Targeting α-amylase enzyme through multi-fold structure-based virtual screening and molecular dynamic simulation

α-Amylase play important role in hydrolyses of α-bonds of large α-linked polysaccharides; thus, it is a potential drug target in diabetes mellites (DM) and its inhibition is one of the therapeutic strategies in DM. With the aim to discover novel and safer therapeutic molecules to combat diabetes, a huge dataset of ∼0.69 billion compounds from ZINC20 database were screened against α-amylase using multi-fold structure-based virtual screening protocol. Based on receptor-based pharmacophore model, docking results, pharmacokinetic profile, molecular interactions with α-amylase, several compounds were retrieved as lead candidates to be further scrutinized in the in vitro assay and in vivo animal testing. Among the selected hits, CP26 exhibited the highest binding free energy in MMGB-SA analysis, followed by CP7 and CP9, which is higher than the binding free energy of acarbose. While CP20 and CP21 showed comparative binding free energy to acarbose. All the selected ligands showed acceptable binding energy range, therefore, several molecules with enhanced efficacy can be designed by derivatizing these molecules. The in-silico results indicates that the selected molecules could serve as potential selective α-amylase inhibitors and can be used for the treatment of diabetes.Communicated by Ramaswamy H. Sarma.

Structure-Based Virtual Screening of Tumor Necrosis Factor-α Inhibitors by Cheminformatics Approaches and Bio-Molecular Simulation

Tumor necrosis factor-α (TNF-α) is a drug target in rheumatoid arthritis and several other auto-immune disorders. TNF-α binds with TNF receptors (TNFR), located on the surface of several immunological cells to exert its effect. Hence, the use of inhibitors that can hinder the complex formation of TNF-α/TNFR can be of medicinal significance. In this study, multiple chem-informatics approaches, including descriptor-based screening, 2D-similarity searching, and pharmacophore modelling were applied to screen new TNF-α inhibitors. Subsequently, multiple-docking protocols were used, and four-fold post-docking results were analyzed by consensus approach. After structure-based virtual screening, seventeen compounds were mutually ranked in top-ranked position by all the docking programs. Those identified hits target TNF-α dimer and effectively block TNF-α/TNFR interface. The predicted pharmacokinetics and physiological properties of the selected hits revealed that, out of seventeen, seven compounds (4, 5, 10, 11, 13-15) possessed excellent ADMET profile. These seven compounds plus three more molecules (7, 8 and 9) were chosen for molecular dynamics simulation studies to probe into ligand-induced structural and dynamic behavior of TNF-α, followed by ligand-TNF-α binding free energy calculation using MM-PBSA. The MM-PBSA calculations revealed that compounds 4, 5, 7 and 9 possess highest affinity for TNF-α; 8, 11, 13-15 exhibited moderate affinities, while compound 10 showed weaker binding affinity with TNF-α. This study provides valuable insights to design more potent and selective inhibitors of TNF-α, that will help to treat inflammatory disorders.