Diabetes is a major health issue that half a billion people affected worldwide. It is a serious, long-term medical condition majorly impacting the lives and well-being of individuals, families, and societies at large. It is amongst the top 10 diseases responsible for the death amongst adults with an expected rise to 10.2% (578 million) by 2030 and 10.9% (700 million) by 2045. The carbohydrates absorbed into the body are hydrolyzed by pancreatic α-amylase and other enzymes, human α-glucosidase. The α-amylase and α-glucosidase are validated therapeutic targets in the treatment of Type II diabetes (T2DM) as they play a vital role in modulating the blood glucose post meal. Herein, we report novel and diverse molecules as potential candidates, with predicted affinity for α-amylase and α-glucosidase. These molecules have been identified via hierarchical multistep docking of small molecules database with the estimated binding free energies. A Glide XP Score cutoff −8.00 kcal/mol was implemented to filter out non potential molecules. Four molecules viz. amb22034702, amb18105639, amb17153304, and amb9760832 have been identified after an exhaustive computational study involving, evaluation of binding interactions and assessment of the pharmacokinetics and toxicity profiles. The in-depth analysis of protein– ligand interactions was performed using a 100ns molecular dynamics (MD) simulation to establish the dynamic stability. Furthermore MM-GBSA based binding free energies were computed for 1000 trajectory snapshots to ascertain the strong binding affinity of these molecules for α-amylase and αglucosidase. The identified molecules can be considered as promising candidates for further drug development through necessary experimental assessments.
A Computational Study of the Protein-Ligand Interactions in CDK2 Inhibitors: Using Quantum Mechanics/Molecular Mechanics Interaction Energy as a Predictor of the Biological Activity
