Small Molecule Inhibitors of Mycobacterium tuberculosis Topoisomerase I Identified by Machine Learning and In Vitro...

Area of Science:

• Medicinal Chemistry
• Computational Biology
• Drug Discovery

Background:

• Tuberculosis (TB), caused by Mycobacterium tuberculosis, remains a leading global infectious cause of death.
• Multi-drug resistant TB presents significant treatment challenges, necessitating novel therapeutic strategies.
• Mycobacterium tuberculosis topoisomerase I is a validated target for developing new anti-TB drugs.

Purpose of the Study:

• To identify novel small molecule inhibitors of Mycobacterium tuberculosis topoisomerase I using a machine learning-based virtual screening approach.
• To explore compounds targeting the divalent ion binding site near the catalytic tyrosine of M. tuberculosis topoisomerase I.

Main Methods:

• Virtual high-throughput screening of over 2 million commercially available compounds using machine learning.
• In vitro topoisomerase I relaxation activity assays to evaluate selected compounds.
• In vitro enzyme assays on analogs of the top-hit compound to understand structure-activity relationships.

Main Results:

• 96 compounds were selected from virtual screening for experimental testing.
• The top-hit compound demonstrated an IC50 of 7 µM in the topoisomerase I relaxation activity assay.
• Analysis of analogs provided insights into the essential molecular scaffold for topoisomerase inhibition.

Conclusions:

• Machine learning-based virtual screening is an effective strategy for identifying novel inhibitors of bacterial topoisomerase I.
• This study successfully identified potential lead compounds for the development of new anti-TB agents.
• The findings support the continued exploration of topoisomerase I as a drug target for combating resistant TB strains.