Computational Screening of Natural Compounds for Identification of Potential Anti-Cancer Agents Targeting MCM7 Protein.

Minichromosome maintenance complex component 7 (MCM7) is involved in replicative licensing and the synthesis of DNA, and its overexpression is a fascinating biomarker for various cancer types. There is currently no effective agent that can prevent the development of cancer caused by the MCM7 protein. However, on the molecular level, inhibiting MCM7 lowers cancer-related cellular growth. With this purpose, this study screened 452 biogenic compounds extracted from the UEFS Natural Products dataset against MCM protein by using the in silico art of technique. The hit compounds UEFS99, UEFS137, and UEFS428 showed good binding with the MCM7 protein with binding energy values of -9.95, -8.92, and -8.71 kcal/mol, which was comparatively higher than that of the control compound ciprofloxacin (-6.50). The hit (UEFS99) with the minimum binding energy was picked for molecular dynamics (MD) simulation investigation, and it demonstrated stability at 30 ns. Computational prediction of physicochemical property evaluation revealed that these hits are non-toxic and have good drug-likeness features. It is suggested that hit compounds UEFS99, UEFS137, and UEFS428 pave the way for further bench work validation in novel inhibitor development against MCM7 to fight the cancers.

Identification of 3-((1-(Benzyl(2-hydroxy-2-phenylethyl)amino)-1-oxo-3-phenylpropan-2-yl)carbamoyl)pyrazine-2-carboxylic Acid as a Potential Inhibitor of Non-Nucleosidase Reverse Transcriptase Inhibitors through InSilico Ligand- and Structure-Based Approaches.

Non-nucleosidase reverse transcriptase inhibitors (NNRTIs) are highly promising agents for use in highly effective antiretroviral therapy. We implemented a rational approach for the identification of promising NNRTIs based on the validated ligand- and structure-based approaches. In view of our state-of-the-art techniques in drug design and discovery utilizing multiple modeling approaches, we report here, for the first time, quantitative pharmacophore modeling (HypoGen), docking, and in-house database screening approaches in the identification of potential NNRTIs. The validated pharmacophore model with three hydrophobic groups, one aromatic ring group, and a hydrogen-bond acceptor explains the interactions at the active site by the inhibitors. The model was implemented in pharmacophore-based virtual screening (in-house and commercially available databases) and molecular docking for prioritizing the potential compounds as NNRTI. The identified leads are in good corroboration with binding affinities and interactions as compared to standard ligands. The model can be utilized for designing and identifying the potential leads in the area of NNRTIs.