Abstract
The COVID-19 pandemic first appeared in Wuhan, China, in December 2019 in a cluster of pneumonia patients. The causative agent was found to be SARS-CoV-2. Here, we are summarizing current treatment strategies and highlighting the role of bioinformatics, molecular modeling, and structural biology during the COVID-19 pandemic. There are different pharmacological treatments, mostly repurposed drugs, employed for the treatment of COVID-19, including antiviral drugs, corticosteroids, biologic drugs, antibiotics, antifungal agents, and anticoagulants. Some immune-based therapies are also under evaluation, including convalescent plasma, IL-1, IL-6 inhibitors, and interferons. Different bioinformatics networks are established to provide information about the structure, transcriptome, and pathogenicity of the virus. The genotyping analysis for SARS-CoV-2 is also useful in identifying different mutations, SNPs, and conservative domains along the viral genome. Cryo-EM and X-ray diffraction had a crucial role in determining the structure of viral proteins such as spike (S) protein, main protease, and RdRp. NMR had a minor role and determining the structure of nucleocapsid (N) protein only. Several docking studies were performed to predict the interaction of certain FDA-approved drugs with known efficacy and toxicity, while others used natural products. Among different study types, in silico drug prediction and repurposing have the lowest risk with less off-target results. Therefore, bioinformatics and in silico studies have an important role during pandemics in providing information about viral structure and function and predicting potential treatments.
Highlights
Once the data of the SARS-CoV-2 genome were published on The National Center for Biotechnology Information (NCBI) and Global Initiative on Sharing All Influenza Data (GISAID) database, various studies related to bioinformatics analysis had appeared providing significant information for the SARSCoV-2 treatment
The computational works in structural biology can be extended to include predicting the 3D structures by using various tools, including AlphaFold as an Ab initio tool based on deep neural networks [142] that have been used in predicting ORF8 [143], NSP6 [144], or other SARS-CoV-2 proteins [145]
The papain-like protease (PLpro) is the least explored target among the characterized molecular targets of the SARS-CoV-2, relative to the 3C-like Main Protease (3CLpro) on which extensive studies have been made, the RNA dependent RNA polymerase (RdRp), which is the target of the only Food and Drug Administration (FDA)-approved drug for Covid-19 treatment, and the S protein used in vaccine development
Summary
The world is recently facing Coronavirus Disease (COVID-19) that threatens public health everywhere. COVID-19 first appeared in Wuhan, China December 2019 in a cluster of pneumonia patients [1] It was pronounced a global pandemic disease by the World Health Organization (WHO) on March 11th, 2020. The causative agent of COVID-19 belongs to RNA β- Coronaviruses It has a 79.5% similarity with the genome of the Severe Acute Respiratory Syndrome Corona-. Binding of the Receptor Binding Domain (RBD) of the S1 subunit to hACE-2 receptor mediates the interaction of the heptad repeat 1 (HR1) and 2 (HR2) domains in its S2 subunit to make a six-helix bundle (6HB) fusion core, bringing cellular and viral membranes in contact to facilitate infection [8]. Chest CT and X-ray Imaging on COVID-19 patients is essential for disease diagnosis and prediction of its progression. Viral load and viral-shedding period are important predictors for disease severity and fetal outcomes among different age groups [32]
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