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

Area of Science:

• Biochemistry
• Pharmacology
• Computational Chemistry

Background:

• Alpha-amylase is a key enzyme in carbohydrate digestion and a therapeutic target for managing diabetes mellitus (DM).
• Inhibiting alpha-amylase activity can help control postprandial hyperglycemia, a hallmark of diabetes.

Purpose of the Study:

• To discover novel and safer therapeutic compounds for diabetes by screening a large chemical library against alpha-amylase.
• To identify potent alpha-amylase inhibitors using structure-based virtual screening.

Main Methods:

• Utilized a multi-fold structure-based virtual screening protocol on approximately 0.69 billion compounds from the ZINC20 database.
• Employed receptor-based pharmacophore modeling, molecular docking, pharmacokinetic profiling, and MMGB/SA analysis to evaluate compound efficacy and interactions with alpha-amylase.

Main Results:

• Identified several lead compounds, including CP26, CP7, and CP9, which demonstrated higher binding free energy than acarbose (a known inhibitor).
• Compounds CP20 and CP21 showed binding free energy comparable to acarbose.
• All selected compounds exhibited acceptable binding energy, suggesting potential for further optimization.

Conclusions:

• The in-silico findings indicate that the identified molecules are potential selective alpha-amylase inhibitors.
• These compounds represent promising candidates for the development of new therapeutic agents for diabetes mellitus.
• Further in vitro and in vivo studies are warranted to validate the therapeutic potential of these identified inhibitors.