ABSTRACT
Severe acute respiratory syndrome corona virus-2 (SARS-CoV-2) main protease (M-Pro) is recognized as an important therapeutic target protein in the drug development for COVID-19. To date, clinical trials of many vaccine and other viral protease inhibitors (PI) are currently under investigation. Undoubtedly, there are chances of possible side effects and ineffectiveness. Thus, the search for natural bio-active molecules is of great interest that will exert antiviral activity as well as have least chances of toxicity. Fungi are considered as bio-enriched source of producing antiviral compounds. This study is focused on identifying potential fungal derived antiviral molecules with good binding affinity against SARS-CoV-2 M-Pro using molecular docking. Semicochliodinol B was identified as the best lead molecule with higher binding affinity (-8.9kcal/mol) as compared to the co-crystalized ligand (-8.5kcal/mol). The results of molecular docking confirm the hydrogen bond interaction of Semicochliodinol B with Glu166 and Asn142 as well as hydrophobic interactions with 20 amino acid residues of SARS-CoV-2 M-Pro. Semicochliodinol B also exhibited good binding affinity against SARS-CoV M-Pro and Middle east respiratory syndrome-related corona virus (MERS-CoV M-Pro), suggesting its broad-spectrum activity. Druglikeness, Absorption, distribution, metabolism, excretion (ADME) and toxicity studies also directed that Semicochliodinol B may become a promising drug candidate and thus it can be further investigated as a potential inhibitor of SARS-CoV-2 M-Pro. Virtual screening was done on fungal-derived antivirals to identify SARS-CoV-2 main protease inhibitors. Semicochliodinol B was identified as a promising lead candidate considering binding affinity and interactions obtained in molecular docking. Broad spectrum antiviral activity of Semicochliodinol B is reported. ADME and toxicity study suggested druglikeness of selected lead Semicochliodinol B.
ABSTRACT
COVID-19 is an exceptionally infectious illness that has affected the general public. Displaying such sicknesses can be critical in the expectation of their effect. The assessment of the COVID patient and its correlation with sequential age is a significant task in the clinic, however, with the help of machine learning algorithms we can effectively model the expectations. Getting factual expectation can be tedious as movements can be inclined to intra-rater changeability, the utilization of strategies that can computerize it, similar to machine learning techniques, is of value. The objective of this part is to introduce the strainer chart, patterns and holes in the examination identified with age and sex assessment that utilize machine learning techniques and also provides the COVID issues with various problems in combination with sieve methods. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.