Computational Approaches to Identify Molecules Binding to Mycobacterium tuberculosis KasA

Area of Science:

• Biochemistry
• Medicinal Chemistry
• Drug Discovery

Background:

• Tuberculosis, caused by Mycobacterium tuberculosis (Mtb), remains a significant global health threat, causing millions of deaths annually.
• Mycolic acid synthesis, crucial for Mtb survival, involves the enzyme β-ketoacyl-ACP synthase (KasA).
• Previous structural studies of KasA provided a foundation for computational drug design.

Purpose of the Study:

• To identify novel small molecules that inhibit KasA activity using computational methods.
• To validate the efficacy of identified compounds through biophysical assays.
• To explore existing drugs as potential leads for tuberculosis treatment.

Main Methods:

• Structure-based pharmacophore modeling and molecular docking studies were performed.
• Machine learning approaches were employed using Assay Central for compound identification.
• Nanoscale differential scanning fluorimetry and microscale thermophoresis were used for experimental validation.

Main Results:

• Computational studies identified several potential KasA inhibitors.
• Experimental validation confirmed binding of identified molecules to KasA.
• The FDA-approved drugs sildenafil and flubendazole were identified as binders with K_d values in the 30-40 μM range.

Conclusions:

• Computational and experimental approaches successfully identified novel small molecules targeting KasA.
• Existing drugs like sildenafil and flubendazole show potential for repurposing in tuberculosis treatment.
• These findings provide a basis for further optimization of KasA inhibitors for anti-tuberculosis drug development.