Abstract
The heavy burden imposed by the COVID-19 pandemic on our society triggered the race toward the development of therapies or preventive strategies. Among these, antibodies and vaccines are particularly attractive because of their high specificity, low probability of drug-drug interaction, and potentially long-standing protective effects. While the threat at hand justifies the pace of research, the implementation of therapeutic strategies cannot be exempted from safety considerations. There are several potential adverse events reported after the vaccination or antibody therapy, but two are of utmost importance: antibody-dependent enhancement (ADE) and cytokine storm syndrome (CSS). On the other hand, the depletion or exhaustion of T-cells has been reported to be associated with worse prognosis in COVID-19 patients. This observation suggests a potential role of vaccines eliciting cellular immunity, which might simultaneously limit the risk of ADE and CSS. Such risk was proposed to be associated with FcR-induced activation of proinflammatory macrophages (M1) by Fu et al. (2020) and Iwasaki and Yang (2020). All aspects of the newly developed vaccine (including the route of administration, delivery system, and adjuvant selection) may affect its effectiveness and safety. In this work we use a novel in silico approach (based on AI and bioinformatics methods) developed to support the design of epitope-based vaccines. We evaluated the capabilities of our method for predicting the immunogenicity of epitopes. Next, the results of our approach were compared with other vaccine-design strategies reported in the literature. The risk of immuno-toxicity was also assessed. The analysis of epitope conservation among other Coronaviridae was carried out in order to facilitate the selection of peptides shared across different SARS-CoV-2 strains and which might be conserved in emerging zootic coronavirus strains. Finally, the potential applicability of the selected epitopes for the development of a vaccine eliciting cellular immunity for COVID-19 was discussed, highlighting the benefits and challenges of such an approach.
Highlights
As of August 6, 2020, more than 19 million cases of COVID-19 were reported worldwide, leading to more than 700 thousands deaths1
Some groups display a noticeable correlation between pHLA immunogenicity and pHLA binding affinity predictions (e.g., Pneumoviridae and Orthomyxoviridae), this trend is not confirmed across all groups
Many investigations aimed at developing vaccines protecting humans and animals from coronaviruses were initiated in the last few decades, setting the basis for the recent scientific advancement in COVID-19 treatment
Summary
As of August 6, 2020, more than 19 million cases of COVID-19 were reported worldwide, leading to more than 700 thousands deaths. The metagenomic RNA sequencing of bronchoalveolar lavage (BAL) fluid sample obtained from that patient led to the identification of the seventh coronavirus (CoV) strain known to infect humans. Coronaviruses are well known human respiratory pathogens associated with the common cold. There are four seasonal coronaviruses infecting humans and they cluster within alphacoronaviruses (HCoV-NL63, HCoV-229E) and betacoronaviruses (HCoVOC43, HCoV-HKU1) genera. Three zoonotic strains were reported – severe acute respiratory syndrome coronavirus (SARS-CoV; 2002–2003), the Middle East respiratory syndrome coronavirus (MERS-CoV; 2012-), and SARS-CoV-2 (2019-), all of which belong to the betacoronavirus genus (Wu A. et al, 2020). The highly pathogenic species cluster in two subgenera – sarbecoviruses (SARS-CoVs) and merbecoviruses (MERS-CoVs) (Hu et al, 2018; Wu F. et al, 2020; Zhou et al, 2020)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
