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System for Efficacy and Cytotoxicity Screening of Inhibitors Targeting Intracellular Mycobacterium tuberculosis
Published on: April 5, 2017
Structure-based design of Mycobacterium tuberculosis PptT-ACP complex inhibitors using derivative design and machine
Badriyah Shadid Alotaibi1, Vivek Dhar Dwivedi2,3, Mohammad Amjad Kamal4,5,6
1Department of Pharmaceutical Sciences, College of Pharmacy, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia.
Folia Microbiologica
|June 2, 2025
Summary
This study identified novel drug candidates targeting Mycobacterium tuberculosis (MTB) lipid biosynthesis to combat drug-resistant tuberculosis (TB). Computational methods revealed Compound_36 as a highly stable and potent inhibitor, offering promising avenues for TB treatment.
Area of Science:
- Computational chemistry and drug discovery
- Molecular modeling and simulation
- Machine learning in pharmacology
Background:
- Tuberculosis (TB), caused by Mycobacterium tuberculosis (MTB), is a major global health concern, exacerbated by multidrug-resistant (MDR) strains.
- Inhibition of the PptT-ACP complex, crucial for MTB lipid biosynthesis, presents a viable therapeutic strategy.
Purpose of the Study:
- To identify and computationally optimize inhibitors targeting the PptT-ACP complex in MTB.
- To design and evaluate novel derivatives of a promising lead compound for enhanced anti-TB activity.
Main Methods:
- Virtual screening of FDA-approved compounds to identify initial leads.
- Molecular dynamics (MD) simulations for stability analysis and derivative design.
- Machine learning (Random Forest Regression) for predicting biological activity (pIC50).
Main Results:
- Mk3207 was identified as a promising lead compound.
- Compound_36 demonstrated superior binding stability and predicted potency (pIC50: 25.64) compared to Mk3207 (pIC50: 26.26) and other derivatives.
- MD simulations, FEL, and PCA confirmed the thermodynamic stability of the top compounds.
Conclusions:
- Computational approaches, including derivative design and machine learning, are effective for developing potent MTB inhibitors.
- Compound_36 and other optimized derivatives represent strong candidates for experimental validation against drug-resistant TB.
