Abstract
The rampant spread of COVID-19, an infectious disease caused by SARS-CoV-2, all over the world has led to over 6.5 million cases and more than 380,000 deaths, and devastated the social, financial and political entities around the world. Without an existing effective medical therapy, vaccines are urgently needed to avoid the spread of this disease. In this study, we propose an in-silico deep learning approach for prediction and design of a multi-epitope vaccine (Deep-Vac-Pred). By combining the in-silico immunotherapeutic and deep neural network strategies, the DeepVacPred computational framework directly predicts 26 potential vaccine subunits from the available SARS-CoV- 2 spike protein sequence. We further use in-silico methods to investigate the linear B-cell epitopes, Cytotoxic T Lymphocytes (CTL) epitopes, Helper T Lymphocytes (HTL) epitopes in the 26 subunit candidates and identify the best 11 of them to construct a multi-epitope vaccine for SARS-CoV-2 virus. The human population coverage, antigenicity, allergenicity, toxicity, physicochemical properties and secondary structure of the designed vaccine are evaluated via state-of-the-art bioinformatic approaches, showing good quality of the designed vaccine. The 3D structure of the designed vaccine is predicted, refined and validated by in-silico tools. Finally, we optimize and insert the codon sequence into a plasmid to ensure the cloning and expression efficiency. In conclusion, this proposed artificial intelligence vaccine discovery framework accelerates the vaccine design process and constructs a 694aa multi- epitope vaccine containing 16 B-cell epitopes, 82 CTL epitopes and 89 HTL epitopes, which is promising to fight the SARS-CoV-2 viral infection and can be further evaluated in clinical studies. Moreover, we trace the RNA mutations of the CoV and make sure our designed vaccine can tackle the recent RNA mutations of the virus.
In silico prediction of B cell epitopes of the hemolysis-associated
protein 1 for vaccine design against leptospirosis