Antiaging, antioxidant flavonoids; synthesis, antimicrobial screening as well as 3D QSAR CoMFA models for the prediction of biological activity

Lumbiny, Bilkis Jahan; Zhang, Hui; Islam, Mahfuzul

doi:10.3329/jasbs.v39i2.17856

Cited by 8 publications

(4 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These factors influencing the antibacterial activity of essential oil chemotypes are summarized and analyzed in the two reviews of Radulovic et al (2013) and Gyawali and Ibrahim (2014). The existing literature concerning QSARs for the prediction of the antibacterial activity of phenolic compounds of slightly higher molecular weight such as phenolic acids, flavonoids, stilbenes, coumarins and quinones still remains limited (Cushnie and Lamb, 2011; Daglia, 2012; Duggirala et al, 2014; Li et al, 2014; Lumbiny et al, 2014; Upadhyay et al, 2014; Fang et al, 2016). Although a QSAR study has not been conducted regarding the antibacterial effect of polyphenols (Larif et al, 2015), descriptors related to the number of hydroxyl functions, electronic effects and lipophilicity are the most common in QSAR models involving polyphenols.…”

Section: Introductionmentioning

confidence: 99%

Antibacterial Properties of Polyphenols: Characterization and QSAR (Quantitative Structure–Activity Relationship) Models

et al. 2019

View full text Add to dashboard Cite

Besides their established antioxidant activity, many phenolic compounds may exhibit significant antibacterial activity. Here, the effect of a large dataset of 35 polyphenols on the growth of 6 foodborne pathogenic or food-spoiling bacterial strains, three Gram-positive ones ( Staphylococcus aureu s, Bacillus subtilis , and Listeria monocytogenes ) and three Gram-negative ones ( Escherichia coli, Pseudomonas aeruginosa , and Salmonella Enteritidis), have been characterized. As expected, the effects of phenolic compounds were highly heterogeneous ranging from bacterial growth stimulation to antibacterial activity and depended on bacterial strains. The effect on bacterial growth of each of the polyphenols was expressed as relative Bacterial Load Difference (BLD) between a culture with and without (control) polyphenols at a 1 g L −1 concentration after 24 h incubation at 37°C. Reliable Quantitative Structure-Activity Relationship (QSAR) models were developed (regardless of polyphenol class or the mechanism of action involved) to predict BLD for E. coli, S . Enteritidis, S. aureu s, and B. subtilis , unlike for L. monocytogenes and P. aeruginosa . L. monocytogenes was generally sensitive to polyphenols whereas P. aeruginosa was not. No satisfactory models predicting the BLD of P. aeruginosa and L. monocytogenes were obtained due to their specific and quite constant behavior toward polyphenols. The main descriptors involved in reliable QSAR models were the lipophilicity and the electronic and charge properties of the polyphenols. The models developed for the two Gram-negative bacteria ( E. coli, S . Enteritidis) were comparable suggesting similar mechanisms of toxic action. This was not clearly observed for the two Gram-positive bacteria ( S. aureu s and B. subtilis ). Interestingly, a preliminary evaluation by Microbial Adhesion To Solvents (MATS) measurements of surface properties of the two Gram-negative bacteria for which QSAR models were based on similar physico-chemical descriptors, revealed that MATS results were also quite similar. Moreover, the MATS results of the two Gram-positive bacterial strains S. aureus and B. subtilis for which QSARs were not based on similar physico-chemical descriptors also strongly differed. These observations suggest that the antibacterial activity of most of polyphenols likely depends on interactions between polyphenols and bacterial cells surface, although the surface properties of the bacterial strains should be further investigated with other techniques than MATS.

show abstract

Section: Introductionmentioning

confidence: 99%

Antibacterial Properties of Polyphenols: Characterization and QSAR (Quantitative Structure–Activity Relationship) Models

et al. 2019

View full text Add to dashboard Cite

show abstract

“…3D-QSAR studies, mainly focus on how changes in 3D structural features such as electrostatic distribution, hydrophobic distribution 6 , hydrogen bond (H-bond) forming ability and orientation 11 of chemical groups affect biological activity 12 . 3D-QSAR eliminates problems such as limitation in the prediction of stereochemistry of tested dataset and lack of recognition ability in search of active compounds 13 suffered by the classical 2D-QSAR studies.…”

mentioning

confidence: 99%

3D-QSAR and docking studies of flavonoids as potent Escherichia coli inhibitors

Fang

Zang

et al. 2016

Sci Rep

View full text Add to dashboard Cite

Flavonoids are potential antibacterial agents. However, key substituents and mechanism for their antibacterial activity have not been fully investigated. The quantitative structure-activity relationship (QSAR) and molecular docking of flavonoids relating to potent anti-Escherichia coli agents were investigated. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were developed by using the pIC50 values of flavonoids. The cross-validated coefficient (q2) values for CoMFA (0.743) and for CoMSIA (0.708) were achieved, illustrating high predictive capabilities. Selected descriptors for the CoMFA model were ClogP (logarithm of the octanol/water partition coefficient), steric and electrostatic fields, while, ClogP, electrostatic and hydrogen bond donor fields were used for the CoMSIA model. Molecular docking results confirmed that half of the tested flavonoids inhibited DNA gyrase B (GyrB) by interacting with adenosine-triphosphate (ATP) pocket in a same orientation. Polymethoxyl flavones, flavonoid glycosides, isoflavonoids changed their orientation, resulting in a decrease of inhibitory activity. Moreover, docking results showed that 3-hydroxyl, 5-hydroxyl, 7-hydroxyl and 4-carbonyl groups were found to be crucial active substituents of flavonoids by interacting with key residues of GyrB, which were in agreement with the QSAR study results. These results provide valuable information for structure requirements of flavonoids as antibacterial agents.

show abstract

“…[14][15][16] Among these, 3D-QSAR can not only examine the effects of different conformations or conformations of receptor-ligand interactions, but also includes richer physicochemical connotations. 17 The most widely used methods in 3D-QSAR are comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). The CoMFA includes both steric and electrostatic fields, and the CoMSIA was no longer limited to the steric and electrostatic fields, but also included hydrophobic, hydrogenbonded acceptor, and hydrogen-bonded donor fields, and so this extension made the results more stable.…”

Section: Introductionmentioning

confidence: 99%