Exploring whole proteome to contrive multi-epitope-based vaccine for NeoCoV: An immunoinformtics and in-silico approach
Neo-Coronavirus (NeoCoV) is a novel Betacoronavirus (β-CoVs or Beta-CoVs) discovered in bat specimens in South Africa during 2011. The viral sequence is highly similar to Middle East Respiratory Syndrome, particularly that of structural proteins. Thus, scientists have emphasized the threat posed by...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2022-08-01
|
| Series: | Frontiers in Immunology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2022.956776/full |
| _version_ | 1818537548307234816 |
|---|---|
| author | Shahkaar Aziz Muhammad Waqas Muhammad Waqas Sobia Ahsan Halim Amjad Ali Aqib Iqbal Maaz Iqbal Ajmal Khan Ahmed Al-Harrasi |
| author_facet | Shahkaar Aziz Muhammad Waqas Muhammad Waqas Sobia Ahsan Halim Amjad Ali Aqib Iqbal Maaz Iqbal Ajmal Khan Ahmed Al-Harrasi |
| author_sort | Shahkaar Aziz |
| collection | DOAJ |
| description | Neo-Coronavirus (NeoCoV) is a novel Betacoronavirus (β-CoVs or Beta-CoVs) discovered in bat specimens in South Africa during 2011. The viral sequence is highly similar to Middle East Respiratory Syndrome, particularly that of structural proteins. Thus, scientists have emphasized the threat posed by NeoCoV associated with human angiotensin-converting enzyme 2 (ACE2) usage, which could lead to a high death rate and faster transmission rate in humans. The development of a NeoCoV vaccine could provide a promising option for the future control of the virus in case of human infection. In silico predictions can decrease the number of experiments required, making the immunoinformatics approaches cost-effective and convenient. Herein, with the aid of immunoinformatics and reverse vaccinology, we aimed to formulate a multi-epitope vaccine that may be used to prevent and treat NeoCoV infection. Based on the NeoCoV proteins, B-cell, cytotoxic T lymphocyte (CTL), and helper T lymphocyte (HTL) epitopes were shortlisted. Four vaccines (Neo-1–4) were devised by fusing shortlisted epitopes with appropriate adjuvants and linkers. The secondary and three-dimensional structures of final vaccines were then predicted. The binding interactions of these potential vaccines with toll-like immune receptors (TLR-2, TLR-3, and TLR-4) and major histocompatibility complex molecules (MHC-I and II) reveal that they properly fit into the receptors’ binding domains. Besides, Neo-1 and Neo-4 vaccines exhibited better docking energies of -101.08 kcal/mol and -114.47 kcal/mol, respectively, with TLR-3 as compared to other vaccine constructs. The constructed vaccines are highly antigenic, non-allergenic, soluble, non-toxic, and topologically assessable with good physiochemical characteristics. Codon optimization and in-silico cloning confirmed efficient expression of the designed vaccines in Escherichia coli strain K12. In-silico immune simulation indicated that Neo-1 and Neo-4 vaccines could induce a strong immune response against NeoCoV. Lastly, the binding stability and strong binding affinity of Neo-1 and Neo-4 with TLR-3 receptor were validated using molecular dynamics simulations and free energy calculations (Molecular Mechanics/Generalized Born Surface Area method). The final vaccines require experimental validation to establish their safety and effectiveness in preventing NeoCoV infections. |
| first_indexed | 2024-12-11T18:52:10Z |
| format | Article |
| id | doaj.art-3f66d171fd84454ba789e0b989dfdcd1 |
| institution | Directory Open Access Journal |
| issn | 1664-3224 |
| language | English |
| last_indexed | 2024-12-11T18:52:10Z |
| publishDate | 2022-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Immunology |
| spelling | doaj.art-3f66d171fd84454ba789e0b989dfdcd12022-12-22T00:54:16ZengFrontiers Media S.A.Frontiers in Immunology1664-32242022-08-011310.3389/fimmu.2022.956776956776Exploring whole proteome to contrive multi-epitope-based vaccine for NeoCoV: An immunoinformtics and in-silico approachShahkaar Aziz0Muhammad Waqas1Muhammad Waqas2Sobia Ahsan Halim3Amjad Ali4Aqib Iqbal5Maaz Iqbal6Ajmal Khan7Ahmed Al-Harrasi8Institute of Biotechnology and Genetic Engineering, the University of Agriculture Peshawar, Peshawar, PakistanNatural and Medical Sciences Research Center, University of Nizwa, Birkat-ul-Mouz, Nizwa, OmanDepartment of Biotechnology and Genetic Engineering, Hazara University Mansehra, Mansehra, PakistanNatural and Medical Sciences Research Center, University of Nizwa, Birkat-ul-Mouz, Nizwa, OmanDepartment of Biotechnology and Genetic Engineering, Hazara University Mansehra, Mansehra, PakistanInstitute of Biotechnology and Genetic Engineering, the University of Agriculture Peshawar, Peshawar, PakistanInstitute of Biotechnology and Genetic Engineering, the University of Agriculture Peshawar, Peshawar, PakistanNatural and Medical Sciences Research Center, University of Nizwa, Birkat-ul-Mouz, Nizwa, OmanNatural and Medical Sciences Research Center, University of Nizwa, Birkat-ul-Mouz, Nizwa, OmanNeo-Coronavirus (NeoCoV) is a novel Betacoronavirus (β-CoVs or Beta-CoVs) discovered in bat specimens in South Africa during 2011. The viral sequence is highly similar to Middle East Respiratory Syndrome, particularly that of structural proteins. Thus, scientists have emphasized the threat posed by NeoCoV associated with human angiotensin-converting enzyme 2 (ACE2) usage, which could lead to a high death rate and faster transmission rate in humans. The development of a NeoCoV vaccine could provide a promising option for the future control of the virus in case of human infection. In silico predictions can decrease the number of experiments required, making the immunoinformatics approaches cost-effective and convenient. Herein, with the aid of immunoinformatics and reverse vaccinology, we aimed to formulate a multi-epitope vaccine that may be used to prevent and treat NeoCoV infection. Based on the NeoCoV proteins, B-cell, cytotoxic T lymphocyte (CTL), and helper T lymphocyte (HTL) epitopes were shortlisted. Four vaccines (Neo-1–4) were devised by fusing shortlisted epitopes with appropriate adjuvants and linkers. The secondary and three-dimensional structures of final vaccines were then predicted. The binding interactions of these potential vaccines with toll-like immune receptors (TLR-2, TLR-3, and TLR-4) and major histocompatibility complex molecules (MHC-I and II) reveal that they properly fit into the receptors’ binding domains. Besides, Neo-1 and Neo-4 vaccines exhibited better docking energies of -101.08 kcal/mol and -114.47 kcal/mol, respectively, with TLR-3 as compared to other vaccine constructs. The constructed vaccines are highly antigenic, non-allergenic, soluble, non-toxic, and topologically assessable with good physiochemical characteristics. Codon optimization and in-silico cloning confirmed efficient expression of the designed vaccines in Escherichia coli strain K12. In-silico immune simulation indicated that Neo-1 and Neo-4 vaccines could induce a strong immune response against NeoCoV. Lastly, the binding stability and strong binding affinity of Neo-1 and Neo-4 with TLR-3 receptor were validated using molecular dynamics simulations and free energy calculations (Molecular Mechanics/Generalized Born Surface Area method). The final vaccines require experimental validation to establish their safety and effectiveness in preventing NeoCoV infections.https://www.frontiersin.org/articles/10.3389/fimmu.2022.956776/fullimmunoinformaticsmulti-epitope vaccinesubunit vaccineepitopes predictionvaccine designNeoCoV |
| spellingShingle | Shahkaar Aziz Muhammad Waqas Muhammad Waqas Sobia Ahsan Halim Amjad Ali Aqib Iqbal Maaz Iqbal Ajmal Khan Ahmed Al-Harrasi Exploring whole proteome to contrive multi-epitope-based vaccine for NeoCoV: An immunoinformtics and in-silico approach Frontiers in Immunology immunoinformatics multi-epitope vaccine subunit vaccine epitopes prediction vaccine design NeoCoV |
| title | Exploring whole proteome to contrive multi-epitope-based vaccine for NeoCoV: An immunoinformtics and in-silico approach |
| title_full | Exploring whole proteome to contrive multi-epitope-based vaccine for NeoCoV: An immunoinformtics and in-silico approach |
| title_fullStr | Exploring whole proteome to contrive multi-epitope-based vaccine for NeoCoV: An immunoinformtics and in-silico approach |
| title_full_unstemmed | Exploring whole proteome to contrive multi-epitope-based vaccine for NeoCoV: An immunoinformtics and in-silico approach |
| title_short | Exploring whole proteome to contrive multi-epitope-based vaccine for NeoCoV: An immunoinformtics and in-silico approach |
| title_sort | exploring whole proteome to contrive multi epitope based vaccine for neocov an immunoinformtics and in silico approach |
| topic | immunoinformatics multi-epitope vaccine subunit vaccine epitopes prediction vaccine design NeoCoV |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2022.956776/full |
| work_keys_str_mv | AT shahkaaraziz exploringwholeproteometocontrivemultiepitopebasedvaccineforneocovanimmunoinformticsandinsilicoapproach AT muhammadwaqas exploringwholeproteometocontrivemultiepitopebasedvaccineforneocovanimmunoinformticsandinsilicoapproach AT muhammadwaqas exploringwholeproteometocontrivemultiepitopebasedvaccineforneocovanimmunoinformticsandinsilicoapproach AT sobiaahsanhalim exploringwholeproteometocontrivemultiepitopebasedvaccineforneocovanimmunoinformticsandinsilicoapproach AT amjadali exploringwholeproteometocontrivemultiepitopebasedvaccineforneocovanimmunoinformticsandinsilicoapproach AT aqibiqbal exploringwholeproteometocontrivemultiepitopebasedvaccineforneocovanimmunoinformticsandinsilicoapproach AT maaziqbal exploringwholeproteometocontrivemultiepitopebasedvaccineforneocovanimmunoinformticsandinsilicoapproach AT ajmalkhan exploringwholeproteometocontrivemultiepitopebasedvaccineforneocovanimmunoinformticsandinsilicoapproach AT ahmedalharrasi exploringwholeproteometocontrivemultiepitopebasedvaccineforneocovanimmunoinformticsandinsilicoapproach |