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Sarvmeili J, Baghban Kohnehrouz B, Gholizadeh A, Shanehbandi D, Ofoghi H. Immunoinformatics design of a structural proteins driven multi-epitope candidate vaccine against different SARS-CoV-2 variants based on fynomer. Sci Rep 2024; 14:10297. [PMID: 38704475 PMCID: PMC11069592 DOI: 10.1038/s41598-024-61025-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Accepted: 04/30/2024] [Indexed: 05/06/2024] Open
Abstract
The ideal vaccines for combating diseases that may emerge in the future require more than simply inactivating a few pathogenic strains. This study aims to provide a peptide-based multi-epitope vaccine effective against various severe acute respiratory syndrome coronavirus 2 strains. To design the vaccine, a library of peptides from the spike, nucleocapsid, membrane, and envelope structural proteins of various strains was prepared. Then, the final vaccine structure was optimized using the fully protected epitopes and the fynomer scaffold. Using bioinformatics tools, the antigenicity, allergenicity, toxicity, physicochemical properties, population coverage, and secondary and three-dimensional structures of the vaccine candidate were evaluated. The bioinformatic analyses confirmed the high quality of the vaccine. According to further investigations, this structure is similar to native protein and there is a stable and strong interaction between vaccine and receptors. Based on molecular dynamics simulation, structural compactness and stability in binding were also observed. In addition, the immune simulation showed that the vaccine can stimulate immune responses similar to real conditions. Finally, codon optimization and in silico cloning confirmed efficient expression in Escherichia coli. In conclusion, the fynomer-based vaccine can be considered as a new style in designing and updating vaccines to protect against coronavirus disease.
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Affiliation(s)
- Javad Sarvmeili
- Department of Plant Breeding and Biotechnology, University of Tabriz, Tabriz, 51666, Iran
| | | | - Ashraf Gholizadeh
- Department of Animal Biology, University of Tabriz, Tabriz, 51666, Iran
| | - Dariush Shanehbandi
- Department of Immunology, Tabriz University of Medical Sciences, Tabriz, 51666, Iran
| | - Hamideh Ofoghi
- Department of Biotechnology, Iranian Research Organization for Science and Technology, Tehran, 33131, Iran
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Ponne S, Kumar R, Vanmathi SM, Brilhante RSN, Kumar CR. Reverse engineering protection: A comprehensive survey of reverse vaccinology-based vaccines targeting viral pathogens. Vaccine 2024; 42:2503-2518. [PMID: 38523003 DOI: 10.1016/j.vaccine.2024.02.087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 01/30/2024] [Accepted: 02/27/2024] [Indexed: 03/26/2024]
Abstract
Vaccines have significantly reduced the impact of numerous deadly viral infections. However, there is an increasing need to expedite vaccine development in light of the recurrent pandemics and epidemics. Also, identifying vaccines against certain viruses is challenging due to various factors, notably the inability to culture certain viruses in cell cultures and the wide-ranging diversity of MHC profiles in humans. Fortunately, reverse vaccinology (RV) efficiently overcomes these limitations and has simplified the identification of epitopes from antigenic proteins across the entire proteome, streamlining the vaccine development process. Furthermore, it enables the creation of multiepitope vaccines that can effectively account for the variations in MHC profiles within the human population. The RV approach offers numerous advantages in developing precise and effective vaccines against viral pathogens, including extensive proteome coverage, accurate epitope identification, cross-protection capabilities, and MHC compatibility. With the introduction of RV, there is a growing emphasis among researchers on creating multiepitope-based vaccines aiming to stimulate the host's immune responses against multiple serotypes, as opposed to single-component monovalent alternatives. Regardless of how promising the RV-based vaccine candidates may appear, they must undergo experimental validation to probe their protection efficacy for real-world applications. The time, effort, and resources allocated to the laborious epitope identification process can now be redirected toward validating vaccine candidates identified through the RV approach. However, to overcome failures in the RV-based approach, efforts must be made to incorporate immunological principles and consider targeting the epitope regions involved in disease pathogenesis, immune responses, and neutralizing antibody maturation. Integrating multi-omics and incorporating artificial intelligence and machine learning-based tools and techniques in RV would increase the chances of developing an effective vaccine. This review thoroughly explains the RV approach, ideal RV-based vaccine construct components, RV-based vaccines designed to combat viral pathogens, its challenges, and future perspectives.
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Affiliation(s)
- Saravanaraman Ponne
- Department of Medical Biotechnology, Aarupadai Veedu Medical College and Hospital, Vinayaka Mission's Research Foundation (Deemed to be University), Kirumampakkam, Puducherry 607402, India
| | - Rajender Kumar
- Division of Glycoscience, Department of Chemistry, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, AlbaNova University Center, Stockholm 106 91, Sweden
| | - S M Vanmathi
- Mahatma Gandhi Medical Advanced Research Institute, Sri Balaji Vidyapeeth (Deemed to be University), Pondicherry 607402, India
| | - Raimunda Sâmia Nogueira Brilhante
- Medical Mycology Specialized Center, Department of Pathology and Legal Medicine, Federal University of Ceará, Fortaleza, Ceará, Brazil
| | - Chinnadurai Raj Kumar
- Mahatma Gandhi Medical Advanced Research Institute, Sri Balaji Vidyapeeth (Deemed to be University), Pondicherry 607402, India.
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Bano N, Kumar A. Immunoinformatics study to explore dengue (DENV-1) proteome to design multi-epitope vaccine construct by using CD4+ epitopes. J Genet Eng Biotechnol 2023; 21:128. [PMID: 37987878 PMCID: PMC10663418 DOI: 10.1186/s43141-023-00592-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 11/06/2023] [Indexed: 11/22/2023]
Abstract
BACKGROUND Immunoinformatics is an emerging interdisciplinary field which integrates immunology, bioinformatics, and computational biology to study the immune system. In this study, we apply immunoinformatics approaches to explore the dengue proteome in order to design a multi-epitope vaccine construct. METHODS We used existing databases and algorithms to predict potential epitopes on dengue proteins and used a bioinformatics approach to identify the most promising epitopes. We then used molecular modelling to develop a multi-epitope construct which could be used as a potential vaccine. The results of this study demonstrate that immunoinformatics is a powerful tool for exploring and designing potential vaccines for infectious diseases like dengue. RESULTS Here, we found four CD4+ epitopes NLKYSVIVTVHTGDQ, ANPIVTDKEKPVNIE, LDPVVYDAKFEKQL, and VGAIALDFKPGTSGS that were used to design vaccine construct. The vaccine construct docked with TLR5. RMSD values suggest that docked complex of TLR5 and vaccine construct have putative stable interaction to induce immunogenic effects on host. CONCLUSIONS Furthermore, our study provides a proof of concept for the use of immunoinformatics approaches in DENV vaccine design. This vaccine can be effective in treating patients infected with DENV virus.
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Affiliation(s)
- Nishat Bano
- Department of Biotechnology, Faculty of Engineering and Technology Rama University, G.T. Road, Kanpur, 209217, India
| | - Ajay Kumar
- Department of Biotechnology, Faculty of Engineering and Technology Rama University, G.T. Road, Kanpur, 209217, India.
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Immunoinformatics-Aided Design of a Peptide Based Multiepitope Vaccine Targeting Glycoproteins and Membrane Proteins against Monkeypox Virus. Viruses 2022; 14:v14112374. [PMID: 36366472 PMCID: PMC9693848 DOI: 10.3390/v14112374] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 10/22/2022] [Accepted: 10/25/2022] [Indexed: 01/31/2023] Open
Abstract
Monkeypox is a self-limiting zoonotic viral disease and causes smallpox-like symptoms. The disease has a case fatality ratio of 3-6% and, recently, a multi-country outbreak of the disease has occurred. The currently available vaccines that have provided immunization against monkeypox are classified as live attenuated vaccinia virus-based vaccines, which pose challenges of safety and efficacy in chronic infections. In this study, we have used an immunoinformatics-aided design of a multi-epitope vaccine (MEV) candidate by targeting monkeypox virus (MPXV) glycoproteins and membrane proteins. From these proteins, seven epitopes (two T-helper cell epitopes, four T-cytotoxic cell epitopes and one linear B cell epitopes) were finally selected and predicted as antigenic, non-allergic, interferon-γ activating and non-toxic. These epitopes were linked to adjuvants to design a non-allergic and antigenic candidate MPXV-MEV. Further, molecular docking and molecular dynamics simulations predicted stable interactions between predicted MEV and human receptor TLR5. Finally, the immune-simulation analysis showed that the candidate MPXV-MEV could elicit a human immune response. The results obtained from these in silico experiments are promising but require further validation through additional in vivo experiments.
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Kaushik V, Jain P, Akhtar N, Joshi A, Gupta LR, Grewal RK, Oliva R, Shaikh AR, Cavallo L, Chawla M. Immunoinformatics-Aided Design and In Vivo Validation of a Peptide-Based Multiepitope Vaccine Targeting Canine Circovirus. ACS Pharmacol Transl Sci 2022. [DOI: 10.1021/acsptsci.2c00130] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Vikas Kaushik
- Domain of Bioinformatics, School of Bio-Engineering and Bio-Sciences, Lovely Professional University, Phagwara 144001, Punjab, India
| | - Pankaj Jain
- Domain of Bioinformatics, School of Bio-Engineering and Bio-Sciences, Lovely Professional University, Phagwara 144001, Punjab, India
| | - Nahid Akhtar
- Domain of Bioinformatics, School of Bio-Engineering and Bio-Sciences, Lovely Professional University, Phagwara 144001, Punjab, India
| | - Amit Joshi
- Domain of Bioinformatics, School of Bio-Engineering and Bio-Sciences, Lovely Professional University, Phagwara 144001, Punjab, India
| | - Lovi Raj Gupta
- Domain of Bioinformatics, School of Bio-Engineering and Bio-Sciences, Lovely Professional University, Phagwara 144001, Punjab, India
| | - Ravneet Kaur Grewal
- Department of Research and Innovation, STEMskills Research and Education Lab Private Limited, Faridabad 121002, Haryana, India
| | - Romina Oliva
- Department of Sciences and Technologies, University Parthenope of Naples, Centro Direzionale Isola C4, I-80143, Naples, Italy
| | - Abdul Rajjak Shaikh
- Department of Research and Innovation, STEMskills Research and Education Lab Private Limited, Faridabad 121002, Haryana, India
| | - Luigi Cavallo
- Physical Sciences and Engineering Division, Kaust Catalysis Center, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia
| | - Mohit Chawla
- Physical Sciences and Engineering Division, Kaust Catalysis Center, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia
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Kaushik V, G SK, Gupta LR, Kalra U, Shaikh AR, Cavallo L, Chawla M. Immunoinformatics Aided Design and In-Vivo Validation of a Cross-Reactive Peptide Based Multi-Epitope Vaccine Targeting Multiple Serotypes of Dengue Virus. Front Immunol 2022; 13:865180. [PMID: 35799781 PMCID: PMC9254734 DOI: 10.3389/fimmu.2022.865180] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2022] [Accepted: 05/05/2022] [Indexed: 02/03/2023] Open
Abstract
Dengue virus (DENV) is an arboviral disease affecting more than 400 million people annually. Only a single vaccine formulation is available commercially and many others are still under clinical trials. Despite all the efforts in vaccine designing, the improvement in vaccine formulation against DENV is very much needed. In this study, we used a roboust immunoinformatics approach, targeting all the four serotypes of DENV to design a multi-epitope vaccine. A total of 13501 MHC II binding CD4+ epitope peptides were predicted from polyprotein sequences of four dengue virus serotypes. Among them, ten conserved epitope peptides that were interferon-inducing were selected and found to be conserved among all the four dengue serotypes. The vaccine was formulated using antigenic, non-toxic and conserved multi epitopes discovered in the in-silico study. Further, the molecular docking and molecular dynamics predicted stable interactions between predicted vaccine and immune receptor, TLR-5. Finally, one of the mapped epitope peptides was synthesized for the validation of antigenicity and antibody production ability where the in-vivo tests on rabbit model was conducted. Our in-vivo analysis clearly indicate that the imunogen designed in this study could stimulate the production of antibodies which further suggest that the vaccine designed possesses good immunogenicity.
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Affiliation(s)
- Vikas Kaushik
- Domain of Bioinformatics, School of Bio-Engineering and Bio-Sciences, Lovely Professional University, Punjab, India
| | - Sunil Krishnan G
- Domain of Bioinformatics, School of Bio-Engineering and Bio-Sciences, Lovely Professional University, Punjab, India
| | - Lovi Raj Gupta
- Domain of Bioinformatics, School of Bio-Engineering and Bio-Sciences, Lovely Professional University, Punjab, India
| | - Utkarsh Kalra
- Department of Research and Innovation, STEMskills Research and Education Lab Private Limited, Faridabad, India
- Department of Data Science, Innopolis University, Innopolis, Russia
| | - Abdul Rajjak Shaikh
- Department of Research and Innovation, STEMskills Research and Education Lab Private Limited, Faridabad, India
- *Correspondence: Abdul Rajjak Shaikh, ; Luigi Cavallo, ; Mohit Chawla, ;
| | - Luigi Cavallo
- Kaust Catalysis Center, Physical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
- *Correspondence: Abdul Rajjak Shaikh, ; Luigi Cavallo, ; Mohit Chawla, ;
| | - Mohit Chawla
- Kaust Catalysis Center, Physical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
- *Correspondence: Abdul Rajjak Shaikh, ; Luigi Cavallo, ; Mohit Chawla, ;
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