Angiotensin-converting enzyme 2 (ACE2), also known as peptidyl-dipeptidase A, belongs to the dipeptidyl carboxydipeptidases family has emerged as a potential antiviral drug target against SARS-CoV-2. Most of the ACE2 inhibitors discovered until now are chemical synthesis; suffer from many limitations related to stability and adverse side effects. However, natural, and selective ACE2 inhibitors that possess strong stability and low side effects can be replaced instead of those chemicals’ inhibitors. To envisage structurally diverse natural entities as an ACE2 inhibitor with better efficacy, a structure-based-pharmacophore model (SBPM) was developed and validated by 20 known selective inhibitors with their correspondence 1166 decoy compounds. The validated SBPM has excellent goodness of hit score and good predictive ability, which has been appointed as a query model for further screening of 11,295 natural compounds. The resultant 23 hits compounds with pharmacophore fit score 75.31 to 78.81 were optimized using in-silico ADMET and molecular docking analysis. Four potential natural inhibitory molecules namely D-DOPA (Amb17613565), L-Saccharopine (Amb6600091), D-Phenylalanine (Amb3940754), and L-Mimosine (Amb21855906) have been selected based onbinding affinity (−7.5, −7.1, −7.1, and −7.0 kcal/mol), respectively. Moreover, 250 ns molecular dynamics (MD) simulations confirmed the structural stability of the ligands within the protein. Additionally, MM/GBSA approach also used to support the stability of molecules to the binding site of the protein that also confirm the stability of the selected four natural compounds. The virtual screening strategy used in this study demonstrated four natural compounds that can be utilized for designing a future class of potential natural ACE2 inhibitor that will block the spike (S) protein dependent entry of SARS-CoV-2 into the host cell.
Renal cell carcinoma (RCC) is a type of cancer that develops in the renal epithelium of the kidney. It is responsible for approximately 3% of adult malignancies, and 90-95% of neoplasms originate from the kidney. Advances in tumor diagnosis, innovative immune therapeutics, and checkpoint inhibitors-based treatment options improved the survival rate of patients with RCC accompanied by different risk factors. RCC Patients with diabetes, hepatitis C virus (HCV), or obesity (OB) may have a comorbidity, and finding the risk factor for better clinical treatment is an urgent issue. Therefore, the study focused on network-based gene expression analysis approaches to learning the impact of RCC on other comorbidities associated with the disease. The study found critical genetic factors and signal transduction pathways that share pathophysiology and commonly use dysregulated genes of the illness. Initially, the study identified 385 upregulated genes and 338 down-regulated genes involved with RCC. OB, chronic kidney disease (CKD), type 2 diabetes (T2D), and HCV significantly shared 28, 14, 5, and 3 genes, respectively. RCC shared one downregulated gene versican (VCAN) with OB and HCV and one downregulated gene oxidase homolog 2 (LOXL2) with OB and CKD. Interestingly, most of the shared pathways were linked with metabolism. The study also identified six prospective biomarkers, signaling pathways, and numerous critical regulatory and associated drug candidates for the disease. We believe that the discovery will help explain these diseases' complicated interplay and aid in developing novel therapeutic targets and drug candidates.
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