Design of a novel multiple epitope-based vaccine: An immune-informatics approach to combat Dengue virus

doi:10.21203/rs.3.rs-5296606/v1

Dengue fever is a vector-borne viral disease that is responsible for 25,000 people deaths per year globally. Elimination of the virus from the bloodstream of affected individuals is the primary goal of the treatment. However, there is no successful dengue vaccine candidate that can prevent this virus to date. The purpose of this study was to develop a potential vaccine by targeting B cell and T cell epitopes of DENV-1, DENV-2, DENV-3, and DENV-4 serotypes by using bioinformatics approaches. Epitopes were predicted from envelopes protein of DENV-1, DENV-2, and DENV-4 and polyprotein of DENV-3. These epitopes were analyzed and selected by layer-by-layer filtration method based on different bioinformatics approaches. DENV-BkS8 and DENV-BkS10 passed all filtration criteria, among all 21 constructed vaccine models. All selected epitope candidates showed good results in worldwide population coverage. DENV-BkS8 and DENV-BkS10 showed good docking properties against TLR 2, TLR4, HLA- A*02:01, and HLA- DRB1*01:01 and promising immunomodulation properties. Vaccine constructs were cloned into PET28a (+) vector for expression study in Escherichia coli. DENV-BkS8 and DENV-BkS10 proved effective in various computer-based immune response analyses. Laboratory-based studies and clinical trials will be needed for further confirmation of the efficacy and safety of vaccines.

B and T-cells

Dynamic simulations

Molecular docking

Dengue virus

Vaccine design.

Dengue virus belonging to Flaviviridae viral family is transmitted by female mosquitoes mainly of the species Aedes aegypti (1). This mosquito becomes a vector when it bites an infected human. This virus remains in the mosquito's gut for up to ten days and can spread the infection to any other individual biting. Dengue virus infects about 390 million people per year and approximately 25,000 people die per year globally (2). About 40% of the world's population is at risk of infection according to World Health Organization (WHO) (3). Available licensed dengue vaccines and several DENV vaccine candidates who are undergoing various stages of clinical trials are not completely safe and effective against South Asian dengue serotypes and others (4, 5). Based on secondary data analysis there is no successful vaccine that can prevent dengue virus completely (2). Dengue virus has four distinct DENV serotypes such as DENV-1, DENV-2, DENV-3, and DENV-4 (2). Each serotypes contain 3 structural proteins such as envelope, core, and membrane protein (6). However, there has been no use of poly-protein and envelope protein together before for multi-epitope based vaccine production based on literature review. So, the aim of this study was to develop a potential vaccine by using envelope proteins of serotypes DENV-1, DENV-2, DENV-3, and poly protein of DENV-4 by using bioinformatics approaches.

Data retrieval, sequence alignment, and selection of proteins

A total of 3100 envelope protein sequences DENV-1, DENV-2, DENV-3, and 3149 polyprotein sequences of serotypes DENV-4 of dengue virus were collected from NCBI. Three individual envelope protein sequences from DNEV 1, DNEV 2, and DNEV 4, respectively, and 1 polyprotein from DNEV 3 were selected based on a high similarity sequence among them using MEGA 11 (7). NCBI protein blasts with the nr database were done for the identification of coverage worldwide (8).

Prediction of epitope

The Immune Epitope Database (IEDB) Linear Epitope Prediction Tool v2.0 was used to predict the B cell epitopes and T cell (MHC-I and MHC-II) epitopes from the selected protein sequences using default parameters (9). This tool is considered a specialized, high-quality, accurate, and powerful tool compared to others which only uses the epitope data from crystallized structures (10). The prediction method was where the method was Bepipred Linear Epitope Prediction 2.0 for B cell epitopes prediction and NetMHCpan 4.1 BA was for MHC-I and MHC-II epitopes. All available human allele (s) were selected (Supplementary Table 1).

Profiling and Selection of epitopes

The Antigenicity, allergen properties, toxicity properties, and homology analysis of epitopes were predicted by Vaxigen 2.0 server (11), AllerTOP (12), ToxinPred server (13), and PIR (Uniprot) server (14), respectively. B cell epitopes that have < 5 amino acids, < 0.4 antigenicity score and probable allergen were excluded from this study, and the highest two antigenicity-scored epitopes from each sequence were selected for vaccine construction (supplementary table 2). T cell epitope which has IC₅₀ values > 100, < 0.4 antigenicity score, probable allergen, < 4 alleles for DNEV 1, DNEV 2, and DNEV 4 and < 5 alleles for DNEV 3 were excluded from this study and remaining top one or two epitopes based on interaction with highest alleles from each list were selected for vaccine construction (Supplementary table 2, Supplementary table 3 and Supplementary table 4).

Multi-epitope vaccine constructs design

All T-cell and B-cell epitopes who are successfully passed the filtration criteria were incorporated into the construction of the multi-epitope vaccine. Linkers were added for forming effective epitope conjugation between epitopes and allow independent immunological activities (15). Adjuvant molecules such as 50S ribosomal L7/L12 (16), LT-IIC (17), Cholera toxin-B (18), RS09 (19), CpG 1018 (20), PADRE (21), and Beta-defensin (22) were used for significant boosting the peptide vaccine’s immunogenicity and longevity (23). All vaccines were designed using standard scientific methods (24). All components of these constructs were identical except for the adjuvant. The general structures of vaccine construction with a study design are presented in Fig. 1.

Prediction of biophysical and biochemical features of vaccine constructs and selection of appropriate vaccine constructs

The Antigenicity, allergen properties, toxicity properties, and homology analysis of all vaccines were predicted by Vaxigen 2.0 server (11), AllerTOP (12), ToxinPred server (13), and PIR (Uniprot) server (14), respectively. Some vaccine constructs were excluded depending on their toxic nature. The remaining vaccine models were filtered based on low protein solubility score predicted by Soluprot v1.0 (25), presence of transmembrane regions (TMRs) analysis via best performing DeepTMHMM server (26), presence of signal peptidase and location of their cleavage site (Sec/SPI, Sec/SPII, Tat/SPI, Tat/SPII, and Sec/SPIII) examined by SignalP-6.0 (27). The physicochemical properties including molecular weight, the number of amino acids, theoretical isoelectric point (pI), estimated half-life, instability index, aliphatic index, and grand average of hydropathicity (GRAVY) index were predicted of finally selected vaccine constructs using ProtParam tool (28) (Table 1).

Secondary and tertiary structure prediction and validation of vaccine constructs

All vaccine constructs which were successfully passed the filtration criteria were submitted to the SIPRED 4.0 server to obtain their secondary structure because it performed protein secondary structure prediction based on position-specific scoring matrices with a prediction accuracy of over 84% (29). The tertiary structure of vaccine models was predicted via the trRosetta server due to its fast and accurate protein structure prediction algorithm (30). trRosetta generated five models of each vaccine construct and predicted per-residue LDDT, estimated TM-score, and predicted inter-residue distance and orientations (Contact, Distance, Omega, Theta, and Phi). Model 1 is selected for each vaccine construct and submitted to GalaxyWEB server which can rebuild unreliable loops or termini of the initial model structures using an optimization-based refinement method and further generate five refined models of each crude model (31). However, only model one of each was selected for submission to the ERRAT tool and PROCHECK tool for validation of protein structure (32).

Molecular docking

A chain of TLR2, TLR4, and HLA- DRB1*01:01, and the D chain of HLA- A*02:01 were downloaded from the RCSB PDB server with the accession number 1fyx (33), 2z62(34), 8euq (35) and 7m8s (36), respectively. TLR2 and TLR4 can induce antiviral immune responses by detecting the virus coat proteins and HLA of class I and class II MHC molecules to help the CD4 + T and CD8 + T cells recognize foreign antigens such as vaccines (37, 38). Receptor molecules were cleaned by Discovery Studio (39) and docking was performed using the ClusPro 2.0 server due to it is the best docking server for protein-to-protein docking (40). Coefficient weights were calculated by the formula E = 0.40Erep + − 0.40Eatt + 600Eelec + 1.00EDARS. A total of thirty models were generated in each docking case and the best model in each docking case was further submitted to the HADDOCK 2.4 server to refine the complex structure (41). Then the docked models were submitted to the PRODIGY server for predicting binding energy (42). For the investigation of interacting residues between docked chains and vaccine construct PDBsum server was utilized (43). Chimera X was used to visualize the vaccine-receptor complex (Supplementary Table 5).

Molecular dynamics simulation

iModS online tool was used to carry out molecular dynamics simulation studies (44). This torsional angles-based analytical tool measures the RMSD values, co-variance among individual residues, eigenvalue of interacting residues, and deformation of the structure to discover the stability of the complexes.

Prediction of population coverage of epitopes

Population coverage of T cell epitopes for measuring the effectiveness of the epitopes of vaccine construction was predicted by using the IEDB’s population coverage tool in default parameters where all the regions of the world were selected (45).

Immune simulation

The potential immune efficacy of these vaccines was measured by using an online C-ImmSim server which determines immune epitope and immune interaction by using the position-specific scoring matrix (PSSM) (46). All parameters were left at their default settings except for time steps during the immune stimulation. The simulation time steps were set to 1050 (1-time step equals 8 h), the default dose of each antigen injection was 1,000, and the time interval between each dose was 30 days means at day 1, 30, and 60, respectively which is the recommended interval between injections for most commercial vaccines. A novel multi-epitope vaccine against Echinococcus granulosus was used as a control (47).

Codon optimization of designed vaccine peptide for expression analysis

The vaccine sequences were reverse-translated by the backtranseq program of EMBOSS 6.0.1 to express the chimeric protein in an expression vector (48). VectorBuilder was used for codon optimization in Escherichia coli due to this tool is used for optimizing sequences with extreme GC content and simple repeats for highly efficient gene synthesis and DNA cloning applications. The results interpret the GC content percentage and the score of the codon adaption index (CAI) of the optimized codons. The expression quality was measured by codon adaption index (CAI) score where the score lies between 0.8-1.0 is considered good, although 1.0 is scrutinized as ideal score (49) and the transcriptional and translational performance is affected if the GC content range outside between 30% -70% (50). SnapGene tool was used for cloning and PCR. The E. coli pET 28a(+) was selected as an expression vector and BamHI and XhoI restriction sites were inserted at the starts and the ends of the constructs that make them feasible to be cloned in vector. SnapGene was also used to amplify the constructs in the in-silico PCR method.

Useful list of all used servers, tools, and software

A list of all utilized available server links were given in supplementary table 5.

Protein sequence retrieval and alignment

100% identical sequences were 437, 247, and 86 for selected sequences AFI71510.1 (DENV-1), ABX25788.1 (DENV-2), and AGD80786.1 (DDENV-4) envelope protein sequences in NCBI (Table 1). 98.997-100% identical sequences were 557 for ACY70809.1 (DENV-3) poly-protein. These results proved that the selected sequences cover the maximum isolates of all DENV serotypes around the world.

Table 1

Distribution of selected sequences based on the hit.
Isolates	DENV-1	DENV-2	DENV-3	DENV-4
Protein type	Envelope Glycoprotein	Envelope Protein	Polyprotein	Envelope Protein
GB (GI)	AFI71510.1 (385718166)	ABX25788.1 (160337001)	ACY70809.1 (263614377)	AGD80786.1 (443927870)
% identity	100	100	98.997-100	100
Hits found	437	247	557	86
Mismatch	0	0	Present	0
Gap open	0	0	0	0
e-value	0	0	0	0
bit score (counts)	1017 (274), 1016(153), 1018(8), 1015(2)	1028 (211), 1027(32), 1023 (2), 1022 (2)	-	-
% positive	100	100	99.26–100	100

Epitopes selection

8 B-cell, 6 T-cell MHC Class I, and 7 T-cell MHC Class II epitopes were selected from 168, 8754, and 4819 epitopes from all selected protein sequences for vaccine construction after a layer by-layer screening depending on the safety of human and interaction of alleles (Supplementary table 1 and Supplementary table 2). Characteristics and properties of all safe selected epitopes were present in Table 2.

Table 2

Characteristics and properties of all selected epitopes
Cell type	Serotype	Sequences	Start	End	Antigenicity score	Allergic nature	Toxicity	Homologues to human protein	Allele number	IC₅₀
B Cell	DNEV 1	SNTTTDSRCPTQGEATLVEE	66	85	0.7023	No	No	No	-	-
	DNEV 1	AKDKPT	35	40	0.4609	No	No	No
	DNEV 2	LPGADKQESNWI	221	232	0.7226	No	No	No
	DNEV 2	AKNKPT	35	40	0.5794	No	No	No
	DNEV 3	INEKEEN	201	207	1.5975	No	No	No
	DNEV 3	KRLEPNWAS	626	634	1.5273	No	No	No
	DNEV 4	DFVEGVSGG	10	18	0.7821	No	No	No
	DNEV 4	EMAETQHGT	311	319	0.6967	No	No	No
T Cell MHC Class I	DNEV 1	LTMKEKSWLV	59	68	1.8097	No	No	No	4	≥ 100
	DNEV 1	KALKLSWFKK	35	44	0.6828	No	No	No	4
	DNEV 2	WTMKILIGV	453	461	0.6229	No	No	No	4
	DNEV 3	MMLPATLAF	37	45	0.8823	No	No	No	7
	DNEV 3	LLMRTSWAL	44	52	1.0382	No	No	No	6
	DNEV 4	TMFGGVSWMV	446	455	0.4593	No	No	No	4
T Cell MHC Class II	DNEV 1	QDLLVTFKTAHAKKQ	234	248	0.6582	No	No	No	11
	DNEV 1	LLVTFKTAHAKKQEV	236	250	0.7179	No	No	No	10
	DNEV 2	SYIIIGVEPGQLKLN	376	390	1.219	No	No	No	6
	DNEV 2	DSYIIIGVEPGQLKL	375	389	1.0338	No	No	No	6
	DNEV 3	KRRLRTLILAPTRVV	1687	1701	0.4961	No	No	No	15
	DNEV 3	LIATFKIQPFLALGF	1202	1216	1.2597	No	No	No	13
	DNEV 4	MILMKMKTKTWLVHK	196	210	0.4377	No	No	No	6

Construction and analysis of vaccine models

Among many specialized cell types of the immune system, CTLs are the main force of cellular immunity that can directly mediate the death of target cells and eliminate circulating antigens in the host body (51), HTLs can assist both humoral and cellular immunity by secreting different cytokines (52), and stimulated B cells play a vital role in the generation of humoral immune responses by differentiate antibody-producing plasma cells (53). Hence, both T-cell epitopes (CTL and HTL epitopes) and B-cell epitopes were incorporated together to construct an effective multi-epitope vaccine. The structures of all 21 preliminary constructed vaccine sequences with all linkers and adjuvants were presented in supplementary table 6. Characteristics and properties of multi-epitope vaccine models were presented in Table 3.

Preliminary analysis of selected vaccine

The biophysical and biochemical features of the multi-epitope vaccine models DENV-BkS8 and DENV-BkS10 are presented in Table 4. There were no trans-membrane domains (supplementary Fig. 1) and signal peptides and their cleavage sites (supplementary Fig. 2) present in DENV-BkS8 and DENV-BkS10 models.

Table 4

Prediction of biophysical and biochemical features of the multi-epitope vaccine constructs
Properties	DENV-BkS8	DENV-BkS10
Number of amino acids	537	461
Molecular weight (Da)	58829.26	49929.01
Theoretical isoelectric point (pI)	9.70	9.72
Total number of negatively charged residues (Asp + Glu)	45	38
Total number of positively charged residues (Arg + Lys	76	66
The estimated half-life (mammalian reticulocytes, in vitro) in hours	30	30
The estimated half-life (yeast, in vivo) in hours	> 20	> 20
The estimated half-life (Escherichia coli, in vivo) in hours	> 10	> 10
The instability index (II)	30.01	29.31
Protein stability	stable	stable
Aliphatic index	80.39	76.96
Grand average of hydropathicity (GRAVY)	-0.328	-0.330

Secondary structure prediction, tertiary structure modeling, refinement, and validation

Secondary structures such as strand, helix, coil, and configuration were predicted in DENV-BkS8 and DENV-BkS10 vaccine models (Supplementary Fig. 3). Overall estimated TM-scores were 0.186 and 0.263 for initially prepared vaccine models of DENV-BkS8 and DENV-BkS10 and orientations (Contact, Distance, Omega, Theta, and Phi were displayed in supplementary Fig. 4. A comparison between the initial and refined structure of constructed vaccines by Glaxy Web was presented in Table 5.

Table 5

Characteristics of the initial and refined structure of constructed vaccines by Glaxy Web.
Vaccine construct	Model	GDT-HA	RMSD	MolProbity	Clash score	Rama favored
DENV-BkS8	Initial	1.0000	0.000	1.001	1.0	96.6
	Model 1	0.9530	0.427	1.246	4.6	97.9
DENV-BkS10	Initial	1.0000	0.000	1.102	1.4	96.5
	Model 1	0.9490	0.421	1.015	2.4	98.7

The corresponding overall quality factors generated by the ERRAT program were 97.734 (DENV-BkS8) and 96.364 (DENV-BkS10). The Ramachandran plot analysis of the refined model DENV-BkS8 showed that 94.6% of residues were located in the most favorable region, 5.2% in the allowed region, and only 0.2% in the disallowed region (Fig. 2). For the refined model DENV-BkS10, 95.6% of residues were suited in the most favored region, 3.4% in the allowed region, and only 1% of residues in the disallowed regions (Fig. 2). The above plot analysis confirmed the excellent quality of the refined 3D models of the DENV-BkS8 and DENV-BkS10 vaccine constructs.

Molecular docking

Among the thirty clusters in each docking case generated by the ClusPro, the best one with the largest cluster size in each docking case was selected. The detailed docking results for all refined docking complexes were presented in Table 6. All corresponding immune receptor complexes showed lower docking scores and binding energy which indicated that both DNEV-BkS8 and DNEV-BkS10 had a stronger affinity for all immune receptors (TLR2, TLR4, HLA-A*02:01, and HLA-DRB1*01:01).

Table 6

The results of molecular docking analysis of vaccine-receptor complex
Server	Parameter	DNEV-BkS8				DNEV-BkS10
Server	Parameter	TLR2 (A chain)	TLR4 (A chain)	HLA- A*02:01 (D chain)	HLA- DRB1*01:01 (A chain)	TLR2 (A chain)	TLR4 (A chain)	HLA- A*02:01 (D chain)	HLA- DRB1*01:01(A chain)
ClusPro v2.0	Center	-1031.6	-951.8	-1037.1	-1164.8	-1228.2	-988.3	-903.4	-1255.7
ClusPro v2.0	Lowest Energy	-1167.9	-1000.0	-1082.1	-1305.8	1398.2	-1140.2	-1103.4	-1437.2
HADDOCK v2.4	HADDOCK score	-233.3 +/- 3.5	-158.4 +/- 0.0	-124.4 +/- 0.0	-90.6 +/- 0.0	-235.1 +/- 1.2	-172.6 +/- 0.0	-214.1 +/- 0.0	-124.4 +/- 0.0
	Cluster size	50	200	200	200	50	200	200	200
	RMSD from the overall lowest-energy structure	0.7 +/- 0.4	0.0 +/- 0.0	0.0 +/- 0.0	0.0 +/- 0.0	0.7 +/- 0.4	0.0 +/- 0.0	0.0 +/- 0.0	0.0 +/- 0.0
	Van der Waals energy	-120.1 +/- 4.4	-81.0 +/- 0.0	-83.5 +/- 0.0	-19.3 +/- 0.0	-137.7 +/- 2.6	-88.2 +/- 0.0	-129.3 +/- 0.0	-83.5 +/- 0.0
	Electrostatic energy	-321.7 +/- 9.3	-208.1 +/- 0.0	-213.1 +/- 0.0	-335.1 +/- 0.0	-233.8 +/- 20.7	-268.0 +/- 0.0	-341.6 +/- 0.0	-213.1 +/- 0.0
	Desolvation energy	-48.9 +/- 1.6	-35.8 +/- 0.0	1.7 +/- 0.0	-4.3 +/- 0.0	-50.6 +/- 5.1	-30.8 +/- 0.0	-16.5 +/- 0.0	1.7 +/- 0.0
	Restraints violation energy	0.0 +/- 0.0	0.0 +/- 0.0	0.0 +/- 0.0	0.0 +/- 0.0	0.0 +/- 0.0	0.0 +/- 0.0	0.0 +/- 0.0	0.0 +/- 0.0
	Buried Surface Area	3236.8 +/- 32.6	2344.5 +/- 0.0	2256.2 +/- 0.0	1156.7 +/- 0.0	3745.4 +/- 55.3	2531.2 +/- 0.0	3255.5 +/- 0.0	2256.2 +/- 0.0
	Z-Score	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
PRODIG	DG (kcal/mol)	-13.3	-13.7	-8.8	-6.7	-12.2	-11.3	-17.8	-13.0
PRODIG	Kd (M) at 25.0°C	1.7e-10	8.7e-11	3.7e-07	1.3e-05	1e-09	5.3e-09	8.4e-14	3e-10
PDBsum	Number of hydrogen bonds	16	8	21	7	13	11	20	21
PDBsum	Number of salt Bridges	3	1	2	3	1	2	2	2

Significant interactions between the designed vaccines (DENV-BkS8 and DENV-BkS10) and the immune receptors (TLR2, TLR4, HLA-A*02:01, and HLA- DRB1*01:01) complexes were seen according to the analysis of docking interaction by PDBsum server (Fig. 3, and Supplementary Fig. 5).

Molecular dynamics simulation

All simulations were done in normal mode by the iMod server. The eigenvalues were 1.146185e-08, 4.310334e-08, 2.857831e-08, 7.141658e-09, 6.203310e-09, 6.044449e-09, 6.684682e-09, and 4.412438e-09 for DNEV-BkS8-TLR2, DNEV-BkS8-TLR4, DNEV-BkS8-HLA-A*02:01, DNEV-BkS8-HLA-DRB1*01:01, DNEV-BkS10-TLR2, DNEV-BkS10-TLR4, DNEV-BkS10-HLA-A*02:01, DNEV-BkS10-HLA-DRB1*01:01, respectively and this value represents the motion stiffness where the lower eigenvalue represent the easier deformation. The main-chain deformability, average RMS, and the variance of the complex in the simulation were displayed in Fig. 4.

The covariance matrix and elastic network were presented in Fig. 5. The covariance matrix indicates coupling between pairs of residues that are computed using the Cα Cartesian coordinates and the equation provided by Ichiye T and Karplus M (54). The elastic network model expresses which pairs of atoms are connected by springs.

Population coverage of T cell epitopes

Total population coverages were 90.1% for class I MHC epitopes and 72.95% for Class II MHC epitopes (Fig. 6). Average number of epitope hits / HLA combinations recognized by the population and minimum number of epitope hits / HLA combinations recognized by 90% of the population (pc90) were 2.94 and 1.0 for class I MHC epitopes and 4.75 and 0.37 for Class II MHC epitopes, respectively. Area-wise population coverage was presented in supplementary table 7.

Immune simulation

The immune simulations of candidate vaccines showed similar immune response patterns compared among DENV-BkS8, DENV-BkS10, and control (Fig. 7 and Supplementary Fig. 6). The elevation of the primary, secondary, and tertiary response by increasing immunoglobulins and the immunocomplexes were observed. According to the simulation results, the DENV-BkS8 and DENV-BkS10 both can induce stronger immunity in the host body compared with control, but DENV-BkS10 was much stronger than that of DENV-BkS8.

Codon optimization and cloning

The initial vaccine sequences and the improved DNA sequences were presented in Supplementary Table 8. The CAI were 0.94 and 0.94 in DENV-BkS8 and DENV-BkS10, respectively after codon optimization. The GC content was 54.07% in the improved DNA sequence of DENV-BkS8 and 54.95% in the improved DNA sequence of DENV-BkS10 which means both vaccine models might well express in E. coli. The inserted optimized codon sequences of DENV-BkS8 and DENV-BkS10 into the pET28a (+) vector and electrophoretogram of enzyme digestion of pET28a (+)-DENV-BkS8 and pET28a (+)-DENV-BkS10 were shown in Fig. 8.

Computational approaches were used to develop an effective vaccine against Dengue virus infections in this study. These vaccines were constructed in as prescreening manner for clinical trials because constructing an artificial environment favors the illustrated approach to design promising vaccines as a time-saving and cost-effective strategy due to minimal chances of failure. These two vaccines with a good immune response, population coverage, and computational cloning properties into PET28a (+) plasmid prove themselves as a potential candidate for clinical trials. However, experimental validation will be essential to ensure the efficacy of the vaccine construct against the Dengue virus, but this study would help eradicate the global threat caused by the Dengue virus by developing an effective vaccine.

Author Contribution

M. B. R. Conceptualization, Methodology, Software, Data curation, Visualization, and Writing- Original draft preparation and S. R. Supervision, Validation, and Writing- Reviewing and Editing.

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Table 3 is available in the Supplementary Files section.

No competing interests reported.

Design of a novel multiple epitope-based vaccine: An immune-informatics approach to combat Dengue virus

Status:

Version 1

Abstract

Figures

INTRODUCTION

METHODOLOGY

Data retrieval, sequence alignment, and selection of proteins

Prediction of epitope

Profiling and Selection of epitopes

Multi-epitope vaccine constructs design

Prediction of biophysical and biochemical features of vaccine constructs and selection of appropriate vaccine constructs

Secondary and tertiary structure prediction and validation of vaccine constructs

Molecular docking

Molecular dynamics simulation

Prediction of population coverage of epitopes

Immune simulation

Codon optimization of designed vaccine peptide for expression analysis

Useful list of all used servers, tools, and software

RESULT AND DISCUSSION

Protein sequence retrieval and alignment

Epitopes selection

Construction and analysis of vaccine models

Preliminary analysis of selected vaccine

Secondary structure prediction, tertiary structure modeling, refinement, and validation

Molecular docking

Molecular dynamics simulation

Population coverage of T cell epitopes

Immune simulation

Codon optimization and cloning

CONCLUSION

Declarations

Author Contribution

References

Table 3

Additional Declarations

Supplementary Files

Status:

Version 1