Back

Computational screening of T-muurolol for an alternative antibacterial solution against Staphylococcus aureus infections: A state-of-the-art phytochemical-based drug discovery approach

Bhattacharya, S.; Khanra, P. K.; Dutta, A.; GUPTA, N.; Tehrani, Z. A.; Severova, L.; Sredl, K.; Dvorak, M.; Cusimamani, E. F.; Cusimamani, E. F.

2024-06-10 pharmacology and toxicology
10.1101/2024.06.07.597877 bioRxiv
Show abstract

Staphylococcus aureus infections present a significant threat to the global healthcare system. The increasing resistance to existing antibiotics and their limited efficacy underscores the urgent need to identify new antibacterial agents with low toxicity to effectively combat various S. aureus infections. Hence, in this study, we have screened T-muurolol for possible interactions with several S. aureus-specific bacterial proteins to establish its potential as an alternative antibacterial agent. Based on binding affinity and interactions with amino acids T-muurolol was identified as a potential inhibitor of S. aureus lipase, dihydrofolate reductase, penicillin-binding protein 2a, D-Ala:D-Ala ligase, and RPP TetM, which indicates its potentiality against S. aureus and its multi-drug resistant strains. Also, T-muurolol exhibited good antioxidant and anti-inflammatory activity by showing strong binding interactions with FAD-dependent NAD(P)H oxidase, and cyclooxygenase-2. Consequently, MD simulation and recalculating binding free energies elucidated its binding interaction stability with targeted proteins. Furthermore, quantum chemical structure analysis based on density functional theory (DFT) depicted a higher EHOMO-LUMO energy gap with a lower chemical potential index, and moderate electrophilicity suggests its chemical hardness and stability and less polarizability and reactivity. Additionally, pharmacological parameters based on ADMET, Lipinskis rules, and bioactivity score validated it as a promising drug candidate with high activity toward ion channel modulators, nuclear receptor ligands, and enzyme inhibitors. In conclusion, the current findings suggest T-muurolol as a promising alternative antibacterial agent that might be a potential phytochemical-based drug against S. aureus. This study also suggests further clinical research before human application. Author SummaryStaphylococcus aureus significantly contributes to human mortality, with over 1 million deaths annually accredited to its infections. At the same time, antimicrobial resistance (AMR) is a critical public health issue, responsible for an estimated 1.27 million deaths globally in 2019. The overuse and abuse of antimicrobials in both human and veterinary medicine are primary drivers of AMR, complicating the treatment of infections and increasing the risks associated with surgeries and other medical events. Despite the availability of antimicrobials such as methicillin, vancomycin, daptomycin, and linezolid, the emergence of multidrug-resistant S. aureus poses a formidable challenge to effective treatment. Due to the limited efficacy and increasing resilience to current antibiotics, there is an urgent need to discover new and effective antibacterial drugs against S. aureus. Since time immemorial, phytochemicals have been valued for their rich biological properties and safety in treating bacterial infections. In this study, we have computationally investigated T-muurolol as a potential alternative antibacterial agent. Our molecular docking and simulation approaches provide insights into the interactions of T-muurolol as an inhibitor of S. aureus-specific bacterial proteins. Additionally, pharmacokinetic and quantum chemical structure analyses offer valuable information about T-muurolols potential as a drug candidate, supporting its further development as an antibacterial agent.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.