In Silico Studies of Indole Derivatives as Antibacterial Agents
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Objectives: Molecular docking and QSAR studies of indole derivatives as antibacterial agents. Methods: In this study, we used a multiple linear regressions (MLR) approach to construct a 2D quantitative structure activity relationship of 14 reported indole derivatives. It was performed on the reported antibacterial activity data of 14 compounds based on theoretical chemical descriptors to construct statistical models that link structural properties of indole derivatives to antibacterial activity. We have also performed molecular docking studies of same compounds by using Maestro module of Schrodinger. A set the molecular descriptors like hydrophobic, geometric, electronic and topological characters were calculated to represent the structural features of compounds. The conventional antibiotics sultamicillin and ampicillin were not used in the model development since their structures are different from those of the created compounds. Biological activity data was first translated into pMIC values (i.e. –log MIC) and used as a dependent variable in QSAR investigation. Results: Compounds with high electronic energy and dipole moment were effective antibacterial agents against S. aureus, indole derivatives with lower κ 2 values were excellent antibacterial agents against MRSA standard strain, and compounds with lower R value and a high 2 χ v value were effective antibacterial agents against MRSA isolate. Conclusion: Compounds 12 and 2 showed better binding score against penicillin binding protein 2 and penicillin binding protein 2a respectively.
Abstract
Objectives: Molecular docking and QSAR studies of indole derivatives as antibacterial agents. Methods: In this study, we used a multiple linear regressions (MLR) approach to construct a 2D quantitative structure activity relationship of 14 reported indole derivatives. It was performed on the reported antibacterial activity data of 14 compounds based on theoretical chemical descriptors to construct statistical models that link structural properties of indole derivatives to antibacterial activity. We have also performed molecular docking studies of same compounds by using Maestro module of Schrodinger. A set the molecular descriptors like hydrophobic, geometric, electronic and topological characters were calculated to represent the structural features of compounds. The conventional antibiotics sultamicillin and ampicillin were not used in the model development since their structures are different from those of the created compounds. Biological activity data was first translated into pMIC values (i.e. –log MIC) and used as a dependent variable in QSAR investigation. Results: Compounds with high electronic energy and dipole moment were effective antibacterial agents against S. aureus, indole derivatives with lower κ 2 values were excellent antibacterial agents against MRSA standard strain, and compounds with lower R value and a high 2 χ v value were effective antibacterial agents against MRSA isolate. Conclusion: Compounds 12 and 2 showed better binding score against penicillin binding protein 2 and penicillin binding protein 2a respectively.
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