Bioinformatics approach for the construction of multiple epitope vaccine against omicron variant of SARS-CoV-2.

The World Health Organization categorized SARS-CoV-2 as a variant of concern, having numerous mutations in spike protein, which have been found to evade the effect of antibodies stimulated by the COVID-19 vaccine. The susceptibility to omicron variant by immunization-induced antibodies are direly required for risk evaluation. To avoid the risk of arising viral illness, the construction of a specific vaccine that triggers the production of targeted antibodies to combat infection remains highly imperative. The aim of the present study is to develop a particular vaccine exploiting bioinformatics approaches which can target B- and T-cells epitopes. Through this approach, novel epitopes of the S protein-SARS-CoV-2 were predicted for the development of a multiple epitope vaccine. Multiple epitopes were selected on the basis of toxicity, immunogenicity and antigenicity, and vaccine subunit was constructed having potential immunogenic properties. The epitopes were linked with 3 types of linker EAAAK, AAY and GPGPG for vaccine construction. Subsequently, vaccine structure was docked with the receptor and cloned in a pET-28a (+) vector. The constructed vaccine was ligated in pET-28a (+) vector in E. coli using the SnapGene tool for the expression study and a good immune response was observed. Several computational tools were used to predict and analyze the vaccine constructed by using spike protein sequence of omicrons. The current study identified a Multi-Epitope Vaccine (MEV) as a significant vaccine candidate that could potentially help the global world to combat SARS-CoV-2 infections.

Design of a novel multiple epitope-based vaccine: An immunoinformatics approach to combat SARS-CoV-2 strains.

BACKGROUND: Since the SARS-CoV-2 outbreak in December 2019 in Wuhan, China, the virus has infected more than 153 million individuals across the world due to its human-to-human transmission. The USA is the most affected country having more than 32-million cases till date. Sudden high fever, pneumonia and organ failure have been observed in infected individuals. OBJECTIVES: In the current situation of emerging viral disease, there is no specific vaccine, or any therapeutics available for SARS-CoV-2, thus there is a dire need to design a potential vaccine to combat the virus by developing immunity in the population. The purpose of present study was to develop a potential vaccine by targeting B and T-cell epitopes using bioinformatics approaches. METHODS: B- and T-cell epitopes are predicted from novel M protein-SARS-CoV-2 for the development of a unique multiple epitope vaccine by applying bioinformatics approaches. These epitopes were analyzed and selected for their immunogenicity, antigenicity scores, and toxicity in correspondence to their ability to trigger immune response. In combination to epitopes, best multi-epitope of potential immunogenic property was constructed. The epitopes were joined using EAAAK, AAY and GPGPG linkers. RESULTS: The constructed vaccine showed good results of worldwide population coverage and promising immune response. This constructed vaccine was subjected to in-silico immune simulations by C-ImmSim. Chimeric protein construct was cloned into PET28a (+) vector for expression study in Escherichia coli using snapgene. CONCLUSION: This vaccine design proved effective in various computer-based immune response analysis as well as showed good population coverage. This study is solely dependent on developing M protein-based vaccine, and these in silico findings would be a breakthrough in the development of an effective vaccine to eradicate SARS-CoV-2 globally.

Machine Intelligence Techniques for the Identification and Diagnosis of COVID-19.

COVID-19, an infectious disease caused by a newly discovered enveloped virus (SARS-CoV-2), was first reported in Wuhan, China, in December 2019 and affected the whole world. The infected individual may develop symptoms such as high fever, cough, myalgia, lymphopenia, respiratory distress syndrome etc., or remain completely asymptomatic after the incubation period of two to fourteen days. As the virus is transmitted by inhaling infectious respiratory droplets that are produced by sneezing or coughing, so early and rapid diagnosis of the disease can prevent infection and transmission. In the current pandemic situation, the medical industry is looking for new technologies to monitor and control the spread of COVID-19. In this context, the current review article highlights the Artificial Intelligence methods that are playing an effective role in rapid, accurate and early diagnosis of the disease via pattern recognition, machine learning, expert system and fuzzy logic by improving cognitive behavior and reducing human error. Auto-encoder deep learning method, α-satellite, ACEMod and heterogeneous graph auto- encoder are AI approaches that determine the transfer rate of virus and are helpful in shaping public health and planning. In addition, CT scan, X-ray, MRI, and RT-PCR are some of the techniques that are being employed in the identification of COVID-19. We hope using AI techniques; the world can emerge from COVID-19 pandemic while mitigating social and economic crisis.