On January 2020, a new coronavirus (officially named SARS-CoV-2) was associated with alarming outbreak of a pneumonia-like illness, which was later named by the WHO as COVID-19, originating from Wuhan City, China. Although many clinical studies involving antiviral and immunomodulatory drug treatments for SARS-CoV-2 all without reported results, no approved drugs have been found to effectively inhibit the virus so far. Full genome sequencing of the virus was done, and uploaded to be freely available for the world scientists to explore. A promising target for SARS-CoV-2 drug design is a chymotrypsin-like cysteine protease (3CLpro), a main protease responsible for the replication and maturation of functional proteins in the life cycle of the SARS coronavirus. Here we aim to explore SARS-CoV-2 3CLpro as possible drug targets based on ligand- protein interactions. In addition, ADME properties of the ligands were also analyzed to predict their drug likeliness. The results revealed Out of 9 ligands, 8 ligands (JFM, X77, RZG, HWH, T8A, 0EN, PEPTIDE and DMS) showed best ADME properties. These findings suggest that these ligands can be used as potential molecules for developing potent inhibitors against SARS-CoV-2 3CLpro, which could be helpful in inhibiting the propagation of the COVID-19. Furthermore, 10 potential amino acids residues were recognized as potential drug binding site (THR25, HIS41, GLY143, SER144, CYS145, MET165, GLU166, GLN189, ASP295 and ARG298). All those amino acid residues were subjected to missense SNP analysis were recognized to affect the structure and function of the protein. These characteristics provide them the promising to be target sites for the fresh generation inhibitors to work with and overcome drug resistance. These findings would be beneficial for the drug development for inhibiting SARS-CoV-2 3CLpro hence assisting the pharmacogenomics effort to manage the infection. of SARS-CoV-2.

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Background: The AGT gene is gene responsible for regulation of protein called angiotensinogen which regulates blood pressure and balances fluids in the body. Hypertension happens due to many causes one of this is the defect in AGT gene. Hypertension usually has no symptoms. However, it is a major risk factor for heart diseases, stroke, kidney failure, and eye problems. Objectives: in this study we use software to analyze the gene using different software and represented statistically and to detect the SNPs that can cause the disease. Material and Method: In this analysis using many software tools that can analyze the nsSNPs retrieved from NCBI website. These software include SIFT, I-mutant, Polyphen-2, PHD SNP and SNP& Go, Projecthop and GeneMANIA. Results: The study showed that from 172 nsSNPs only 46 nsSNPs were deleterious while 126 were tolerated using SIFT. Two were benign, 11 were possibly damaging and 33 were probably damaging by Polyphen-2. Using Provean, 19 nsSNPs were neutral and 27 were deleterious. For PHD-SNP software 20 nsSNPs were disease related and 18 were neutral. Also SNPs were checked using SNP & Go software that showed 32 neutral nsSNPs and 14 nsSNPs were disease associated variants. Using I-Mutant software 13 nsSNPs increase the stability of the protein and 33 decrease the protein stability. Conclusions: In conclusion, extensive functional and structural analyses are carried out to predict potentially damaging and deleterious nsSNPs of AGT gene using bioinformatics and computational methods. In the study, 14 high confidence damaging nsSNPs are identified from 172 nsSNPs. Although bioinformatics tools have their limitations, the results from the present study may be convenient in future for further population based research activities and towards development of accuracy medicines.