Designing a multi-epitope vaccine against SARS-CoV-2: an immunoinformatic approach

封面

如何引用文章

全文:

详细

Background. An outbreak of SARS-CoV-2 in 2019 has brought a great challenge to public health and rapid identification of immune epitopes for designing an effective vaccine for different variants of SARS-CoV-2 is necessary at the time of the pandemic. Rational, rapid, and precise vaccine design, especially vaccine antigen identification and optimization by in silico methods of bioinformatics, structural biology, and immunoinformatic is critical to efficient vaccine development against the SARS-CoV-2 virus. The aim of this study was to develop a particular novel and effective vaccines vaccine using bioinformatics approaches and resources that can target B- and T-cell epitopes to combat SARS-CoV-2 infection. 

Materials and methods. The variants of SARS-CoV-2 (Alpha, Beta, Delta, and Omicron strains) spike protein were selected for designing the vaccine. The B-cell, T-cell, and IFNg-inducing epitopes were predicted. The beta-defensin-3 protein was selected as adjuvant and predicted epitopes were connected using suitable linkers. The vaccine’s allergenicity, antigenicity, physicochemical characteristics, 2D and 3D structure modeling, and molecular docking were evaluated for the final construct. 

Results. The in silico results showed that the multi-epitope vaccine has a stable structure and can induce humoral and cellular immune responses against SARS-CoV-2. 

Conclusion. B-cell and T-cell epitopes on spike protein were identified and recommended for design and confirmation of in vivo evaluation for multi-epitope peptides as vaccines against SARS-CoV-2.

作者简介

V. Alamdari-Palang

Shiraz University of Medical Sciences

Email: razban_vahid@yahoo.com

MSc, PhD Candidate, Department of Molecular Medicine

伊朗伊斯兰共和国, Shiraz

Z. Dehghan

Shiraz University of Medical Sciences

Email: razban_vahid@yahoo.com

PhD, Researcher, Department of Comparative Biomedical Sciences, School of Advanced Medical Sciences and Technologies

伊朗伊斯兰共和国, Shiraz

M. Kian

Shiraz University of Medical Sciences

Email: razban_vahid@yahoo.com

DVM, PhD Candidate, Department of Comparative Biomedical Sciences, School of Advanced Medical Sciences and Technologies

伊朗伊斯兰共和国, Shiraz

S. Zonar

Islamic Azad University

Email: razban_vahid@yahoo.com

MSc, Researcher, Department of Biology, Sciences and Research Branch

伊朗伊斯兰共和国, Tehran

J. Fallahi

Shiraz University of Medical Sciences

Email: razban_vahid@yahoo.com

PhD, Assistant Professor, Department of Molecular Medicine, School of Advanced Medical Sciences and Technologies

伊朗伊斯兰共和国, Shiraz

M. Sisakht

Shiraz University of Medical Sciences

Email: razban_vahid@yahoo.com

PhD, Researcher, Department of Molecular Medicine, School of Advanced Medical Sciences and Technologies

伊朗伊斯兰共和国, Shiraz

S. Khajeh

Shiraz University of Medical Sciences

Email: razban_vahid@yahoo.com

PhD, Assistant Professor, Bone and Joint Diseases Research Center

伊朗伊斯兰共和国, Shiraz

V. Razban

Shiraz University of Medical Sciences

编辑信件的主要联系方式.
Email: razban_vahid@yahoo.com

PhD, Associate Professor, Department of Molecular Medicine, School of Advanced Medical Sciences and Technologies

伊朗伊斯兰共和国, Shiraz

参考

  1. Acter T., Uddin N., Das J., Akhter A., Choudhury T.R., Kim S. Evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as coronavirus disease 2019 (COVID-19) pandemic: a global health emergency. Sci. Total Environ., 2020, no. 730: 138996. doi: 10.1016/j.scitotenv.2020.138996
  2. Ahmed S.F., Quadeer A.A., McKay M.R. Preliminary identification of potential vaccine targets for the COVID-19 coronavirus (SARS-CoV-2) based on SARS-CoV immunological studies. Viruses, 2020, vol. 12, no. 3: 254. doi: 10.3390/v12030254
  3. Al-Rohaimi A.H., Al Otaibi F. Novel SARS-CoV-2 outbreak and COVID19 disease; a systemic review on the global pandemic. Genes Dis., 2020, vol. 7, no. 4, pp. 491–501. doi: 10.1016/j.gendis.2020.06.004
  4. Amanat F., Krammer F. SARS-CoV-2 vaccines: status report. Immunity, 2020, vol. 52, no. 4, pp. 583–589. doi: 10.1016/j.immuni.2020.03.007
  5. Anderegg M.A., Liu M., Saganas C., Montani M., Vogt B., Huynh-Do U., Fuster D.G. De novo vasculitis after mRNA-1273 (Moderna) vaccination. Kidney Int., 2021, vol. 100, no. 2, pp. 474–476. doi: 10.1016/j.kint.2021.05.016
  6. 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, vol. 14, no. 8, pp. 529–532. doi: 10.1093/protein/14.8.529
  7. Ashfaq U.A., Saleem S., Masoud M.S., Ahmad M., Nahid N., Bhatti R., Almatroudi A., Khurshid M. Rational design of multi epitope-based subunit vaccine by exploring MERS-COV proteome: reverse vaccinology and molecular docking approach. PLoS One, 2021, vol. 16, no. 2: e0245072. doi: 10.1371/journal.pone.0245072
  8. Ather A., Patel B., Ruparel N.B., Diogenes A., Hargreaves K.M. Coronavirus disease 19 (COVID-19): implications for clinical dental care. J. Endod., 2020, vol. 46, no. 5, pp. 584–595. doi: 10.1016/j.joen.2020.03.008
  9. Bahrami M., Kamalinejad M., Latifi S.A., Seif F., Dadmehr M. Cytokine storm in COVID-19 and parthenolide: preclinical evidence. Phytother Res., 2020, vol. 34, no. 10, pp. 2429–2430. doi: 10.1002/ptr.6776
  10. Bansal S., Perincheri S., Fleming T., Poulson C., Tiffany B., Bremner R.M., Mohanakumar T. Cutting edge: circulating exosomes with COVID spike protein are induced by BNT162b2 (Pfizer-BioNTech) vaccination prior to development of antibodies: a novel mechanism for immune activation by mRNA vaccines. J. Immunol., 2021, vol. 207, no. 10, pp. 2405–2410. doi: 10.4049/jimmunol.2100637
  11. Cascella M., Mauro I., De Blasio E., Crispo A., Del Gaudio A., Bimonte S., Cuomo A., Ascierto P.A. Rapid and impressive response to a combined treatment with single-dose tocilizumab and NIV in a patient with COVID-19 pneumonia/ARDS. Medicina (Kaunas), 2020, vol. 56, no. 8: 377. doi: 10.3390/medicina56080377
  12. Chaudhri G., Quah B.J., Wang Y., Tan A.H., Zhou J., Karupiah G., Parish C.R. T cell receptor sharing by cytotoxic T lymphocytes facilitates efficient virus control. Proc. Natl Acad. Sci. USA, 2009, vol. 106, no. 35, pp. 14984–14989. doi: 10.1073/pnas.0906554106
  13. Chen W.H., Strych U., Hotez P.J., Bottazzi M.E. The SARS-CoV-2 vaccine pipeline: an overview. Curr. Trop. Med. Rep., 2020, vol. 7, no. 2, pp. 61–64. doi: 10.1007/s40475-020-00201-6
  14. Dariushnejad H., Ghorbanzadeh V., Akbari S., Hashemzadeh P. Designing a multi-epitope peptide vaccine against COVID-19 variants utilizing in-silico tools. Iranian Journal of Medical Microbiology, 2021, vol. 15, no. 5, pp. 592–605. doi: 10.30699/ijmm.15.5.592
  15. Desta I.T., Porter K.A., Xia B., Kozakov D., Vajda S. Performance and its limits in rigid body protein-protein docking. Structure, 2020, vol. 28, no. 9, pp. 1071–1081 e3. doi: 10.1016/j.str.2020.06.006
  16. Dhanda S.K., Vir P., Raghava G.P. Designing of interferon-gamma inducing MHC class-II binders. Biol. Direct., 2013, vol. 8, no. 1: 30. doi: 10.1186/1745-6150-8-30
  17. Farnudian-Habibi A., Mirjani M., Montazer V., Aliebrahimi S., Katouzian I., Abdolhosseini S., Rahmani A., Keyvani H., Ostad S.N., Rad-Malekshahi M. Review on approved and inprogress COVID-19 vaccines. Iran. J. Pharm. Res., 2022, vol. 21, no. 1: e124228. doi: 10.5812/ijpr.124228
  18. Gasteiger E., Hoogland C., Gattiker A., Wilkins M.R., Appel R.D., Bairoch A. Protein identification and analysis tools on the ExPASy server. In: The proteomics protocols handbook, 2005, pp. 571–607. doi: 10.1385/1-59259-890-0:571
  19. Ghaebi M., Osali A., Valizadeh H., Roshangar L., Ahmadi M. Vaccine development and therapeutic design for 2019-nCoV/SARS-CoV-2: challenges and chances. J. Cell. Physiol., 2020, vol. 235, no. 12, pp. 9098–9109. doi: 10.1002/jcp.29771
  20. Guo G., Ye L., Pan K., Chen Y., Xing D., Yan K., Chen Z., Ding N., Li W., Huang H., Zhang L., Li X., Xue X. New insights of emerging SARS-CoV-2: epidemiology, etiology, clinical features, clinical treatment, and prevention. Front. Cell. Dev. Biol., 2020, no. 8: 410. doi: 10.3389/fcell.2020.00410
  21. Ishack S., Lipner S.R. Bioinformatics and immunoinformatics to support COVID-19 vaccine development. J. Med. Virol., 2021, vol. 93, no. 9, pp. 5209–5211. doi: 10.1002/jmv.27017
  22. Jones I., Roy P. Sputnik V COVID-19 vaccine candidate appears safe and effective. Lancet, 2021, vol. 397, no. 10275, pp. 642–643. doi: 10.1016/S0140-6736(21)00191-4
  23. Kang S., Peng W., Zhu Y., Lu S., Zhou M., Lin W., Wu W., Huang S., Jiang L., Luo X., Deng M. Recent progress in understanding 2019 novel coronavirus (SARS-CoV-2) associated with human respiratory disease: detection, mechanisms and treatment. Int. J. Antimicrob. Agents, 2020, vol. 55, no. 5: 105950. doi: 10.1016/j.ijantimicag.2020.105950
  24. Kardani K., Bolhassani A., Namvar A. An overview of in silico vaccine design against different pathogens and cancer. Expert. Rev. Vaccines, 2020, vol. 19, no. 8, pp. 699–726. doi: 10.1080/14760584.2020.1794832
  25. Karwaciak I., Salkowska A., Karas K., Dastych J., Ratajewski M. Nucleocapsid and spike proteins of the coronavirus SARS-CoV-2 induce IL6 in monocytes and macrophages-potential implications for cytokine storm syndrome. Vaccines (Basel), 2021, vol. 9, no. 1: 54. doi: 10.3390/vaccines9010054
  26. Knight T.E. Severe Acute Respiratory Syndrome Coronavirus 2 and Coronavirus Disease 2019: a clinical overview and primer. Biopreserv Biobank, 2020, vol. 18, no. 6, pp. 492–502. doi: 10.1089/bio.2020.0066
  27. 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, vol. 12, no. 2, pp. 255–278. doi: 10.1038/nprot.2016.169
  28. 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, vol. 26, no. 2, pp. 283–291. doi: 10.1107/s0021889892009944
  29. Lavigne R., Seto D., Mahadevan P., Ackermann H.W., Kropinski A.M. Unifying classical and molecular taxonomic classification: analysis of the Podoviridae using BLASTP-based tools. Res. Microbiol., 2008, vol. 159, no. 5, pp. 406–414. doi: 10.1016/j.resmic.2008.03.005
  30. Lee S., Nguyen M.T. Recent advances of vaccine adjuvants for infectious diseases. Immune Netw., 2015, vol. 15, no. 2, pp. 51–57. doi: 10.4110/in.2015.15.2.51
  31. Magnan C.N., Zeller M., Kayala M.A., Vigil A., Randall A., Felgner P.L., Baldi P. High-throughput prediction of protein antigenicity using protein microarray data. Bioinformatics, 2010, vol. 26, no. 23, pp. 2936–2943. doi: 10.1093/bioinformatics/btq551
  32. Malik J.A., Ahmed S., Mir A., Shinde M., Bender O., Alshammari F., Ansari M., Anwar S. The SARS-CoV-2 mutations versus vaccine effectiveness: New opportunities to new challenges. J. Infect. Public Health, 2022, vol. 15, no. 2, pp. 228–240. doi: 10.1016/j.jiph.2021.12.014
  33. María R.A.R., Arturo C.V.J., Alicia J.A., Paulina M.L.G., Gerardo A.O. The impact of bioinformatics on vaccine design and development. Vaccines, no. 22017, pp. 3–6.
  34. Martin J.E., Louder M.K., Holman L.A., Gordon I.J., Enama M.E., Larkin B.D., Andrews C.A., Vogel L., Koup R.A., Roederer M., Bailer R.T., Gomez P.L., Nason M., Mascola J.R., Nabel G.J., Graham B.S., Team V.R.C.S. A SARS DNA vaccine induces neutralizing antibody and cellular immune responses in healthy adults in a Phase I clinical trial. Vaccine, 2008, vol. 26, no. 50, pp. 6338–6343. doi: 10.1016/j.vaccine.2008.09.026
  35. McGuffin L.J., Bryson K., Jones D.T. The PSIPRED protein structure prediction server. Bioinformatics, 2000, vol. 16, no. 4, pp. 404–405. doi: 10.1093/bioinformatics/16.4.404
  36. Pandey S.C., Pande V., Sati D., Upreti S., Samant M. Vaccination strategies to combat novel corona virus SARS-CoV-2. Life Sci., 2020, no. 256: 117956. doi: 10.1016/j.lfs.2020.117956
  37. Ponomarenko J., Bui H.H., Li W., Fusseder N., Bourne P.E., Sette A., Peters B. ElliPro: a new structure-based tool for the prediction of antibody epitopes. BMC Bioinformatics, 2008, vol. 9, no. 1: 514. doi: 10.1186/1471-2105-9-514
  38. Roy A., Kucukural A., Zhang Y. I-TASSER: a unified platform for automated protein structure and function prediction. Nat. Protoc., 2010, vol. 5, no. 4, pp. 725–738. doi: 10.1038/nprot.2010.5
  39. Saadi M., Karkhah A., Nouri H.R. Development of a multi-epitope peptide vaccine inducing robust T cell responses against brucellosis using immunoinformatics based approaches. Infect. Genet. Evol., 2017, vol. 51, pp. 227–234. doi: 10.1016/j.meegid.2017.04.009
  40. Sadat S.M., Aghadadeghi M.R., Yousefi M., Khodaei A., Sadat Larijani M., Bahramali G. Bioinformatics analysis of SARS-CoV-2 to approach an effective vaccine candidate against COVID-19. Mol. Biotechnol., 2021, vol. 63, no. 5, pp. 389–409. doi: 10.1007/s12033-021-00303-0
  41. Saha S., Raghava G.P. Prediction of continuous B-cell epitopes in an antigen using recurrent neural network. Proteins, 2006, vol. 65, no. 1, pp. 40–48. doi: 10.1002/prot.21078
  42. Saif L.J. Vaccines for COVID-19: perspectives, prospects, and challenges based on candidate SARS, MERS, and animal coronavirus vaccines. Eur. Med. J., 2020. doi: 10.33590/emj/200324
  43. Sarkar B., Ullah M.A., Araf Y., Rahman M.S. Engineering a novel subunit vaccine against SARS-CoV-2 by exploring immunoinformatics approach. Inform. Med. Unlocked, 2020, no. 21: 100478. doi: 10.1016/j.imu.2020.100478
  44. Satarker S., Nampoothiri M. Structural proteins in severe acute respiratory syndrome coronavirus-2. Arch. Med. Res., 2020, vol. 51, no. 6, pp. 482–491. doi: 10.1016/j.arcmed.2020.05.012
  45. Shehata M.M., Mahmoud S.H., Tarek M., Al-Karmalawy A.A., Mahmoud A., Mostafa A., M. Elhefnawi M., Ali M.A. In silico and in vivo evaluation of SARS-CoV-2 predicted epitopes-based candidate vaccine. Molecules, 2021, vol. 26, no. 20: 6182. doi: 10.3390/molecules26206182
  46. Shereen M.A., Khan S., Kazmi A., Bashir N., Siddique R. COVID-19 infection: origin, transmission, and characteristics of human coronaviruses. J. Adv. Res., 2020, vol. 24, pp. 91–98. doi: 10.1016/j.jare.2020.03.005
  47. Sisakht M., Bemani P., Ghadim M.B. A., Rahimi A., Sakhteman A. PyProtModel: An easy to use GUI for comparative protein modeling. J. Mol. Graph. Model., 2022, no. 112: 108134. doi: 10.1016/j.jmgm.2022.108134
  48. Soria-Guerra R.E., Nieto-Gomez R., Govea-Alonso D.O., Rosales-Mendoza S. An overview of bioinformatics tools for epitope prediction: implications on vaccine development. J. Biomed. Inform., 2015, vol. 53, pp. 405–414. doi: 10.1016/j.jbi.2014.11.003
  49. Srivastava S., Verma S., Kamthania M., Kaur R., Badyal R.K., Saxena A.K., Shin H.J., Kolbe M., Pandey K.C. Structural basis for designing multiepitope vaccines against COVID-19 infection: in silico vaccine design and validation. JMIR Bioinform. Biotechnol., 2020, vol. 1, no. 1: e19371. doi: 10.2196/19371
  50. Tahir Ul Qamar M., Alqahtani S.M., Alamri M.A., Chen L.L. Structural basis of SARS-CoV-2 3CL(pro) and anti-COVID-19 drug discovery from medicinal plants. J. Pharm. Anal., 2020, vol. 10, no. 4, pp. 313–319. doi: 10.1016/j.jpha.2020.03.009
  51. Tourani M., Samavarchi Tehrani S., Movahedpour A., Rezaei Arablouydareh S., Maleksabet A., Savardashtaki A., Ghasemnejad Berenji H., Taheri-Anganeh M. Design and evaluation of a multi-epitope vaccine for COVID-19: an in silico approach. Health. Science Monitor, 2023, vol. 2, no. 3, pp. 180–204. doi: 10.61186/hsm.2.3.180
  52. Vellingiri B., Jayaramayya K., Iyer M., Narayanasamy A., Govindasamy V., Giridharan B., Ganesan S., Venugopal A., Venkatesan D., Ganesan H., Rajagopalan K., Rahman P., Cho S.G., Kumar N.S., Subramaniam M.D. COVID-19: a promising cure for the global panic. Sci. Total. Environ., 2020, no. 725: 138277. doi: 10.1016/j.scitotenv.2020.138277
  53. WHO. COVID-19 weekly epidemiological update, edition 134, 16 March 2023. 2023.
  54. Yarian F., Dehghan Z., Lari A., Ahangarzadeh S., Sharifnia Z., Shahzamani K., Shahidi S. Development of polyepitopic immunogenic contrast against hepatitis C virus 1a-6a genotype by in silico approach. Biomedical and Biotechnology Research Journal (BBRJ), 2020, vol. 4, no. 4, pp. 355–364. doi: 10.4103/bbrj.bbrj_186_20
  55. Zhou P., Yang X.L., Wang X.G., Hu B., Zhang L., Zhang W., Si H.R., Zhu Y., Li B., Huang C.L., Chen H.D., Chen J., Luo Y., Guo H., Jiang R.D., Liu M.Q., Chen Y., Shen X.R., Wang X., Zheng X.S., Zhao K., Chen Q.J., Deng F., Liu L.L., Yan B., Zhan F.X., Wang Y.Y., Xiao G.F., Shi Z.L. A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature, 2020, vol. 579, no. 7798, pp. 270–273. doi: 10.1038/s41586-020-2012-7

补充文件

附件文件
动作
1. JATS XML
下载
2. Figure 1. Schematic workflow of in silico prediction and evaluation of the peptide based multi-epitope vaccine

下载 (141KB)
索引源数据
3. Figure 2. Final construct of the multi-epitope vaccine

下载 (53KB)
索引源数据
4. Figure 3. Secondary structure analysis of multi-epitope vaccine using PSIPRED server

下载 (257KB)
索引源数据
5. Figure 4. A) Tertiary structure of final vaccine construct refinement by PyProtModel software, B) Ramachandran plot analysis of structure predicted by PROCHEK server

下载 (101KB)
索引源数据
6. Figure 5. The three conformational B-cell epitopes predicted by the ElliPro tool in the multi-epitope vaccine

下载 (180KB)
索引源数据
7. Figure 6. Molecular docking between multi-epitope vaccine and with TLR3, TLR4, and TLR8 (A–C, respectively)

下载 (225KB)
索引源数据

版权所有 © Alamdari-Palang V., Dehghan Z., Kian M., Zonar S., Fallahi J., Sisakht M., Khajeh S., Razban V., 2025

Creative Commons License
此作品已接受知识共享署名 4.0国际许可协议的许可

Согласие на обработку персональных данных с помощью сервиса «Яндекс.Метрика»

1. Я (далее – «Пользователь» или «Субъект персональных данных»), осуществляя использование сайта https://journals.rcsi.science/ (далее – «Сайт»), подтверждая свою полную дееспособность даю согласие на обработку персональных данных с использованием средств автоматизации Оператору - федеральному государственному бюджетному учреждению «Российский центр научной информации» (РЦНИ), далее – «Оператор», расположенному по адресу: 119991, г. Москва, Ленинский просп., д.32А, со следующими условиями.

2. Категории обрабатываемых данных: файлы «cookies» (куки-файлы). Файлы «cookie» – это небольшой текстовый файл, который веб-сервер может хранить в браузере Пользователя. Данные файлы веб-сервер загружает на устройство Пользователя при посещении им Сайта. При каждом следующем посещении Пользователем Сайта «cookie» файлы отправляются на Сайт Оператора. Данные файлы позволяют Сайту распознавать устройство Пользователя. Содержимое такого файла может как относиться, так и не относиться к персональным данным, в зависимости от того, содержит ли такой файл персональные данные или содержит обезличенные технические данные.

3. Цель обработки персональных данных: анализ пользовательской активности с помощью сервиса «Яндекс.Метрика».

4. Категории субъектов персональных данных: все Пользователи Сайта, которые дали согласие на обработку файлов «cookie».

5. Способы обработки: сбор, запись, систематизация, накопление, хранение, уточнение (обновление, изменение), извлечение, использование, передача (доступ, предоставление), блокирование, удаление, уничтожение персональных данных.

6. Срок обработки и хранения: до получения от Субъекта персональных данных требования о прекращении обработки/отзыва согласия.

7. Способ отзыва: заявление об отзыве в письменном виде путём его направления на адрес электронной почты Оператора: info@rcsi.science или путем письменного обращения по юридическому адресу: 119991, г. Москва, Ленинский просп., д.32А

8. Субъект персональных данных вправе запретить своему оборудованию прием этих данных или ограничить прием этих данных. При отказе от получения таких данных или при ограничении приема данных некоторые функции Сайта могут работать некорректно. Субъект персональных данных обязуется сам настроить свое оборудование таким способом, чтобы оно обеспечивало адекватный его желаниям режим работы и уровень защиты данных файлов «cookie», Оператор не предоставляет технологических и правовых консультаций на темы подобного характера.

9. Порядок уничтожения персональных данных при достижении цели их обработки или при наступлении иных законных оснований определяется Оператором в соответствии с законодательством Российской Федерации.

10. Я согласен/согласна квалифицировать в качестве своей простой электронной подписи под настоящим Согласием и под Политикой обработки персональных данных выполнение мною следующего действия на сайте: https://journals.rcsi.science/ нажатие мною на интерфейсе с текстом: «Сайт использует сервис «Яндекс.Метрика» (который использует файлы «cookie») на элемент с текстом «Принять и продолжить».