prediction of cytotoxic T lymphocytes epitopes

Identification of potential epitopes is a crucial step in subunit vaccine design to achieve immunologic significance in multi-epitope vaccine candidates. Using the NetCTL 1.2 server (http://www.cbs.dtu.dk/services/NetCTL/), the cytotoxic T lymphocyte epitopes in vaccine-candidate protein sequences were examined. Using a cut-off value of 0.75, 12 MHC class I supertypes were chosen for this study to predict 9-mer CTL epitopes [35]. The NetCTL 1.2 server incorporates the prediction of peptide MHC class I binding, proteasomal C terminal cleavage, and TAP transport efficiency [36].

Prediction of Helper T Lymphocytes epitopes

The NetMHC II 2.3 server (http://www.cbs.dtu.dk/services/NetMHCII-2.3/) was engaged to predict the binding of 15-mer peptides to a 7-allele human leukocyte antigen (HLA) such as HLA-DRB1*01:01, HLA-DRB1*01:03, HLA-DRB1*03:01, HLA-DRB1*04:01, HLA-DRB1*04:02, HLA-DRB1*04:04, and HLA-DRB1*07:01 using artificial neural network (ANNs). The threshold values for stronger and weaker binding were established at 2% and 10%, respectively [36].

The predicted CTL and HTL epitopes were subjected to antigenicity, allergenicity, toxicity, and conservancy analyses using the Vaxijen 2.0, AllerTOP 2.0, ToxinPred, and Epitope Conservancy Analysis, respectively.

Prediction of Interferon-gamma (INF-ꝩ), Tumour Necrosis Factor (TNF), and Interleukin-10 (IL-10)

The selected HTL epitopes were used to predict the ability of HTL epitopes to induce INF- ꝩ, TNF, and IL-10 immune responses by using the IFN-Pred (https://webs.iiitd.edu.in/raghava/ifnepitope), TNFepitope Server (https://webs.iiitd.edu.in/raghava/tnfepitope/), and IL-10Pred (https://webs.iiitd.edu.in/raghava/il10pred) using Support Vector Machine (SVM) based method, respectively [37].

Construction of Multiepitope Vaccine candidate sequence

The highly immunogenic, non-toxic, non-allergenic, and antigenic epitopes served as the building blocks for the multiepitope vaccine. The CTL, HTL, and linear B-cell epitopes were joined together in this structure using the linkers AAY (Ala-Ala-Tyr), GPGPG (Gly-Pro-Gly-Pro-Gly), and KK (bi-lysine), respectively [38]. The 50S ribosomal L7/L12 protein, Locus RL7_MYCTU (accession number: P9WHE3), was introduced as an adjuvant to the N-terminal of the vaccine construct via an EAAAK linker to enhance the immunogenicity of the multiepitope vaccination.

Evaluation of Physiological, and solubility properties

The physiological properties of the multiepitope vaccine construct were evaluated by using Protparam (https://web.expasy.org/protparam/). This tool computed parameters such as the molecular weight, theoretical pI, amino acid composition, atomic composition, extinction coefficient, estimated half-life, instability index (II), aliphatic index, and grand average of hydropathicity (GRAVY) [39]. Additionally, the SOLpro server (http://scratch.proteomics.ics.uci.edu) was used to examine the construct vaccine's solubility. This tool uses the SVM architecture method to specify the protein's propensity to become soluble after being overexpressed in Escherichia coli [40].

Prediction of Secondary Structure and Tertiary Structure

PBDsum (https://www.ebi.ac.uk/thornton-srv/software/PDBsum1/) was used to generate the secondary structure of the vaccine construct. It used a highly accurate prediction algorithm and two feed-forward neural networks to examine PSI-BLAST (Position-specific integrated-BLAST) output [41].

The ab-initio 3D structure of the vaccine construct was created using AlphaFold v.2 integrated into the Galaxy Europe server (https://usegalaxy.eu) using the monomer preset model from AlphaFold's full database. The generated 3D structures were evaluated using ERRAT, and Ramachandra plot details via PROCHECK [21].

Refinement and validation of tertiary structure

The GalaxyRefine server (http://galaxy.seoklab.org/cgi-bin/submit.cgi?type=improve) was used to improve the vaccine construct 3D models produced by AlphaFold. To enhance the quality of the local structure, the GalaxyRefine server applied a refinement technique that was successfully validated in CASP10-based refinement experiments [42].

The validation of the tertiary structure is crucial for the development of vaccines since it highlights any potential problems with the predicted model [43]. The ProSA-web server was utilized to validate the refined 3D vaccine construct model, in which the initial 3D structure and the refined structure of the vaccine candidate were compared. The overall quality of the improved vaccine candidate structure was calculated using the ProSA-web server and expressed as a Z-score [44]. The structure is likely to have faults if the Z-score is beyond the usual range for native proteins. The non-bonded atom-atom interactions linked to the revised 3D model of the vaccine construct were examined using the ERRAT web server (http://services.mbi.ucla.edu/ERRAT/) [45].

The quality of the refined 3D model of the vaccine candidate was further validated by generating a Ramachandra plot using the PROCHECK tool. The Ramachandra plot statistics provide data on the overall amount of amino acid residues discovered in the favorable, allowed, and disallowed regions and state that a score higher than 85% could be deemed an appropriate 3D model [21].

Prediction of Discontinuous B-Cell Epitopes

In the refined 3D model multiepitope vaccine construct, discontinuous or conformational B-cell epitopes were recognized using Ellipro (https://tools.iedb.org/ellipro). ElliPro assigns a protrusion index (PI) value to each projected epitope to estimate the ellipsoid shape of the protein as well as the pI of the residues and nearby cluster residues. For epitope prediction, the default parameters were applied, with a minimum score of 0.5 and a maximum score of 0.6 [46].

Disulfide Engineering

Disulfide engineering increases the stability of protein structure in the vaccine build by including new disulfide bonds through cysteine modification of protein structure residues [47]. To incorporate the disulfide bonds in the improved vaccine design, the Disulfide by Design2 (DbD2) server (https://cptweb.cpt.wayne.edu/DbD2/) was used to suggest residue pairs that had the potential for mutation [48].

In-silico Immune Simulation

The C-ImmSim server (http://150.146.2.1/C-IMMSIM/index.php) was used to perform in-silico immune response profiling of the vaccine construct. The humoral and cellular profile of the mammalian immune system against the vaccine construct is addressed on this server [49].

Three vaccination doses were administered at recommended intervals of 28 days following the literature [36]. The total number of simulation steps was 1000, and the injection time steps were 1, 84, and 168. The default setting was applied in the rest of the simulation.

Molecular Docking Simulation

The ClusPro server and MOE 2022.02 (Molecular Operational Environment) software were applied to determine the binding affinity of a refined 3D model of vaccine construct and immune receptors. The protein-protein docking was performed between the refined 3D model of the vaccine construct and Toll-like receptors including TLR-2 (PDB ID: 6NIG), TLR-3 (PDB ID: 1ZIW), TLR-4 (PDB ID:4G8A, 3FXI). Further, the refined 3D model of the vaccine construct was docked with MHC-I (HLA-A*02:01; PDB:1AKJ) and MHC-II (HLA-DR1; PDB:1KG0). Prior to docking, the connected ligands, heteroatoms, and all chains other than A in the PDB structures of the TLR-2, TLR-3, TLR-4, MHC-I, and MHC-II were removed [50]. The docked complexes created by ClusPro sever were further investigated by the PRODIGY web server to estimate the binding affinity score as kcal/mol and dissociation constant at 37 ºC. Further, protein-protein docking was performed using MOE software to predict the best pose of vaccine construct and efficient binding at the receptor binding domain. MOE utilized a rigid body refinement approach to estimate the accuracy of the prediction concerning the ClusPro server. Using the PBDSum server, the produced vaccine-receptor complex with the greatest binding affinity value (kcal/mol) was chosen for analysis and visualization [51].

Molecular Dynamics Simulation

The molecular dynamics simulation analysis performed for the vaccine construct revealed the most significant result in the molecular docking study. The iMODS web server (http://imods.Chaconlab.org/) was used for molecular dynamics analysis [21].

Codon Optimization and In-silico Cloning

The final multiepitope vaccine construct sequence was reverse translated into nucleotide sequence using the Backtranseq server (https://www.ebi.ac.uk/Tools/st/emboss_backtranseq/). Java Codon Adaptation Too (Jcat) server (http://www.jcat.de/) was used for reverse translation, codon optimization, and calculating the codon adaptation index (CAI) value, and GC content of multiepitope vaccine construct in Escherichia coli K12 [52]. Three features were applied to avoid the rho-independent transcription terminators, prokaryotic ribosome binding site, and cleavage sites of restriction enzyme. Additionally, the 3' and 5' ends of the DNA sequence encoding the vaccine construct were each tagged with the XbaI and XhoI restriction sites. Finally, the optimized sequence of the final vaccine construct tagged with restriction sites was inserted into the pET28a (+) expression vector between the XhoI, and XbaI restriction sites using SnapGene v.7.0 software (http://snapgene.com/free-trial) [32].

Retrieval of Streptococcus mutans protein sequences

The genome of eighteen S. mutans generated 2761 CDS as core-proteome of which five proteins were identified as the best vaccine candidate proteins that satisfied all the vaccine characterization criteria (Table S2).

Prediction of B-cell lymphocyte epitopes

SVMTrip server predicted 14 recommended 20-mer linear B-cell epitopes with greater than 0.8 scores from the five-vaccine candidate proteins including PBP2X, PBP2b, MurG, ATP-F, and SMU_624. The four B-cell epitopes with high binding affinity scores of 1, antigenic, non-toxigenic, non-allergenic, and conservancy were chosen for the vaccine design (Table 1).

Table 1

List of selected linear B-cell epitopes predicted by SVMTrip server
Sl. No	Protein	Location	Peptide Sequence	Score
1.	PBP2X	190–209	PNGTFASQFIGLAQIKENKD	1
2.	PBP2b	102–121	DIKANAKKLADMVTLTESKV	1
3.	MurG	199–218	SAGAKVFNQFISDTPELTKH	1
4.	ATP-F	132–151	DLSVLLAEKIMAKNLDKTAQ	1
5.	AGPAT	226–245	AFTYLFSWFASFVWNPEKHR	1

Prediction of cytotoxic T lymphocytes epitopes

The NetCTL 1.2 server predicted 77 (9-mer) CTL epitopes from 5 vaccine candidate proteins that comply with the cut-off values of 0.75. Out of these predicted CTL epitopes, 10 CTL epitopes were chosen based on their high scores of MHC class I binding affinity, antigenicity, non-allergenicity, and non-toxicity (Table 2).

Table 2

List of selected CTL epitopes predicted by NetCTL 1.2 server
Protein	Peptide Sequence	MHC supertype A1binding affinity	ReScale Binding affinity	C-terminal cleavage affinity	Transport efficiency	Prediction Score
PBP2X	LSGSNDYIY	0.479	2.0338	0.4883	2.754	2.2448
PBP2X	LSFAQGFAY	0.4052	1.7204	0.8636	3.029	2.0014
PBP2b	LTASQSLEY	0.7955	3.3775	0.8953	2.779	3.6508
	YTAMQLAQY	0.6555	2.783	0.8258	3.041	3.0589
	TSSPRGQIY	0.5872	2.493	0.9516	3.041	2.7878
MurG	RVDYVTEMY	0.62	2.6325	0.9614	3.079	2.9306
MurG	SSDDNTEIK	0.3065	1.3013	0.8676	0.436	1.4532
ATP-F	GTSLGNLLI	0.2004	0.8509	0.5643	0.471	0.9591
AGPAT	WAANGNIHY	0.5014	2.1287	0.9696	2.976	2.423
AGPAT	QMDTEVATF	0.2896	1.2296	0.9366	2.354	1.4877

Prediction of Helper T Lymphocytes epitopes

The NetMHC II server identified a total of 64 15-mer HTL epitopes from 5 vaccine candidate proteins that had a binding affinity to HLA-DRB1*01:01, HLA-DRB1*01:03, HLA-DRB1*03:01, HLA-DRB1*04:01, HLA-DRB1*04:02, HLA-DRB1*04:04, and HLA-DRB1*07:01.

Table 3

List of selected HTL epitopes that could induce the INF-ꝩ and TNF
Protein	MHC-II Allele	Peptide sequence	INF-ꝩ	TNF	IL-10
PBP2X	HLA-DRB1*01:01	TLYSASLGGPVIKVG	1.0937	-	-
PBP2b	HLA-DRB101:01/HLA-DRB101:03	TFTLISNRINLLFFL	1.05	0.47	0.654631
MurG	HLA-DRB101:01/HLA-DRB103:01	ESDLSMGLANRIAYK	0.6066	-	-
ATP-F	HLA-DRB103:01/HLA-DRB103:01	ADLSVLLAEKIMAKN	0.9561	0.46	-
AGPAT	HLA-DRB101:01/HLA-DRB101:03	VFYTYLRSLLVFLIW	1.3433	0.48	1.381912

Prediction of Interferon-gamma (INF-ꝩ), Tumour Necrosis Factor (TNF), and Interleukin-10 (IL-10)

Among the predicted HTL epitopes, five 15-mer HTL epitopes were selected from each vaccine candidate protein that holds high binding affinity, antigenicity non-allergenicity, non-toxicity, and lastly capable of inducing INF-ꝩ. Three HTL epitopes of vaccine candidate proteins (PBP2b, ATP-F, and AGPAT) owned the TNF-inducing ability, while vaccine candidate proteins (PBP2b, and AGPAT) had IL-10-inducing capability (Table 3).

Construction of Multiepitope Vaccine candidate sequence

The multiepitope vaccine candidate consists of 10 CTL epitopes, 5 HTL epitopes, and 5 linear B-cell epitopes that were fused by using AAY, GPGPG, and KK linkers, respectively. In addition to these epitopes, 50S ribosomal L7/L12 protein was applied to the N-terminal of the vaccine construct sequence via the EAAAK linker (Fig. 1).

Evaluation of Physiological, and solubility properties

The final multiepitope vaccine construct consists of 464 amino acids with a molecular weight of 49.7 kDa. The theoretical isoelectric point (pI) and GRAVY of the vaccine construct were estimated to be 8.20, and − 0.030, respectively. The instability index (II) value of the vaccine construct was 21.81, suggesting the vaccine construct is stable.

The vaccine construct has a high aliphatic index score of 87.67 indicating that this vaccine has thermostability. The vaccine construct has a half-life period of 30 hours in mammalian reticulocytes in vitro and more than 20 hours in yeast, and 10 hours in Escherichia coli, in vivo. Further, the SOLpro server estimated that the vaccine construct was soluble with a probability of 0.898173. The AllerTOP v.20 and ANTIGENpro servers suggested that the vaccine construct was non-allergen, and antigen (0.556701), respectively. The VaxiJen v.20 server predicated that the vaccine construct has a score of 0.5464 (probable antigen) with a bacterial model at a threshold of 0.4.

Prediction of Secondary Structure and Tertiary Structure

The secondary structure of the multiepitope vaccine construct generated by PDBsum comprises 21 helices, 37 helix-helix interactions, 10 beta-turns, and 9 gamma-turns (Fig. S1).

The tertiary structure of the multiepitope vaccine construct modeled by AlphaFold v.2 tools revealed five 3D models. Among these 3D models, the Rank1 model was found to be the most confident model based on the ERRAT and Ramachandra plot (PROCHECK) evaluations. This 3D model has an overall quality factor, ERRAT of 96.5087, and 87.5% residues in favor regions of the Ramachandra plot, 11.8% in additional allowed regions, 0.5% in generously allowed regions, and 0.2% in the disallowed regions (Fig. 2).

Refinement and validation of tertiary structure

The GalaxyRefine server-generated 5 top Models from the above Rank1 model. Based on the structural quality for all the generated 3D models, model 4 predicted the most significant scores concerning GDT-HA (0.9623), RMSD (0.378), MolProbity (1.190), Clash score (4.1), Poor rotamers (0.3), and Rama favored (98.5) (Table S3). This model was preferred for further validation using the z-score of ProSA, ERRAT, and Ramachandra plots via PROCHECK. The Z-score calculated by the ProSA server for the initial model and the refined model of the multiepitope vaccine construct were − 7.66 and − 7.72, respectively (Fig. 2). The refined model 4 has an improved overall quality factor of 96.5432. The Ramachandra plot analysis via PROCHEK revealed that 97.1% of residues were in the most favored regions, 2.7% of residues were in additional allowed regions, no residues were found in generously allowed regions, and 0.2% of residues were in the disallowed regions (Fig. 2).

Prediction of Discontinuous B-Cell Epitopes

The refined tertiary structure of the multiepitope vaccine construct was analyzed for discontinuous or conformational B-cell epitopes. The ElliPro server recognized 227 residues localized in eleven discontinuous B-cell epitopes with scores ranging from 0.509 to 0.846. The B-cell epitope confirmation sizes vary from 5 to 54 residues. The B-cell discontinuous epitopes with a score value of more than 0.70 were shown in Fig. S2.

Disulfide Engineering

The refined model of the vaccine construct was engineered by the addition of new disulfide bonds using the DbD2 server. This sever predicted that the 39 residue pairs had the potential to form disulfide bonds, in which 4 residue pairs were selected for disulfide bonds formation including ALA250-ALA262, ALA325-SER345, GLY335-VAL338, and Leu435-ALA439. The residue pairs were analyzed based on the x³ angle between the − 87º and + 97º. The wild-type and mutant vaccine construct models were shown in Fig. S3.

In-silico Immune Simulation response

The immune simulation response triggered by the multiepitope vaccine was analyzed by the C-ImmSim server. After every administration of the vaccine dose, there was an increase in the level of antigen count, 6.8 x 10⁵ per mL on 1st dose, and ~ 5 x 10⁵ per mL on 2nd and 3rd doses that subsequently neutralized on the 5th day of injection. This was followed by a rise in the production of secondary immunological (IgM + IgG) response to ~ 1.8 x 10⁵ titer scale per mL after ~ 5 days of the 3rd dose injection (Fig. 3a). The level of IgM elevated to 1.0 x 10⁵ on an arbitrary scale after ~ 5 days of the 3rd dose injection. Also, the population of IgG1 + IgG2 reached a peak value of ~ 8 x 10⁴ on an arbitrary scale after ~ 5 days of the 3rd dose injection (Fig. 3a). Elevation of antibody secretion after secondary and tertiary immunological responses, resulting in the production of B-cell population, intensified antigen clearance upon successive exposures (Fig. 3b). The total B-cell population rose to 600 cells/mm³ after 25 days of 1st dose and further amplified attaining a peak of 720 cells/mm³ after 5 days of 3rd dose and declining gradually after that (Fig. 3b). The level of memory B-cells started elevation from 150 cells/mm³ after the 1st dose and attended the highest peak of 500 cells/mm³ after the 3rd dose. Further, the level of memory B-cells up-regulation lasted ~ 350 days which was the entire simulation period (Fig. 3c). Additionally, the activation of cell-mediated immune response event was observed due to an incline in the population of active HTL, and CTL (Fig. 3d & 3e). The total HTL population elevated to 400 cells/mm³ after the 10 days of 1st dose and attained a peak of 1.16 x 10⁴ cells/mm³ after 2 days of the 2nd dose. Further, HTL increased proliferation up to 1.9 x 10⁴ cell/mm³ after 12 days of the 3rd dose (Fig. 3d). The memory HTL had the highest population of 1.8 x 10³/mm³ after 2 days of the 3rd dose and lasted its up regulation to 350 days (Fig. 3d). The number of resting CTL population dropped vigorously after the first vaccine dose (Fig. 3e). In addition, the level of pro-inflammatory IFN-γ was elevated to 4.2 x 10⁵ ng/mL after 12 days of 1st dose and slightly decreased to 3.7 x 10⁵ ng/mL after 15 days of 3rd dose (Fig. 3f). However, the minimal level of anti-inflammatory cytokines, IL-10, and IL-2 were apparent (Fig. 3f).

Molecular Docking Simulation

The protein-protein docking was performed using ClusPro server using the immune receptors (TLRs) as receptors (targets), and refined multiepitope vaccine construct as ligand to determine the strength of binding energy or affinity of the multiepitope vaccine (antigen) molecule toward the immune receptor molecules. These antigen molecule-immune receptor molecules interactions are very essential for transporting antigen molecules and promoting both humoral and cell-mediated immune response pathways.

Table 4

Molecular docking results of multiepitope vaccine with TLRs and MHC molecules
Receptors	PDB ID	Energy Score (kcal/mol)	Binding affinity (ΔG)	Dissociation constant kd (M)	No. of H-bonds	MOE binding affinity (kcal/mol)
TLR-2	6NIG	-1114.7	-11.6	3.10E-09	12	-70.3042
TLR-3	1ZIW	-1134.3	-20	2.10E-15	34	-81.1515
TLR-4	4G8A	-1397.7	-20.3	1.20E-15	31	-84.3597
TLR-4	3FXI	-1257.8	-17.2	2.10E-13	28	-98.4945
MHC-I	1AKJ	-1068.3	-15	2.10E-12	22	-85.4256
MHC-II	1KG0	-1128.4	-8.6	5.3E-07	6	-62.6244

The ClusPro predicted that the docking of TLR-2 (PDB: 6NIG) and refined multiepitope vaccine construct generated 30 clusters, of which cluster 0 consist of the highest members (98) with a low energy score of -1114.7 kcal/mol. Further, the evaluation of its binding affinity using the PRODIGY server revealed − 11.6 ΔG (kcal mol-1), and a dissociation constant of 3.10E-09 kd (M) at 37 ºC (Fig. 4a & Table 4). The TLR-2-refined multiepitope vaccine complex generated 12 hydrogens (H) bonds, 3 salt bridges, and 163 non-bonded contacts (Fig. 4b). The refined multiepitope vaccine residues involved in hydrogen bond formation with a bond distance < 2.84 Å were ARG308(2x), LYS26, ASN237, TYR311, LYS312, SER263, LEU264(2x), ARG188(2x), and VAL268 (Fig. 4c). Additionally, the MOE v.2022.2 software predicted that this TLR-2 exhibits a binding affinity of -70.3042 kcal/mol.

The docking of the refined multiepitope vaccine construct with TLR-3 (PDB: 1ZIW) using the ClusPro server predicted low energy scores of -1134.3 kcal/mol. The multiepitope vaccine construct -TLR-3 (PDB: 1ZIW) complex has the lowest binding affinity of -20 ΔG (kcal mol-1) and dissociation constant of 2.10E-15 kd (M) at 37 ºC (Fig. 5a & Table 4). The TLR-3-refined multiepitope vaccine complex created 34 H-bonds, 5 salt bridges, and 254 non-bonded contacts (Fig. 5b). The refined multiepitope vaccine residues that engaged H-bonds with a bond distance < 3.22 Å involved SER137, ASP300, TYR240, ARG308, SER139, SER186, ARG188(4x), GLU15(4x), LYS14, MET16, SER22, ASP246(2x), ALA262, GLY253(2x), THR258(3x), TYR260 (2x), PRO254(2x), GLN164, LYS433, LYS464(2x), and TRP450 (Fig. 5c). Also, MOE software showed that the multiepitope vaccine construct-TLR-3 (PDB: 1ZIW) complex has a binding affinity of -81.1515 kcal/mol.

The refined multiepitope vaccine construct docked with TLR-4 (PDB: 4G8A), and TLR-4 (PDB: 3FXI) by the ClusPro server suggesting that the multiepitope vaccine construct-TLR-4 (PDB: 4G8A) has low energy score of -1397.7 kcal/mol and displayed the lower binding affinity of -20.3 ΔG (kcal mol-1), and dissociation constant of 1.20E-15 kd (M) at 37 ºC than the multiepitope vaccine construct- TLR-4 (PDB: 3FXI) complex (Fig. 6& Table 4). The TLR-4 (PDB: 4G8A)-refined multiepitope vaccine complex formed 31 H-bonds, 10 salt bridges, and 270 non-bonded contacts (Fig. 6b). The multiepitope vaccine residues involved in H-bonds with bond distance < 3.08 Å were LYS464(2x), LYS442(2x) LYS460, TYR446, LYS433, TYR159, SER208, SER209(4x), ASP210(2x), GLU202, ASP198, PRO254, TYR195(2x), THR258, TYR260, ASP246(2x), ARG188(3x), ILE238, GLY236, and ARG308(2x) (Fig. 6c). Additionally, docking of multiepitope vaccine construct with TLR-4 (PDB: 3FXI) by MOE software predicted that this protein-protein interaction exhibited a lower binding affinity of -98.4945 kcal/mol (Table 4).

Furthermore, the multiepitope vaccine construct was docked with MHC-I (PDB: 1AKJ), and MHC-II (PDB: 1KG0) using the ClusPro server showing that the MHC-I (PDB: 1AKJ) has low energy score of -1068.3 kcal/mol with a binding affinity of -15 ΔG (kcal mol-1), and dissociation constant of 2.10E-12 kd (M) at 37 ºC (Fig. 7a & Table 4). The PBDSum revealed that the MHC-I-multiepitope vaccine complex created 22 H-bonds, 2 salt-bridges, and 213 non-bonded contacts (Fig. 7b). The multiepitope vaccine involved in H-bonds with bond length < 3.10 Å were LEU259(2x), ASP198, VAL268(2x), GLY272, SER263(4x), ALA262, LEU264, GLY265(2x), GLY266, THR258, GLY257, PRO314, LYS370, ASP374, SER283, and GLY313 (Fig. 7c).

While the multiepitope vaccine construct-MHC-II (PDB: 1KG0) complex exhibited a low energy score of -1128.4 kcal/mol with a binding affinity of -8.6 ΔG (kcal mol-1) and dissociation constant of 5.3E-07 kd (M) at 37 ºC (Fig. 7d & Table 4). The multiepitope vaccine interaction with MHC-II involved 6 H-bonds, 5 salt-bridges, and 128 non-bonded contacts (Fig. 7e). The multi-epitope vaccine that engaged in H-bonds with bond distance < 3.16 Å were LYS312(3x), LEU 264, ARG188, and HIS239 (Fig. 7f).

Additional evaluation of binding affinity using MOE software revealed that the multiepitope vaccine construct-MHC-I (PDB: 1AKJ) complex displayed the lowest binding affinity of -85.4256 kcal/mol than the multiepitope vaccine construct-MHC-II (PDB: 1KG0) complex (-62.6244 kcal/mol) (Table 4).

Molecular Dynamics Simulation

The most significant results obtained from molecular docking studies were the docking of multiepitope vaccine construct with TLR-3 (PDB: 1ZIW), and TLR-4 (PBD:4G8A). The molecular dynamics simulation of the vaccine-TLR-3, and vaccine-TLR-4 (PDB: 4G8A) complexes using iMODS server is illustrated in Fig. 8. The study was conducted to generate conformational morphing trajectories of the molecule, and this enable to inspect of the compatibility and ultimate stability of the complex. In this analysis, the structural mobility and flexibility are categorized by deformability and B-factor plots (Fig. 8). In the deformability plot, the hinge region defined the non-rigid helical contents (Fig. 8), and in D-factor distortions are the atomic positional fluctuations (Fig. 8). The estimation of eigenvalue represents the motion stiffness of the complex and its value depends on the energy required to deform the structure. In this Normal Mode Analysis (NMA), the eigenvalue of TLR-3 (PDB: 1ZIW)-vaccine complex was 2.339113E-05, while the eigenvalue of TLR-4 (PBD:4G8A)-vaccine complex was 1.881333E-05. The TLR-4 (PBD:4G8A)-vaccine complex shows a very low eigenvalue, indicating a stronger deformability structure and more stability (Fig. 8). The variance plots showed an inverse relationship with the eigenvalues to each normal mode, in which the purple and red bar plots represent individual and cumulative variance, respectively (Fig. 8). The covariance matrix showed that motions are categorized in different modes, in which the red, blue, and white colors signify variations of residue pairs among the correlated, anti-correlated, or uncorrelated motions, respectively (Fig. 8). Finally, the elastic network model suggested that the pairs of atoms linked via springs according to the degree of stiffness between them, in which springs direct the atomic connection of the complex, and darker grey dots indicate stiffer strings (Fig. 8).

All these results suggest stable binding interactions with compact conformation and minor fluctuations in the vaccine–TLR3, and vaccine-TLR4 complexes.

Codon Optimization and In-silico Cloning

The codon optimization of reverse translated multiepitope vaccine construct by Jcat server against E. coli K12 strain score Codon Adaptation Index (CAI) value of the improved sequence was 0.99 and GC% of 49.13. The optimized codon sequence of multiepitope vaccine construct tagged with restriction sites of XhoL (CTCGAG) at the 3’ terminal and XbaI (TCTAGA) at the 5’ terminal for the generation of cohesive ends after restriction digestion. Finally, the recombinant plasmid sequence was constructed by insertion of adapted codon sequences into the vector pET28a (+) using SnapGene v. 7.0 software. The total obtained length of circular plasmid pET28a (+) along with the insert was found to be 6590 base pairs, while the insert size was 1404 base pairs (Fig. 9).

Streptococcus mutans is a well-recognized cariogenic factor for dental caries in humans [53]. This bacterium has the ability to produce biofilm and promote the adhesion and attachment of other bacteria and leading to cause several serious infections such as bacteremia, atheromatous plaques of atherosclerosis, and infective endocarditis [54–56]. S. mutans inhabits primarily in biofilms on the tooth enamel, known as dental plaque [1]. The biofilm produced by S. mutans supports resistance to different antimicrobial agents, and human immune clearance [57–60]. Thus, preventing the infection caused by this bacterium still becomes one of the most challenging issues in oral dental practice.

With the continuous emergence of antibiotic resistance in S. mutans, there needs an alternative strategy to prevent the infection, especially dental caries. Several studies had reported the application of several antimicrobial agents, and monovalent vaccines, however, these agents have limitations in triggering biofilm formation and inducing long last immune responses [61–63]. In this regard, peptide-based vaccines are an alternative approach, nevertheless, these monovalent vaccines provide short immune responses, and their development process is labor-intensive, expensive, and time-consuming [64, 65]. Due to advances in immunoinformatics, the introduction of multi-valent (multiepitope) based vaccines are considered over the conventional vaccine, since these multiepitope vaccines have cost-effectiveness, improved safety, and the ability to induce both native and acquired immune responses [66–68] as well as coverage diverse serotypes and geographic prevalence [17]. Currently, studies on a multiepitope vaccine against the S. mutans have been limited and no advanced treatment is available for dental caries and it has long-lasting prevention [69]. Therefore, to address these challenges, we proposed a multiepitope vaccine by fusing five highly conserved antigenic proteins of PBP2X, PBP2b, MurG, ATP-F, and AGPAT using immunoinformatics approach to contrive a multiepitope vaccine of S. mutans.

In this study, the subtractive genomic approach of S. mutans core-genome identified class B penicillin-binding proteins (PBP2X and PBP2b), MurG, ATP-F, and AGPAT. It was reported that PBP2X and PBP2b are essential in S. pneumonia for the septal and peripheral peptidoglycan synthesis, respectively [70, 71] and considered important targets for the beta-lactam antibiotics [72]. Previous studies suggested that these proteins have high immunogenic properties and could be used as vaccine candidates in Neisseria meningitidis, and Staphylococcus aureus [73, 74]. MurG is an essential bacterial glycosyltransferase that is involved in the biosynthesis of peptidoglycan which is a target for the design of new antibiotics [75]. The ATP synthase b (ATP-F) was found to have antigenic potential (Table S2) and the study conducted by Modhammand Nahian Rahman also suggested that epitopes of this protein were non-allergenic, non-toxic, and immunogenic properties that have the ability to trigger a host defense mechanism [76]. 1-acylglycerol-3-phosphate O-acyltransferase (AGPAT) is involved in the de-novo synthesis of triacylglycerol (TAG) and glycerophospholipids and thus is important for lipid storage/transport and membrane biogenesis (Röttig and Steinbüchel, 2013). AGPAT has not been reported as a vaccine candidate, however, it was suggested that this protein elicits an immune response due to its high antigenic properties [77]. The information on these five proteins as vaccine candidates were limited, however, these proteins owned the high antigenicity (Table S2) defined by Vaxign [30] as well as AntigenPro [31] and were chosen as vaccine candidates for the construction of multiepitope vaccine.

Several bioinformatic tools have been employed on these selected five vaccine candidate proteins to identify linear B and T-cell epitopes that could trigger both cellular and humoral immunity [23] and incorporate them into the multiepitope vaccine construction. The T-cell epitopes are essential for adaptive immune response and are sufficient to cooperate with MHC molecules [78]. The CTL epitopes with antigenicity aid in the recognition of foreign antigen fragments on MHC-I molecules and neutralized the target cells while in case of HTL epitopes prediction, several epitopes have been selected that has a binding affinity to HLA-DRB1*01:01, HLA-DRB1*01:03, HLA-DRB1*03:01, HLA-DRB1*04:01, HLA-DRB1*04:02, HLA-DRB1*04:04, and HLA-DRB1*07:01. Geographical differences impact the development of vaccines because they influence the variety of HLA allele expression throughout the world [79].

The HTL epitopes are to be selected based on their ability to produce INF-ꝩ, TNF, and IL-10 cytokines which play key functions against pathogens [80]. According to Kang et al., 2018, INF-ꝩ plays a critical role in the activation of both innate and adaptive immune responses [81]. In addition to helping diagnose diseases, B-cell epitopes are important in the creation of epitope vaccines. The MHC-II-processed epitopes are delivered to T cells by these cells and recognized by B-cell receptors. T-cell receptors were able to recognize its properties as a result. According to Naveed et al., 2022, B cells can also develop into memory cells and plasma cells which generate antibodies [82]. The development of an immunotherapeutic and preventive vaccination depends on the prediction of these epitopes.

According to Apostólico et al., 2016, an adjuvant is crucial for enhancing and directing the adaptive immune response to vaccination antigens [84]. The 50S ribosomal L7/L12 protein was employed in this investigation as an adjuvant, and it has been reported that this protein has been demonstrated to encourage dendritic cells, CD4+, CD8+, and INF-producing cells to mature after the simulation of naive T-cells [85]. The predicted CTL, HTL, and B-cells epitopes were linked together using AAY, GPGPG, and KK linkers, respectively. These linkers are flexible and hydrophilic amino acids that help to prevent domain disruption [86]. To improve the immunogenic qualities of the multiepitope vaccine, an EAAAK, a stiff linker, was fused between the adjuvant and the epitope sequences [87]. The multiepitope vaccine that was created demonstrated improved antigenicity and stability scores.

To facilitate subsequent experimental evaluations of the vaccination and the effective setup of in-vitro and in-vivo experiments, the final multiepitope vaccine was assessed for its physiochemical properties. The purpose multiepitope vaccine has a molecular weight of 49.7 kDa, which is considered a good vaccine candidate and could be easier to clone and express in an expression system [88]. The theoretical pI of the multiepitope vaccine was estimated as 8.20, suggesting that the vaccine is basic. The constructed multiepitope vaccine has a negative GRAVY value (-0.030) indicating that the vaccine is hydrophilic and has a high degree of solubility, suggesting that the designed vaccine will interact better with water molecules [19]. Considering that this multiepitope vaccine's half-lives in mammalian, yeast, and bacterial cells are 30, 20, and 10 hours correspondingly, it is possible to infer that it could expose the immune system for a longer length of time and elicit more immunological responses [32, 89].

The 3D structure of the multiepitope vaccine generated by AlphaFold v.2 was then refined by the GalaxyRefine server. In the initial 3D vaccine model, the Ramachandra plot revealed that 87.5% of residues were in favor regions, which was then improved to 97.1% residues in favor regions after structure refinement. Also, the Z-score was the designed multiepitope vaccine calculated by the ProSA server was improved from − 7.66 to -7.72 after structural refinement. The Z-score with more negative values implies that the refined 3D model of a multiepitope vaccine has high structural quality [90]. Discontinuous B-cell epitope plays an important role in humoral immune responses by the production of antibodies [91]. In this study, the ElliPro server identified 11 discontinuous B-cell epitopes in the refined 3D model of the multiepitope vaccine, revealing that the designed multiepitope vaccine has the capability to induce large amounts of antibody production. Enhancing protein stability is of vital importance in numerous biological and therapeutic applications [79]. To increase the protein's thermostability, four residue pair of mutations in the ALA250-ALA262, ALA325-SER345, GLY335-VAL338, and Leu435-ALA439 residue pairs were made, along with disulfide engineering.

In addition, the immunological simulation outcomes showed that the vaccine formulation induced an antibody cascade against S. mutans that closely mimicked the typical immune response to pathogen infections. After vaccinations, secondary and tertiary immune responses were found to be stronger than initial immune responses, with significant numbers of antibodies being generated and antigens being eliminated [79]. This study found that the multiepitope vaccine was effective in boosting both cell-mediated and humoral immunity, as evidenced by an increase in B-cells (memory B-cell and plasma B-cell) and T-cells (cytotoxic and helper T-cell) for up to 350 days after immunization. The amount of IFN-ꝩ, a pro-inflammatory protein, increased in this study before falling after the third dose. It was clear that there were very few anti-inflammatory cytokines present.

Additionally, to examine the capability of the constructed multiepitope vaccine to bind with TLRs (Toll-Like Receptors) on immune cells, the TLR-2, TLR-3, and TLR-4 were docked with the proposed multiepitope vaccine. TLRs on immune cells identify pathogens that have penetrated the mucosal barrier and trigger the adaptive immune response [92]. This docking experiment demonstrated that the TLR-4 has the lowest binding energy of -1397.7 kcal/mol and revealed a binding affinity of -20.3 ΔG (kcal mol-1), indicating the highest binding affinity with the lowest dissociation constant (1.20E-15 kd (M). According to a previous study, during bacterial infection, TLR-4 is activated by toxins or lipopolysaccharides and causes pro-inflammatory reactions that aid in the elimination of invasive pathogens [93]. TLR-3 appears to be a preventive factor against infections since it detects viral load [94]. TLR2, on the other hand, has been demonstrated to have a protective role during infection by inducing a potent pro-inflammatory response that is thought to be helpful for bacterial clearance [95, 96]. The docking investigation revealed that the TLR-2 and TLR-3 had a high affinity for interacting with the designed multiepitope vaccine which possesses a low dissociation constant. These findings revealed that the vaccine could potentially elicit an immunological response and remove the burdens of S. mutans infections.

Furthermore, the docking of the multiepitope vaccine construct with MHC-I, and MHC-II molecules showed reliable interaction with low energy scores. These molecules play critical roles in pathogen clearance [95]. MHC-I molecules convey intracellular pathogen-derived peptides to cytotoxic CD8 + T lymphocytes, causing infected cells to be eliminated. MHC-II molecules activate immune responses and coordinate pathogen clearance by presenting extracellular pathogen-derived peptides to CD4 + T cells [97]. These MHC molecules are essential to simulate together for effective immune recognition and response against infections. According to this docking result analysis, the multiepitope vaccine has shown reasonable docking energy with these immune receptors, suggesting that they may be able to trigger the subsequent immunological responses [98]. Using a molecular dynamics simulation of the docked complex, it was possible to determine the stability and mobility of the immune receptor-multiepitope vaccination complex in the mimic biological environment. The TLR-3-vaccine complex and the TLR-4-vaccine complex both have high eigenvalue values, according to the molecular dynamic analysis. Compared to the TLR-4-vaccine complex, the TLR-3-vaccine complex showed higher eigenvalues, indicating a lower degree of deformation and improved stability [99]. When compared to other protein complexes, the TLR-4-vaccine complex has a low eigenvalue, which could indicate that it vibrates at a low frequency or is not very flexible. A low eigenvalue often indicates that the protein complex is stiffer and less flexible in terms of stability (Sayed et al. 2020). In some circumstances, rigidity in protein complexes might be advantageous since it may signify a clearly defined and stable structure [73].

The E. coli cell culture technique is frequently used for mass production of recombinant proteins. To ensure efficient expression in the host, codon optimization for E. coli strain K12 was prepared. A codon adaptability index (CAI) of 0.99 and a GC content of 49.13% were discovered to guarantee a better level of protein production in E. coli expression systems [100, 101]. Also, it was suggested that for target organism expression, a CAI higher than 0.890 and a GC content between 30 and 70% are optimal [102].

In this study, a novel multiepitope vaccine against Streptococcus mutans, a well-recognized cariogenic factor for dental caries and associated serious infections, was proposed using an immunoinformatics approach. Docking studies with TLRs and MHC molecules suggested potential immunological activation and recognition, and molecular dynamics simulations indicated stable interactions between the vaccine and immune receptors. This multiepitope vaccine offers a promising alternative strategy to prevent dental caries and associated infections, though further experimental evaluations are required to validate its efficacy and safety.

Acknowledgments

The authors greatly acknowledge and express Multidisciplinary Research Unit (MRU), Jorhat Medical College and Hospital, Jorhat, India for providing the facilities for computational analysis.

Authors contributions

R.S.N and B.D.P: conceptualization, writing original draft preparation. R.S.N: methodology, software, validation. R.S.N and B.D.P: analyze the data. A.B.T: provide computing facilities. A.B.T and C.F: reviewing and editing. C.F: supervising. All the authors contributed to the article and approved the submission version.

Conflict of interest

The authors declare no competing interests.

Ethical approval

Not applicable

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Immunoinformatics-Driven Multiepitope Vaccine Targeting Streptococcus mutans for Dental Caries Prevention

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Abstract

Figures

Introduction

Materials and Methods

Prediction of cytotoxic T lymphocytes epitopes

Prediction of Helper T Lymphocytes epitopes

Prediction of Interferon-gamma (INF-ꝩ), Tumour Necrosis Factor (TNF), and Interleukin-10 (IL-10)

Construction of Multiepitope Vaccine candidate sequence

Evaluation of Physiological, and solubility properties

Prediction of Secondary Structure and Tertiary Structure

Refinement and validation of tertiary structure

Prediction of Discontinuous B-Cell Epitopes

Disulfide Engineering

In-silico Immune Simulation

Molecular Docking Simulation

Molecular Dynamics Simulation

Codon Optimization and In-silico Cloning

Results

Prediction of B-cell lymphocyte epitopes

Prediction of cytotoxic T lymphocytes epitopes

Prediction of Helper T Lymphocytes epitopes

Prediction of Interferon-gamma (INF-ꝩ), Tumour Necrosis Factor (TNF), and Interleukin-10 (IL-10)

Construction of Multiepitope Vaccine candidate sequence

Evaluation of Physiological, and solubility properties

Prediction of Secondary Structure and Tertiary Structure

Refinement and validation of tertiary structure

Prediction of Discontinuous B-Cell Epitopes

Disulfide Engineering

In-silico Immune Simulation response

Molecular Docking Simulation

Molecular Dynamics Simulation

Codon Optimization and In-silico Cloning

Discussion

Conclusion

Declarations

References

Additional Declarations

Supplementary Files

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