Debarati Basu, Makaut WB
Identification of antigen is a major step for the development of the vaccine. Artificial intelligence including deep learning systems is useful for identifying vaccine targets utilizing genomic and proteomic information. An artificial intelligence-based platform was developed by the researchers of Amity University, Noida, and Baylor College of Medicine in Houston, USA. This platform was effective in speeding up the development of vaccines for highly infectious diseases such as COVID-19 and Chagas Disease.
This study’s results were published in the PubMed and UK-based journal Scientific Reports. The artificial intelligence platform is tested on 40 different types of pathogens that include SARS-CoV-2 (causative agent of Covid-19), Vibro Cholerae (agent of cholera), Plasmodium falciparum (agent of malaria), Mycobacterium tuberculosis (agent of TB), etc. The lead authors of this study are Dr. Rawal and Dr. Peter Hotez and the co-authors are Dr. Maria Elena Bottazzi and Dr. Ulrich Steych. Dr. Rawal is the Associate Professor & Project Director, Amity Institute of Biotechnology. Dr. Peter is the Dean of one of the prestigious institutes, the National School of Tropical Medicine. Dr. Maria is the Co-Dean of Baylor College of Medicine. Dr. Ulrich is BCM Associate Professor.
(The above image has been taken from https://www.desfollowupstudy.org/centers_baylor.asp)
Some features of the study
- The study shows a new computational system utilized for the discovery and analysis of novel vaccine targets. This discovery and analysis can lead to the designing of a multi-epitope subunit vaccine candidate. This platform also consists of reverse vaccinology and immune-informatics tool. These tools can be utilized for screening genomic and proteomic datasets of different pathogens. The pathogens mainly include Trypanosoma cruzi, Plasmodium falciparum, and Vibrio cholera. These are used for identifying potential vaccine candidates (PVC).
- It includes highly antigenic and immunogenic MHC class I, MHC class II and B cell epitopes. These were extracted from high-ranking target vaccines.
- The designed vaccines consist of 24 epitopes, 3 adjuvant, and 4 linkers. They were analyzed to determine physiochemical properties. It was obtained by using various tools which include docking analysis.
- The designed vaccine is supposed to be soluble, non-toxic, and stable, and doesn’t cause allergies. They are supposed to give cross-protection against Trypanosoma species and strains.
Significance of the study
- Dr. Rawal said that one of the important features of artificial intelligence is its ability to combine some hundred parameters to develop various thousand proteins and genes. It is utilized to achieve the proper targets and is further used in designing vaccines by utilizing these proteins. He further developed a cloud-based server. This server can be utilized throughout the world by researchers for analysis of proteins and genes and further used as possible vaccine targets.
- Amity University stated that during the sure of the Delta variant Covid-19 strain the research team was involved with several pharmaceutical and biotechnology companies. The team’s engagement with them was mainly for the customized deployment for commercial-scale applications. It is mainly carried out for the development of new vaccines which will be effective against possible infectious diseases. Amity University further stated that the platform was tested by the researchers on various experimentally known vaccine targets. It consists of the vaccines that are available in the market. The researcher’s team is analyzing the entire genome and proteome of Trypanosoma cruzi (T. cruzi) which is an important pathogen.
- The computational biology platform was validated by utilizing more than 335 experimentally verified antigens. These antigens belong to 40 varied pathogens. The system was found to correctly predict the results with the majority having decent accuracy levels. One of the advantages in favor of the artificial intelligence platform is the use of target vaccines which includes vaccines approved by the FDA. The future scope includes injecting mice with these modified vaccines. It then includes observing of the modified vaccines are non-toxic and immunogenic before they enter the clinical trial phase.
- According to Dr. Strych that the study is in the initial phase and so it will be early to tell their effectiveness on patients. But the initial data is suggestive that the platform will be effective in various ways. According to Dr. Bottazzi, an ideal vaccine target should be different from its host proteins. It is mainly done to avoid any cross-reaction and possible side effects. As a result, special care should be taken during the study.
(The image of Amity University, Noida has been taken from https://www.amity.edu/about-university.aspx)
Conclusion
The study consists of an ensuing approach that uses both bioinformatics and computational algorithms. These are utilized for predicting possible vaccine targets in pathogens. The study also comprises reverse vaccinology methods. It is utilized for designing an in-silico multi-epitope subunit vaccine. This designed vaccine is effective against Chagas disease (CD), a protozoan infection caused by Trypanosoma cruzi (T. cruzi). The datasets are utilized in developing an artificial intelligence platform for predicting vaccine targets. The main objective of the study is to develop possible multi-epitope vaccines against CD. This disease has caused an endemic in Latin America and is dreadful in other parts of the world also. The study is in its initial stage (involving in-silico approaches or computational biology) so more studies are required for determining the immunogenicity and safety of the designed vaccines.
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Reference:
- Rawal, K., Sinha, R., Abbasi, B. A., Chaudhary, A., Nath, S. K., Kumari, P., Preeti, P., Saraf, D., Singh, S., Mishra, K., Gupta, P., Mishra, A., Sharma, T., Gupta, S., Singh, P., Sood, S., Subramani, P., Dubey, A. K., Strych, U., … Bottazzi, M. E. (2021). Identification of vaccine targets in pathogens and design of a vaccine using computational approaches. Scientific Reports, 11(1), 17626. https://www.nature.com/articles/s41598-021-96863-x
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