Integrated Immuno and bioinformatics assisted novel epitope vaccine against HIV infection: a study based on complete genome

Abstract

Background

As the HIV-based complication is still going on with the high infection and mortality rate, it requires a novel strategy to combat this infection due to the unavailability of proper therapeutic. Therefore, we utilize integrated immuno and bioinformatics approaches in this study to design a peptide vaccine against HIV infection by targeting its complete genome.

Methods

The complete genome sequence was analyzed, and the potential B and T cells were predicted. Among the predicted epitopes, the promising ones were selected and further used with the adjuvant and linker to formulate a vaccine candidate. The vaccine was modeled, and its activity and stability towards the TLRs were analyzed via docking and dynamics (500ns). The vaccine-generated immune activity and expression via in-silico cloning were also evaluated.

Results

A total of 6 B cells, 7 CTL, and 6 HTL were identified as an immunodominant epitope and used for vaccine formulation. These epitopes were fused together via linkers, and their efficiency and constancy were enhanced with Adjuvant, PADRE epitope, and His-tag. Further, the formulated vaccine shows high population coverage and stable features based on the 2D and 3D assessments. The docking investigation demonstrated the strong activity of the vaccine towards the TLR2 and TLR3, having binding affinity − 10.8 kcal/mol-1 and − 15.8 kcal/mol-1, and also disclosed remarkable constancy based on the 500ns simulation period. The vaccine-assisted immune simulation and expression level in the vector revealed a robust immune response towards the host based on the vaccination and a significant expression level.

Conclusions

Based on the integrated approach and validation steps, the overall finding suggests that the formulated vaccine may have strongly immunodominant properties and could combat the infection.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12985-025-02764-0.

Introduction

Human Immunodeficiency Virus (HIV) is one the leading endemics, and due to its complex mechanism, it is the most challenging virus for the world to design therapeutics against, as it leads to severe complications in the infected person and can lead to death [1,2]. According to the WHO current situation report, by the end of 2022, there was a total of an estimated 39.0 million people living with HIV [3]. Over time, acquired immunodeficiency syndrome (AIDS) is the most severe stage of complication due to HIV infection. Moreover, HIV has mainly two classes, i.e., HIV-1 and HIV-2, and due to highly severe complications, HIV-1 is the leading infection in comparison to HIV-2 and leads to AIDS in the advanced stage; apart from that, the complications due to the HIV-2 were seen in the Central and Western Africa mostly. Furthermore, HIV-1 and HIV-2 have several subtypes, which makes them more threatening to global health. HIV-1 is classified based on its severity, i.e., M for major and found commonly. It is also further subdivided into several subtypes (A to K), N for non-major and rarely found (Especially in central Africa), O for outlier is less common (Especially found in central and west Africa),and the recently involved is scarce P [2, 4–6]. Moreover,the complex genome of HIV lies mainly on Gag, Pol, and Env. Gag protein is encoded by the gag gene, which maintains the life cycle through the cleaved by individual proteins such as Matrix (bringing the virus fragment towards the outermost layer of the host cell), Capsid (To protect the genetic material in the early stage), Nucleocapsid (Binding, packaging, and assisting in maturation). Gag-pol precursor encodes the Pol protein, and it also contains the enzymatic genes for viral replication, such as Protease (cleaving the encoded genes during maturation), Reverse Transcriptase (transcribing the viral particle into DNA in the initial stage), and Integrase (an amalgamation of viral particle into host) [7–10]. Env, i.e.,envelope protein, is essential as it interacts with the host cell (CD4 receptor) for viral transmission [8, 9, 11]. Apart from that, the viral genome is also encoded by six regulatory genes, i.e., Tat protein (Essential for transcription of virus particle),Rev protein (Essential for the transportation of viral RNA to the host cell), Nef protein (Essential for the modulation of the immune response), Vif protein (Essential to overcome from antiviral response from the host), Vpr protein (Essential for viral replication through the cell cycle arrest in Growth 2 phase), and Vpu protein (Crucial for releasing viral particle from the infected cells) [9, 12–14]. Antiretroviral therapy (ART) was utilized to overcome this severe infection; however, it cannot completely eradicate the infection. Different vaccines were also developed and could not deal with the infection due to different circumstances and limitations. Moreover, a highly potent vaccine, i.e., RV144, was also limited to 31.2% protection against this infection. As the infection cases and fatalities were increasing, the development of therapeutics (Drugs and Vaccines) is still required to combat this infection [15–19].

Furthermore, the recent interest in subunit vaccine development has been seen worldwide, and promising results have been found as it does not consist of any live pathogenic components. Developing a vaccine against HIV infection is needed as the earlier developed failed to evoke an effective immune response. However, several studies were performed against HIV and its related target protein [8, 9, 20, 21] and it has certain limitations, such as selecting target protein based on antigenicity, allergenicity and essential crucial immunogen-assisted validation. Moreover, to deal with the HIV complication, selecting the potential immunogenic target and their immunodominant epitopes with promising immunogenic activity, such as antigenic, non-allergenic, and non-toxic, is crucial in the initial steps to effectively trigger the immune response.

Therefore, this study extensively attempts to develop an epitope-based peptide vaccine comprising the immunodominant epitopes against HIV infection. As the genes mentioned above, proteins have an essential role in the HIV infection mechanism; Gag, Pol, and Env, along with six regulatory genes, were used.

Methodology

Collection of the entire HIV-1 proteome and its immunological prediction

All proteins encoded in the complete genome of HIV-1 were collected via the NCBI (https://www.ncbi.nlm.nih.gov/) in the FASTA format using accession ID: AF033819 [22].Further, the antigenicity and allergenicity were examined via VaxiJen v2.0 (http://www.jenner.ac.uk/VaxiJen) considering threshold (0.4) and target organism (virus) [23] and AllerTOP (http://www.pharmfac.net/allertop) [24] using default parameters as the sequence utilized in the peptide vaccine must have antigenic and non-allergenic features. The complete, precise methodology is in Fig. 1.

Fig. 1.

Overall involved steps behind the vaccine design against HIV

Prediction of B-cell epitopes and its immunological assessment

The B cell epitope is vital in vaccine formulation to generate antibodies against the target disease. Moreover, for the prediction, two subsequent prediction servers, i.e., ABCpred (http://crdd.osdd.net/raghava/abcpred/) [25] and BepiPred 2.0 (http://tools.iedb.org/bcell/) [26], were incorporated, considering all default parameters. Further, the epitopes present in these two servers were subjected to antigenicity, allergenicity, and toxicity assessment utilizing the VaxiJen v2.0 [23], and parameters were kept as mentioned above, AllerTOP [24], and ToxinPred (http://crdd.osdd.net/raghava/toxinpred/) [27] web servers considering the default parameters.

Prediction of cytotoxic T-cell (CTL) and T-helper cell (HTL) and its immunological assessment

Tepitool (http://tools.iedb.org/tepitool/) sever was used to identify CTL and HTL epitopes [28]. In the case of the CTL (9mers), the list of available 27 sets of alleles associated with different HLA supertypes [29] and for HTL (15mers), the list of available 15 sets of alleles related to various DR was selected, representing the high population coverage; these restricted alleles were considered to identify MHC I and MHC II binding epitope within the protein sequence, considering the IEDB recommended and its default parameter [28] followed by the percentile rank. Epitopes with three or more restricted alleles (to ensure broad population coverage) were subjected to immunogenic-assisted screening, followed by antigen, allergen, and toxin properties analysis, as mentioned above. Moreover, as HIV have diverse strains, the Epitope conservancy of the selected B and T cells was accomplished via the IEDB Conservancy Analysis tool (http://tools.iedb.org/conservancy/) considering the different parameters and steps as previoulsy reported [30, 31].

Population coverage of vaccine construct

A peptide vaccine must be constructed to protect the high world population based on its allele distribution. The population coverage (http://tools.iedb.org/population/) [32] at IEDB was employed to calculate coverage of the selected CTL (MHC I) and HTL (MHC II) epitopes considering Class I and II combined, with the area set as ‘World’ parameters.

Construction of vaccine candidate and its immunological and physiochemical analysis

Based on their promising immunodominant attributes, the identified and selected epitopes were integrated to formulate vaccine candidates. The B cell was fused with the KK, CTL was fused with AAY, and HTL was fused with the GPGPG linker, whereas the B cell with CTL and CTL with HTL were fused with the AAY and GPGPG linkers [8, 20, 33]. Further, to overcome the weak immunogenicity and enhance the response, Adjuvant (β-defensin) as an at N-terminal was linked to EAAAK linker and just after PADRE having 13 mer sequences (helpful for activating the antigen-specific immune response) and at the C-terminal His-tag sequence was adjoined using RVRR linker for the recognition of protein and its purification [34, 35]. Moreover, the immunological assessment was carried out as mentioned above. In addition, the ProtParam [36] and Protein sol [37] were employed to compute the constructed vaccine candidate’s physiochemical properties and solubility following the default parameters. In addition to overcoming the autoimmunity, the final construct sequence was further processed with the human proteome (Taxon Id; 9606) for similarity search characterized by the BLASTp tool [38].

Secondary structure assessment of chimeric vaccine construct

The secondary structure of the constructed vaccine candidate was characterized by employing the SOPMA [39] and PSIPRED [40] web servers, considering the default parameters. The structure assessment will help to understand the features and structure representation in the secondary structure followed by the helix, coil, and extended strand.

3D structure prediction, refinement, and validations

In order to design efficient vaccine model structure, 4 consecutive modelling servers were employed Phyre2 (http://www.sbg.bio.ic.ac.uk/phyre2/html/page.cgi? id=index) [41], Swiss model (https://swissmodel.expasy.org/) [42], I-TASSER (https://zhanggroup.org/I-TASSER/) [43], and Robetta (https://robetta.bakerlab.org/) [44] server. Based on the system prioritization the promising modeled structures among these sever were further selected and refined to enhance the quality of the constructed tertiary model and create a more efficient form via GalaxyRefine (https://galaxy.seoklab.org/index.html) [45]. The quality assessment of the selected refined model was characterized by two subsequent stages, i.e., SWISS-MODEL’s Structure Assessment (https://swissmodel.expasy.org/assess) [46] and ProSA web (https://prosa.services.came.sbg.ac.at/prosa.php) [47] tool, followed by the generation of the Ramachandran plot and Z score.

Assessment of discontinuous B cell epitope

The presence of discontinuous B cells in vaccine development is crucial as it can help in antigen recognition, antibody specificity, and many other areas. The ElliPro [48] server (http://tools.iedb.org/ellipro/) was employed to predict the epitopes, employing an initial score of 0.5 and an optimal distance of 6 and considering the final vaccine model as an input [49].

Molecular interaction analysis between the vaccine and TLRs

The molecular interaction between the vaccine (final selected refined model) and TLRs (TLR2 and 3) was computed via ClusPro 2.0 (https://cluspro.org/help.php) server [50] which is grounded on the PIPER algorithm following the default parameters. The structures of Toll-like receptors 2 and 3 were collected via RCSB in PDB format. Based on the lowest energy scoring and docking efficiency, docked complexes were selected from the output data. Further, the best-selected docking complex was used to calculate binding affinity via PRODIGY (https://rascar.science.uu.nl/prodigy/) [51]. The molecular interaction within the docked complex was visualized through the PDBsum web server and PyMOL software [52, 53].

MD simulation of TLRs with vaccine

To understand the stability of the molecular connection in the docked complex the MD Simulations were carried out to get comprehensive activity on the variations and conformational deviations in the complex [54]. To achieve this, the complex was exposed to 500ns molecular dynamics (MD) simulations using GROMACS 2023 with a time step of 2fs [55, 56]. The examination was computed with periodic boundary conditions, and the force field was derived from the CHARMM-GUI server [57]. The complex is immersed in TIP3P water molecules followed by neutralization via introducing Na and Cl ions and simulated by adding 0.15 M NaCl. The structure was minimized via CHARMM36 [58, 59].Further, the other parameters were used as earlier reported previously [56,58–60].The trajectories were examined through VMD and GROMACS [59].

Evaluation of the immune response of designed vaccine

The immune response generated through the designed vaccine was examined through the C-ImmSim (https://kraken.iac.rm.cnr.it/C-IMMSIM/index.php?page=1) (A machine learning-assisted application) to understand the ability to generate antibodies and cytokines [61]. The final constructed vaccine sequence was utilized as an input for this evaluation. In this study, the designed vaccine was administered at three doses followed by a four-week interval following the previously utilized strategy. The vaccine was administered at time steps 1, 84, and 168, corresponding to the primary, secondary, and tertiary immune responses, respectively, with no LPS considered [8,33].

Cloning and expression assessments

The Codon Adaption tool (JCat) optimized the vaccine sequence, followed by the E. coli expression system employing the default parameters [62]. For the maximum expression of the query sequence, the optimum CAI value is 1.0, while > 0.8 is regarded as a good score, and the GC content is 30-70% [8]. SnapGene (https://www.snapgene.com/) software were utilized to incorporate the vaccine in pET-28a(+), and their expression was examined [20, 63].

Results and discussion

Collection of the entire HIV-1 proteome and its immunological prediction

The whole genome associated each target sequence was collected via the NCBI database, and the partial sequence was replaced with the corresponding complete sequence and was further subjected to immunogenic-assisted screening to identify a promising one. The screening based on the antigenicity and allergenicity revealed that among the nine retrieved proteins, only 7 were (Supplementary Table 1) found to have the required feature (Highlighted in blue) and utilized for promising epitope identification.

Prediction of B-cell epitopes and its immunological assessment

The B cell epitopes were characterized by two subsequent servers, BepiPred 2.0 and ABCpred. The overlapped epitope predicted through these servers within each protein sequence was further utilized for the immunological assessment. The antigenicity, allergenicity, and toxicity analysis revealed several promising epitopes within each target protein (Supplementary Tables 2 to 8); however, for the immunodominant vaccine formulation, one epitope having the highest antigen score from each target was selected as in Table 1.

Table 1.

Selected immunodominant b-cell epitope within each protein with their immunological feature

Sl.No	Start	Epitope	Antigen	Allergen	Toxic
Gag
1.	24	GGKKKYKLKHIVWASR	1.4907 (Antigen)	No	No
Gag-Pol
2.	24	GGKKKYKLKHIVWASR	1.4907 (Antigen)	No	No
Env
3.	208	VSFEPIPIHYCAPAGF	1.3361 (Antigen)	No	No
Rev
4.	86	NEDCGTSGTQGVGSPQ	0.7840 (Antigen)	No	No
Tat
5.	40	TKALGISYGRKKRRQR	1.4325 (Antigen)	No	No
Vif
6.	74	TGERDWHLGQGVSIEW	1.9108 (Antigen)	No	No
Vpu
7.	67	MGVEMGHHAPWDVDDL	1.1882 (Antigen)	No	No

Prediction of cytotoxic T-cell (CTL) and T-helper cell (HTL) and its immunological assessment

Based on the 9 mer and 15 mer for the CTL and HTL with the available restricted alles set, the epitope was identified employing the TepiTool sever, considering IEDB-assisted parameters. The predicted epitope was subjected to the analysis of antigen, allergen, and toxin properties (Supplementary Tables 9 to 22). The immunological assessment revealed several promising epitopes within each target protein; however, among the promising epitopes from each target protein, 1 epitope based on the highest antigen score, non-allergen, and non-toxic profile was selected from the identified CTL and HTL epitopes and further utilized for the vaccine formulation. The selected epitopes with their immunological assessment are represented in Table 2. Moreover, the conservancy analysis revealed that the selected epitope is highly conserved, followed by their minimum and maximum identity [30], which ensures that the selected epitope may enhance vaccine efficacy and address HIV genetic diversity.

Table 2.

List of selected (CTL and HTL) epitopes within the proteins with their associated immunological properties

Sl.No	Position	Epitope	Alleles	Antigen	Allergen	Toxic
Cytotoxic T-cell epitopes
Gag
1.	28–36	KYKLKHIVW	HLA-A*24:02 HLA-A*23:01 HLA-A*32:01 HLA-B*57:01	1.6115 (Antigen)	No	No
Gag-Pol
2.	480–488	RQGTVSFNF	HLA-A*32:01 HLA-B*15:01 HLA-A*23:01 HLA-A*24:02 HLA-A*30:02	2.0637 (Antigen)	No	No
Env
3.	208–216	VSFEPIPIH	HLA-A*30:02 HLA-A*03:01 HLA-A*11:01	2.2018 (Antigen)	No	No
Rev
4.	67–75	SAEPVPLQL	HLA-B*53:01 HLA-B*51:01 HLA-B*35:01 HLA-A*01:01 HLA-A*02:06 HLA-B*58:01 HLA-B*07:02 HLA-A*68:02 HLA-B*08:01	1.4534 (Antigen)	No	No
Tat
5.	41–49	KALGISYGR	HLA-A*31:01 HLA-A*11:01 HLA-A*33:01 HLA-A*68:01	1.7375 (Antigen)	No	No
Vif
6.	84–92	GVSIEWRKK	HLA-A*11:01 HLA-A*03:01 HLA-A*30:01	3.0282 (Antigen)	No	No
Vpu
7.	24–32	SIVIIEYRK	HLA-A*11:01 HLA-A*68:01 HLA-A*03:01	1.9806 (Antigen)	No	No
T helper cell epitopes
Gag
1.	214–228	RVHPVHAGPIAPGQM	HLA-DRB1*01:01 HLA-DRB1*04:01 HLA-DRB1*07:01 HLA-DRB1*09:01 HLA-DRB1*12:01 HLA-DRB4*01:01	1.6066 (Antigen)	No	No
Gag-Pol
2.	214–228	RVHPVHAGPIAPGQM	HLA-DRB1*01:01 HLA-DRB1*04:01 HLA-DRB1*07:01 HLA-DRB1*09:01 HLA-DRB1*12:01 HLA-DRB4*01:01	1.6066 (Antigen)	No	No
Env
3.	490–504	KIEPLGVAPTKAKRR	HLA-DRB1*03:01 HLA-DRB1*13:02 HLA-DRB5*01:01	1.9736 (Antigen)	No	No
Rev
4.	60–74	LGTYLGRSAEPVPLQ	HLA-DRB1*01:01 HLA-DRB1*07:01 HLA-DRB1*08:02 HLA-DRB1*09:01 HLA-DRB1*11:01 HLA-DRB1*13:02 HLA-DRB1*15:01 HLA-DRB3*02:02 HLA-DRB5*01:01	1.0953 (Antigen)	No	No
Tat
5.	40–54	TKALGISYGRKKRRQ	HLA-DRB1*03:01 HLA-DRB1*13:02 HLA-DRB5*01:01	1.6611 (Antigen)	No	No
Vif
6.	81–95	LGQGVSIEWRKKRYS	HLA-DRB1*03:01 HLA-DRB1*11:01 HLA-DRB1*13:02	1.7980 (Antigen)	No	No
Vpu
7.	2–16	QPIPIVAIVALVVAI	HLA-DRB1*01:01 HLA-DRB1*07:01 HLA-DRB1*12:01 HLA-DRB1*15:01	0.9177 (Antigen)	No	No

Population coverage of vaccine component

The selected CTL and HTL epitopes (Table 2) for vaccine formulation were used to calculate world population coverage combinedly via IEDB population coverage online tool. It shows that the vaccine component covered 97.98% of the world population. Most of the designed peptide vaccine’s population coverage confirmed its effectiveness worldwide, as shown in Fig. 2A.

Fig. 2.

Illustration of population covergae and vaccine construction. (A) Representataion of epitopes corresponding to their coverage, (B) Representataion of steps and the involved attributes in peptide-based vaccine construction

Construction of vaccine candidate and its immunological and physiochemical analysis

The promising B cell, CTL, and HTL epitopes were ultimately selected and fused with the different linkers based on immunogenic-assisted screening. Interestingly, the final selected B and HTL epitopes within the Gag and Pol proteins have similar promising highly antigenic epitopes; one epitope was selected, representing highly immunodominant properties. A total of 6 B cells, 7 CTL, and 6 HTL were adjoined together using the KK, AAY, and GPGPG linkers. In addition, to boost the immune response, antigenic specific recognition, protein purification, and recognition the β-defensin at the N through the EAAAK and PADRE (AKFVAAWTLKAAA) after the adjuvant and His-Tag at the C terminal via RVRR linker was introduced to fused the final construct [8, 20]. Finally, a 388-sequence-length vaccine was formulated, comprised of the promising B cell, CTL, and HTL epitopes from each selected protein, as shown in Fig. 2B.

Before further utilization, the immunological profile was analyzed, and the designed vaccine was found to have immunodominant properties. Moreover, the physiochemical properties were assessed through the ProtParam sever, and the various calculated attributes were obtained and shown in Table 3. The overall assessment revealed that the developed vaccine has a favorable feature followed by its promising immunological nature, physiochemical properties such as the instability 39, i.e., is under range (below 40), GRAVY value is negative indicating hydrophilic properties and high solubility, as its obtained value (0.611) is higher than the experimental control level (0.45) [37]. The BLASTp search revealed no significant similarity with the human host.

Table 3.

Calculated Immuno and physiochemical properties of the vaccine construct

Sl.No	Properties	Remarks
1.	Antigen	Yes (1.0813)
2.	Allergen	No
3.	Molecular Weight	42237.31
4.	Theoretical pI	10.34
5.	Instability index	39.00
6.	Aliphatic index	72.99
7.	GRAVY	-0.497
8.	Estimated half-life	30 h (mammalian reticulocytes; in vitro). > 20 h (yeast; in vivo) > 10 h (Escherichia coli; in vivo)
9.	Solubility	0.611

Secondary structure assessment of chimeric vaccine construct

The secondary structure of the constructed peptide vaccine was accomplished with two different online servers, i.e., SOPMA and PSIPRED, considering default parameters. This result shows that the secondary structure contains an Alpha helix (21.65%), Random coil (49.23%), extended strand (21.65%), and beta-turn (7.47%) assessed by SOPMA server. Their graphical representation and other features are in Fig. 3.

Fig. 3.

Diagrammatic representation of predicted secondary structure via PSIPRED server

3-D structure prediction, refinement, and validations

The 4 structure modeling servers (Phyre2, Swiss model, I-TASSER, and Robetta) having different backend algorithms were employed for the 3D structure prediction. The model generated through the Robetta servers was suitable for these servers. Interestingly, as the Robetta server designed five models of the sequence, these model has the same confidence score for all predicted models. Model 1 was utilized for structure refinement through the Galaxy Refine. Among the generated 5 refined models, model 1 was adopted based on highly Rama favoured and less clashing scores (Supplementary Table 23). The SWISS-MODEL structure assessment and ProSA web validated the selected model. The initial Ramachandran favored was 90.93%, and for the refined structure, it was 94.82%. The Z score value was initially -7.61 and, after refinement, was -7.68. The overall assessment shows a promising 3D structure and effective profile. The initial and refined structure and its assessment are illustrated in Fig. 4(A & B).

Fig. 4.

Illustration of the 3D structure of the vaccine construct and its evaluation. (A) Representation of the initial 3D structure along with the Ramachandran plot and Z-score plot (B) Representation of the refined improved 3D structure and z-score value

Assessment of discontinuous B cell epitope

The discontinuous B cell epitope was asses through the ElliPro server using the model structure of the vaccine.A total of 5 epitopes were identified within the vaccine construct,having varying lengths from 18 to 70. However,the epitopes were characterized based on their scores of 0.595 to 0.825. The overall residue identified within the vaccine is comprised of 212. The representation of the top two based on their score are in Fig. 5. Whereas rest were shows in Supplementary Fig. 1.

Fig. 5.

Illustration of top two discontinuous epitope in the vaccine construct. The epitopes are represented in yellow balls; the remaining vaccine portion is in stick form

Molecular interaction analysis between the vaccine and TLRs

The vaccine was subject to docking with the TLR-2 and 3 (PDB ID: 3a7c and 2a0z) to check for possible molecular interaction. The ClusPro 2.0 was employed to compute docking between TLR-2 and 3 and vaccine components. Among the top ten displayed docked clusters, the docking complex with the lowest energy score value was selected for analysis. Model 4 for the vaccine with TLR-2 and Model 1 for the vaccine with TLR3 showed the − 1401.5 kcal mol^− 1 and − 1078.6 kcal mol^− 1 lowest energy score shown in Supplementary Table 24(highlighted in blue color), which indicated the spontaneous reactivity between vaccine and TLR-2 and 3 receptors. The binding affinities of the selected docking were − 10.8 kcal mol^− 1 and − 15.8 kcal mol^− 1 obtained from the PRODIGY server. To anticipate the interaction and other properties, the 2D interaction was visualized through the PDBsum, revealing that the vaccine with TLR2 and the vaccine with TLR3 have 8 and 30 hydrogen bonds. It also shares the interface residue 30 with 20 for TLR2-vaccine and 42 interface residues with 35 for TLR3-vaccine, respectively (Supplementary Fig. 2). In addition, the molecular interaction was visualized through PyMOL software, shown in Fig. 6.

Fig. 6.

Molecular interaction of vaccine with TLRs. (A) Residue is involved in the interaction between the vaccine and TLR2, and (B) Vaccine with TLR3

MD simulation of TLRs with vaccine

The dynamics behind the interaction within the complex were examined over 500ns to examine the stability and solvent exposure of the system. Moreover, in the case of RMSD, the higher number of residues will lead to conformational changes compared to a smaller size. The RMSD of TLR2 and 3 with the vaccine shows the stability of the complex after 200ns (TLR2 with the vaccine) (Fig. 7A) and 100ns (TLR3 with the vaccine) (Fig. 8A) as indicated by the plateauing of the RMSD values. Further, the RMSF of chain A (TLR) and chain B (Vaccine) were individually illustrated and examined to gain individual insight into the residue within the complex. The TLR2 and Vaccine show a minor fluctuation in both cases and lie within the range, i.e., of TLR2 from 0.21 to 0.7Å where, whereas the Vaccine is 0.2 to 1.4Å (Fig. 7B and C) which demonstrated the slightly higher fluctuations. Similarly, the TLR3 and vaccine show an average fluctuation within the range followed by 0.0 to 0.6Å and 0.2 to 1.2Å (Fig. 8B and C). Moreover, based on the structural behaviour, these demonstrated stable interaction with the vaccine, followed by flexibility in its binding form. Further, the docked complex compactness was examined via the Radius of Gyration (Rg), and the remarkable compactness within the complex over time in TLR2 with the vaccine (Fig. 7D) and TLR3 with the vaccine (Fig. 8D) demonstrated the signifying stable folding and limited expansion over the simulation period. Additionally, the SASA revealed the range of TLR2 between 260 and 286 nm, and the vaccine in 269 to 325 nm (Fig. 7E) revealed slightly higher exposure. In contrast, the TLR3 was 295 to 320 nm, and the vaccine was 273 to 325 nm (Fig. 8E). The overall analysis confirms that the TLRs with the vaccine have stable binding and structural integrity followed by minimal fluctuations [60,64].

Fig. 7.

The dynamics series calculations of TLR2 with the vaccine. (A) RMSD intended on over 500 ns trajectory of TLR2 and vaccine, (B) RMSF of the TLR2 (Chain A), (C) RMSF of the vaccine, (D) RGYR of the TLR2 and vaccine component, and (E) Surface accessibility of TLR2 with vaccine over 500ns period

Fig. 8.

The dynamics series calculations of TLR3 with the vaccine. (A) RMSD intended on over 500 ns trajectory of TLR2 and vaccine, (B) RMSF of the TLR3 (Chain A), (C) RMSF of the vaccine, (D) RGYR of the TLR3 and vaccine component, and (E) Surface accessibility of TLR3 with vaccine over 500ns period

Evaluation of the immune response of designed vaccine

The anticipation of the vaccine’s ability to have an effective response was computed through the C-ImmSim. The vaccine construct demonstrates robust immune activity across primary, secondary, and tertiary immune responses followed by their time stesp of injection. In Fig. 9A, the generation of antibody types shows the highest levels for the IgM and IgG combination, followed by IgM alone, IgG1 + IgG2, IgG1, and IgG2, indicating a progression in immune response, particularly during secondary and tertiary exposure. As shown in Fig. 9B, cytokine and interleukin concentrations peak within the first 50 days, with IFN-γ and IL-2 levels notably elevated, highlighting the vaccine’s capacity to evoke a strong immune response. Figure 9C illustrates that active B cell populations are significantly stimulated, maintaining a heightened state due to the robust immune reaction. Similarly, Fig. 9D and E show increased helper T (Th) and cytotoxic T (Tc) lymphocyte populations, with Tc cells continuing to rise over time. This immunogenic profile underscores the potential of the peptide vaccine to trigger a vigorous immune activity against the viral infection.

Fig. 9.

Immune simulation profile of vaccine construct. (A) Representation of immunoglobulin and immunocomplex response to antigen; (B) Represent increased concentration of interleukin and cytokine levels. (C) The level of active B-cell population (D & E) The increase in the population of helper T lymphocyte (Th) cells and cytotoxic T lymphocytes upon repeat antigen represent the higher immune response

Expression analysis of vaccine through in Silico cloning

The final vaccine construct sequence was subjected to codon optimization using the Java codon adaptation tool (JCAT). The sequences were reverse-translated into DNA sequences to achieve the maximum expression level in the E. coli K12 strain. The optimized sequence length was 1164 bp, the codon adaptation index was 1.0, and the GC content was 52.23%, which indicates an adequate range for the maximum expression in the E. coli system. Finally, using the SnapGene software, the optimized sequence was cloned in pET-28a(+) for the expression, as shown in Fig. 10. In contrast, the blue represents the inserted vaccine, and the other represents the vector region.

Fig. 10.

Cloned sequence (Red color) into E. coli expression vector pET28a (+) using SnapGene software

Discussion

Due to the unavailability of proper therapeutics, HIV infection is still going on, with severe complications within the infected patients. To deal with and control this infection, researchers applied and designed treatments, and, unfortunately, they cannot reduce and combat the infection completely [15, 65]. The HIV infection within the host targets the CD4 + receptor and starts the mechanism, followed by transcription, generation of new viral proteins, and many more to complete the mechanism. However, infection begins with the host-pathogen interaction via the attachment of the envelop protein with the CD4+; the other viral protein and regulatory target al.so play a vital role in the mechanism [2, 10, 17]. At present computational approach followed by the amalgamation of immunoinformatics and bioinformatics [66–70] shows a remarkable response in peptide-based vaccine developments [20, 60]. Therefore, this study employed steps of the immunoinformatic along with bioinformatics approach to categorize and screen out the immunodominant B and T cell epitopes among all the targets within the complete genome for the construction of a multi-epitope-based vaccine that elicit together humoral and cellular immune response towards the infection. Among the complete genome-based 9 targets, 7 were found suitable for the investigation, and 2 were excluded from further investigation due to the non-potential properties. For vaccine development, the selected epitope must have a wide range of features, such as immunodominant properties, and be applicable for high coverage. A total of 6 B cells, 7 MHC-I, and 6 MHC-II epitopes were found suitable for the vaccine design. The epitope-based vaccine was constructed along with the Adjuvant (To improve the effectiveness) and suitable linker [8, 71]. Additionally, the His-tag was adjoined for solubility and stability enhancements. To reduce the infection, the vaccine must have antigenic features as it will lead to long-term immunity, high protection, and many more. The designed vaccine comprises 388-sequence-length, having an antigenic score of 1.0813 and non-allergenic properties. Moreover, the previous study reported a vaccine antigenic score of 0.8778 (HIV-1a) and 0.8028 (HIV-1b) by Sher et al. [9] 0.6789 by Habib et al. [20] and 0.45 by Pandey et al. [8] suggest the formulated vaccine in this study can lead to a remarkable response. At present, the infection and transition rate of HIV infection is high worldwide; however, based on the comprised epitopes and their restricted alleles, the formulated vaccine revealed 97.98% worldwide population coverage, which is nearly more or less similar to previously reported study [9, 21]. The physiochemical and solubility of the vaccine show highly promising activity, based on reported earlier data [8, 9]. The secondary assessment revealed that the vaccine has helix (21.65%), coil (49.23%), and strand (21.65%), which is outperformed by the prior data that was reported (helix: 15.43%, coil: 75.43% and strand: 9.14%) by Pandey et al. [8] and comparably nearly similar to the reported data [20], the presence of high helix within the construct indicate the remarkable stability. To actively bind and initiate immune activity, the vaccine structure must have proper folding to ensure proper activity. The structure of our designed vaccine demonstrated favourable region, i.e., 94.82%. and the Z-score value, i.e., -7.68, is nearly higher than the earlier reported data [8, 20].. The conformational B cell epitope within the designed vaccine will help reduce the risk of immune evasion and lead to a robust immune response towards the infection. A further study reported that targeting TLR2 ad 3 in the case of HIV will be beneficial as the TLR2 can easily recognize the viral components and enhance the immune activity and the production of inflammatory cytokines. In contrast, the TLR3 activation will help induce the antiviral response, followed by the activation of interferons and other cytokines vital to combat and reduce the viral response [64, 72, 73]. The formulated vaccine is strongly bound with the TLR2 and TLR3 via numerous types of molecular connectivity followed by the − 10.8 kcal mol^− 1 and − 15.8 kcal mol^− 1 binding affinity followed by 8 and 30 Hydrogen bond that ensures the vaccine may able to trigger the response effectively [74]. Studies found that targeting these TLRs can be a potential lead [19, 64]. Zubair et al. found that the their designed vaccine have strong actvity towards the TLR3 and TLR5 based on affinity among other TLRs [64]. Habib et al. found that among the other TLRs, the vaccine demonstrated higher activity towards the TLR2 followed by 12 H bond [20]. In the form of vaccine activity towards the TLRs, the formulated vaccine is nearly similar and higher then the earlier reported study. Further, simulation over 500ns revealed the stability of the vaccine towards TLR2 and TLR3 with minor deviation, whereas the RMSD demonstrated the system stabilises after 200ns (TLR2-Vaccine) and 100ns (TLR3-Vaccine), RMSF range was varied from 0.21 to 0.7 Å for TLR2 and Vaccine, and 0.0 to 0.6 and 0.2 to 1.2 for the TLR3 and Vaccine. Subsequently, the Rg demonstrate the significant compactness of the complex, and the SASA revealed the conformational changes of the system [19, 21, 64]. To analyses the vaccine-TLRs complex through simulation was also performed by the researcher previously. Moreover, the designed vaccine and its stability towards the TLRs are nearly similar to what previously reported outcomes [21, 64]. The vaccine-assisted immune response based on the steps of injection revealed the generation of different IgM, IgG1 + IgG2, IgG1, and IgG2 antibodies, and their combination, followed by the high peak of interleukin and cytokine levels based on the dosing revealed remarkable response followed by prolonged memory [20, 65] which will help to control and clear antigens. Moreover, similar to other investigations, the immune activity responded, and the activation of antigen-presenting cells based on the time frame was found promising and ideal to trigger the immune system [21, 64, 75]. Additionally, the expression level of a vaccine in the pET28a (+) vector was examined, and the optimised sequence revealed 52.23% GC content along with 1 CAI value, demonstrating the obtained values lie in a high expression level. Moreover, the in silico cloning of formulated vaccine in the pET28a (+) vector was found ideal, as reported with the GC% 51.97% by Pandey et al. [8] 52.04% (HIV-1a) and 51.48% (HIV-b) by Habib et al. [20] 41.76% and 41.75 by Heidarnejad et al. [74] and ensures the formulated vaccine in this study can lead to a remarkable response. Based on the overall finding, the study shows that the designed multiple-epitope vaccine construct comprised of B and T cells holds remarkable activity against HIV infection. However, further in vitro and in vivo validation is essential, as also discussed in previous studies [69]. To address the HIV infection, the vaccine and drugs were designed; however, it was unable to reduce the infection completely. The infection depletes the CD4 + count level, further reducing immune system activity. One of the major obstacles behind the successful vaccine is less immune response activity [15–17, 64]. Therefore, in this investigation, the formulated vaccine is grounded on the comprising immunodominant B and T cell epitopes which target both cellular and humoral immune and can trigger robust immune responses to combat HIV. The significant highly immunogenic properties of the formulated vaccine suggest its effectiveness and capability to fight HIV infection. Moreover, this work has some limitations. The integrated immuno and bioinformatics use various tools, servers, and databases to formulate this vaccine. However, the vaccine has shown potent activity; future steps should include laboratory validation of the vaccine model, assessing its efficacy across diverse populations, and evaluating vaccine-induced immune responses to the infection, followed by subsequent phases of clinical trials.

Conclusion

HIV infection is one of the ongoing pandemics worldwide, and their complication is still going on. This study examined the complete genome target sequence via an integrated immunoinformatics and bioinformatics approach, and highly promising multi-epitope-based vaccines were designed. The designed vaccines can powerfully evoke the target receptor along with high stability, and the generation of immune response based on vaccination via immune simulation suggests the remarkable immune activity within the host. The expression of the vaccine in the target vector shows the high GC constant followed by the CAI value that ensures the high expression of the vaccine. Overall study suggests the successful vaccine has a remarkable response, and the involved steps can also be utilized for other pathogens.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Author contributions

S.K.M. conceived, designed, and performed the study. A.G., N.K., and G.S. contributed to data curation, software analysis, validation, and visualization. T.A., M.Z., and A.A.M. were involved in data curation, software analysis, investigation, resource acquisition, and formal analysis. Y.K.T. and J.J.G. supervised the project, contributed to resource acquisition and formal analysis, and reviewed and edited the manuscript. All authors reviewed and approved the submitted version of the manuscript

Funding

The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through large group Research Project under grant number RGP2/406/45.

Data availability

The supplemental material for this article can be found in its online version.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.