Using Chemoinformatics, Bioinformatics, and Bioassay to Predict and Explain the Antibacterial Activity of...

Discovering of new and effective antibiotics is a major issue facing scientists today. Luckily, the development of computer science offers new methods to overcome this issue. In this study, a set of computer software was used to predict the antibacterial activity of nonantibiotic Food and Drug Administration (FDA)-approved drugs, and to explain their action by possible binding to well-known bacterial protein targets, along with testing their antibacterial activity against Gram-positive and Gram-negative bacteria. A three-dimensional virtual screening method that relies on chemical and shape similarity was applied using rapid overlay of chemical structures (ROCS) software to select candidate compounds from the FDA-approved drugs database that share similarity with 17 known antibiotics. Then, to check their antibacterial activity, disk diffusion test was applied on Staphylococcus aureus and Escherichia coli. Finally, a protein docking method was applied using HYBRID software to predict the binding of the active candidate to the target receptor of its similar antibiotic. Of the 1,991 drugs that were screened, 34 had been selected and among them 10 drugs showed antibacterial activity, whereby drotaverine and metoclopramide activities were without precedent reports. Furthermore, the docking process predicted that diclofenac, drotaverine, (S)-flurbiprofen, (S)-ibuprofen, and indomethacin could bind to the protein target of their similar antibiotics. Nevertheless, their antibacterial activities are weak compared with those of their similar antibiotics, which can be potentiated further by performing chemical modifications on their structure.