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. 2022 Aug 3:13:956776.
doi: 10.3389/fimmu.2022.956776. eCollection 2022.

Exploring whole proteome to contrive multi-epitope-based vaccine for NeoCoV: An immunoinformtics and in-silico approach

Shahkaar Aziz  1 , Muhammad Waqas  2   3 , Sobia Ahsan Halim  2 , Amjad Ali  3 , Aqib Iqbal  1 , Maaz Iqbal  1 , Ajmal Khan  2 , Ahmed Al-Harrasi  2
Affiliations

Exploring whole proteome to contrive multi-epitope-based vaccine for NeoCoV: An immunoinformtics and in-silico approach

Shahkaar Aziz et al. Front Immunol. .

Abstract

Neo-Coronavirus (NeoCoV) is a novel Betacoronavirus (β-CoVs or Beta-CoVs) discovered in bat specimens in South Africa during 2011. The viral sequence is highly similar to Middle East Respiratory Syndrome, particularly that of structural proteins. Thus, scientists have emphasized the threat posed by NeoCoV associated with human angiotensin-converting enzyme 2 (ACE2) usage, which could lead to a high death rate and faster transmission rate in humans. The development of a NeoCoV vaccine could provide a promising option for the future control of the virus in case of human infection. In silico predictions can decrease the number of experiments required, making the immunoinformatics approaches cost-effective and convenient. Herein, with the aid of immunoinformatics and reverse vaccinology, we aimed to formulate a multi-epitope vaccine that may be used to prevent and treat NeoCoV infection. Based on the NeoCoV proteins, B-cell, cytotoxic T lymphocyte (CTL), and helper T lymphocyte (HTL) epitopes were shortlisted. Four vaccines (Neo-1-4) were devised by fusing shortlisted epitopes with appropriate adjuvants and linkers. The secondary and three-dimensional structures of final vaccines were then predicted. The binding interactions of these potential vaccines with toll-like immune receptors (TLR-2, TLR-3, and TLR-4) and major histocompatibility complex molecules (MHC-I and II) reveal that they properly fit into the receptors' binding domains. Besides, Neo-1 and Neo-4 vaccines exhibited better docking energies of -101.08 kcal/mol and -114.47 kcal/mol, respectively, with TLR-3 as compared to other vaccine constructs. The constructed vaccines are highly antigenic, non-allergenic, soluble, non-toxic, and topologically assessable with good physiochemical characteristics. Codon optimization and in-silico cloning confirmed efficient expression of the designed vaccines in Escherichia coli strain K12. In-silico immune simulation indicated that Neo-1 and Neo-4 vaccines could induce a strong immune response against NeoCoV. Lastly, the binding stability and strong binding affinity of Neo-1 and Neo-4 with TLR-3 receptor were validated using molecular dynamics simulations and free energy calculations (Molecular Mechanics/Generalized Born Surface Area method). The final vaccines require experimental validation to establish their safety and effectiveness in preventing NeoCoV infections.

Keywords: NeoCoV; epitopes prediction; immunoinformatics; multi-epitope vaccine; subunit vaccine; vaccine design.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Schematic illustration of the overall strategy implemented in this study to design multi-epitope-based vaccine from NeoCoV whole proteome.
Figure 2
Figure 2
Global population coverage of selected T-cell epitopes (MHC-I, MHC-II, and combined MHC) based on their corresponding HLA binding alleles.
Figure 3
Figure 3
The predicted secondary structure features, relative surface accessibility, and disorder regions of (A) Neo-1 (B) Neo-4 using NetsrurfP-3.0 server.
Figure 4
Figure 4
Validation of the predicted 3D structure models of NeoCoV vaccines with PROCHECK and ProSA-webserver.
Figure 5
Figure 5
NeoCoV vaccine constructs (Neo-1 and Neo-4) docked with human Toll-like receptor-3 (TLR3). The best-docked vaccine-TLR3complex is depicted in the middle. Neo-1 and Neo-4 are represented with a silver cartoon model, and TLR3 is shown in a light green surface model. The interacting residues of Neo-1 and Neo-4 with TLR3 receptor are shown on either side. Hydrogen bonds are indicated in blue lines. The color of interacting residues reflects the properties of amino acid.
Figure 6
Figure 6
In silico restriction cloning of Neo-1−4 vaccine constructs. Using the SnapGene program, the final vaccine constructs codon-optimized sequence (red color) was cloned between the XhoI (158) and NdeI (936) restriction enzyme loci in the pET-28a (+) expression vector (black color). Efficient expression of the designed constructs can be carried out in E. coli strain K12 for effective vaccine production.
Figure 7
Figure 7
The immune response of Neo-1 and Neo-4 evaluated using C-Immsim server (A) Response of antibodies and antibodies complex to antigen (B) Total count per entity state of B-lymphocytes (C) Count of plasma B-lymphocytes (D) Total count per entity state of CD4 T helper lymphocyte (E) Total count per entity state of CD8 T-cytotoxic lymphocytes (F) cytokine concentrations and interleukin in various states shown in a smaller graph with the Simpson index shown in dotted line. In three successive immunological reactions, all units are expressed in cells/mm3.
Figure 8
Figure 8
Molecular dynamics simulation of Neo-1 and TLR-3 complex at 110ns (A) Root means square deviation (RMSD) plot of the complex, representing mild fluctuations (B) The Root means square fluctuation (RMSF) plot of the docked complex (C) The Radius of Gyration plot of the docked complex (D) Free energy landscapes (FELs) of the docked complex. High, intermediate, and low/stable energy states are shown in red, yellow/green, and light-to-dark blue color in the graph, respectively (E) Alterations in Solvent-Accessible Surface Area (SASA) profile of the docked complex during the simulation.
Figure 9
Figure 9
Molecular dynamics simulation of Neo-4 and TLR-3 complex at 110ns (A) Root means square deviation (RMSD) plot of the complex, representing mild fluctuations (B) The Root means square fluctuation (RMSF) plot of the docked complex (C) The Radius of Gyration plot of the docked complex (D) Free energy landscapes (FELs) of the docked complex. High, intermediate, and low/stable energy states are shown in red, yellow/green, and light-to dark blue color in the graph, respectively (E) Alterations in Solvent-Accessible Surface Area (SASA) profile of the docked complex during the simulation.
Figure 10
Figure 10
Proposed immune route in response to a multiepitope-based subunit vaccine in the host. The vaccine enters the cell through toll-like receptors (TLRs) and attaches to dendritic cells (DC) and macrophages (MP), triggering an innate immune response. The vaccine’s epitopes are then digested by antigen-presenting cells (APC) and presented to T-cells, which triggered an adaptive immunological response. T-cells activate other immune cells or destroy infected cells directly (cellular immune response), whilst plasma B-cells (PLBC) create antibodies to neutralize viruses and memory B-cells (MBC) preserve all the information needed to mount a powerful immune response in the event of re-infection (humoral immune response). TCR, T-cell receptors; CTL, cytotoxic T lymphocytes; IFNs, interferons; NK, natural killer cells; CD, cluster of differentiation; MHC, major histocompatibility complex; TCR, T-cell receptors; CTL, cytotoxic T lymphocytes; IFNs, interferons; NK, natural killer cells; PLBC, Plasma B-cells.
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References

    1. Ur Rehman MF, Fariha C, Anwar A, Shahzad N, Ahmad M, Mukhtar S, et al. . Novel coronavirus disease (COVID-19) pandemic: A recent mini review. Comput Struct Biotechnol J (2021) 19:612–23. doi: 10.1016/j.csbj.2020.12.033 - DOI - PMC - PubMed
    1. Yan H, Wang X, Xiong Q, Cao L, Ma C, Liu C, et al. . Close relatives of MERS-CoV in bats use ACE2 as their functional receptors. bioRxiv[preprint] (2022). doi: 10.21203/rs.3.rs-1292418/v1 - DOI - PMC - PubMed
    1. Hok L, Rimac H, Mavri J, Vianello R. COVID-19 infection and neurodegeneration: Computational evidence for interactions between the SARS-CoV-2 spike protein and monoamine oxidase enzymes. Comput Struct Biotechnol J (2022) 20:1254–63. doi: 10.1016/j.csbj.2022.02.020 - DOI - PMC - PubMed
    1. Mohd HA, Al-Tawfiq JA, Memish ZA. Middle East respiratory syndrome coronavirus (MERS-CoV) origin and animal reservoir. Virol J (2016) 13:1–7. doi: 10.1186/s12985-016-0544-0 - DOI - PMC - PubMed
    1. Hassan MM, Hussain MA, Kambal S, Elshikh AA, Gendeel OR, Ahmed SA, et al. . NeoCoV is closer to MERS-CoV than SARS-CoV. Infect Diseases: Res Treat (2020) 13:1178633720930711. doi: 10.1177/1178633720930711 - DOI - PMC - PubMed