Data processing algorithms for the in silico SARS-CoV-2 epitope prediction and vaccine development

2021 ◽  
Vol 26 (jai2021.26(2)) ◽  
pp. 08-13
Author(s):  
Sprindzuk M ◽  
◽  
Vladyko A ◽  
Titov L ◽  
◽  
...  

Based on literature analysis and own bioinformatics and virology research experience, authors propose multistep data processing algorithms, designed for the objectives of assisting the SARS-CoV-2 epitope vaccine production. Epitope vaccines are expected to provoke a weaker but safer response of the vaccinated person. Methodologies of reverse bioengineering, vaccinology and synthetic peptide manufacturing have a promising future to combat COVID-19 brutal disease. The significant mutational variability and evolution of the SARS-CoV-2, which is more typical for natural animal-borne viruses, are the hurdle for the effective and robust vaccine application and therefore require multidisciplinary research and prevention measures on the international level of cooperation. However, we can expect that other viruses with different nature and content may be labelled as SARS-CoV-2. In this case metagenomics is an important discipline for COVID-19 discovery. High quality reliable virus detection is still an unresolved question for improvement and optimization. It is of upmost importance to develop the in silico and in vitro methods for the vaccine recipient reaction prediction and monitoring as techniques of the so-called modern personalized medicine. Many questions can`t be solved applying exclusively in silico techniques and only can be discovered in vitro and in vivo, demanding significant time and money investments. Future experiments also should be directed at the discovery of optimal vaccine adjuvants, vectors and epitope ensembles, as well as the personal characteristics of citizens of a certain region. This research would require several more years of meticulous large-scale laboratory and clinical work in various centers of biomedical institutions worldwide

2008 ◽  
Vol 9 (S1) ◽  
Author(s):  
Upinder S Bhalla ◽  
Radhika Madhavan ◽  
Ashesh Dhawale ◽  
Mehrab Modi ◽  
Raamesh Deshpande ◽  
...  
Keyword(s):  

Author(s):  
Rania Francis ◽  
Marion Le Bideau ◽  
Priscilla Jardot ◽  
Clio Grimaldier ◽  
Didier Raoult ◽  
...  

AbstractSARS-CoV-2, a novel coronavirus infecting humans, is responsible for the current COVID-19 global pandemic. If several strains could be isolated worldwide, especially for in-vitro drug susceptibility testing and vaccine development, few laboratories routinely isolate SARS-CoV-2. This is due to the fact that the current co-culture strategy is highly time consuming and requires working in a biosafety level 3 laboratory. In this work, we present a new strategy based on high content screening automated microscopy (HCS) allowing large scale isolation of SARS-CoV-2 from clinical samples in 1 week. A randomized panel of 104 samples, including 72 tested positive by RT-PCR and 32 tested negative, were processed with our HCS procedure and were compared to the classical isolation procedure. Isolation rate was 43 % with both strategies on RT-PCR positive samples, and was correlated with the initial RNA viral load in the samples, where we obtained a positivity threshold of 27 Ct. Co-culture delays were shorter with HCS strategy, where 80 % of the positive samples were recovered by the third day of co-culture, as compared to only 25 % with the classic strategy. Moreover, only the HCS strategy allowed us to recover all the positive elements after 1 week of co-culture. This system allows rapid and automated screening of clinical samples with minimal operator work load, thus reducing the risks of contamination.


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