Comparative Computational Analysis of Dirithromycin and Azithromycin in Search for a Potent Drug against COVID-19 caused by SARS-CoV-2: Evidence from molecular docking and dynamic simulation.

Due to the emergency and uncontrolled situation caused by the COVID-19 pandemic that arising in the entire world, it is necessary to choose available drugs that can inhibit or prevent the disease. Therefore, the repurposing of the commercial antibiotic, dirithromycin has been screened for the first time against fifteen receptors and compared to the azithromycin using a molecular docking approach to identify possible SARS-CoV-2 inhibitors. Our docking results showed that dirithromycin fit significantly in the Furin catalytic pocket having the lowest binding score (-9.9 Kcal/mol) with respect to azithromycin (-9.4 Kcal/mol) and can interact and block both Asp154 and Ser368 residues by Van der Walls interaction as well as bound to His194 and Ser368 residues via hydrogen bonds. Good results were also obtained with the Tmprss-2 receptor. A Molecular Dynamic simulation was assessed to confirm this interaction. Additionally, detailed receptor-ligand interactions with SARS-CoV-2 and pro-inflammatory mediators were investigated suggesting more target information with interesting results. The findings of this study are very efficient and provide a basis for the development of dirithromycin for clinical trial applications to be efficient in treating SARS-CoV-2 infections.

In silico screening of TMPRSS2 SNPs that affect its binding with SARS-CoV2 spike protein and directly involved in the interaction affinity changes

In this paper, we used in silico analysis to shed light on the possible interaction between TMPRSS2 and SARS-CoV2 spike (S) protein by examining the role of TMPRSS2 single nucleotide polymorphisms (SNPs) in relation with susceptibility and inter-individual variability of SARS-CoV2 infection. First, we used molecular docking of human TMPRSS2 protein to predict the binding site of TMPRSS2, especially the TMPRSS2 link loops, in order to assess the effect TMPRSS2 SNPs. The latter lead to missense variants on the interaction between TMPRSS2 and SARS-CoV2 S protein. In a second step, we further refine our analysis by performing a structure-function analysis of the complexes using PyMol software, and finally by MD simulations to validate the as-obtained results. Our findings show that 17 SNPs among the 692 natural TMPRSS2 coding variants are in positions to influence the binding of TMPRSS2 with the viral S protein. All of them give more important interaction energy as assessed by docking. Among the 17 SNPs, four missense variants E389A, K392Q, T393S and Q438E lead to "directly increasing" the interaction affinity and 2 missense variants R470I and Y416C cause it "directly decreasing". The R470I and Y416C present in African and American population, respectively. While the other 4 SNP variants (E389A; K392Q; T393S and Q438E) are present only in the European population, which could link the viral infection susceptibility to demographic, geographic and genetic factors.