Identification of potent compounds against SARs-CoV-2: An in-silico based drug searching against Mpro.

The worldwide pandemic of coronavirus disease 2019 (COVID-19) along with the various newly discovered major SARS-CoV-2 variants, including B.1.1.7, B.1.351, and B.1.1.28, constitute the Variant of Concerns (VOC). It's difficult to keep these variants from spreading over the planet. As a result of these VOCs, the fifth wave has already begun in several countries. The rapid spread of VOCs is posing a serious threat to human civilization. There is currently no specific medicine available for the treatment of COVID-19. Here, we present the findings of methods that used a combination of structure-assisted drug design, virtual screening, and high-throughput screening to swiftly generate lead compounds against Mpro protein of SARs-CoV-2. Therapeutics, in addition to vaccinations, are an essential element of the healthcare response to COVID-19's persistent threat. In the current study, we designed the efficient compounds that may combat all emerging variants of SARs-CoV-2 by targeting the common Mpro protein. The present study was aimed to discover new compounds that may be proposed as new therapeutic agents to treat COVID-19 infection without any adverse effects. For this purpose, a computational-based virtual screening of 352 in-house synthesized compounds library was performed through molecular docking and Molecular Dynamics (MD) simulation approach. As a result, four novel potent compounds were successfully shortlisted by implementing certain pharmacological, physiological, and ADMET criteria i.e., compounds 3, 4, 21, and 22. Furthermore, MD simulations were performed to evaluate the stability and dynamic behavior of these compounds with Mpro complex for about 30 ns. Eventually, compound 22 was found to be highly potent against Mpro protein and was further evaluated by applying 100 ns simulations. Our findings showed that these shortlisted compounds may have potency to treat the COVID-19 infection for which further experimental validation is proposed as part of a follow-up investigation.

Immunoinformatic approach for the construction of multi-epitopes vaccine against omicron COVID-19 variant.

The newly discovered SARS-CoV-2 Omicron variant B.1.1.529 is a Variant of Concern (VOC) announced by the World Health Organization (WHO). It's becoming increasingly difficult to keep these variants from spreading over the planet. The fifth wave has begun in several countries because of Omicron variant, and it is posing a threat to human civilization. As a result, we need effective vaccination that can tackle Omicron SARS-CoV-2 variants that are bound to emerge. Therefore, the current study is an initiative to design a peptide-based chimeric vaccine that may potentially battle SARS-CoV-2 Omicron variant. As a result, the most relevant epitopes present in the mutagenic areas of Omicron spike protein were identified using a set of computational tools and immunoinformatic techniques to uncover common MHC-1, MHC-II, and B cell epitopes that may have the ability to influence the host immune mechanism. A final of three epitopes from CD8+ T-cell, CD4+ T-cell epitopes, and B-cell were shortlisted from spike protein, and that are highly antigenic, IFN-γ inducer, as well as overlapping for the construction of twelve vaccine models. As a result, the antigenic epitopes were coupled with a flexible and stable peptide linker, and the adjuvant was added at the N-terminal end to create a unique vaccine candidate. The structure of a 3D vaccine candidate was refined, and its quality was assessed by using web servers. However, the applied immunoinformatic study along with the molecular docking and simulation of 12 modeled vaccines constructs against six distinct HLAs, and TLRs (TLR2, and TLR4) complexes revealed that the V1 construct was non-allergenic, non-toxic, highly immunogenic, antigenic, and most stable. The vaccine candidate's stability was confirmed by molecular dynamics investigations. Finally, we studied the expression of the suggested vaccination using codon optimization and in-silico cloning. The current study proposed V1 Multi-Epitope Vaccine (MEV) as a significant vaccine candidate that may help the scientific community to treat SARS-CoV-2 infections.

Reverse vaccinology approach for multi-epitope centered vaccine design against delta variant of the SARS-CoV-2.

The ongoing COVID-19 outbreak, initially identified in Wuhan, China, has impacted people all over the globe and new variants of concern continue to threaten hundreds of thousands of people. The delta variant (first reported in India) is currently classified as one of the most contagious variants of SARS-CoV-2. It is estimated that the transmission rate of delta variant is 225% times faster than the alpha variant, and it is causing havoc worldwide (especially in the USA, UK, and South Asia). The mutations found in the spike protein of delta variant make it more infective than other variants in addition to ruining the global efficacy of available vaccines. In the current study, an in silico reverse vaccinology approach was applied for multi-epitope vaccine construction against the spike protein of delta variant, which could induce an immune response against COVID-19 infection. Non-toxic, highly conserved, non-allergenic and highly antigenic B-cell, HTL, and CTL epitopes were identified to minimize adverse effects and maximize the efficacy of chimeric vaccines that could be developed from these epitopes. Finally, V1 vaccine construct model was shortlisted and 3D modeling was performed by refinement, docking against HLAs and TLR4 protein, simulation and in silico expression. In silico evaluation showed that the designed chimeric vaccine could elicit an immune response (i.e., cell-mediated and humoral) identified through immune simulation. This study could add to the efforts of overcoming global burden of COVID-19 particularly the variants of concern.

Re-purposing of hepatitis C virus FDA approved direct acting antivirals as potential SARS-CoV-2 protease inhibitors.

A new coronavirus strain called as SARS-CoV-2 has emerged from Wuhan, China in late 2019 and it caused a worldwide pandemic in a few months. After the Second World War, it is the biggest calamity observed as there is no specific US Food and Drugs Administration (USFDA) approved drug or vaccine available globally for the treatment. Several clinical trials are ongoing for therapeutic alternatives, however with little success rate. Considering that the time is crucial, the drug repurposing and data obtained from in silico models are one of the most important approaches to identify possible lead inhibitors against SARS-CoV-2. More recently, the Direct Acting Antivirals (DAAs) are emerged as the most promising drugs to control viral infection. The Main Protease (Mpro), a key enzyme in the SARS-CoV-2 replication cycle, is found close homolog to the Hepatitis C Virus (HCV) protease and could be susceptible of blocking its activity by DAAs. In the current study, the DAAs were investigated as antivirals using structure based computational approach against Mpro of SARS-CoV-2 to propose them as new therapeutics. In total, 20 DAAs of HCV, including a reference compound O6K were docked against Mpro. The docked structures were examined and resulted in the identification of six highly promising DAAs i.e. beclabuvir, elbasvir, paritaprevir, grazoprevir, simeprevir, and asunapevir exhibiting high theoretical binding affinity to Mpro from SARS-CoV-2 in comparison to other DAAs. Furthermore, the post docking analysis revealed that Cys145, Glu166, His163, Thr26, His41, and Met165 played potential role for the binding of these DAAs inside binding site of Mpro. Furthermore, the correlation between binding energies were found in accord with the results from the reported IC50s for some DAAs. Overall, the current study provides insight to combat COVID-19 using FDA-approved DAAs as repurposed drugs.

Identification of chymotrypsin-like protease inhibitors of SARS-CoV-2 via integrated computational approach.

Recently, the world has witnessed outbreak of a novel Coronavirus (SARS-CoV-2), the virus which initially emerged in Wuhan, China has now made its way to a large part of the world, resulting in a public emergency of international concern. The functional importance of Chymotrypsin-like protease (3CLpro) in viral replication and maturation turns it into an attractive target for the development of effective antiviral drugs against SARS and other coronaviruses. At present, there is no standard drug regime nor any vaccine available against the infection. The rapid development and identification of efficient interventions against SARS-CoV-2 remains a major challenge. Based on the available knowledge of closely related coronavirus and their safety profiles, repurposing of existing antiviral drugs and screening of available databases is considered a near term strategic and economic way to contain the SARS-CoV-2 pandemic. Herein, we applied computational drug design methods to identify Chymotrypsin-like protease inhibitors from FDA approved antiviral drugs and our in-house database of natural and drug-like compounds of synthetic origin. As a result three FDA approved drugs (Remdesivir, Saquinavir and Darunavir) and two natural compounds (. flavone and coumarine derivatives) were identified as promising hits. Further, MD simulation and binding free energy calculations were performed to evaluate the dynamic behavior, stability of protein-ligand contact, and binding affinity of the hit compounds. Our results indicate that the identified compounds can inhibit the function of Chymotrypsin-like protease (3CLpro) of Coronavirus. Considering the severity of the spread of coronavirus, the current study is in-line with the concept of finding the new inhibitors against the vital pathway of the corona virus to expedite the process of drug discovery.Communicated by Ramaswamy H. Sarma.