Design of an Epitope-Based Vaccine Against MERS-CoV
Department of Botany and Microbiology, College of Science, King Saud University, Riyadh 11451, Saudi Arabia
Medicina 2024, 60(10), 1632; https://doi.org/10.3390/medicina60101632
Submission received: 23 August 2024
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Revised: 1 October 2024
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Accepted: 4 October 2024
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Published: 6 October 2024
(This article belongs to the Special Issue Public Health in the Post-pandemic Era)
Abstract
:Background and Objectives: Middle East Respiratory Syndrome (MERS) is a viral respiratory illness caused by a coronavirus called Middle East respiratory syndrome. In the current study, immunoinformatics studies were applied to design an epitope-based vaccine construct against Middle East Respiratory Syndrome. Materials and Methods: In this study, epitopes base vaccine construct was designed against MERS using immunoinformatics approach. Results: In this approach, the targeted proteins were screened, and probable antigenic, non-allergenic, and good water-soluble epitopes were selected for vaccine construction. In vaccine construction, the selected epitopes were joined by GPGPG linkers, and a linear multi-epitope vaccine was constructed. The vaccine construct underwent a physiochemical property analysis. The 3D structure of the vaccine construct was predicted and subjected to refinement. After the refinement, the 3D model was subjected to a molecular docking analysis, TLRs (TLR-3 and TLR-9) were selected as receptors for vaccine construct, and the molecular docking analysis study determined that the vaccine construct has binding ability with the targeted receptor. Conclusions: The docking analysis also unveils that the vaccine construct can properly activate immune system against the target virus however experimental validation is needed to confirm the in silico findings further.
1. Introduction
Middle East Respiratory Syndrome (MERS) is a viral respiratory illness caused by the Middle East respiratory syndrome coronavirus (MERS-CoV) [1]. The virus can be transmitted from camels to humans through direct physical contact, with limited human-to-human transmission also possible. Most diagnosed cases of MERS have resulted in severe respiratory disease, leading to high mortality and morbidity rates [2]. While the majority of MERS cases have been reported in countries within or near the Arabian Peninsula, some travel-related cases have been identified in countries outside of this region [3]. In some instances, MERS may be asymptomatic. However, symptoms typically begin within 1 to 2 weeks after infection, often around 5 days post-exposure, but they can manifest up to 14 days later [4]. Common symptoms of MERS include fever, chills, coughing, sore throat, runny nose, difficulty breathing, and muscle aches.
Immunoprophylaxis against viral illnesses involves the use of vaccines or antibody-containing preparations to provide immunologic protection to susceptible individuals against specific diseases. Immunization can either be active or passive [5]. Active immunity is achieved by stimulating the body’s immune response. Reverse vaccinology is a novel approach to vaccine design that leverages the rapidly advancing ability to sequence entire genomes of microorganisms and apply bioinformatics analyses to the data [5]. Predictive modeling is used to identify new pathogen targets that are ideally conserved and elicit protective responses. These candidate targets are then expressed and screened using human serum from individuals with effective immunity and evaluated in murine models [6]. This process can lead to the development of optimal vaccines, especially against fatal viruses [7]. The use of vaccines has led to a significant improvement in global health, saving numerous lives, reducing treatment costs, and enhancing the quality of life for both humans and animals [8]. Traditional vaccines were developed empirically, often with little to no understanding of how they modulate the immune system [9]. Despite advancements in vaccine design, there are still immune-related concerns, particularly in specific vulnerable populations and in cases of emerging or re-emerging infectious diseases [10].
2. Research Methods
The research flow is given in Figure 1.
The MERS spike protein (accession id: K9N5Q8 · SPIKE_MERS1) was selected from the UniProt database and retrieved for an immunoinformatic analysis [11]. To find all the reported epitope datasets of the protein, the IEDB Epitope Source platform [12] was utilized, where the spike protein served as an antigen, while MERS was considered as an organism. In the epitope section, “Any Epitope” was opted, which includes linear, discontinuous, and non-peptide epitopes. In the Assay tab, the T cell, B cell, and MHC ligand options were selected [13]. In the “MHC Restriction” window, any option was opted for, including Class I, Class II, and non-classical. In the case of “Host Tab”, the Human host was selected. By doing so, 3474 epitopes were identified. After thoughtful investigation, the duplicate epitopes were discarded, and only single and non-redundant epitopes were selected for additional analysis. The database can be accessed at https://www.uniprot.org/uniprotkb/K9N5Q8/entry (accessed on 1 July 2024) spike glycoprotein OS = Middle East respiratory syndrome-related coronavirus; OX = 1335626; GN = S; PE = 3; SV = 1.
2.1. Epitopes Clustering Analysis
The collected set of epitopes was then subjected to an epitope cluster analysis using IEDB Epitope Cluster Tool v2.0 [14]. This tool identifies a group of epitopes while considering sequence identity. The identity cut-off was set to 70%. The maximum and minimum lengths of the epitopes were not required. The clustering algorithm used involved breaking the connected clusters to obtain a clear consensus sequence.
2.2. Selection of Potential Epitopes
The representative and consensus epitopes from the epitope clusters were then investigated for several different checks such as antigenicity, allergenicity, toxicity, homology against human proteome, IFN-gamma production, and binding affinity for DRB*0101 alleles. The DRB1*0101 allele is one of the alleles of the HLA-DRB1 gene, part of the Human Leukocyte Antigen (HLA) system which encodes proteins involved in presenting antigens (peptides derived from pathogens or self-proteins) to CD4+ T cells [14]. The antigenicity of the opted epitopes was assessed using VaxiJen v2.0 [15]. The target organism selected was a virus, and the antigenicity cut-off was allowed to be set as default (>0.5) [8,16,17,18]. VaxiJen is an online server designed to predict protective antigens and subunit vaccines. The allergenicity of the antigenic epitopes was investigated using AllerTOP v2.0 [19]. The server uses auto cross-covariance transformation of the given peptide sequence into uniform equal-length vectors. The epitopes are classified either as allergen or non-allergen. Next, the selected epitopes were analyzed for toxicity, which was carried out using the ToxinPred online server [20]. The non-toxic epitopes were selected for further analyses. For comparative homology analysis, the shortlisted epitopes were subjected to homology analysis against the human proteome with a sequence identity value of ≤30% and a bit score of 100%. The selection of interferon-gamma (IFN-γ) epitopes is key in designing potent vaccine candidates as it helps provide crucial CD4+ T helper cell and CD8+ T cytotoxic cell immunity against the infectious pathogen. Finally, MHCphred analysis was performed to evaluate the epitopes’ ability to interact with the DRB*0101 allele [21,22]. This allele is predominant in the human population; thus, any epitope interacting with the allele can lead to the formation of robust and targeted protective immune responses.
2.3. Proposed Vaccine Construct Engineering and mRNA Design
The vaccine construct of the mRNA sequence was designed in the following order from the N to C terminus: m7GCap and UTR at the 5′ end, signal peptide, linker (EAAAK), RpfE adjuvant, Linker (GPGPG), CD4+helper T lymphocytes, linker (AYY), CD8+ cytotoxic T lymphocytes, MITD sequence, stop codon, and UTR-poly A tail at 3′ end [23].
2.4. Structure Modeling and Post-Structure Processing
The proposed vaccine sequence was then used for 3D structure construction using the 3Dpro online server in SCRATCH. The vaccine structure was further improved by modeling the structure loops, followed by refinements using the GalaxyLoop and GalaxyRefine servers, respectively [24]. To increase the structure strength, disulfide bonds were introduced into the structure using Design 2.0 [25]. The vaccine sequence was then reverse-translated to nucleotide so it could be cloned into an pET-28a(+) expression vector [26]. This was achieved using the SnapGene software.
2.5. Evaluating the Vaccine’s Immune Signaling Pathway Potential
The profiling of generated immune responses by the designed vaccine was carried out using C-ImmSim server simulations [27]. The tool is often best when used with other tools like Vaxim and SIMVACS because it works on an agent-based model where the vaccine sequence is considered as an antigen and employs the position-specific scoring matrix algorithm for predicting immunological responses. The immune reactions and interactions with the vaccine antigen were then modeled through machine learning algorithms. The immunological responses were simulated in three anatomical compartments such as tertiary lymph nodes, thymus, and bone marrow. During this simulation analysis, the number of simulation steps set was 1000, the random seed value was set to 12,345, and simulation volume value was 10 [28].
2.6. The Molecular Docking of the Vaccine Protein with TLR-3 and 9
Molecular docking analysis was carried out in order to analyze the binding ability of the vaccine with immune cell receptor; for the docking analysis, cluspro 2.p webserver was used, and population coverage analysis was carried out using the IEDB database (http://tools.iedb.org/population/) (accessed on 15 July 2024) [29].
3. Results and Discussion
The epitopes were retrieved from IEDB and subjected to an immunoinformatics filter as mentioned in Table 1, and only probable antigenic, non-allergenic, good water-soluble, and IFN-gamma inducers were selected for vaccine designing in order to activate the proper immune system against the target pathogen. These epitopes were selected using machine learning-based classification models trained on biological epitopes that were confirmed through lab experiments. The query genome of the mentioned virus was processed through the trained machine learning model incorporated in the IEDB database, which predicted the most significant epitopes from the genome. Furthermore, the epitopes were prioritized based on their chemical and biological properties, such as antigenicity, toxicity, allergenicity, and MHC binding, using various tools trained on confirmed biological amino acid sequences with known properties. This approach of selection was used by numerous research articles presenting supported results and conclusions [30,31,32].
3.1. Multi-Epitope Vaccine Construction and Refinement
The selected epitopes were linked by GPGPG linkers and further connected to an adjuvant, generating a linear sequence. In the vaccine construct, the epitopes were arranged such that the B cell epitopes appeared first, followed by the T cell epitopes. The obtained sequence was then used for structural modeling. The structure was refined by galaxyweb, and the server generated the top 5 models based on different parameters; the top 1 structure was selected for docking analysis. Overall, the refinement results are presented in Table 2, and the refined structure plus the exact sequence of the vaccine construct are presented in Figure 2. The GPGPG linkers are mostly used in constructing in silico vaccine constructs because they make the vaccine more rigid and highly exposed to immune receptors and they avoid self-complementarity between used epitopes [33]. This type of linker has been used by multiple authors and has shown to be effective, yielding promising results. Furthermore, the modeled vaccine construct was structurally refined to make it more relaxed and highly stable for further analysis. This approach is commonly used to prepare a vaccine for docking studies. Galaxy-web employs highly accepted force fields, where the structure is examined and relaxed under all possible conditions, particularly considering the bond angles between the amino acids [34,35,36].
3.2. Disulfide Engineering
Disulfide engineering was carried out to stabilize the structure of the vaccine construct further; the amino acids’ positions and pairs, chi 3 values, and energy are presented in Table 3. Furthermore, Figure 3 presents the mutation and wild type of the vaccine construct. Disulfide by Design 2.0 is one of the most significant biotechnological webservers that is used in advanced research; the incorporation of disulfide bonds can improve the stability of the protein [37,38,39].
3.3. Molecular Docking Analysis
As mentioned, an effective vaccine candidate should have the potential for significant binding efficiency with an immune receptor. This will enable the immune system to recognize it and trigger a robust and effective immune response against the vaccine. To ensure the effectiveness of our designed vaccine, docking studies were performed where the vaccine was virtually docked to the TL3 and TL9 proteins that are present on the surface of host macrophages. This was carried out on purpose because these immune receptors almost have the potential to bind to foreign substances, thus promoting a series of immune reactions [40]. These receptors are mostly used in vaccine designing studies where scientists check the efficacy of their proposed vaccine [41]. Upon docking, the results reveal that the vaccine and target receptors have proper binding ability, indicating that the vaccine construct can activate an immune response against the target pathogens. ClusPro 2.0 generated docked complexes, as shown in Table 4 and Table 5. Additionally, the docked conformations are illustrated in Figure 4 and Figure 5. The docking results suggest that the vaccine can effectively interact with the target receptor and trigger an immune response against the pathogen.
3.4. Host Immune Simulation
To confirm the significance of the designed vaccine candidate, immune simulations were conducted to observe the extent of the immune response it provoked. This model is trained on antigen-based proteins and can predict the magnitude of immune responses triggered by the query antigens. In the human C-immune simulation analysis, various antibodies and cytokines were observed in response to the vaccine construct. IgM and IgG were reported in high concentrations, followed by IgM1 and IgG2. Among the cytokines, IFN-gamma was observed in the highest concentration, followed by IL-4, IL-12, TGF-b, TNF-a, IL-10, IL-6, IFN-b, IL-18, and IL-23, as represented by different colored peaks. Figure 6 present different antibodies and cytokines generated in response to the vaccine construct. Furthermore, the overall immune responses are depicted in Figure 7 and Figure 8.
4. Conclusions
Designing and developing vaccines is a complex process, but computational immunology techniques have the potential to significantly reduce the workload. Epitope-based vaccines are both feasible and effective in eliciting a protective immune response. In this study, we predicted B and T cell epitopes for target pathogens, and the identified minimal epitope sets may serve as promising vaccine candidates for future use. The population coverage analysis suggests that the proposed epitopes could be effective across a significant portion of the human population. Overall, the immunological analysis, along with structural and physicochemical characterizations, indicates that the vaccine candidate requires further in vitro and in vivo validation.
Funding
The author express their sincere appreciation to the Researchers Supporting Project Number (RSPD2024R632), King Saud University, Riyadh, Saudi Arabia.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
The data generated in the work is presented in the manuscript.
Acknowledgments
The author express their sincere appreciation to the Researchers Supporting Project Number (RSPD2024R632), King Saud University, Riyadh, Saudi Arabia.
Conflicts of Interest
The author declares no conflict of interest.
References
- Gastanaduy, P.A. Update: Severe respiratory illness associated with Middle East respiratory syndrome coronavirus (MERS-CoV)—Worldwide, 2012–2013. Morb. Mortal. Wkly. Rep. 2013, 62, 480. [Google Scholar]
- Azhar, E.I.; Hui, D.S.C.; Memish, Z.A.; Drosten, C.; Zumla, A. The middle east respiratory syndrome (MERS). Infect. Dis. Clin. 2019, 33, 891–905. [Google Scholar] [CrossRef] [PubMed]
- Williams, H.A.; Dunville, R.L.; Gerber, S.I.; Erdman, D.D.; Pesik, N.; Kuhar, D.; Mason, K.A.; Haynes, L.; Rotz, L.; St. Pierre, J. CDC’s early response to a novel viral disease, middle east respiratory syndrome coronavirus (MERS-CoV), September 2012–May 2014. Public Health Rep. 2015, 130, 307–317. [Google Scholar] [CrossRef] [PubMed]
- Ramadan, N.; Shaib, H. Middle East respiratory syndrome coronavirus (MERS-CoV): A review. Germs 2019, 9, 35. [Google Scholar] [CrossRef] [PubMed]
- Goldenthal, K.L.; Midthun, K.; Zoon, K.C. Control of Viral Infections and Diseases; University of Texas Medical Branch at Galveston: Galveston, TX, USA, 2011. [Google Scholar]
- Bhattacharjee, M.; Banerjee, M.; Mukherjee, A. In silico designing of a novel polyvalent multi-subunit peptide vaccine leveraging cross-immunity against human visceral and cutaneous leishmaniasis: An immunoinformatics-based approach. J. Mol. Model. 2023, 29, 99. [Google Scholar] [CrossRef] [PubMed]
- Abraham Peele, K.; Srihansa, T.; Krupanidhi, S.; Ayyagari, V.S.; Venkateswarulu, T.C. Design of multi-epitope vaccine candidate against SARS-CoV-2: A in-silico study. J. Biomol. Struct. Dyn. 2021, 39, 3793–3801. [Google Scholar] [CrossRef]
- Gul, S.; Ahmad, S.; Ullah, A.; Ismail, S.; Khurram, M.; Tahir ul Qamar, M.; Hakami, A.R.; Alkhathami, A.G.; Alrumaihi, F.; Allemailem, K.S. Designing a Recombinant Vaccine against Providencia rettgeri Using Immunoinformatics Approach. Vaccines 2022, 10, 189. [Google Scholar] [CrossRef] [PubMed]
- Alshammari, A.; Alasmari, A.F.; Alharbi, M.; Ali, N.; Muhseen, Z.T.; Ashfaq, U.A.; Ud-Din, M.; Ullah, A.; Arshad, M.; Ahmad, S. Novel Chimeric Vaccine Candidate Development against Leptotrichia buccalis. Int. J. Environ. Res. Public. Health 2022, 19, 10742. [Google Scholar] [CrossRef]
- Ud-Din, M.; Albutti, A.; Ullah, A.; Ismail, S.; Ahmad, S.; Naz, A.; Khurram, M.; Haq, M.U.; Afsheen, Z.; Bakri, Y.E.; et al. Vaccinomics to Design a Multi-Epitopes Vaccine for Acinetobacter baumannii. Int. J. Environ. Res. Public. Health 2022, 19, 5568. [Google Scholar] [CrossRef]
- Boutet, E.; Lieberherr, D.; Tognolli, M.; Schneider, M.; Bansal, P.; Bridge, A.J.; Poux, S.; Bougueleret, L.; Xenarios, I. UniProtKB/Swiss-Prot, the manually annotated section of the UniProt KnowledgeBase: How to use the entry view. Plant Bioinform. Methods Protoc. 2016, 1374, 23–54. [Google Scholar]
- Dhanda, S.K.; Mahajan, S.; Paul, S.; Yan, Z.; Kim, H.; Jespersen, M.C.; Jurtz, V.; Andreatta, M.; Greenbaum, J.A.; Marcatili, P. IEDB-AR: Immune epitope database—Analysis resource in 2019. Nucleic Acids Res. 2019, 47, W502–W506. [Google Scholar] [CrossRef]
- Vita, R.; Mahajan, S.; Overton, J.A.; Dhanda, S.K.; Martini, S.; Cantrell, J.R.; Wheeler, D.K.; Sette, A.; Peters, B. The immune epitope database (IEDB): 2018 update. Nucleic Acids Res. 2019, 47, D339–D343. [Google Scholar] [CrossRef]
- Blackwell, J.M.; Jamieson, S.E.; Burgner, D. HLA and infectious diseases. Clin. Microbiol. Rev. 2009, 22, 370–385. [Google Scholar] [CrossRef]
- Doytchinova, I.A.; Flower, D.R. VaxiJen: A server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC Bioinform. 2007, 8, 4. [Google Scholar] [CrossRef]
- Rida, T.; Ahmad, S.; Ullah, A.; Ismail, S.; Tahir ul Qamar, M.; Afsheen, Z.; Khurram, M.; Saqib Ishaq, M.; Alkhathami, A.G.; Alatawi, E.A. Pan-Genome Analysis of Oral Bacterial Pathogens to Predict a Potential Novel Multi-Epitopes Vaccine Candidate. Int. J. Environ. Res. Public. Health 2022, 19, 8408. [Google Scholar] [CrossRef]
- Ullah, A.; Rehman, B.; Khan, S.; Almanaa, T.N.; Waheed, Y.; Hassan, M.; Naz, T.; ul Haq, M.; Muhammad, R.; Sanami, S. An In Silico Multi-epitopes Vaccine Ensemble and Characterization Against Nosocomial Proteus penneri. Mol. Biotechnol. 2023, 1–16. [Google Scholar] [CrossRef]
- Ullah, A.; ul Haq, M.; Iqbal, M.; Irfan, M.; Khan, S.; Muhammad, R.; Ullah, A.; Khurram, M.; Alharbi, M.; Alasmari, A.F. A computational quest for identifying potential vaccine candidates against Moraxella lacunata: A multi-pronged approach. J. Biomol. Struct. Dyn. 2024, 42, 2976–2989. [Google Scholar] [CrossRef] [PubMed]
- Dimitrov, I.; Flower, D.R.; Doytchinova, I. AllerTOP-a server for in silico prediction of allergens. BMC Bioinform. 2013, 14, S4. [Google Scholar] [CrossRef] [PubMed]
- Sharma, N.; Naorem, L.D.; Jain, S.; Raghava, G.P.S. ToxinPred2: An improved method for predicting toxicity of proteins. Brief Bioinform. 2022, 23, bbac174. [Google Scholar] [CrossRef]
- Abbas, G.; Zafar, I.; Ahmad, S.; Azam, S.S. Staphylococcus hominis native mitral valve bacterial endocarditis (SBE) in a patient with hypertrophic obstructive cardiomyopathy. Heart Lung 2007, 36, 380–382. [Google Scholar] [CrossRef]
- Malik, M.; Khan, S.; Ullah, A.; Hassan, M.; Ahmad, S.; Al-Harbi, A.I.; Sanami, S.; Abideen, S.A.; Irfan, M.; Khurram, M. Proteome-wide Screening of Potential Vaccine Targets against Brucella melitensis. Vaccines 2023, 11, 263. [Google Scholar] [CrossRef]
- Schlake, T.; Thess, A.; Fotin-Mleczek, M.; Kallen, K.-J. Developing mRNA-vaccine technologies. RNA Biol. 2012, 9, 1319–1330. [Google Scholar] [CrossRef]
- Heo, L.; Park, H.; Seok, C. GalaxyRefine: Protein structure refinement driven by side-chain repacking. Nucleic Acids Res. 2013, 41, W384–W388. [Google Scholar] [CrossRef] [PubMed]
- Bibi, S.; Ullah, I.; Zhu, B.; Adnan, M.; Liaqat, R.; Kong, W.B.; Niu, S. In silico analysis of epitope-based vaccine candidate against tuberculosis using reverse vaccinology. Sci. Rep. 2021, 11, 1149. [Google Scholar] [CrossRef] [PubMed]
- Jaydari, A.; Nazifi, N.; Forouharmehr, A. Computational design of a novel multi-epitope vaccine against Coxiella burnetii. Hum. Immunol. 2020, 81, 596–605. [Google Scholar] [CrossRef]
- Rapin, N.; Lund, O.; Castiglione, F. C-Immsim 10.1 server. PLoS Pathog. 2012, 8, 293. [Google Scholar]
- Ahmad, S.; Waheed, Y.; Ismail, S.; Abbasi, S.W.; Najmi, M.H. A computational study to disclose potential drugs and vaccine ensemble for COVID-19 conundrum. J. Mol. Liq. 2021, 324, 114734. [Google Scholar] [CrossRef]
- Asad, Y.; Ahmad, S.; Rungrotmongkol, T.; Ranaghan, K.E.; Azam, S.S. Immuno-informatics driven proteome-wide investigation revealed novel peptide-based vaccine targets against emerging multiple drug resistant Providencia stuartii. J. Mol. Graph. Model. 2018, 80, 238–250. [Google Scholar] [CrossRef]
- Reginald, K.; Chan, Y.; Plebanski, M.; Poh, C.L. Development of peptide vaccines in dengue. Curr. Pharm. Des. 2018, 24, 1157–1173. [Google Scholar] [CrossRef]
- Maleki, A.; Russo, G.; Parasiliti Palumbo, G.A.; Pappalardo, F. In silico design of recombinant multi-epitope vaccine against influenza A virus. BMC Bioinform. 2021, 22, 617. [Google Scholar] [CrossRef] [PubMed]
- Devi, A.; Chaitanya, N.S.N. In silico designing of multi-epitope vaccine construct against human coronavirus infections. J. Biomol. Struct. Dyn. 2021, 39, 6903–6917. [Google Scholar] [CrossRef] [PubMed]
- Arai, R.; Ueda, H.; Kitayama, A.; Kamiya, N.; Nagamune, T. Design of the linkers which effectively separate domains of a bifunctional fusion protein. Protein Eng. 2001, 14, 529–532. [Google Scholar] [CrossRef]
- Ullah, A.; Shahid, F.A.; Haq, M.U.; Tahir ul Qamar, M.; Irfan, M.; Shaker, B.; Ahmad, S.; Alrumaihi, F.; Allemailem, K.S.; Almatroudi, A. An integrative reverse vaccinology, immunoinformatic, docking and simulation approaches towards designing of multi-epitopes based vaccine against monkeypox virus. J. Biomol. Struct. Dyn. 2023, 41, 7821–7834. [Google Scholar] [CrossRef]
- Alshammari, A.; Alharbi, M.; Alghamdi, A.; Alharbi, S.A.; Ashfaq, U.A.; Tahir ul Qamar, M.; Ullah, A.; Irfan, M.; Khan, A.; Ahmad, S. Computer-Aided Multi-Epitope Vaccine Design against Enterobacter xiangfangensis. Int. J. Environ. Res. Public. Health 2022, 19, 7723. [Google Scholar] [CrossRef] [PubMed]
- Khan, S.; Irfan, M.; Hameed, A.R.; Ullah, A.; Abideen, S.A.; Ahmad, S.; Haq, M.U.; El Bakri, Y.; Al-Harbi, A.I.; Ali, M. Vaccinomics to design a multi-epitope-based vaccine against monkeypox virus using surface-associated proteins. J. Biomol. Struct. Dyn. 2023, 41, 10859–10868. [Google Scholar] [CrossRef]
- Craig, D.B.; Dombkowski, A.A. Disulfide by Design 2.0: A web-based tool for disulfide engineering in proteins. BMC Bioinform. 2013, 14, 346. [Google Scholar] [CrossRef] [PubMed]
- Yousaf, M.; Ullah, A.; Sarosh, N.; Abbasi, S.W.; Ismail, S.; Bibi, S.; Hasan, M.M.; Albadrani, G.M.; Talaat Nouh, N.A.; Abdulhakim, J.A. Design of Multi-Epitope Vaccine for Staphylococcus saprophyticus: Pan-Genome and Reverse Vaccinology Approach. Vaccines 2022, 10, 1192. [Google Scholar] [CrossRef] [PubMed]
- Yousaf, M.; Ismail, S.; Ullah, A.; Bibi, S. Immuno-informatics profiling of monkeypox virus cell surface binding protein for designing a next generation multi-valent peptide-based vaccine. Front. Immunol. 2022, 13, 1035924. [Google Scholar] [CrossRef]
- Marongiu, L.; Gornati, L.; Artuso, I.; Zanoni, I.; Granucci, F. Below the surface: The inner lives of TLR4 and TLR9. J. Leukoc. Biol. 2019, 106, 147–160. [Google Scholar] [CrossRef] [PubMed]
- Singh, H.; Jakhar, R.; Sehrawat, N. Designing spike protein (S-Protein) based multi-epitope peptide vaccine against SARS COVID-19 by immunoinformatics. Heliyon 2020, 6, e05528. [Google Scholar] [CrossRef]
Figure 1.
A schematic diagram illustrating the construction and processing of a multi-epitope vaccine. This study begins with the selection of a MERS-specific spike protein from a biological database. Epitopes were then predicted from the query protein and prioritized using various machine learning classification models. The filtered epitopes were linked together to form a complete three-dimensional structure, which was subsequently subjected to immune simulations to predict the immune response against the vaccine construct. The resulting vaccine candidate, which demonstrated potential immune stimulation and structural stability, was further evaluated for performance through docking studies with known immune receptors.
Figure 1.
A schematic diagram illustrating the construction and processing of a multi-epitope vaccine. This study begins with the selection of a MERS-specific spike protein from a biological database. Epitopes were then predicted from the query protein and prioritized using various machine learning classification models. The filtered epitopes were linked together to form a complete three-dimensional structure, which was subsequently subjected to immune simulations to predict the immune response against the vaccine construct. The resulting vaccine candidate, which demonstrated potential immune stimulation and structural stability, was further evaluated for performance through docking studies with known immune receptors.
Figure 2.
This figure presents the sequence of the vaccine construct, where the adjuvant is highlighted in blue, the linkers are in yellow, and epitopes are not highlighted. Furthermore, the 3D structure of vaccine was modeled. This model explains a highly packed, completely modeled structure vaccine construct with multiple grooves, predicting the effective binding of immune receptors.
Figure 2.
This figure presents the sequence of the vaccine construct, where the adjuvant is highlighted in blue, the linkers are in yellow, and epitopes are not highlighted. Furthermore, the 3D structure of vaccine was modeled. This model explains a highly packed, completely modeled structure vaccine construct with multiple grooves, predicting the effective binding of immune receptors.
Figure 3.
The wild-type (A) and mutated structure (B) of the vaccine construct. In the mutated vaccine structure, yellow represents di sulfide bonds. In both structures, white shows the loop, blue shows the sheet, and pink shows the ribbon secondary structure elements.
Figure 3.
The wild-type (A) and mutated structure (B) of the vaccine construct. In the mutated vaccine structure, yellow represents di sulfide bonds. In both structures, white shows the loop, blue shows the sheet, and pink shows the ribbon secondary structure elements.
Figure 4.
The Vaccine_TLR-3 complex. The red color represents the TLR-3 candidate, while green represents the vaccine. The figure shows that the vaccine is purely docked to the side domain of TLR-3, thus showing effective binding.
Figure 4.
The Vaccine_TLR-3 complex. The red color represents the TLR-3 candidate, while green represents the vaccine. The figure shows that the vaccine is purely docked to the side domain of TLR-3, thus showing effective binding.
Figure 5.
The Vaccine_TLR-9 complex, where the yellow color represents TLR-9, while the green color represents the vaccine candidate. The figure shows that the vaccine is purely docked to the middle region of TLR-9, thus showing effective binding.
Figure 5.
The Vaccine_TLR-9 complex, where the yellow color represents TLR-9, while the green color represents the vaccine candidate. The figure shows that the vaccine is purely docked to the middle region of TLR-9, thus showing effective binding.
Figure 6.
Antibody and cytokine levels toward multi-epitope vaccine construct. (A) shows that upon the introduction of the vaccine antigen (black curve), starting from day 5, the adaptive immune response was highly activated. Large amounts of IgM and IgG, along with their subtypes, were produced in response to the vaccine candidate, which is a typical reaction when a foreign pathogen enters the body. (B) The results show cytokine production against the vaccine candidate. Large amounts of IFN-gamma along with other cytokines are produced, which clearly represent the robust activation of the immune system against the vaccine construct.
Figure 6.
Antibody and cytokine levels toward multi-epitope vaccine construct. (A) shows that upon the introduction of the vaccine antigen (black curve), starting from day 5, the adaptive immune response was highly activated. Large amounts of IgM and IgG, along with their subtypes, were produced in response to the vaccine candidate, which is a typical reaction when a foreign pathogen enters the body. (B) The results show cytokine production against the vaccine candidate. Large amounts of IFN-gamma along with other cytokines are produced, which clearly represent the robust activation of the immune system against the vaccine construct.
Figure 7.
Immune simulation reports. Legend: Act = active, Intern = the internalized Ag, Pres II = presenting on MHC II, Dup = in the mitotic cycle, Anergic = anergic, Resting = not active.
Figure 7.
Immune simulation reports. Legend: Act = active, Intern = the internalized Ag, Pres II = presenting on MHC II, Dup = in the mitotic cycle, Anergic = anergic, Resting = not active.
Figure 8.
Immune simulation reports. Legend: Act = active, Intern = the internalized Ag, Pres II = presenting on MHC II, Dup = in the mitotic cycle, Anergic = anergic, Resting = not active.
Figure 8.
Immune simulation reports. Legend: Act = active, Intern = the internalized Ag, Pres II = presenting on MHC II, Dup = in the mitotic cycle, Anergic = anergic, Resting = not active.
Table 1.
Epitopes and their immunoinformatics screening. The selected peptides in the table were screened through machine learning classification models which predict and select the most significant peptides that might have confirmed mentioned properties (for detail, see text). The mentioned epitopes have properties in an accepted range that could withstand the normal desired properties of a normal highly efficient epitope. The epitopes in the table are ranked based on antigenicity score in descending order. NA: not applicable.
Table 1.
Epitopes and their immunoinformatics screening. The selected peptides in the table were screened through machine learning classification models which predict and select the most significant peptides that might have confirmed mentioned properties (for detail, see text). The mentioned epitopes have properties in an accepted range that could withstand the normal desired properties of a normal highly efficient epitope. The epitopes in the table are ranked based on antigenicity score in descending order. NA: not applicable.
| Selected Peptide | Type | Start and End Amino Acid | Antigenicity | Allergenicity | Toxicity | Homology | IFN-Gamma Peptide |
|---|---|---|---|---|---|---|---|
| FVSKDVKFENLTNLPPPLLN | Consensus | 101–119 | 1.1267 | Probable Non-Allergen | No | No | No |
| LDAGLNGDFNLTLLQVP | Consensus | 625–642 | 0.8180 | Probable Non-Allergen | No | No | Yes |
| CDNYDDYDFEPQKI | Consensus | 687–702 | 1.4164 | Probable Non-Allergen | No | No | Yes |
| CPMMDLGNSTITDLGV | Consensus | 135–151 | 1.2037 | Probable Non-Allergen | No | No | Yes |
| CCDYEEYDLEPHK | Consensus | 745–758 | 1.1076 | Probable Allergen | NA | NA | NA |
| ITTLNTRYVAPQVTF | Consensus | 514–528 | 1.0017 | Probable Non-Allergen | No | No | Yes |
| LKNRCCDRYEEYD | Consensus | 457–469 | 0.9942 | Probable Allergen | NA | NA | NA |
| ISPAAISSNCYS | Consensus | 320–333 | 0.9397 | Probable Allergen | NA | NA | NA |
| LFSVNDFTCSQISPAAIASN | Consensus | 945–965 | 0.8997 | Probable Allergen | No | No | Yes |
| EEYDLEPHKIHVH | Consensus | 638–650 | 0.8688 | Probable Non-Allergen | No | No | Yes |
| FMHVGYYPSNHIEVAS | Consensus | 637–652 | 0.8584 | Probable Non-Allergen | No | No | Yes |
| MTDNLQMAFVIS | Singleton | 661–673 | 0.821 | Probable Non-Allergen | No | No | Yes |
| IVDIQQTFFDKTWPRPIDVK | Consensus | 104–124 | 0.8024 | Probable Allergen | NA | NA | NA |
| GSSFGNFSANNKSGAYFN | Consensus | 639–657 | 0.7847 | Probable Allergen | NA | NA | NA |
| LLSNSTGTDFKD | Singleton | 388–400 | 0.7804 | Probable Non-Allergen | No | No | Yes |
| KNSQSSPIIPGF | Singleton | 398–410 | 0.7804 | Probable Non-Allergen | No | No | No |
| LALQEVVKALNESYIDLKELGNY | Consensus | 502–524 | 0.7702 | Probable Non-Allergen | No | No | No |
| FSDIKTHKSQPLNAG | Consensus | 601–615 | 0.7665 | Probable Non-Allergen | No | No | No |
| GFTTTNAFKVQ | Consensus | 362–373 | 0.7624 | Probable Allergen | NA | NA | NA |
| IIPGFGGNFNLKLLEPVS | Consensus | 302–320 | 0.7494 | Probable Non-Allergen | No | No | Yes |
| FNITEDEAEWFGITQNAQGVHL | Consensus | 111–133 | 0.7405 | Probable Non-Allergen | No | No | Yes |
| EHDEWFGITQDT | Singleton | 399–410 | 0.7036 | Probable Allergen | NA | NA | NA |
| FQNISNLPPPLLN | Consensus | 1134–1147 | 0.7032 | Probable Non-Allergen | No | No | Yes |
| IDCGHDDLAQLRCS | Consensus | 456–471 | 0.6975 | Probable Allergen | NA | NA | NA |
| EQAEGVECDFSLLL | Consensus | 815–829 | 0.6827 | Probable Non-Allergen | No | No | Yes |
| DQLNSTYFKLSI | Singleton | 771–783 | 0.6751 | Probable Non-Allergen | No | No | Yes |
| DGIVIRIGQNANKTGSVI | Consensus | 823–841 | 0.6684 | Probable Allergen | NA | NA | NA |
| LAFSKVQEAVNA | Singleton | 697–709 | 0.668 | Probable Allergen | No | No | No |
| LPPPLLNNQTDL | Singleton | 1275–1287 | 0.658 | Probable Non-Allergen | No | No | Yes |
| MAATGVISSMTDNLQMAF | Consensus | 1134–1151 | 0.634 | Probable Allergen | NA | NA | NA |
| EQAEGFECDFSPLLSGTPPQV | Consensus | 786–807 | 0.6256 | Probable Non-Allergen | No | No | Yes |
| DKSWPRPIDPAAA | Consensus | 965–988 | 0.6222 | Probable Allergen | NA | NA | NA |
| IIPGFGGDFNLT | Singleton | 602–614 | 0.622 | Probable Non-Allergen | No | No | Yes |
| FMLGSSVGNFSNGS | Consensus | 236–250 | 0.6195 | Probable Non-Allergen | No | No | Yes |
| GYHPSQHIEVVA | Singleton | 404–416 | 0.6166 | Probable Non-Allergen | No | No | Yes |
| LFASVKSSQSSPII | Consensus | 345–359 | 0.6133 | Probable Allergen | NA | NA | NA |
| AGYKVLPPLYDPNMEAAYTSSL | Consensus | 928–950 | 0.6025 | Probable Non-Allergen | No | No | Yes |
| IIPHSIRSIQSDRKAWAAFYVYKLQPLTF | Consensus | 887–901 | 0.6015 | Probable Non-Allergen | No | No | Yes |
| STGSRSARSAIEDLLFKVTIADP | Consensus | 125–147 | 0.5925 | Probable Non-Allergen | No | No | Yes |
| IGAAANSTGTVIISP | Consensus | 625–639 | 0.5861 | Probable Allergen | NA | NA | NA |
| FVYDFDNIG | Consensus | 114–123 | 0.5855 | Probable Non-Allergen | No | No | Yes |
| GQSLCALPDTPSTLTPRSVRSVPGEMRLAS | Consensus | 732–760 | 0.5429 | Probable Non-Allergen | No | No | Yes |
| FSAYSGDIPHYVQPGQYTP | Consensus | 507–525 | 0.54 | Probable Non-Allergen | No | No | Yes |
| EFSCDGISPDAI | Singleton | 687–699 | 0.5325 | Probable Allergen | NA | NA | NA |
| ATDCSDGNYNRNASLNSF | Consensus | 514–531 | 0.5206 | Probable Allergen | NA | NA | NA |
| ACEHITTMMQFS | Consensus | 634–644 | 0.5155 | Probable Allergen | NA | NA | NA |
| GSSFYAPEPITSLNTKYVAPQ | Consensus | 333–355 | 0.4965 | Probable Allergen | NA | NA | NA |
| DGYIRRAIDCGFNDLSQLCSYE | Consensus | 367–386 | 0.4774 | Probable Non-Allergen | No | No | Yes |
| KITIADPGYMQGYDDCMQQGPASARDLICAQYVAG | Consensus | 896–931 | 0.4675 | Probable Non-Allergen | No | No | Yes |
| FAYPLSMKSYMQ | Singleton | 281–293 | 0.4593 | Probable Allergen | NA | NA | NA |
| KNVSSQGPNFQE | Singleton | 205–217 | 0.4589 | Probable Non-Allergen | No | No | Yes |
| IFATAPANLTISKPSSYS | Consensus | 976–994 | 0.4536 | Probable Non-Allergen | No | No | Yes |
| EPIDMNKADGVIYPGRTYS | Consensus | 231–248 | 0.4392 | Probable Allergen | NA | NA | NA |
| LFVEDCLPLGQSLCA | Consensus | 967–692 | 0.4392 | Probable Allergen | NA | NA | NA |
| EMCLASIAFNHPIQVDQLNSSYFK | Consensus | 850–873 | 0.4138 | Probable Non-Allergen | No | No | Yes |
| HFVYDAYNLVGYYSDDGNYYCV | Consensus | 233–253 | 0.412 | Probable Allergen | NA | NA | NA |
| GSSVGNYYNGYP | Singleton | 1041–1053 | 0.4007 | Probable Non-Allergen | No | No | Yes |
| GCSVGNFSDGKM | Singleton | 783–795 | 0.3891 | NA | NA | NA | NA |
| LILDYFSYPLSMKSDLSVSSS | Consensus | 364–384 | 0.388 | NA | NA | NA | NA |
| FATYHTPATDCSDGNY | Consensus | 532–549 | 0.3792 | NA | NA | NA | NA |
| ITTFMPQFSRMTQSALRMR | Consensus | 1041–1061 | 0.3727 | NA | NA | NA | NA |
| FGAISASIGDIIQRLDLEQDAQIDRLI | Consensus | 366–387 | 0.3635 | NA | NA | NA | NA |
| GNHCPAGNSYTSFATYHT | Consensus | 1265–1283 | 0.3452 | NA | NA | NA | NA |
| CPKEFANDTKIASQLGN | Consensus | 146–164 | 0.3352 | NA | NA | NA | NA |
| NAKADGIIYPTGKSYSNI | Consensus | 541–557 | 0.3262 | NA | NA | NA | NA |
| ILPPPLLSNST | Consensus | 442–452 | 0.3216 | NA | NA | NA | NA |
| LLGNSXGIDFQDELDEFFKNVSTSIPNFGS | Consensus | 951–981 | 0.3057 | NA | NA | NA | NA |
| CVLGLVNSSLVEDCK | Consensus | 712–728 | 0.3009 | NA | NA | NA | NA |
| GNMFRFASLPVY | Singleton | 463–475 | 0.2847 | NA | NA | NA | NA |
| DQLNSSYKLSIP | Singleton | 771–784 | 0.284 | NA | NA | NA | NA |
| CGISPDAIARGCYS | Consensus | 374–387 | 0.2786 | NA | NA | NA | NA |
| LDFKEELEEFFK | Singleton | 856–567 | 0.2785 | NA | NA | NA | NA |
| ASAYGLCDAANPTNCIAPVNG | Consensus | 784–805 | 0.2607 | NA | NA | NA | NA |
| DAVNNAQALSKLAS | Consensus | 459–463 | 0.2396 | NA | NA | NA | NA |
| AAIASNCYSSLID | Consensus | 931–943 | 0.2103 | NA | NA | NA | NA |
| LLGSIAGAGWTAGLSSFAAI | Consensus | 98–117 | 0.1785 | NA | NA | NA | NA |
| GYFIKTNNTIVDEWS | Consensus | 114–128 | 0.1375 | NA | NA | NA | NA |
| AANSTGNLIISSS | Consensus | 1187–1198 | 0.1231 | NA | NA | NA | NA |
| FDNIIGFHSDDGNYY | Consensus | 604–619 | 0.1103 | NA | NA | NA | NA |
| DVSKADGIIPQ | Consensus | 187–198 | 0.0638 | NA | NA | NA | NA |
| AKINQALHGANLRQDSVRNL | Consensus | 116–135 | 0.0607 | NA | NA | NA | NA |
| KLIANKFNQALGAMQTGF | Consensus | 254–271 | 0.0337 | NA | NA | NA | NA |
| HSDGNYYCVRPCVS | Consensus | 456–468 | −0.0086 | NA | NA | NA | NA |
| DLYGGNMFQFATPV | Consensus | 410–425 | −0.0297 | NA | NA | NA | NA |
| CNGFQKCEQLLREYGQFCA | Consensus | 435–448 | −0.0311 | NA | NA | NA | NA |
| ITKPLKYSYINKCSRLLSDDRTEVPQ | Consensus | 491–515 | −0.0554 | NA | NA | NA | NA |
| LMQDESVANLFSDIKTHKS | Consensus | 639–657 | −0.0873 | NA | NA | NA | NA |
| EKLLEQYGQFCS | Singleton | 523–534 | −0.0925 | NA | NA | NA | NA |
| AFVAQQLVRSEAAR | Consensus | 149–161 | −0.1895 | NA | NA | NA | NA |
| MYLYSAAHADPNRFILGKLY | Consensus | 84–104 | −0.3463 | NA | NA | NA | NA |
| GFAKCEKLLEQY | Singleton | 968–980 | −0.3673 | NA | NA | NA | NA |
| DQSFKDELEEFF | Singleton | 1214–1240 | −0.3752 | NA | NA | NA | NA |
| EQEVQIDRLING | Singleton | 987–997 | −0.4307 | NA | NA | NA | NA |
Table 2.
The refined models of the vaccine construct generated by the galaxyweb server. Almost five models are generated from the predicted vaccine structure, showing comparative clash and stability scores (last column, model). The top model has the lowest clash score and Root Mean Square Deviation (RMSD), and the highest global distance test–high accuracy (GDT-HA) model value was selected for further analysis.
Table 2.
The refined models of the vaccine construct generated by the galaxyweb server. Almost five models are generated from the predicted vaccine structure, showing comparative clash and stability scores (last column, model). The top model has the lowest clash score and Root Mean Square Deviation (RMSD), and the highest global distance test–high accuracy (GDT-HA) model value was selected for further analysis.
| Model | GDT-HA | RMSD | MolProbity | Clash Score | Poor | Model |
|---|---|---|---|---|---|---|
| Initial | 1.0000 | 0.000 | 3.802 | 124.4 | 6.8 | 87.3 |
| MODEL 1 | 0.9110 | 0.502 | 2.197 | 17.6 | 0.3 | 92.9 |
| MODEL 2 | 0.9047 | 0.530 | 2.336 | 20.8 | 1.3 | 93.4 |
| MODEL 3 | 0.9085 | 0.520 | 2.260 | 21.1 | 1.0 | 93.1 |
| MODEL 4 | 0.9097 | 0.512 | 2.175 | 19.9 | 1.0 | 94.4 |
| MODEL 5 | 0.9066 | 0.513 | 2.225 | 19.4 | 1.0 | 93.1 |
Table 3.
Pairs of amino acid selected for disulfide bonds. # represents residue number.
| Residues1 Seq # | Residues1 AA | Residues2 Seq # | Residues2 AA | Chi3 | Energy |
|---|---|---|---|---|---|
| 3 | PRO | 40 | ILE | 101.55 | 4.59 |
| 8 | ASP | 14 | HIS | 126.98 | 3.65 |
| 17 | GLN | 20 | THR | 91.37 | 0.67 |
| 40 | ILE | 45 | ASN | 119.1 | 6.36 |
| 89 | TRP | 94 | PRO | 113.14 | 4.49 |
| 94 | PRO | 108 | ALA | −67.22 | 1.94 |
| 100 | ILE | 104 | ASN | 119.75 | 1.75 |
| 114 | ASP | 117 | TYR | −115.58 | 5.25 |
| 117 | TYR | 119 | GLN | −104.25 | 3.99 |
| 135 | PHE | 139 | GLY | −88.2 | 3.57 |
| 140 | PRO | 158 | ASN | −71.39 | 5.81 |
| 144 | VAL | 150 | SER | 77.6 | 3.93 |
| 146 | GLN | 149 | SER | 86.9 | 3.39 |
| 161 | TYR | 170 | PRO | 101.78 | 4.59 |
| 163 | LYS | 167 | GLY | 97.48 | 3.31 |
| 164 | LEU | 167 | GLY | 114.04 | 1.57 |
| 166 | ILE | 189 | PRO | −78.73 | 6.1 |
| 168 | PRO | 177 | ASN | 108.77 | 2.71 |
| 171 | GLY | 174 | ASN | −111.44 | 7.95 |
| 175 | ILE | 178 | LEU | −92.5 | 4 |
| 183 | PRO | 217 | LEU | −103.94 | 5.86 |
| 218 | ASN | 237 | ASN | −116.83 | 6.08 |
| 228 | GLY | 232 | PRO | −68.03 | 2.7 |
| 234 | PHE | 238 | PHE | −63.92 | 6.41 |
| 247 | PRO | 257 | PRO | 121.42 | 3.06 |
| 250 | GLY | 290 | GLY | −86.8 | 3.77 |
| 253 | PHE | 256 | GLY | −91.29 | 5.01 |
| 260 | GLY | 263 | ILE | 121.84 | 6.03 |
| 295 | ASP | 321 | PHE | −88.18 | 2.56 |
| 309 | CYS | 321 | PHE | −77.33 | 1.61 |
| 310 | ASP | 313 | LEU | 107.72 | 5.77 |
| 313 | LEU | 318 | GLY | 108.9 | 2.53 |
| 322 | MET | 336 | ARG | −61.42 | 5.7 |
| 324 | GLY | 335 | GLY | −86.84 | 4.62 |
| 325 | SER | 328 | GLY | 102.97 | 4.66 |
| 339 | ILE | 360 | TRP | −65.91 | 2.85 |
| 348 | GLY | 353 | ILE | 84.25 | 3.34 |
| 362 | PRO | 366 | MET | 80.19 | 6.16 |
| 378 | GLY | 381 | ASP | −112.52 | 3.12 |
Table 4.
The docking scores of the vaccine and Toll-like receptor 3. Different clusters of amino acids contributing to docking are shown. The top cluster has 47 amino acids involved, which have a central lowest energy of −1000.4 kcal/mol and a comparative overall lowest energy of −1033.2 kcal/mol, respectively. The lower the binding energy, the more tightly the vaccine will bind to the immune receptors.
Table 4.
The docking scores of the vaccine and Toll-like receptor 3. Different clusters of amino acids contributing to docking are shown. The top cluster has 47 amino acids involved, which have a central lowest energy of −1000.4 kcal/mol and a comparative overall lowest energy of −1033.2 kcal/mol, respectively. The lower the binding energy, the more tightly the vaccine will bind to the immune receptors.
| Cluster | Members | Representative | Weighted Score |
|---|---|---|---|
| 0 | 47 | Center | −1000.4 |
| Lowest Energy | −1033.2 | ||
| 1 | 37 | Center | −835.7 |
| Lowest Energy | −835.7 | ||
| 2 | 30 | Center | −890.0 |
| Lowest Energy | −975.4 | ||
| 3 | 27 | Center | −813.8 |
| Lowest Energy | −903.8 | ||
| 4 | 24 | Center | −801.0 |
| Lowest Energy | −888.1 | ||
| 5 | 19 | Center | −906.6 |
| Lowest Energy | −946.3 | ||
| 6 | 19 | Center | −902.1 |
| Lowest Energy | −902.1 | ||
| 7 | 18 | Center | −881.3 |
| Lowest Energy | −886.3 | ||
| 8 | 18 | Center | −960.8 |
| Lowest Energy | −960.8 | ||
| 9 | 17 | Center | −900.9 |
| Lowest Energy | −918.8 | ||
| 10 | 16 | Center | −853.9 |
| Lowest Energy | −940.1 | ||
| 11 | 15 | Center | −839.4 |
| Lowest Energy | −951.9 | ||
| 12 | 14 | Center | −792.0 |
| Lowest Energy | −911.9 | ||
| 13 | 13 | Center | −943.0 |
| Lowest Energy | −943.0 | ||
| 14 | 12 | Center | −905.4 |
| Lowest Energy | −905.4 | ||
| 15 | 12 | Center | −882.5 |
| Lowest Energy | −883.4 | ||
| 16 | 12 | Center | −851.5 |
| Lowest Energy | −851.5 | ||
| 17 | 12 | Center | −837.8 |
| Lowest Energy | −1040.0 | ||
| 18 | 11 | Center | −1033.1 |
| Lowest Energy | −1033.1 | ||
| 19 | 11 | Center | −881.3 |
| Lowest Energy | −881.3 | ||
| 20 | 11 | Center | −871.7 |
| Lowest Energy | −871.7 |
Table 5.
The docking scores of the vaccine and Toll-like receptor 9. Different clusters of amino acids contributing to docking are shown. The top cluster has 47 amino acids involved, which have a central lowest energy of −1036.7 kcal/mol and a comparative overall lowest energy of −1203.3 kcal/mol, respectively. The lower the binding energy, the more tightly the vaccine will bind to the immune receptors.
Table 5.
The docking scores of the vaccine and Toll-like receptor 9. Different clusters of amino acids contributing to docking are shown. The top cluster has 47 amino acids involved, which have a central lowest energy of −1036.7 kcal/mol and a comparative overall lowest energy of −1203.3 kcal/mol, respectively. The lower the binding energy, the more tightly the vaccine will bind to the immune receptors.
| Cluster | Members | Representative | Weighted Score |
|---|---|---|---|
| 0 | 30 | Center | −1036.7 |
| Lowest Energy | −1203.3 | ||
| 1 | 24 | Center | −1318.0 |
| Lowest Energy | −1318.0 | ||
| 2 | 23 | Center | −1068.5 |
| Lowest Energy | −1228.9 | ||
| 3 | 22 | Center | −995.4 |
| Lowest Energy | −1147.7 | ||
| 4 | 21 | Center | −1068.5 |
| Lowest Energy | −1298.6 | ||
| 5 | 21 | Center | −1055.6 |
| Lowest Energy | −1172.7 | ||
| 6 | 21 | Center | −1009.1 |
| Lowest Energy | −1295.4 | ||
| 7 | 20 | Center | −1212.1 |
| Lowest Energy | −1242.1 | ||
| 8 | 19 | Center | −991.7 |
| Lowest Energy | −1338.5 | ||
| 9 | 18 | Center | −1016.1 |
| Lowest Energy | −1206.8 | ||
| 10 | 18 | Center | −1140.1 |
| Lowest Energy | −1217.3 | ||
| 11 | 16 | Center | −1030.0 |
| Lowest Energy | −1424.7 | ||
| 12 | 16 | Center | −1199.1 |
| Lowest Energy | −1199.1 | ||
| 13 | 16 | Center | −1150.4 |
| Lowest Energy | −1300.8 | ||
| 14 | 16 | Center | −1125.0 |
| Lowest Energy | −1155.1 | ||
| 15 | 14 | Center | −1138.0 |
| Lowest Energy | −1138.0 | ||
| 16 | 14 | Center | −1084.5 |
| Lowest Energy | −1122.5 | ||
| 17 | 13 | Center | −1108.6 |
| Lowest Energy | −1151.3 | ||
| 18 | 13 | Center | −1215.2 |
| Lowest Energy | −1215.2 | ||
| 19 | 13 | Center | −1047.6 |
| Lowest Energy | −1095.5 | ||
| 20 | 13 | Center | −1003.2 |
| Lowest Energy | −1218.4 |
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© 2024 by the author. Published by MDPI on behalf of the Lithuanian University of Health Sciences. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
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MDPI and ACS Style
Almanaa, T.N. Design of an Epitope-Based Vaccine Against MERS-CoV. Medicina 2024, 60, 1632. https://doi.org/10.3390/medicina60101632
AMA Style
Almanaa TN. Design of an Epitope-Based Vaccine Against MERS-CoV. Medicina. 2024; 60(10):1632. https://doi.org/10.3390/medicina60101632
Chicago/Turabian StyleAlmanaa, Taghreed N. 2024. "Design of an Epitope-Based Vaccine Against MERS-CoV" Medicina 60, no. 10: 1632. https://doi.org/10.3390/medicina60101632
APA StyleAlmanaa, T. N. (2024). Design of an Epitope-Based Vaccine Against MERS-CoV. Medicina, 60(10), 1632. https://doi.org/10.3390/medicina60101632
