In silico screening of dicoumarols as potential Mur B enzyme inhibitors in Mycobacterium tuberculosis: molecular docking, ADME, QSAR and MD simulations.
Mansi, Pankaj Khanna, Shilpa Yadav, Asmita Singh, Leena Khanna
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引用次数: 0
Abstract
UDP-N-acetylenolpyruvoylglucosamine reductase (Mur B) from Mycobacterium tuberculosis has gathered significant pharmaceutical interest as a pivotal target because of its essential role in bacterial viability. This study employed computational methods to screen and assess the inhibitory potential of dicoumarol derivatives against the Mur B protein. A diverse set of dicoumarols, sourced from PubChem and Zinc database, is subjected to molecular docking, ADME studies, and MD simulations to elucidate interacting modes and stability. A QSAR model was constructed for dicoumarol derivatives based on known inhibitor MIC values against Staphylococcus aureus and Mur B. The four best dicoumarols (CID142097979, CID54716867, CID91962283, CID54705236) aligned well with the model. Subsequently, these dicoumarols were scrutinized via 200 ns MD simulations and MM-PBSA analysis to assess their complex stability with Mur B protein. Various MD simulation parameters such as RMSD, RMSF, Rg, H-bonds, PCA, and FEL were employed. The 200 ns MD simulation analysis outcomes indicated that the Mur B-CID54705236 complex exhibited the highest stability and possessed the binding energy of -59.96 kcal/mol further verifying its stability. The post-dynamic simulation analysis showed four hydrogen bond formations with Ser70, Asn71, Leu72 and Gln137 residues at the active site of Mur B. Overall, these results underscored dicoumarol derivatives as potential Mur B inhibitors and these findings can serve as a basis for further in vitro studies against Mur B protein.
期刊介绍:
The Journal of Biomolecular Structure and Dynamics welcomes manuscripts on biological structure, dynamics, interactions and expression. The Journal is one of the leading publications in high end computational science, atomic structural biology, bioinformatics, virtual drug design, genomics and biological networks.