Identification of a promising inhibitor from Illicium verum (star anise) against the main protease of SARS-CoV-2: insights from the computational study.

SARS-CoV-2, the causing agent of coronavirus disease (COVID-19), first broke out in Wuhan and rapidly spread worldwide, resulting in a global health emergency. The lack of specific drugs against the coronavirus has made its spread challenging to control. The main protease (Mpro) is a key enzyme of SARS-CoV-2 used as a key target in drug discovery against the coronavirus. Medicines derived from plant phytoconstituents have been widely exploited to treat various diseases. The present study has evaluated the potential of Illicium verum (star anise) phytoconstituents against Mpro by implementing a computational approach. We performed molecular docking and molecular dynamics simulation study with a set of 60 compounds to identify their potential to inhibit the main protease (Mpro) of SARS-CoV-2. DFT study and post dynamics free energy calculations were also performed to strengthen the findings. The identified four compounds by docking study exhibited the highest potential compared to other selected phytoconstituents. Further, density functional theory (DFT) calculation, molecular dynamics simulation and post dynamics MM-GBSA energy calculation predicted Verimol-G as a potential compound, which formed stable interactions through the catalytic dyad residues. The HOMO orbital energy (-0.250038) from DFT and the post dynamics binding free energy calculation (-73.33 Kcal/mol) correlate, suggesting Verimol-G is the best inhibitor compared to the other phytoconstituents. This compound also complies with the ADME properties of drug likeliness. Thus, based on a computational study, we suggest that Verimol G may be developed as a potential inhibitor against the main protease to combat COVID-19.Communicated by Ramaswamy H. Sarma.

Structural and functional insights into the spike protein mutations of emerging SARS-CoV-2 variants.

Since the emergence of the first case of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus (SARS-CoV-2), the viral genome has constantly undergone rapid mutations for better adaptation in the host system. These newer mutations have given rise to several lineages/ variants of the virus that have resulted in high transmission and virulence rates compared to the previously circulating variants. Owing to this, the overall caseload and related mortality have tremendously increased globally to > 233 million infections and > 4.7 million deaths as of Sept. 28th, 2021. SARS-CoV-2, Spike (S) protein binds to host cells by recognizing human angiotensin-converting enzyme 2 (hACE2) receptor. The viral S protein contains S1 and S2 domains that constitute the binding and fusion machinery, respectively. Structural analysis of viral S protein reveals that the virus undergoes conformational flexibility and dynamicity to interact with the hACE2 receptor. The SARS-CoV-2 variants and mutations might be associated with affecting the conformational plasticity of S protein, potentially linked to its altered affinity, infectivity, and immunogenicity. This review focuses on the current circulating variants of SARS-CoV-2 and the structure-function analysis of key S protein mutations linked with increased affinity, higher infectivity, enhanced transmission rates, and immune escape against this infection.

Identification of bioactive molecule from Withania somnifera (Ashwagandha) as SARS-CoV-2 main protease inhibitor.

SARS-CoV-2 is the causative agent of COVID-19 and has been declared as pandemic disease by World Health Organization. Lack of targeted therapeutics and vaccines for COVID-2019 have triggered the scientific community to develop new vaccines or drugs against this novel virus. Many synthetic compounds and antimalarial drugs are undergoing clinical trials. The traditional medical practitioners widely use Indian medicinal plant Withania somnifera (Ashwagandha) natural constituents, called withanolides for curing various diseases. The main protease (Mpro) of SARS-CoV-2 plays a vital role in disease propagation by processing the polyproteins which are required for its replication. Hence, it denotes a significant target for drug discovery. In the present study, we evaluate the potential of 40 natural chemical constituents of Ashwagandha to explore a possible inhibitor against main protease of SARS-CoV-2 by adopting the computational approach. The docking study revealed that four constituents of Ashwagandha; Withanoside II (-11.30 Kcal/mol), Withanoside IV (-11.02 Kcal/mol), Withanoside V (-8.96 Kcal/mol) and Sitoindoside IX (-8.37 Kcal/mol) exhibited the highest docking energy among the selected natural constituents. Further, MD simulation study of 100 ns predicts Withanoside V possess strong binding affinity and hydrogen-bonding interactions with the protein active site and indicates its stability in the active site. The binding free energy score also correlates with the highest score of -87.01 ± 5.01 Kcal/mol as compared to other selected compounds. In conclusion, our study suggests that Withanoside V in Ashwagandha may be serve as a potential inhibitor against Mpro of SARS-CoV-2 to combat COVID-19 and may have an antiviral effect on nCoV.Communicated by Ramaswamy H. Sarma.

Computational Intelligence in Drug Repurposing for COVID-19

Severe acute respiratory syndrome is a viral respiratory infection known as COVID-19, which is caused by a novel coronavirus, called SARS-associated coronavirus-2 (SARS-CoV-2). Considering it as an international concern, WHO declared COVID-19 as the “sixth public health emergency” and has termed it as ‘pandemic”. Currently, no specific drugs are available, and studies about COVID-19 treatment are still in progress. As the world is facing a major challenge in trying to adapt and defend itself against this new pandemic disease, computational intelligence offers a new hope that a cure to this disease might be developed faster than ever before. Many targets for the design of drugs have been already identified, and studies are in progress to explore these potential targets. Computational approaches like virtual screening, molecular docking, machine learning, deep learning and natural language processing (NLP) play a vital role in drug repurposing studies. Repurposing drugs involves discovering novel drug-target interactions and their use against the treatment of different diseases. This strategy has regained significant interest to develop a drug against the COVID-19 considering this pandemic scenario, and offers the best chance to identify potent drugs from the list of approved drugs. Various research efforts are currently focusing on the identification of existing drugs which might be useful in mitigating the infection and some compounds namely favipiravir, remdesivir, lopinavir, hydroxychloroquine etc. are in the final stage of human testing. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.