Shape-based virtual screening, docking, and molecular dynamics simulations to identify Mtb-ASADH inhibitors

Area of Science:

• Computational chemistry and drug discovery
• Biochemistry and enzymology
• Microbiology and infectious diseases

Background:

• Aspartate β-semialdehyde dehydrogenase (ASADH) is crucial for essential amino acid biosynthesis in microbes.
• ASADH inhibition is a promising strategy for developing new drugs against Mycobacterium tuberculosis (Mtb).
• Understanding molecular interactions is key to designing effective ASADH inhibitors.

Purpose of the Study:

• To identify potent inhibitors of Mtb-ASADH using in silico methods.
• To understand the molecular recognition interactions of a lead compound (β-AFP) with Mtb-ASADH.
• To screen virtual compound databases for novel Mtb-ASADH inhibitors.

Main Methods:

• Molecular docking and molecular dynamics simulations were used to analyze ligand-enzyme interactions.
• A shape-based virtual screening approach was employed using ZINC and NCI databases.
• Hits were filtered using ADME/toxicity predictions and further validated by docking and MD simulations.

Main Results:

• Key amino acids (Arg99, Glu224, Cys130, Arg249, His256) were identified as critical for Mtb-ASADH inhibition, particularly through H-bonding with Arg99 and Arg249.
• Virtual screening identified 20 compounds with characteristics suitable for competitive inhibition, showing at least three H-bonding interactions with key residues.
• MD simulations confirmed the binding stability and key interactions of two selected compounds (NSC4862 and ZINC02534243) with Mtb-ASADH.

Conclusions:

• The study successfully identified potential novel inhibitors of Mtb-ASADH through a validated in silico approach.
• The identified compounds, exhibiting specific H-bonding patterns, can serve as a basis for developing more potent anti-tubercular agents.
• This computational strategy provides a framework for future drug discovery efforts targeting ASADH enzymes.