Designing an Epitope-Based Peptide Vaccine Derived from RNA-Dependent RNA Polymerase (RdRp) against Dengue Virus Serotype 2
Abstract
:1. Introduction
2. Materials and Methods
2.1. DENV-2 Sequences Alignment and Reconstruction of Phylogenetic Tree
2.2. B-Cell Linear Epitope Analysis
2.3. Immunological Properties Analysis
2.4. Receptor Preparation
2.5. Peptide Modeling and Molecular Docking
2.6. Molecular Dynamics Simulation Study
2.7. MM/GBSA Binding-Free Energy Calculation
3. Results and Discussion
3.1. DENV-2 Sequences Collection
3.2. Multiple Sequence Alignment
3.3. Phylogenetic Tree Analysis
3.4. B-Cell Epitope Analysis
3.5. Analysis of Epitope Immunological Properties
3.6. Target Receptor Preparation
3.7. Protein Modeling and Molecular Docking
3.8. Molecular Dynamics Simulations Study
3.9. Binding-Free Energy
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Cox, J.; Mota, J.; Sukupolvi-Petty, S.; Diamond, M.S.; Rico-Hesse, R. Mosquito Bite Delivery of Dengue Virus Enhances Immunogenicity and Pathogenesis in Humanized Mice. J. Virol. 2012, 86, 7637–7649. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Bhatt, S.; Gething, P.W.; Brady, O.J.; Messina, J.P.; Farlow, A.W.; Moyes, C.L.; Drake, J.M.; Brownstein, J.S.; Hoen, A.G.; Sankoh, O.; et al. The Global Distribution and Burden of Dengue. Nature 2013, 496, 504–507. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Rothman, A.L.; Ennis, F.A. Dengue Vaccine: The Need, the Challenges, and Progress. J. Infect. Dis. 2016, 214, 825–827. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Olivera-Botello, G.; Coudeville, L.; Fanouillere, K.; Guy, B.; Chambonneau, L.; Noriega, F.; Jackson, N. Tetravalent Dengue Vaccine Reduces Symptomatic and Asymptomatic Dengue Virus Infections in Healthy Children and Adolescents Aged 2-16 Years in Asia and Latin America. J. Infect. Dis. 2016, 214, 994–1000. [Google Scholar] [CrossRef] [Green Version]
- Marintcheva, B. (Ed.) Chapter 8—Viruses as tools for vaccine, development. In Harnessing the Power of Viruses; Academic Press: Cambridge, MA, USA, 2018; pp. 217–242. ISBN 978-0-12-810514-6. [Google Scholar]
- Tam, J.P. Synthetic Peptide Vaccine Design: Synthesis and Properties of a High-Density Multiple Antigenic Peptide System. Proc. Natl. Acad. Sci. USA 1988, 85, 5409–5413. [Google Scholar] [CrossRef] [Green Version]
- Dillon, P.M.; Slingluff, C.L. Peptide Vaccine: Overview. In Cancer Therapeutic Targets; Marshall, J.L., Ed.; Springer: New York, NY, USA, 2017; pp. 427–439. ISBN 978-1-4419-0717-2. [Google Scholar]
- Murugesan, A.; Manoharan, M. Dengue Virus. In Emerging and Reemerging Viral Pathogens; Academic Press: Cambridge, MA, USA, 2020; pp. 281–359. [Google Scholar] [CrossRef]
- Gubler, D.J. Dengue and Dengue Hemorrhagic Fever. Clin. Microbiol. Rev. 1998, 11, 480–496. [Google Scholar] [CrossRef] [Green Version]
- Wang, W.-H.; Urbina, A.N.; Chang, M.R.; Assavalapsakul, W.; Lu, P.-L.; Chen, Y.-H.; Wang, S.-F. Dengue Hemorrhagic Fever—A Systemic Literature Review of Current Perspectives on Pathogenesis, Prevention and Control. J. Microbiol. Immunol. Infect. 2020, 53, 963–978. [Google Scholar] [CrossRef]
- Norshidah, H.; Vignesh, R.; Lai, N.S. Updates on Dengue Vaccine and Antiviral: Where Are We Heading? Molecules 2021, 26, 6768. [Google Scholar] [CrossRef]
- Khan, N.U.; Danish, L.; Khan, H.U.; Shah, M.; Ismail, M.; Ali, I.; Petruzziello, A.; Sabatino, R.; Guzzo, A.; Botti, G.; et al. Prevalence of Dengue Virus Serotypes in the 2017 Outbreak in Peshawar, KP, Pakistan. J. Clin. Lab. Anal. 2020, 34, e23371. [Google Scholar] [CrossRef]
- Fried, J.R.; Gibbons, R.V.; Kalayanarooj, S.; Thomas, S.J.; Srikiatkhachorn, A.; Yoon, I.-K.; Jarman, R.G.; Green, S.; Rothman, A.L.; Cummings, D.A.T. Serotype-Specific Differences in the Rrsk of Dengue Hemorrhagic Fever: An Analysis of Data Collected in Bangkok, Thailand from 1994 to 2006. PLoS Negl. Trop. Dis. 2010, 4, e617. [Google Scholar] [CrossRef] [Green Version]
- Vicente, C.R.; Herbinger, K.-H.; Fröschl, G.; Malta Romano, C.; de Souza Areias Cabidelle, A.; Cerutti Junior, C. Serotype Influences on Dengue Severity: A Cross-Sectional Study on 485 Confirmed Dengue Cases in Vitória, Brazil. BMC Infect. Dis. 2016, 16, 320. [Google Scholar] [CrossRef] [Green Version]
- Martina, B.E.E.; Koraka, P.; Osterhaus, A.D.M.E. Dengue Virus Pathogenesis: An Integrated View. Clin. Microbiol. Rev. 2009, 22, 564–581. [Google Scholar] [CrossRef] [Green Version]
- Norazharuddin, H.; Lai, N.S. Roles and Prospects of Dengue Virus Non-Structural Proteins as Antiviral Targets: An Easy Digest. Malays. J. Med. Sci. 2018, 25, 6–15. [Google Scholar] [CrossRef]
- Davidson, A.D. Chapter 2 New Insights into Flavivirus Nonstructural Protein 5. In Advances in Virus Research; Academic Press: Cambridge, MA, USA, 2009; Volume 74, pp. 41–101. [Google Scholar]
- Gebhard, L.G.; Filomatori, C.V.; Gamarnik, A.V. Functional RNA Elements in the Dengue Virus Genome. Viruses 2011, 3, 1739–1756. [Google Scholar] [CrossRef]
- Obi, J.O.; Gutiérrez-Barbosa, H.; Chua, J.V.; Deredge, D.J. Current Trends and Limitations in Dengue Antiviral Research. Trop. Med. Infect. Dis. 2021, 6, 180. [Google Scholar] [CrossRef]
- Shimizu, H.; Saito, A.; Mikuni, J.; Nakayama, E.E.; Koyama, H.; Honma, T.; Shirouzu, M.; Sekine, S.-I.; Shioda, T. Discovery of a Small Molecule Inhibitor Targeting Dengue Virus NS5 RNA-Dependent RNA Polymerase. PLoS Negl. Trop. Dis. 2019, 13, e0007894. [Google Scholar] [CrossRef] [Green Version]
- Alves, R.P.D.S.; Pereira, L.R.; Fabris, D.L.N.; Salvador, F.S.; Santos, R.A.; Zanotto, P.M.d.A.; Romano, C.M.; Amorim, J.H.; Ferreira, L.C.d.S. Production of a Recombinant Dengue Virus 2 NS5 Protein and Potential Use as a Vaccine Antigen. Clin. Vaccine Immunol. 2016, 23, 460–469. [Google Scholar] [CrossRef] [Green Version]
- Fadaka, A.O.; Sibuyi, N.R.S.; Martin, D.R.; Goboza, M.; Klein, A.; Madiehe, A.M.; Meyer, M. Immunoinformatics Design of a Novel Epitope-Based Vaccine Candidate against Dengue Virus. Sci. Rep. 2021, 11, 19707. [Google Scholar] [CrossRef]
- Sami, S.A.; Marma, K.K.S.; Mahmud, S.; Khan, M.A.N.; Albogami, S.; El-Shehawi, A.M.; Rakib, A.; Chakraborty, A.; Mohiuddin, M.; Dhama, K.; et al. Designing of a Multi-Epitope Vaccine against the Structural Proteins of Marburg Virus Exploiting the Immunoinformatics Approach. ACS Omega 2021, 6, 32043–32071. [Google Scholar] [CrossRef]
- Uno, N.; Ross, T.M. Dengue Virus and the Host Innate Immune Response. Emerg. Microbes Infect. 2018, 7, 167. [Google Scholar] [CrossRef] [Green Version]
- Pati, R.; Shevtsov, M.; Sonawane, A. Nanoparticle Vaccines against Infectious Diseases. Front. Immunol. 2018, 9, 2224. [Google Scholar] [CrossRef] [Green Version]
- Zaneti, A.B.; Yamamoto, M.M.; Sulczewski, F.B.; Almeida, B.d.S.; Souza, H.F.S.; Ferreira, N.S.; Maeda, D.L.N.F.; Sales, N.S.; Rosa, D.S.; Ferreira, L.C.d.S.; et al. Dendritic Cell Targeting Using a DNA Vaccine Induces Specific Antibodies and CD4(+) T Cells to the Dengue Virus Envelope Protein Domain III. Front. Immunol. 2019, 10, 59. [Google Scholar] [CrossRef]
- Cohn, L.; Delamarre, L. Dendritic Cell-Targeted Vaccines. Front. Immunol. 2014, 5, 255. [Google Scholar] [CrossRef] [Green Version]
- Jonny, J.; Putranto, T.A.; Sitepu, E.C.; Irfon, R. Dendritic Cell Vaccine as a Potential Strategy to End the COVID-19 Pandemic. Why Should It Be Ex Vivo? Expert Rev. Vaccines 2022, 21, 1111–1120. [Google Scholar] [CrossRef]
- Liu, K. Dendritic Cells. In Encyclopedia of Cell Biology; Bradshaw, R.A., Stahl, P.D., Eds.; Academic Press: Waltham, MA, USA, 2016; pp. 741–749. ISBN 978-0-12-394796-3. [Google Scholar]
- Kastenmüller, W.; Kastenmüller, K.; Kurts, C.; Seder, R.A. Dendritic Cell-Targeted Vaccines—Hope or Hype? Nat. Rev. Immunol. 2014, 14, 705–711. [Google Scholar] [CrossRef]
- Tarassoff, C.P.; Arlen, P.M.; Gulley, J.L. Therapeutic Vaccines for Prostate Cancer. Oncologist 2006, 11, 451–462. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Rahman, M.M.; Puspo, J.A.; Adib, A.A.; Hossain, M.E.; Alam, M.M.; Sultana, S.; Islam, A.; Klena, J.D.; Montgomery, J.M.; Satter, S.M.; et al. An Immunoinformatics Prediction of Novel Multi-Epitope Vaccines Candidate against Surface Antigens of Nipah Virus. Int. J. Pept. Res. Ther. 2022, 28, 123. [Google Scholar] [CrossRef] [PubMed]
- Tallei, T.E.; Tumilaar, S.G.; Niode, N.J.; Fatimawali, F.; Kepel, B.J.; Idroes, R.; Effendi, Y.; Sakib, S.A.; Emran, T. Bin Potential of Plant Bioactive Compounds as SARS-CoV-2 Main Protease (Mpro) and Spike (S) Glycoprotein Inhibitors: A Molecular Docking Study. Scientifica 2020, 2020, 6307457. [Google Scholar] [CrossRef] [PubMed]
- Laskowski, R.A.; MacArthur, M.W.; Moss, D.S.; Thornton, J.M. PROCHECK: A Program to Check the Stereochemical Quality of Protein Structures. J. Appl. Crystallogr. 1993, 26, 283–291. [Google Scholar] [CrossRef]
- Kleywegt, G.J.; Jones, T.A. Phi/Psi-Chology: Ramachandran Revisited. Structure 1996, 4, 1395–1400. [Google Scholar] [CrossRef] [Green Version]
- Kozakov, D.; Hall, D.R.; Xia, B.; Porter, K.A.; Padhorny, D.; Yueh, C.; Beglov, D.; Vajda, S. The ClusPro Web Server for Protein-Protein Docking. Nat. Protoc. 2017, 12, 255–278. [Google Scholar] [CrossRef]
- Laskowski, R.A.; Jabłońska, J.; Pravda, L.; Vařeková, R.S.; Thornton, J.M. PDBsum: Structural Summaries of PDB Entries. Protein Sci. 2018, 27, 129–134. [Google Scholar] [CrossRef]
- Brooks, B.R.; Brooks, C.L., III; Mackerell, A.D., Jr.; Nilsson, L.; Petrella, R.J.; Roux, B.; Won, Y.; Archontis, G.; Bartels, C.; Boresch, S.; et al. CHARMM: The Biomolecular Simulation Program. J. Comput. Chem. 2009, 30, 1545–1614. [Google Scholar] [CrossRef] [Green Version]
- Jo, S.; Kim, T.; Iyer, V.G.; Im, W. CHARMM-GUI: A Web-Based Graphical User Interface for CHARMM. J. Comput. Chem. 2008, 29, 1859–1865. [Google Scholar] [CrossRef]
- Lee, J.; Cheng, X.; Swails, J.M.; Yeom, M.S.; Eastman, P.K.; Lemkul, J.A.; Wei, S.; Buckner, J.; Jeong, J.C.; Qi, Y.; et al. CHARMM-GUI Input Generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM Simulations Using the CHARMM36 Additive Force Field. J. Chem. Theory Comput. 2016, 12, 405–413. [Google Scholar] [CrossRef]
- Best, R.B.; Zhu, X.; Shim, J.; Lopes, P.E.M.; Mittal, J.; Feig, M.; Mackerell, A.D.J. Optimization of the Additive CHARMM All-Atom Protein Force Field Targeting Improved Sampling of the Backbone φ, ψ and Side-Chain χ(1) and χ(2) Dihedral Angles. J. Chem. Theory Comput. 2012, 8, 3257–3273. [Google Scholar] [CrossRef] [Green Version]
- Phillips, J.C.; Braun, R.; Wang, W.; Gumbart, J.; Tajkhorshid, E.; Villa, E.; Chipot, C.; Skeel, R.D.; Kalé, L.; Schulten, K. Scalable Molecular Dynamics with NAMD. J. Comput. Chem. 2005, 26, 1781–1802. [Google Scholar] [CrossRef] [Green Version]
- Price, D.J.; Brooks, C.L., III. A Modified TIP3P Water Potential for Simulation with Ewald Summation. J. Chem. Phys. 2004, 121, 10096–10103. [Google Scholar] [CrossRef]
- Nosé, S. A Molecular Dynamics Method for Simulations in the Canonical Ensemble. Mol. Phys. 1984, 52, 255–268. [Google Scholar] [CrossRef]
- Nosé, S.; Klein, M.L. Constant Pressure Molecular Dynamics for Molecular Systems. Mol. Phys. 1983, 50, 1055–1076. [Google Scholar] [CrossRef]
- Grest, G.S.; Kremer, K. Molecular Dynamics Simulation for Polymers in the Presence of a Heat Bath. Phys. Rev. A Gen. Phys. 1986, 33, 3628–3631. [Google Scholar] [CrossRef]
- Darden, T.; York, D.; Pedersen, L. Particle Mesh Ewald: An N⋅log(N) Method for Ewald Sums in Large Systems. J. Chem. Phys. 1993, 98, 10089–10092. [Google Scholar] [CrossRef] [Green Version]
- Essmann, U.; Perera, L.; Berkowitz, M.L.; Darden, T.; Lee, H.; Pedersen, L.G. A Smooth Particle Mesh Ewald Method. J. Chem. Phys. 1995, 103, 8577–8593. [Google Scholar] [CrossRef] [Green Version]
- Ryckaert, J.-P.; Ciccotti, G.; Berendsen, H.J.C. Numerical Integration of the Cartesian Equations of Motion of a System with Constraints: Molecular Dynamics of n-Alkanes. J. Comput. Phys. 1977, 23, 327–341. [Google Scholar] [CrossRef] [Green Version]
- Genheden, S.; Ryde, U. Comparison of End-Point Continuum-Solvation Methods for the Calculation of Protein-Ligand Binding Free Energies. Proteins 2012, 80, 1326–1342. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Wang, E.; Sun, H.; Wang, J.; Wang, Z.; Liu, H.; Zhang, J.Z.H.; Hou, T. End-Point Binding Free Energy Calculation with MM/PBSA and MM/GBSA: Strategies and Applications in Drug Design. Chem. Rev. 2019, 119, 9478–9508. [Google Scholar] [CrossRef] [PubMed]
- Bai, Q.; Tan, S.; Xu, T.; Liu, H.; Huang, J.; Yao, X. MolAICal: A Soft Tool for 3D Drug Design of Protein Targets by Artificial Intelligence and Classical Algorithm. Brief. Bioinform. 2020, 22, bbaa161. [Google Scholar] [CrossRef]
- Muhammed, Y.; Yusuf Nadabo, A.; Pius, M.; Sani, B.; Usman, J.; Anka Garba, N.; Mohammed Sani, J.; Opeyemi Olayanju, B.; Zeal Bala, S.; Garba Abdullahi, M.; et al. SARS-CoV-2 Spike Protein and RNA Dependent RNA Polymerase as Targets for Drug and Vaccine Development: A Review. Biosaf. Health 2021, 3, 249–263. [Google Scholar] [CrossRef]
- Liu, X.; Yang, X.; Lee, C.A.; Moustafa, I.M.; Smidansky, E.D.; Lum, D.; Arnold, J.J.; Cameron, C.E.; Boehr, D.D. Vaccine-Derived Mutation in Motif D of Poliovirus RNA-Dependent RNA Polymerase Lowers Nucleotide Incorporation Fidelity. J. Biol. Chem. 2013, 288, 32753–32765. [Google Scholar] [CrossRef]
- Oany, A.R.; Sharmin, T.; Chowdhury, A.S.; Jyoti, T.P.; Hasan, M.A. Highly Conserved Regions in Ebola Virus RNA Dependent RNA Polymerase May Be Act as a Universal Novel Peptide Vaccine Target: A Computational Approach. Silico Pharmacol. 2015, 3, 7. [Google Scholar] [CrossRef] [Green Version]
- Yu, Y.; Santat, L.A.; Choi, S. 6-Bioinformatics Packages for Sequence Analysis. In Applied Mycology and Biotechnology; Arora, D.K., Berka, R.M., Singh, G.B., Eds.; Elsevier: Singapore, 2006; Volume 6, pp. 143–160. [Google Scholar]
- Louie, B.; Higdon, R.; Kolker, E. A Statistical Model of Protein Sequence Similarity and Function Similarity Reveals Overly-Specific Function Predictions. PLoS ONE 2009, 4, e7546. [Google Scholar] [CrossRef] [Green Version]
- Tay, M.Y.F.; Smith, K.; Ng, I.H.W.; Chan, K.W.K.; Zhao, Y.; Ooi, E.E.; Lescar, J.; Luo, D.; Jans, D.A.; Forwood, J.K.; et al. The C-Terminal 18 Amino Acid Region of Dengue Virus NS5 Regulates Its Subcellular Localization and Contains a Conserved Arginine Residue Essential for Infectious Virus Production. PLoS Pathog. 2016, 12, e1005886. [Google Scholar] [CrossRef] [Green Version]
- Tallei, T.E.; Kolondam, B.J. DNA Barcoding of Sangihe Nutmeg (Myristica Fragrans) Using MatK Gene. HAYATI J. Biosci. 2015, 22, 41–47. [Google Scholar] [CrossRef] [Green Version]
- Martinez, D.R.; Yount, B.; Nivarthi, U.; Munt, J.E.; Delacruz, M.J.; Whitehead, S.S.; Durbin, A.P.; de Silva, A.M.; Baric, R.S. Antigenic Variation of the Dengue Virus 2 Genotypes Impacts the Neutralization Activity of Human Antibodies in Vaccinees. Cell Rep. 2020, 33, 108226. [Google Scholar] [CrossRef]
- Jespersen, M.C.; Peters, B.; Nielsen, M.; Marcatili, P. BepiPred-2.0: Improving Sequence-Based B-Cell Epitope Prediction Using Conformational Epitopes. Nucleic Acids Res. 2017, 45, W24–W29. [Google Scholar] [CrossRef] [Green Version]
- Larsen, J.E.P.; Lund, O.; Nielsen, M. Improved Method for Predicting Linear B-Cell Epitopes. Immunome Res. 2006, 2, 2. [Google Scholar] [CrossRef] [Green Version]
- Javadi Mamaghani, A.; Arab-Mazar, Z.; Heidarzadeh, S.; Ranjbar, M.M.; Molazadeh, S.; Rashidi, S.; Niazpour, F.; Naghi Vishteh, M.; Bashiri, H.; Bozorgomid, A.; et al. In-Silico Design of a Multi-Epitope for Developing Sero-Diagnosis Detection of SARS-CoV-2 Using Spike Glycoprotein and Nucleocapsid Antigens. Netw. Model. Anal. Health Inform. Bioinform. 2021, 10, 61. [Google Scholar] [CrossRef]
- Li, W.; Joshi, M.D.; Singhania, S.; Ramsey, K.H.; Murthy, A.K. Peptide Caccine: Progress and Challenges. Vaccines 2014, 2, 515–536. [Google Scholar] [CrossRef] [Green Version]
- Sarkar, B.; Ullah, M.A.; Araf, Y.; Islam, N.N.; Zohora, U.S. Immunoinformatics-Guided Designing and in Silico Analysis of Epitope-Based Polyvalent Vaccines against Multiple Strains of Human Coronavirus (HCoV). Expert Rev. Vaccines 2021, 1–21. [Google Scholar] [CrossRef]
- Yang, Z.; Bogdan, P.; Nazarian, S. An in Silico Deep Learning Approach to Multi-Epitope Vaccine Design: A SARS-CoV-2 Case Study. Sci. Rep. 2021, 11, 3238. [Google Scholar] [CrossRef]
- Bloem, K.; Vuist, I.M.; van der Plas, A.-J.; Knippels, L.M.J.; Garssen, J.; García-Vallejo, J.J.; van Vliet, S.J.; van Kooyk, Y. Ligand Binding and Signaling of Dendritic Cell Immunoreceptor (DCIR) Is Modulated by the Glycosylation of the Carbohydrate Recognition Domain. PLoS ONE 2013, 8, e66266. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Marcou, G.; Rognan, D. Optimizing Fragment and Scaffold Docking by Use of Molecular Interaction Fingerprints. J. Chem. Inf. Model. 2007, 47, 195–207. [Google Scholar] [CrossRef] [PubMed]
- Thévenet, P.; Shen, Y.; Maupetit, J.; Guyon, F.; Derreumaux, P.; Tufféry, P. PEP-FOLD: An Updated de Novo Structure Prediction Server for Both Linear and Disulfide Bonded Cyclic Peptides. Nucleic Acids Res. 2012, 40, W288–W293. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Kastritis, P.L.; Bonvin, A.M.J.J. On the Binding Affinity of Macromolecular Interactions: Daring to Ask Why Proteins Interact. J. R. Soc. Interface 2013, 10, 20120835. [Google Scholar] [CrossRef]
- Sadarangani, M.; Marchant, A.; Kollmann, T.R. Immunological Mechanisms of Vaccine-Induced Protection against COVID-19 in Humans. Nat. Rev. Immunol. 2021, 21, 475–484. [Google Scholar] [CrossRef]
- Abelian, A.; Dybek, M.; Wallach, J.; Gaye, B.; Adejare, A. Chapter 6—Pharmaceutical Chemistry. In Remington: The Science and Practice of Pharmacy; Adejare, A.B., Ed.; Academic Press: Cambridge, MA, USA, 2021; pp. 105–128. ISBN 978-0-12-820007-0. [Google Scholar]
- McRee, D.E. 3-Computational Techniques. In Practical Protein Crystallography, 2nd ed.; McREE, D.E., Ed.; Academic Press: San Diego, CA, USA, 1999; pp. 91–269.cp1. ISBN 978-0-12-486052-0. [Google Scholar]
- Pylaeva, S.; Brehm, M.; Sebastiani, D. Salt Bridge in Aqueous Solution: Strong Structural Motifs but Weak Enthalpic Effect. Sci. Rep. 2018, 8, 13626. [Google Scholar] [CrossRef] [Green Version]
- Karplus, M.; McCammon, J.A. Molecular Dynamics Simulations of Biomolecules. Nat. Struct. Biol. 2002, 9, 646–652. [Google Scholar] [CrossRef]
- Radwan, A.; Mahrous, G.M. Docking Studies and Molecular Dynamics Simulations of the Binding Characteristics of Waldiomycin and Its Methyl Ester Analog to Staphylococcus Aureus Histidine Kinase. PLoS ONE 2020, 15, e0234215. [Google Scholar] [CrossRef]
- Zhou, R.; Eleftheriou, M.; Hon, C.-C.; Germain, R.S.; Royyuru, A.K.; Berne, B.J. Massively Parallel Molecular Dynamics Simulations of Lysozyme Unfolding. IBM J. Res. Dev. 2008, 52, 19–30. [Google Scholar] [CrossRef]
- Lobanov, M.Y.; Bogatyreva, N.S.; Galzitskaya, O.V. Radius of Gyration as an Indicator of Protein Structure Compactness. Mol. Biol. 2008, 42, 623–628. [Google Scholar] [CrossRef]
- Becker, W.; Bhattiprolu, K.C.; Gubensäk, N.; Zangger, K. Investigating Protein-Ligand Interactions by Solution Nuclear Magnetic Resonance Spectroscopy. Chemphyschem 2018, 19, 895–906. [Google Scholar] [CrossRef] [Green Version]
- Hou, T.; Wang, J.; Li, Y.; Wang, W. Assessing the Performance of the MM/PBSA and MM/GBSA Methods. 1. The Accuracy of Binding Free Energy Calculations Based on Molecular Dynamics Simulations. J. Chem. Inf. Model. 2011, 51, 69–82. [Google Scholar] [CrossRef]
Accession Number | Origin | Year of Sample Collection | Organism |
---|---|---|---|
ACQ44500 | Belize | 2002 | Homo sapiens |
AHG23135 | Nicaragua | 2007 | H. sapiens |
ABV02465 | Mexico | 2005 | H. sapiens |
AER45462 | Guatemala | 2009 | H. sapiens |
ADA60759 | Colombia | 2007 | H. sapiens |
AET43238 | Venezuela | 2007 | H. sapiens |
AGN94892 | Brazil | 2002 | H. sapiens |
AHG23133 | Puerto Rico | 2006 | H. sapiens |
AGX15366 | Peru | 2009 | H. sapiens |
AAW31412 | Cuba | 1997 | H. sapiens |
ACQ44499 | Saint Kitts | 2001 | H. sapiens |
ACK57816 | Guadeloupe | 2006 | H. sapiens |
BAD36760 | Dominica | 2001 | H. sapiens |
ACQ44492 | Virgin Island | 2005 | H. sapiens |
ACK57811 | French Guiana | 2006 | H. sapiens |
ACS32033 | Jamaica | 2007 | H. sapiens |
ACK57806 | Suriname | 2005 | H. sapiens |
AGO67249 | Vietnam | 1995 | H. sapiens |
AHG23169 | Cambodia | 2008 | H. sapiens |
ACY70785 | Thailand | 2001 | H. sapiens |
ACQ44517 | Papua New Guinea | 2008 | H. sapiens |
AHZ61501 | Taiwan | 2014 | H. sapiens |
AHA80987 | Pakistan | 2010 | H. sapiens |
AIH13924 | Jeddah | 2014 | H. sapiens |
ACS32039 | Sri Lanka | 2004 | H. sapiens |
AFZ40225 | India | 2009 | H. sapiens |
AGF87124 | China | 2012 | H. sapiens |
ADK26435 | Guam | 2001 | H. sapiens |
BAD42415 | Sumatera-Indonesia | 1998 | H. sapiens |
AHG06340 | Makassar-Indonesia | 2007 | H. sapiens |
AAW51407 | Jakarta-Indonesia | 2004 | H. sapiens |
AAK67712 | Australia | 1993 | H. sapiens |
AKA55069 | Singapore | 2012 | H. sapiens |
ABW06614 | Brunei | 2006 | H. sapiens |
ADM26236 | Tonga | 1974 | H. sapiens |
ADM26220 | Fiji | 1971 | H. sapiens |
ADM26228 | Tahiti | 1971 | H. sapiens |
ADM26222 | New Caledonia | 1972 | H. sapiens |
ADM26222 | Samoa | 1972 | H. sapiens |
AEW50183 (DENV-4) | Brazil | 2010 | H. sapiens |
AFX65868 (DENV-4) | Brazil | 2010 | H. sapiens |
Cluster | Origin of Specimen |
---|---|
I (American Genotype) | Brazil, Puerto Rico, Peru |
II (American Genotype) |
|
III (Asian Genotype) |
|
IV (Pacific Islands) | Samoa, Tahiti, Tonga, Fiji, New Caledonia |
Start | End | Peptide | Length |
---|---|---|---|
5 | 29 | DVDLGSGTRNIGIESEIPNLDIIGK | 25 |
33 | 51 | KIKQEHETSWHYDQDHPYK | 19 |
60 | 69 | ETKQTGSASS | 10 |
97 | 122 | TPFGQQRVFKEKVDTRTQEPKEGTKK | 26 |
146 | 155 | REEFTRKVRS | 10 |
163 | 196 | FTDENKWKSAREAVEDSGFWELVDKERNLHLEGK | 34 |
205 | 220 | MGKREKKLGEFGKAKG | 16 |
246 | 259 | HWFSRENSLSGVEG | 14 |
293 | 306 | TLEDLKNEEMVTNH | 14 |
345 | 354 | RRDQRGSGQV | 10 |
378 | 392 | VFKSIQHLTVTEEIA | 15 |
398 | 405 | ARVGRERL | 8 |
435 | 455 | GKVRKDIQQWEPSRGWNDWTQ | 21 |
483 | 500 | ELIGRARISQGAGWSLRE | 18 |
537 | 557 | WVPTSRTTWSIHATHEWMTTE | 21 |
570 | 597 | ENPWMEDKTPVESWEEIPYLGKREDQWC | 28 |
601 | 611 | IGLTSRATWAK | 11 |
624 | 635 | IGNEEYTDYMPS | 12 |
Epitope | Length | Antigenicity | Allergenicity | Toxicity | Homology to Human |
---|---|---|---|---|---|
DVDLGSGTRNIGIESEIPNLDIIGK | 25 | Antigen (1.0462) | Non- allergen (0.69) | Non-toxin (−0.78) | Non-Homolog |
KIKQEHETSWHYDQDHPYK | 19 | Antigen (0.4439) | Non-allergen (0.64) | Non-toxin (−0.70) | Non-Homolog |
MGKREKKLGEFGKAKG | 16 | Antigen (1.3969) | Non-allergen (0.59) | Non-toxin (−0.45) | Non-Homolog |
GKVRKDIQQWEPSRGWNDWTQ | 21 | Non-antigen (−0.2845) | Non- allergen (0.64) | Non-toxin (−1.04) | Non-Homolog |
VFKSIQHLTVTEEIA | 15 | Antigen (0.3651) | Non-allergen (0.64) | Non-toxin (−1.17) | Non-Homolog |
ELIGRARISQGAGWSLRE | 18 | Antigen (0.6645) | Non-allergen (0.59) | Non-toxin (−1.43) | Non-Homolog |
WVPTSRTTWSIHATHEWMTTE | 21 | Antigen (0.8736) | Non-allergen (0.67) | Non-toxin (−1.50) | Non-Homolog |
Ramachandran Criteria | Percentage |
---|---|
Most Favored Regions | 90.1% |
Additional allowed regions | 9.6% |
Generously allowed regions | 0.1% |
Disallowed regions | 0.1% |
−35.784 ± 0.2933 | 0 | 3.0796 | −38.8636 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the authors. 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/).
Share and Cite
Wahongan, I.F.; Suoth, E.J.; Fatimawali; Alhumaid, S.; Albayat, H.; Aljeldah, M.; Al Shammari, B.R.; Mashraqi, M.M.; Alshehri, A.A.; Sulaiman, T.; et al. Designing an Epitope-Based Peptide Vaccine Derived from RNA-Dependent RNA Polymerase (RdRp) against Dengue Virus Serotype 2. Vaccines 2022, 10, 1734. https://doi.org/10.3390/vaccines10101734
Wahongan IF, Suoth EJ, Fatimawali, Alhumaid S, Albayat H, Aljeldah M, Al Shammari BR, Mashraqi MM, Alshehri AA, Sulaiman T, et al. Designing an Epitope-Based Peptide Vaccine Derived from RNA-Dependent RNA Polymerase (RdRp) against Dengue Virus Serotype 2. Vaccines. 2022; 10(10):1734. https://doi.org/10.3390/vaccines10101734
Chicago/Turabian StyleWahongan, Irma F., Elly J. Suoth, Fatimawali, Saad Alhumaid, Hawra Albayat, Mohammed Aljeldah, Basim R. Al Shammari, Mutaib M. Mashraqi, Ahmad A. Alshehri, Tarek Sulaiman, and et al. 2022. "Designing an Epitope-Based Peptide Vaccine Derived from RNA-Dependent RNA Polymerase (RdRp) against Dengue Virus Serotype 2" Vaccines 10, no. 10: 1734. https://doi.org/10.3390/vaccines10101734
APA StyleWahongan, I. F., Suoth, E. J., Fatimawali, Alhumaid, S., Albayat, H., Aljeldah, M., Al Shammari, B. R., Mashraqi, M. M., Alshehri, A. A., Sulaiman, T., Turkistani, S. A., Alwashmi, A. S. S., Garout, M., Tallei, T. E., & Rabaan, A. A. (2022). Designing an Epitope-Based Peptide Vaccine Derived from RNA-Dependent RNA Polymerase (RdRp) against Dengue Virus Serotype 2. Vaccines, 10(10), 1734. https://doi.org/10.3390/vaccines10101734