Identification of vaccine targets in pathogens and design of a vaccine using computational approaches
Abstract Antigen identification is an important step in the vaccine development process. Computational approaches including deep learning systems can play an important role in the identification of vaccine targets using genomic and proteomic information. Here, we present a new computational system t...
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Nature Portfolio
2021
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oai:doaj.org-article:cf9224e382204f6f982079d52b357e142021-12-02T15:28:46ZIdentification of vaccine targets in pathogens and design of a vaccine using computational approaches10.1038/s41598-021-96863-x2045-2322https://doaj.org/article/cf9224e382204f6f982079d52b357e142021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-96863-xhttps://doaj.org/toc/2045-2322Abstract Antigen identification is an important step in the vaccine development process. Computational approaches including deep learning systems can play an important role in the identification of vaccine targets using genomic and proteomic information. Here, we present a new computational system to discover and analyse novel vaccine targets leading to the design of a multi-epitope subunit vaccine candidate. The system incorporates reverse vaccinology and immuno-informatics tools to screen genomic and proteomic datasets of several pathogens such as Trypanosoma cruzi, Plasmodium falciparum, and Vibrio cholerae to identify potential vaccine candidates (PVC). Further, as a case study, we performed a detailed analysis of the genomic and proteomic dataset of T. cruzi (CL Brenner and Y strain) to shortlist eight proteins as possible vaccine antigen candidates using properties such as secretory/surface-exposed nature, low transmembrane helix (< 2), essentiality, virulence, antigenic, and non-homology with host/gut flora proteins. Subsequently, highly antigenic and immunogenic MHC class I, MHC class II and B cell epitopes were extracted from top-ranking vaccine targets. The designed vaccine construct containing 24 epitopes, 3 adjuvants, and 4 linkers was analysed for its physicochemical properties using different tools, including docking analysis. Immunological simulation studies suggested significant levels of T-helper, T-cytotoxic cells, and IgG1 will be elicited upon administration of such a putative multi-epitope vaccine construct. The vaccine construct is predicted to be soluble, stable, non-allergenic, non-toxic, and to offer cross-protection against related Trypanosoma species and strains. Further, studies are required to validate safety and immunogenicity of the vaccine.Kamal RawalRobin SinhaBilal Ahmed AbbasiAmit ChaudharySwarsat Kaushik NathPriya KumariP. PreetiDevansh SarafShachee SinghKartik MishraPranjay GuptaAstha MishraTrapti SharmaSrijanee GuptaPrashant SinghShriya SoodPreeti SubramaniAman Kumar DubeyUlrich StrychPeter J. HotezMaria Elena BottazziNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-25 (2021) |
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DOAJ |
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DOAJ |
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EN |
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Medicine R Science Q |
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Medicine R Science Q Kamal Rawal Robin Sinha Bilal Ahmed Abbasi Amit Chaudhary Swarsat Kaushik Nath Priya Kumari P. Preeti Devansh Saraf Shachee Singh Kartik Mishra Pranjay Gupta Astha Mishra Trapti Sharma Srijanee Gupta Prashant Singh Shriya Sood Preeti Subramani Aman Kumar Dubey Ulrich Strych Peter J. Hotez Maria Elena Bottazzi Identification of vaccine targets in pathogens and design of a vaccine using computational approaches |
| description |
Abstract Antigen identification is an important step in the vaccine development process. Computational approaches including deep learning systems can play an important role in the identification of vaccine targets using genomic and proteomic information. Here, we present a new computational system to discover and analyse novel vaccine targets leading to the design of a multi-epitope subunit vaccine candidate. The system incorporates reverse vaccinology and immuno-informatics tools to screen genomic and proteomic datasets of several pathogens such as Trypanosoma cruzi, Plasmodium falciparum, and Vibrio cholerae to identify potential vaccine candidates (PVC). Further, as a case study, we performed a detailed analysis of the genomic and proteomic dataset of T. cruzi (CL Brenner and Y strain) to shortlist eight proteins as possible vaccine antigen candidates using properties such as secretory/surface-exposed nature, low transmembrane helix (< 2), essentiality, virulence, antigenic, and non-homology with host/gut flora proteins. Subsequently, highly antigenic and immunogenic MHC class I, MHC class II and B cell epitopes were extracted from top-ranking vaccine targets. The designed vaccine construct containing 24 epitopes, 3 adjuvants, and 4 linkers was analysed for its physicochemical properties using different tools, including docking analysis. Immunological simulation studies suggested significant levels of T-helper, T-cytotoxic cells, and IgG1 will be elicited upon administration of such a putative multi-epitope vaccine construct. The vaccine construct is predicted to be soluble, stable, non-allergenic, non-toxic, and to offer cross-protection against related Trypanosoma species and strains. Further, studies are required to validate safety and immunogenicity of the vaccine. |
| format |
article |
| author |
Kamal Rawal Robin Sinha Bilal Ahmed Abbasi Amit Chaudhary Swarsat Kaushik Nath Priya Kumari P. Preeti Devansh Saraf Shachee Singh Kartik Mishra Pranjay Gupta Astha Mishra Trapti Sharma Srijanee Gupta Prashant Singh Shriya Sood Preeti Subramani Aman Kumar Dubey Ulrich Strych Peter J. Hotez Maria Elena Bottazzi |
| author_facet |
Kamal Rawal Robin Sinha Bilal Ahmed Abbasi Amit Chaudhary Swarsat Kaushik Nath Priya Kumari P. Preeti Devansh Saraf Shachee Singh Kartik Mishra Pranjay Gupta Astha Mishra Trapti Sharma Srijanee Gupta Prashant Singh Shriya Sood Preeti Subramani Aman Kumar Dubey Ulrich Strych Peter J. Hotez Maria Elena Bottazzi |
| author_sort |
Kamal Rawal |
| title |
Identification of vaccine targets in pathogens and design of a vaccine using computational approaches |
| title_short |
Identification of vaccine targets in pathogens and design of a vaccine using computational approaches |
| title_full |
Identification of vaccine targets in pathogens and design of a vaccine using computational approaches |
| title_fullStr |
Identification of vaccine targets in pathogens and design of a vaccine using computational approaches |
| title_full_unstemmed |
Identification of vaccine targets in pathogens and design of a vaccine using computational approaches |
| title_sort |
identification of vaccine targets in pathogens and design of a vaccine using computational approaches |
| publisher |
Nature Portfolio |
| publishDate |
2021 |
| url |
https://doaj.org/article/cf9224e382204f6f982079d52b357e14 |
| work_keys_str_mv |
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| _version_ |
1718387230847795200 |