Virtual Screening of Novel Benzothiozinone Derivatives to Predict Potential Inhibitors of Mycobacterium Tuberculosis Kinases 2D-QSAR, Molecular Docking, MM-PBSA Dynamics Simulations, and ADMET Properties

Abstract

Mycobacterium tuberculosis, the infectious agent behind tuberculosis (TB), underscores the significance of targeting enzymes such as arabinosyltransferases in drug development efforts. Benzothiozinone derivatives, which have been assessed for their effectiveness against TB, present a promising avenue for treatment. Utilizing a high virtual screening quantitative structure-activity relationship (QSAR-VS), a set of forty Benzothiozinone (C1-C40) compounds were investigated to build a robust model with satisfactory performance metrics (<i>R</i>² = 0.82, <i>R</i>²<i>_adj</i> = 0.78, <i>N_test</i> = 10, <i>R</i>²<i>_test</i> = 0.70). This model enabled the creation of databases containing new derivatives for screening drug-like properties and predicting MIC activity in TB treatment. The best-scoring compounds were screened by molecular docking with Mycobacterium tuberculosis kinases A and B (PDB code: 6B2P) and validated by molecular dynamics simulations to elucidate the most stable drug-protein interactions. Additionally, the MM-PBSA analysis shows that the strongest binding occurs in complexes X3, X4, and X6 with Δ<i>G_bind</i> values of -8.2, -15.3, and -12.0 kcal/mol, respectively. Our in silico study aims to prospect these new anti-tubercular drugs and their potential development through perspective in vitro and in vivo assays.