Abstract
Twenty-four cannabinoids active against MRSA SA1199B and XU212 were optimized at WB97XD/6-31G(d,p), and several molecular descriptors were obtained. Using a multiple linear regression method, several mathematical models with statistical significance were obtained. The robustness of the models was validated, employing the leave-one-out cross-validation and Y-scrambling methods. The entire data set was docked against penicillin-binding protein, iso-tyrosyl tRNA synthetase, and DNA gyrase. The most active cannabinoids had high affinity to penicillin-binding protein (PBP), whereas the least active compounds had low affinities for all of the targets. Among the cannabinoid compounds, Cannabinoid 2 was highlighted due to its suitable combination of both antimicrobial activity and higher scoring values against the selected target; therefore, its docking performance was compared to that of oxacillin, a commercial PBP inhibitor. The 2D figures reveal that both compounds hit the protein in the active site with a similar type of molecular interaction, where the hydroxyl groups in the aromatic ring of cannabinoids play a pivotal role in the biological activity. These results provide some evidence that the anti-Staphylococcus aureus activity of these cannabinoids may be related to the inhibition of the PBP protein; besides, the robustness of the models along with the docking and Quantitative Structure–Activity Relationship (QSAR) results allow the proposal of three new compounds; the predicted activity combined with the scoring values against PBP should encourage future synthesis and experimental testing.
Highlights
Staphylococcus aureus is known as the principal cause of human bacterial infections around the world
The structures of twenty-four cannabinoid compounds isolated from Cannabis sativa and tested in vitro against several methicillin-resistantStaphylococcus aureus (MRSA) Staphylococcus aureus were retrieved from Appenddino et al along with their activity reported as minimum inhibitory concentrations, MICs
Using the different multiple regression methods, three mathematical models that describe the quantitative structure–antimicrobial activity relationship were found for the two Staphylococcus aureus strains, SA-1199B and XU212
Summary
Staphylococcus aureus is known as the principal cause of human bacterial infections around the world. Even though the infections are not symptomatic in many cases, in immunosuppressed patients, this bacterial infection can have lethal consequences such as pneumonia, meningitis, or septicemias. Staphylococcus aureus presents high morbidity in clinical infections, mainly in less developed countries where access to medicines is complicated [1]. The problem becomes more serious considering the appearance of methicillin-resistant. Staphylococcus aureus (MRSA) strains every year around the planet with a high rate of resistance [2]. Despite the urgent need for new antimicrobial agents, the discovery rates are low; only one class of antibiotics has been introduced in the last 30 years [3,4]. Searching, designing, Crystals 2020, 10, 692; doi:10.3390/cryst10080692 www.mdpi.com/journal/crystals
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