Clinical and Experimental Vaccine Research

The Korean Vaccine Society (대한백신학회)
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Insights into structural vaccinology harnessed for universal coronavirus vaccine development

  • Chin Peng Lim (School of Pharmaceutical Sciences, Universiti Sains Malaysia) ;
  • Chiuan Herng Leow (Institute for Research in Molecular Medicine, Universiti Sains Malaysia) ;
  • Hui Ting Lim (Institute for Research in Molecular Medicine, Universiti Sains Malaysia) ;
  • Boon Hui Kok (Institute for Research in Molecular Medicine, Universiti Sains Malaysia) ;
  • Candy Chuah (Faculty of Medicine, Asian Institute of Medical Science and Technology University) ;
  • Jonas Ivan Nobre Oliveira (Department of Biophysics and Pharmacology, Bioscience Center, Federal University of Rio Grande do Norte) ;
  • Malcolm Jones (School of Veterinary Science, The University of Queensland) ;
  • Chiuan Yee Leow (School of Pharmaceutical Sciences, Universiti Sains Malaysia)
  • Received : 2024.04.07
  • Accepted : 2024.05.15
  • Published : 2024.07.31
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Abstract

Structural vaccinology is pivotal in expediting vaccine design through high-throughput screening of immunogenic antigens. Leveraging the structural and functional characteristics of antigens and immune cell receptors, this approach employs protein structural comparison to identify conserved patterns in key pathogenic components. Molecular modeling techniques, including homology modeling and molecular docking, analyze specific three-dimensional (3D) structures and protein interactions and offer valuable insights into the 3D interactions and binding affinity between vaccine candidates and target proteins. In this review, we delve into the utilization of various immunoinformatics and molecular modeling tools to streamline the development of broad-protective vaccines against coronavirus disease 2019 variants. Structural vaccinology significantly enhances our understanding of molecular interactions between hosts and pathogens. By accelerating the pace of developing effective and targeted vaccines, particularly against the rapidly mutating severe acute respiratory syndrome coronavirus 2 and other prevalent infectious diseases, this approach stands at the forefront of advancing immunization strategies. The combination of computational techniques and structural insights not only facilitates the identification of potential vaccine candidates but also contributes to the rational design of vaccines, fostering a more efficient and targeted approach to combatting infectious diseases.

Keywords

Acknowledgement

The authors acknowledge the funding support provided for this work by the Ministry of Higher Education Malaysia for Fundamental Research Grant Scheme with Project Code: FRGS/1/2022/SKK0/USM/02/5 and FRGS/1/2021/SKK06/USM/02/12.

References

  1. Lim CP, Kok BH, Lim HT, et al. Recent trends in next generation immunoinformatics harnessed for universal coronavirus vaccine design. Pathog Glob Health 2023;117:134-51. 
  2. Nuccitelli A, Cozzi R, Gourlay LJ, et al. Structure-based approach to rationally design a chimeric protein for an effective vaccine against Group B Streptococcus infections. Proc Natl Acad Sci U S A 2011;108:10278-83. 
  3. Schneewind O, Missiakas D. Structural vaccinology to thwart antigenic variation in microbial pathogens. Proc Natl Acad Sci U S A 2011;108:10029-30. 
  4. Dormitzer PR, Grandi G, Rappuoli R. Structural vaccinology starts to deliver. Nat Rev Microbiol 2012;10:807-13. 
  5. Swanson KA, Settembre EC, Shaw CA, et al. Structural basis for immunization with postfusion respiratory syncytial virus fusion F glycoprotein (RSV F) to elicit high neutralizing antibody titers. Proc Natl Acad Sci USA 2011;108:9619-24. 
  6. Beernink PT, Shaughnessy J, Braga EM, et al. A meningococcal factor H binding protein mutant that eliminates factor H binding enhances protective antibody responses to vaccination. J Immunol 2011;186:3606-14. 
  7. Pajon R, Beernink PT, Granoff DM. Design of meningococcal factor H binding protein mutant vaccines that do not bind human complement factor H. Infect Immun 2012;80:2667-77. 
  8. Beernink PT, Granoff DM. Bactericidal antibody responses induced by meningococcal recombinant chimeric factor H-binding protein vaccines. Infect Immun 2008;76:2568-75. 
  9. da Silva MK, Fulco UL, Junior ED, Oliveira JI. Moving targets: COVID-19 vaccine efficacy against Omicron subvariants. Mol Ther 2022;30:2644-5. 
  10. Marty AM, Jones MK. The novel Coronavirus (SARS-CoV2) is a one health issue. One Health 2020;9:100123. 
  11. Rappuoli R, De Gregorio E, Del Giudice G, et al. Vaccinology in the post-COVID-19 era. Proc Natl Acad Sci USA 2021;118:e2020368118. 
  12. Dormitzer PR, Ulmer JB, Rappuoli R. Structure-based antigen design: a strategy for next generation vaccines. Trends Biotechnol 2008;26:659-67. 
  13. Rappuoli R, Bottomley MJ, D'Oro U, Finco O, De Gregorio E. Reverse vaccinology 2.0: human immunology instructs vaccine antigen design. J Exp Med 2016;213:469-81. 
  14. Pallesen J, Wang N, Corbett KS, et al. Immunogenicity and structures of a rationally designed prefusion MERS-CoV spike antigen. Proc Natl Acad Sci U S A 2017;114:E7348-57. 
  15. Juraszek J, Rutten L, Blokland S, et al. Stabilizing the closed SARS-CoV-2 spike trimer. Nat Commun 2021;12:244. 
  16. Bangaru S, Ozorowski G, Turner HL, et al. Structural analysis of full-length SARS-CoV-2 spike protein from an advanced vaccine candidate. Science 2020;370:1089-94. 
  17. Hsieh CL, Goldsmith JA, Schaub JM, et al. Structure-based design of prefusion-stabilized SARS-CoV-2 spikes. Science 2020;369:1501-5. 
  18. He L, Lin X, Wang Y, et al. Single-component, self-assembling, protein nanoparticles presenting the receptor binding domain and stabilized spike as SARS-CoV-2 vaccine candidates. Sci Adv 2021;7:eabf1591. 
  19. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol 1990;215:403-10. 
  20. Pearson WR, Lipman DJ. Improved tools for biological sequence comparison. Proc Natl Acad Sci USA 1988;85:2444-8. 
  21. Brusic V, Petrovsky N. Immunoinformatics and its relevance to understanding human immune disease. Expert Rev Clin Immunol 2005;1:145-57. 
  22. Wang L, Jiang T. On the complexity of multiple sequence alignment. J Comput Biol 1994;1:337-48. 
  23. Edgar RC, Batzoglou S. Multiple sequence alignment. Curr Opin Struct Biol 2006;16:368-73. 
  24. Stanfield RL, Wilson IA. Protein-peptide interactions. Curr Opin Struct Biol 1995;5:103-13. 
  25. Lohning AE, Levonis SM, Williams-Noonan B, Schweiker SS. A practical guide to molecular docking and homology modelling for medicinal chemists. Curr Top Med Chem 2017;17:2023-40. 
  26. Xiang Z. Advances in homology protein structure modeling. Curr Protein Pept Sci 2006;7:217-27. 
  27. Chothia C, Lesk AM. The relation between the divergence of sequence and structure in proteins. EMBO J 1986;5:823-6. 
  28. Anasir MI, Poh CL. Structural vaccinology for viral vaccine design. Front Microbiol 2019;10:738. 
  29. Purcell AW, McCluskey J, Rossjohn J. More than one reason to rethink the use of peptides in vaccine design. Nat Rev Drug Discov 2007;6:404-14. 
  30. Meng XY, Zhang HX, Mezei M, Cui M. Molecular docking: a powerful approach for structure-based drug discovery. Curr Comput Aided Drug Des 2011;7:146-57. 
  31. Waqas M, Haider A, Sufyan M, Siraj S, Sehgal SA. Determine the potential epitope based peptide vaccine against novel SARS-CoV-2 targeting structural proteins using immunoinformatics approaches. Front Mol Biosci 2020;7:227. 
  32. Chaudhuri A. Comparative analysis of non structural protein 1 of SARS-CoV2 with SARS-CoV1 and MERS-CoV: an in silico study. J Mol Struct 2021;1243:130854. 
  33. Ghorbani A, Zare F, Sazegari S, Afsharifar A, Eskandari MH, Pormohammad A. Development of a novel platform of virus-like particle (VLP)-based vaccine against COVID-19 by exposing epitopes: an immunoinformatics approach. New Microbes New Infect 2020;38:100786. 
  34. Akhand MR, Azim KF, Hoque SF, et al. Genome based evolutionary lineage of SARS-CoV-2 towards the development of novel chimeric vaccine. Infect Genet Evol 2020;85:104517. 
  35. Madeira F, Park YM, Lee J, et al. The EMBL-EBI search and sequence analysis tools APIs in 2019. Nucleic Acids Res 2019;47(W1):W636-41. 
  36. Rahman MS, Hoque MN, Islam MR, et al. Epitope-based chimeric peptide vaccine design against S, M and E proteins of SARS-CoV-2, the etiologic agent of COVID-19 pandemic: an in silico approach. PeerJ 2020;8:e9572. 
  37. Banerjee A, Santra D, Maiti S. Energetics and IC50 based epitope screening in SARS CoV-2 (COVID 19) spike protein by immunoinformatic analysis implicating for a suitable vaccine development. J Transl Med 2020;18:281. 
  38. Rakib A, Sami SA, Islam MA, et al. Epitope-based immunoinformatics approach on nucleocapsid protein of severe acute respiratory syndrome-coronavirus-2. Molecules 2020;25:5088. 
  39. Srivastava S, Kamthania M, Singh S, Saxena AK, Sharma N. Structural basis of development of multi-epitope vaccine against Middle East respiratory syndrome using in silico approach. Infect Drug Resist 2018;11:2377-91. 
  40. Ayyagari VS, T C V, K AP, Srirama K. Design of a multiepitope-based vaccine targeting M-protein of SARSCoV2: an immunoinformatics approach. J Biomol Struct Dyn 2022;40:2963-77. 
  41. Larkin MA, Blackshields G, Brown NP, et al. Clustal W and Clustal X version 2.0. Bioinformatics 2007;23:2947-8. 
  42. Bhatnager R, Bhasin M, Arora J, Dang AS. Epitope based peptide vaccine against SARS-COV2: an immune-informatics approach. J Biomol Struct Dyn 2021;39:5690-705. 
  43. Jakhar R, Kaushik S, Gakhar SK. 3CL hydrolase-based multiepitope peptide vaccine against SARS-CoV-2 using immunoinformatics. J Med Virol 2020;92:2114-23. 
  44. Papadopoulos JS, Agarwala R. COBALT: constraint-based alignment tool for multiple protein sequences. Bioinformatics 2007;23:1073-9. 
  45. Awadelkareem EA, Mohammed NO, Gaafar BB, Ali SA. Epitope-based peptide vaccine design against spike protein (S) of novel coronavirus (2019-nCoV): an immunoinformatics approach. Res Sq [Preprint] 2020 Jun 9. https://doi.org/10.21203/rs.3.rs-30076/v1 
  46. Panda PK, Arul MN, Patel P, et al. Structure-based drug designing and immunoinformatics approach for SARSCoV-2. Sci Adv 2020;6:eabb8097. 
  47. Kuraku S, Zmasek CM, Nishimura O, Katoh K. aLeaves facilitates on-demand exploration of metazoan gene family trees on MAFFT sequence alignment server with enhanced interactivity. Nucleic Acids Res 2013;41(Web Server issue):W22-8. 
  48. Cuspoca AF, Diaz LL, Acosta AF, et al. A multi-epitope vaccine against SARS-CoV-2 directed towards the Latin American population: an immunoinformatics approach. Res Sq [Preprint] 2020 Sep 2. https://doi.org/10.21203/rs.3.rs-70414/v1 
  49. Bailey TL, Gribskov M. Combining evidence using p-values: application to sequence homology searches. Bioin-formatics 1998;14:48-54. 
  50. Rehman HM, Mirza MU, Ahmad MA, et al. A putative prophylactic solution for COVID-19: development of novel multiepitope vaccine candidate against SARS-COV-2 by comprehensive immunoinformatic and molecular modelling approach. Biology (Basel) 2020;9:296. 
  51. Sievers F, Wilm A, Dineen D, et al. Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. Mol Syst Biol 2011;7:539. 
  52. Sievers F, Higgins DG. Clustal Omega, accurate alignment of very large numbers of sequences. Methods Mol Biol 2014;1079:105-16.
  53. Rice P, Longden I, Bleasby A. EMBOSS: the European Molecular Biology Open Software Suite. Trends Genet 2000;16:276-7. 
  54. Heo L, Park H, Seok C. GalaxyRefine: protein structure refinement driven by side-chain repacking. Nucleic Acids Res 2013;41(Web Server issue):W384-8. 
  55. Jain N, Shankar U, Majee P, Kumar A. Scrutinizing the SARS-CoV-2 protein information for designing an effective vaccine encompassing both the T-cell and B-cell epitopes. Infect Genet Evol 2021;87:104648. 
  56. Khairkhah N, Aghasadeghi MR, Namvar A, Bolhassani A. Design of novel multiepitope constructs-based peptide vaccine against the structural S, N and M proteins of human COVID-19 using immunoinformatics analysis. PLoS One 2020;15:e0240577. 
  57. Yazdani Z, Rafiei A, Yazdani M, Valadan R. Design an efficient multi-epitope peptide vaccine candidate against SARS-CoV-2: an in silico analysis. Infect Drug Resist 2020;13:3007-22. 
  58. Wiederstein M, Sippl MJ. ProSA-web: interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic Acids Res 2007;35(Web Server issue):W407-10. 
  59. Bhattacharya M, Sharma AR, Patra P, et al. Development of epitope-based peptide vaccine against novel coronavirus 2019 (SARS-COV-2): immunoinformatics approach. J Med Virol 2020;92:618-31. 
  60. Sanami S, Zandi M, Pourhossein B, et al. Design of a multi-epitope vaccine against SARS-CoV-2 using immunoinformatics approach. Int J Biol Macromol 2020;164:871-83. 
  61. Almofti YA, Abd-elrahman KA, Gassmallah SA, Salih MA. Multi epitopes vaccine prediction against severe acute respiratory syndrome (SARS) coronavirus using immunoinformatics approaches. Am J Microbiol Res 2018;6:94-114. 
  62. Shi J, Zhang J, Li S, et al. Epitope-based vaccine target screening against highly pathogenic MERS-CoV: an in silico approach applied to emerging infectious diseases. PLoS One 2015;10:e0144475. 
  63. Abdelmageed MI, Abdelmoneim AH, Mustafa MI, et al. Design of a multiepitope-based peptide vaccine against the E protein of human COVID-19: an immunoinformatics approach. Biomed Res Int 2020;2020:2683286. 
  64. Mahapatra SR, Sahoo S, Dehury B, et al. Designing an efficient multi-epitope vaccine displaying interactions with diverse HLA molecules for an efficient humoral and cellular immune response to prevent COVID-19 infection. Expert Rev Vaccines 2020;19:871-85. 
  65. Joshi A, Joshi BC, Mannan MA, Kaushik V. Epitope based vaccine prediction for SARS-COV-2 by deploying immuno-informatics approach. Inform Med Unlocked 2020;19:100338. 
  66. Cheng J, Randall AZ, Sweredoski MJ, Baldi P. SCRATCH: a protein structure and structural feature prediction server. Nucleic Acids Res 2005;33(Web Server issue):W72-6. 
  67. Naz A, Shahid F, Butt TT, Awan FM, Ali A, Malik A. Designing multi-epitope vaccines to combat emerging coronavirus disease 2019 (COVID-19) by employing immuno-informatics approach. Front Immunol 2020;11:1663. 
  68. Bhattacharya D, Nowotny J, Cao R, Cheng J. 3Drefine: an interactive web server for efficient protein structure refinement. Nucleic Acids Res 2016;44(W1):W406-9. 
  69. Sarkar B, Ullah MA, Johora FT, Taniya MA, Araf Y. Immunoinformatics-guided designing of epitope-based subunit vaccines against the SARS coronavirus-2 (SARSCoV-2). Immunobiology 2020;225:151955. 
  70. Devi A, Chaitanya NS. In silico designing of multi-epitope vaccine construct against human coronavirus infections. J Biomol Struct Dyn 2021;39:6903-17. 
  71. Moura RR, Agrelli A, Santos-Silva CA, et al. Immunoinformatic approach to assess SARS-CoV-2 protein S epitopes recognised by the most frequent MHC-I alleles in the Brazilian population. J Clin Pathol 2021;74:528-32. 
  72. Kuriata A, Gierut AM, Oleniecki T, et al. CABS-flex 2.0: a web server for fast simulations of flexibility of protein structures. Nucleic Acids Res 2018;46(W1):W338-43. 
  73. Rahman N, Ali F, Basharat Z, et al. Vaccine design from the ensemble of surface glycoprotein epitopes of SARS-CoV-2: an immunoinformatics approach. Vaccines (Basel) 2020;8:423. 
  74. Tahir Ul Qamar M, Rehman A, Tusleem K, et al. Designing of a next generation multiepitope based vaccine (MEV) against SARS-COV-2: Immunoinformatics and in silico approaches. PLoS One 2020;15:e0244176. 
  75. Kolinski A. Protein modeling and structure prediction with a reduced representation. Acta Biochim Pol 2004;51:349-71. 
  76. Blaszczyk M, Jamroz M, Kmiecik S, Kolinski A. CABSfold: server for the de novo and consensus-based prediction of protein structure. Nucleic Acids Res 2013;41(Web Server issue):W406-11. 
  77. Tahir Ul Qamar M, Shahid F, Aslam S, et al. Reverse vaccinology assisted designing of multiepitope-based subunit vaccine against SARS-CoV-2. Infect Dis Poverty 2020;9:132. 
  78. Tian W, Chen C, Lei X, Zhao J, Liang J. CASTp 3.0: computed atlas of surface topography of proteins. Nucleic Acids Res 2018;46(W1):W363-7. 
  79. Kumar TA. CFSSP: Chou and Fasman secondary structure prediction server. Wide Spectr 2013;1:15-9. 
  80. Pettersen EF, Goddard TD, Huang CC, et al. UCSF Chimera: a visualization system for exploratory research and analysis. J Comput Chem 2004;25:1605-12. 
  81. Goddard TD, Huang CC, Meng EC, et al. UCSF ChimeraX: meeting modern challenges in visualization and analysis. Protein Sci 2018;27:14-25. 
  82. Pettersen EF, Goddard TD, Huang CC, et al. UCSF ChimeraX: structure visualization for researchers, educators, and developers. Protein Sci 2021;30:70-82. 
  83. Meng EC, Goddard TD, Pettersen EF, et al. UCSF ChimeraX: tools for structure building and analysis. Protein Sci 2023;32:e4792. 
  84. Tahir Ul Qamar M, Saleem S, Ashfaq UA, Bari A, Anwar F, Alqahtani S. Epitope-based peptide vaccine design and target site depiction against Middle East respiratory syndrome coronavirus: an immune-informatics study. J Transl Med 2019;17:362. 
  85. Mukherjee S, Tworowski D, Detroja R, Mukherjee SB, Frenkel-Morgenstern M. Immunoinformatics and structural analysis for identification of immunodominant epitopes in SARS-CoV-2 as potential vaccine targets. Vaccines (Basel) 2020;8:290. 
  86. Abd Albagi SO, Al-Nour MY, Elhag M, et al. A multiple peptides vaccine against COVID-19 designed from the nucleocapsid phosphoprotein (N) and spike glycoprotein (S) via the immunoinformatics approach. Inform Med Unlocked 2020;21:100476. 
  87. Chen Z, Ruan P, Wang L, Nie X, Ma X, Tan Y. T and B cell epitope analysis of SARS-CoV-2 S protein based on immunoinformatics and experimental research. J Cell Mol Med 2021;25:1274-89. 
  88. Enayatkhani M, Hasaniazad M, Faezi S, et al. Reverse vaccinology approach to design a novel multi-epitope vaccine candidate against COVID-19: an in silico study. J Biomol Struct Dyn 2021;39:2857-72. 
  89. Krogh A, Larsson B, von Heijne G, Sonnhammer EL. Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J Mol Biol 2001;305:567-80. 
  90. Hallgren J, Tsirigos KD, Pedersen MD, et al. DeepTM-HMM predicts alpha and beta transmembrane proteins using deep neural networks. BioRxiv [Preprint] 2022 Apr 8. https://doi.org/10.1101/2022.04.08.487609 
  91. Jyotisha, Singh S, Qureshi IA. Multi-epitope vaccine against SARS-CoV-2 applying immunoinformatics and molecular dynamics simulation approaches. J Biomol Struct Dyn 2022;40:2917-33. 
  92. Bency J, Helen M. Novel epitope based peptides for vaccine against SARS-CoV-2 virus: immunoinformatics with docking approach. Int J Res Med Sci 2020;8:2385-94. 
  93. Sarkar B, Ullah MA, Araf Y, Rahman MS. Engineering a novel subunit vaccine against SARS-CoV-2 by exploring immunoinformatics approach. Inform Med Unlocked 2020;21:100478. 
  94. Ward JJ, McGuffin LJ, Bryson K, Buxton BF, Jones DT. The DISOPRED server for the prediction of protein disorder. Bioinformatics 2004;20:2138-9. 
  95. Corral-Lugo A, Lopez-Siles M, Lopez D, McConnell MJ, Martin-Galiano AJ. Identification and analysis of unstructured, linear B-cell epitopes in SARS-CoV-2 virion proteins for vaccine development. Vaccines (Basel) 2020;8:397. 
  96. Craig DB, Dombkowski AA. Disulfide by Design 2.0: a web-based tool for disulfide engineering in proteins. BMC Bioinformatics 2013;14:346. 
  97. Chauhan V, Rungta T, Rawat M, Goyal K, Gupta Y, Singh MP. Excavating SARS-coronavirus 2 genome for epitope-based subunit vaccine synthesis using immunoinformatics approach. J Cell Physiol 2021;236:1131-47. 
  98. Robert X, Gouet P. Deciphering key features in protein structures with the new ENDscript server. Nucleic Acids Res 2014;42(Web Server issue):W320-4. 
  99. Zhang J, Liang Y, Zhang Y. Atomic-level protein structure refinement using fragment-guided molecular dynamics conformation sampling. Structure 2011;19:1784-95. 
  100. Ko J, Park H, Heo L, Seok C. GalaxyWEB server for protein structure prediction and refinement. Nucleic Acids Res 2012;40(Web Server issue):W294-7. 
  101. Kumar J, Qureshi R, Sagurthi SR, Qureshi IA. Designing of nucleocapsid protein based novel multi-epitope vaccine against SARS-COV-2 using immunoinformatics approach. Int J Pept Res Ther 2021;27:941-56. 
  102. Samad A, Ahammad F, Nain Z, et al. Designing a multi-epitope vaccine against SARS-CoV-2: an immunoinformatics approach. J Biomol Struct Dyn 2022;40:14-30. 
  103. Herrera LR. Immunoinformatics approach in designing a novel vaccine using epitopes from all the structural proteins of SARS-CoV-2. Biomed Pharmacol J 2020;13:1845-62. 
  104. Heo L, Lee H, Seok C. GalaxyRefineComplex: refinement of protein-protein complex model structures driven by interface repacking. Sci Rep 2016;6:32153. 
  105. Ko J, Park H, Seok C. GalaxyTBM: template-based modeling by building a reliable core and refining unreliable local regions. BMC Bioinformatics 2012;13:198. 
  106. Linding R, Russell RB, Neduva V, Gibson TJ. GlobPlot: exploring protein sequences for globularity and disorder. Nucleic Acids Res 2003;31:3701-8. 
  107. Sen TZ, Jernigan RL, Garnier J, Kloczkowski A. GOR V server for protein secondary structure prediction. Bioinformatics 2005;21:2787-8. 
  108. Kumar N, Sood D, Chandra R. Design and optimization of a subunit vaccine targeting COVID-19 molecular shreds using an immunoinformatics framework. RSC Adv 2020;10:35856-72. 
  109. Scott WR, Hunenberger PH, Tironi IG, et al. The GROMOS biomolecular simulation program package. J Phys Chem A 1999;103:3596-607. 
  110. Zhang Y. I-TASSER server for protein 3D structure prediction. BMC Bioinformatics 2008;9:40. 
  111. Safavi A, Kefayat A, Mahdevar E, Abiri A, Ghahremani F. Exploring the out of sight antigens of SARS-CoV-2 to design a candidate multi-epitope vaccine by utilizing immunoinformatics approaches. Vaccine 2020;38:7612-28. 
  112. Khan S, Shaker B, Ahmad S, et al. Towards a novel peptide vaccine for Middle East respiratory syndrome coronavirus and its possible use against pandemic COVID-19. J Mol Liq 2021;324:114706. 
  113. Kumar A, Kumar P, Saumya KU, Kapuganti SK, Bhardwaj T, Giri R. Exploring the SARS-CoV-2 structural proteins for multi-epitope vaccine development: an in-silico approach. Expert Rev Vaccines 2020;19:887-98. 
  114. Wallace AC, Laskowski RA, Thornton JM. LIGPLOT: a program to generate schematic diagrams of protein-ligand interactions. Protein Eng 1995;8:127-34. 
  115. Laskowski RA, Swindells MB. LigPlot+: multiple ligand-protein interaction diagrams for drug discovery. J Chem Inf Model 2011;51:2778-86. 
  116. Wang D, Mai J, Zhou W, et al. Immunoinformatic analysis of T- and B-cell epitopes for SARS-CoV-2 vaccine design. Vaccines (Basel) 2020;8:355. 
  117. Wu S, Zhang Y. LOMETS: a local meta-threading-server for protein structure prediction. Nucleic Acids Res 2007;35:3375-82. 
  118. Kumar S, Stecher G, Li M, Knyaz C, Tamura K. MEGA X: molecular evolutionary genetics analysis across computing platforms. Mol Biol Evol 2018;35:1547-9. 
  119. Xu D, Zhang Y. Improving the physical realism and structural accuracy of protein models by a two-step atomiclevel energy minimization. Biophys J 2011;101:2525-34. 
  120. Gupta R, Jung E, Brunak S. NetNGlyc 1.0 server: prediction of N-glycosylation sites in human proteins. Kongens Lyngby: DTU Bioinformatics; 2004. 
  121. Steentoft C, Vakhrushev SY, Joshi HJ, et al. Precision mapping of the human O-GalNAc glycoproteome through SimpleCell technology. EMBO J 2013;32:1478-88. 
  122. Blom N, Sicheritz-Ponten T, Gupta R, Gammeltoft S, Brunak S. Prediction of post-translational glycosylation and phosphorylation of proteins from the amino acid sequence. Proteomics 2004;4:1633-49. 
  123. Blom N, Gammeltoft S, Brunak S. Sequence and structure-based prediction of eukaryotic protein phosphorylation sites. J Mol Biol 1999;294:1351-62. 
  124. Petersen B, Lundegaard C, Petersen TN. NetTurnP: neural network prediction of beta-turns by use of evolutionary information and predicted protein sequence features. PLoS One 2010;5:e15079. 
  125. Laskowski RA, Hutchinson EG, Michie AD, Wallace AC, Jones ML, Thornton JM. PDBsum: a web-based database of summaries and analyses of all PDB structures. Trends Biochem Sci 1997;22:488-90. 
  126. Lizbeth RG, Jazmin GM, Jose CB, Marlet MA. Immunoinformatics study to search epitopes of spike glycoprotein from SARS-CoV-2 as potential vaccine. J Biomol Struct Dyn 2021;39:4878-92. 
  127. Thevenet P, Shen Y, Maupetit J, Guyon F, Derreumaux P, Tuffery P. PEP-FOLD: an updated de novo structure prediction server for both linear and disulfide bonded cyclic peptides. Nucleic Acids Res 2012;40(Web Server issue): W288-93. 
  128. Baruah V, Bose S. Immunoinformatics-aided identification of T cell and B cell epitopes in the surface glycoprotein of 2019-nCoV. J Med Virol 2020;92:495-500. 
  129. Singh S, Singh H, Tuknait A, et al. PEPstrMOD: structure prediction of peptides containing natural, non-natural and modified residues. Biol Direct 2015;10:73. 
  130. Kaur H, Garg A, Raghava GP. PEPstr: a de novo method for tertiary structure prediction of small bioactive peptides. Protein Pept Lett 2007;14:626-31. 
  131. Kelley LA, Mezulis S, Yates CM, Wass MN, Sternberg MJ. The Phyre2 web portal for protein modeling, prediction and analysis. Nat Protoc 2015;10:845-58. 
  132. Laskowski RA, Watson JD, Thornton JM. ProFunc: a server for predicting protein function from 3D structure. Nucleic Acids Res 2005;33(Web Server issue):W89-93. 
  133. Wallner B, Elofsson A. Can correct protein models be identified? Protein Sci 2003;12:1073-86. 
  134. Jones DT. Protein secondary structure prediction based on position-specific scoring matrices. J Mol Biol 1999;292:195-202. 
  135. McGuffin LJ, Bryson K, Jones DT. The PSIPRED protein structure prediction server. Bioinformatics 2000;16:404-5. 
  136. Benkert P, Tosatto SC, Schomburg D. QMEAN: a comprehensive scoring function for model quality assessment. Proteins 2008;71:261-77. 
  137. Wang S, Li W, Liu S, Xu J. RaptorX-Property: a web server for protein structure property prediction. Nucleic Acids Res 2016 8;44(W1):W430-5. 
  138. Kim DE, Chivian D, Baker D. Protein structure prediction and analysis using the Robetta server. Nucleic Acids Res 2004;32(Web Server issue):W526-31. 
  139. Pontius J, Richelle J, Wodak SJ. Deviations from standard atomic volumes as a quality measure for protein crystal structures. J Mol Biol 1996;264:121-36. 
  140. Almagro Armenteros JJ, Tsirigos KD, Sonderby CK, et al. SignalP 5.0 improves signal peptide predictions using deep neural networks. Nat Biotechnol 2019;37:420-3. 
  141. Levin JM, Robson B, Garnier J. An algorithm for secondary structure determination in proteins based on sequence similarity. FEBS Lett 1986;205:303-8. 
  142. Geourjon C, Deleage G. SOPMA: significant improvements in protein secondary structure prediction by consensus prediction from multiple alignments. Comput Appl Biosci 1995;11:681-4. 
  143. Yang Y, Faraggi E, Zhao H, Zhou Y. Improving protein fold recognition and template-based modeling by employing probabilistic-based matching between predicted one-dimensional structural properties of query and corresponding native properties of templates. Bioinformatics 2011;27:2076-82. 
  144. Schwede T, Kopp J, Guex N, Peitsch MC. SWISS-MODEL: an automated protein homology-modeling server. Nucleic Acids Res 2003;31:3381-5. 
  145. Guex N, Peitsch MC. SWISS-MODEL and the Swiss-Pd-bViewer: an environment for comparative protein modeling. Electrophoresis 1997;18:2714-23. 
  146. Radivojac P, Vacic V, Haynes C, et al. Identification, analysis, and prediction of protein ubiquitination sites. Proteins 2010;78:365-80. 
  147. Krieger E, Joo K, Lee J, et al. Improving physical realism, stereochemistry, and side-chain accuracy in homology modeling: four approaches that performed well in CASP8. Proteins 2009;77(Suppl 9):114-22. 
  148. Peng J, Xu J. RaptorX: exploiting structure information for protein alignment by statistical inference. Proteins 2011;79(Suppl 10):161-71. 
  149. Kallberg M, Margaryan G, Wang S, Ma J, Xu J. RaptorX server: a resource for template-based protein structure modeling. Methods Mol Biol 2014;1137:17-27. 
  150. Yang J, Roy A, Zhang Y. Protein-ligand binding site recognition using complementary binding-specific substructure comparison and sequence profile alignment. Bioinformatics 2013;29:2588-95. 
  151. Abraham MJ, Murtola T, Schulz R, et al. GROMACS: high performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 2015;1:19-25. 
  152. van Zundert GC, Rodrigues JP, Trellet M, et al. The HAD DOCK2.2 web server: user-friendly integrative modeling of biomolecular complexes. J Mol Biol 2016;428:720-5. 
  153. OriginLab Corporation. Northampton (MA): OriginLab Corporation; 2016. 
  154. Liu IH, Lo YS, Yang JM. PAComplex: a web server to infer peptide antigen families and binding models from TCR-pMHC complexes. Nucleic Acids Res 2011;39(Web Server issue):W254-60. 
  155. Tina KG, Bhadra R, Srinivasan N. PIC: protein interactions calculator. Nucleic Acids Res 2007;35(Web Server issue):W473-6. 
  156. Kozakov D, Hall DR, Xia B, et al. The ClusPro web server for protein-protein docking. Nat Protoc 2017;12:255-78. 
  157. Schneidman-Duhovny D, Inbar Y, Nussinov R, Wolfson HJ. PatchDock and SymmDock: servers for rigid and symmetric docking. Nucleic Acids Res 2005;33(Web Server issue):W363-7. 
  158. Case DA, Cheatham TE 3rd, Darden T, et al. The Amber biomolecular simulation programs. J Comput Chem 2005;26:1668-88. 
  159. Hornak V, Abel R, Okur A, Strockbine B, Roitberg A, Simmerling C. Comparison of multiple Amber force fields and development of improved protein backbone parameters. Proteins 2006;65:712-25. 
  160. Morris GM, Huey R, Lindstrom W, et al. AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J Comput Chem 2009;30:2785-91. 
  161. Trott O, Olson AJ. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 2010;31:455-61. 
  162. Kurcinski M, Jamroz M, Blaszczyk M, Kolinski A, Kmiecik S. CABS-dock web server for the flexible docking of peptides to proteins without prior knowledge of the binding site. Nucleic Acids Res 2015;43(W1):W419-24. 
  163. Blaszczyk M, Kurcinski M, Kouza M, et al. Modeling of protein-peptide interactions using the CABS-dock web server for binding site search and flexible docking. Methods 2016;93:72-83. 
  164. Kozakov D, Beglov D, Bohnuud T, et al. How good is automated protein docking? Proteins 2013;81:2159-66. 
  165. Cheeseright T, Mackey M, Rose S, Vinter A. Molecular field extrema as descriptors of biological activity: definition and validation. J Chem Inf Model 2006;46:665-76. 
  166. Bauer MR, Mackey MD. Electrostatic complementarity as a fast and effective tool to optimize binding and selectivity of protein-ligand complexes. J Med Chem 2019;62:3036-50. 
  167. Kuhn M, Firth-Clark S, Tosco P, Mey AS, Mackey M, Michel J. Assessment of binding affinity via alchemical free-energy calculations. J Chem Inf Model 2020;60:3120-30. 
  168. Devaurs D, Antunes DA, Hall-Swan S, et al. Using parallelized incremental meta-docking can solve the conformational sampling issue when docking large ligands to proteins. BMC Mol Cell Biol 2019;20:42. 
  169. Mashiach E, Schneidman-Duhovny D, Andrusier N, Nussinov R, Wolfson HJ. FireDock: a web server for fast interaction refinement in molecular docking. Nucleic Acids Res 2008;36(Web Server issue):W229-32. 
  170. Lee H, Heo L, Lee MS, Seok C. GalaxyPepDock: a protein-peptide docking tool based on interaction similarity and energy optimization. Nucleic Acids Res 2015;43(W1):W431-5. 
  171. Friesner RA, Banks JL, Murphy RB, et al. Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J Med Chem 2004;47:1739-49. 
  172. Halgren TA, Murphy RB, Friesner RA, et al. Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. J Med Chem 2004;47:1750-9. 
  173. Tovchigrechko A, Vakser IA. GRAMM-X public web server for protein-protein docking. Nucleic Acids Res 2006; 34(Web Server issue):W310-4. 
  174. Tovchigrechko A, Vakser IA. Development and testing of an automated approach to protein docking. Proteins 2005;60:296-301. 
  175. Singh A, Copeland MM, Kundrotas PJ, Vakser IA. GRA MM web server for protein docking. Methods Mol Biol 2024;2714:101-12. 
  176. Weng G, Wang E, Wang Z, et al. HawkDock: a web server to predict and analyze the protein-protein complex based on computational docking and MM/GBSA. Nucleic Acids Res 2019;47(W1):W322-30. 
  177. Yan Y, Zhang D, Zhou P, Li B, Huang SY. HDOCK: a web server for protein: protein and protein-DNA/RNA docking based on a hybrid strategy. Nucleic Acids Res 2017;45(W1):W365-73. 
  178. Macindoe G, Mavridis L, Venkatraman V, Devignes MD, Ritchie DW. HexServer: an FFT-based protein docking server powered by graphics processors. Nucleic Acids Res 2010;38(Web Server issue):W445-9. 
  179. Zhou P, Jin B, Li H, Huang SY. HPEPDOCK: a web server for blind peptide-protein docking based on a hierarchical algorithm. Nucleic Acids Res 2018;46(W1):W443-50. 
  180. Xu X, Yan C, Zou X. MDockPeP: an ab-initio protein-peptide docking server. J Comput Chem 2018;39:2409-13. 
  181. Hospital A, Andrio P, Fenollosa C, Cicin-Sain D, Orozco M, Gelpi JL. MDWeb and MDMoby: an integrated web-based platform for molecular dynamics simulations. Bioinformatics 2012;28:1278-9. 
  182. Eswar N, Webb B, Marti-Renom MA, et al. Comparative protein structure modeling using Modeller. Curr Protoc Bioinformatics 2006;Chapter 5:Unit-5.6. 
  183. Phillips JC, Hardy DJ, Maia JD, et al. Scalable molecular dynamics on CPU and GPU architectures with NAMD. J Chem Phys 2020;153:044130. 
  184. Duhovny D, Nussinov R, Wolfson HJ. Efficient unbound docking of rigid molecules. In: Guigo R, Gusfield D. editors. Algorithms in bioinformatics. Berlin: Springer Berlin Heidelberg; 2002. p. 185-200. 
  185. Campos DM, Silva MK, Barbosa ED, Leow CY, Fulco UL, Oliveira JI. Exploiting reverse vaccinology approach for the design of a multiepitope subunit vaccine against the major SARS-CoV-2 variants. Comput Biol Chem 2022;101:107754. 
  186. Kozakov D, Brenke R, Comeau SR, Vajda S. PIPER: an FFT-based protein docking program with pairwise potentials. Proteins 2006;65:392-406. 
  187. Xue LC, Rodrigues JP, Kastritis PL, Bonvin AM, Vangone A. PRODIGY: a web server for predicting the binding affinity of protein-protein complexes. Bioinformatics 2016;32:3676-8. 
  188. Humphrey W, Dalke A, Schulten K. VMD: visual molecular dynamics. J Mol Graph 1996;14:33-8. 
  189. Pierce BG, Wiehe K, Hwang H, Kim BH, Vreven T, Weng Z. ZDOCK server: interactive docking prediction of protein-protein complexes and symmetric multimers. Bioinformatics 2014;30:1771-3. 
  190. Camacho CJ, Gatchell DW, Kimura SR, Vajda S. Scoring docked conformations generated by rigid-body protein-protein docking. Proteins 2000;40:525-37. 
  191. Comeau SR, Gatchell DW, Vajda S, Camacho CJ. ClusPro: an automated docking and discrimination method for the prediction of protein complexes. Bioinformatics 2004;20:45-50. 
  192. Connolly ML. Solvent-accessible surfaces of proteins and nucleic acids. Science 1983;221:709-13. 
  193. Campos DM, Fulco UL, de Oliveira CB, Oliveira JI. SARS-CoV-2 virus infection: targets and antiviral pharmacological strategies. J Evid Based Med 2020;13:255-60. 
  194. Campos DM, Oliveira CB, Andrade JM, Oliveira JI. Fighting COVID-19. Braz J Biol 2020;80:698-701. 
  195. Centers for Disease Control and Prevention. COVID Data Tracker [Internet]. Atlanta (GA): Centers for Disease Control and Prevention; 2023 [cited 2023 May 17]. Available from: https://covid.cdc.gov/covid-data-tracker/#variant-proportions 
  196. Yee PT, Laa Poh C. Impact of genetic changes, pathogenicity and antigenicity on Enterovirus- A71 vaccine development. Virology 2017;506:121-9. 
  197. Peeters M, Toure-Kane C, Nkengasong JN. Genetic diversity of HIV in Africa: impact on diagnosis, treatment, vaccine development and trials. AIDS 2003;17:2547-60. 
  198. Koff WC, Berkley SF. A universal coronavirus vaccine. Science 2021;371:759. 
  199. Wall EC, Wu M, Harvey R, et al. Neutralising antibody activity against SARS-CoV-2 VOCs B.1.617.2 and B.1.351 by BNT162b2 vaccination. Lancet 2021;397:2331-3. 
  200. Ahmed SF, Quadeer AA, McKay MR. Preliminary identification of potential vaccine targets for the COVID-19 coronavirus (SARS-CoV-2) based on SARS-CoV immunological studies. Viruses 2020;12:254. 
  201. Ou X, Liu Y, Lei X, et al. Characterization of spike glycoprotein of SARS-CoV-2 on virus entry and its immune cross-reactivity with SARS-CoV. Nat Commun 2020;11:1620. 
  202. Lee CH, Koohy H. In silico identification of vaccine targets for 2019-nCoV. F1000Res 2020;9:145. 
  203. de Oliveira Campos DM, da Silva MK, Silva de Oliveira CB, Fulco UL, Nobre Oliveira JI. Effectiveness of COVID-19 vaccines against Omicron variant. Immunotherapy 2022;14:903-4. 
  204. Stanekova Z, Vareckova E. Conserved epitopes of influenza A virus inducing protective immunity and their prospects for universal vaccine development. Virol J 2010;7:351. 
  205. Giurgea LT, Han A, Memoli MJ. Universal coronavirus vaccines: the time to start is now. NPJ Vaccines 2020;5:43.