1H, 13C and 2D NMR were obtained on Bruker Avance III 400 MHz spectrophotometer using either deuterated chloroform (CDCl3) or methanol (MeOD) at room temperature. High- resolution mass spectrometry (HRMS) was done on a Waters Micromass LCT Premier TOF- MS instrument. Infrared (IR) spectra were recorded on a Perkin Elmer Spectrum 100 Fourier transform infrared spectrophotometer (FT-IR). Ultraviolet-visible (UV-Vis) spectra were
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recorded on a Varian Cary UV-Vis spectrophotometer. Column chromatography was performed using Merck silica gel 60 (0.040-0.063 mm), a R CH-20 (Merck) Sephadex column, and 20 × 20 aluminum sheets coated with silica gel 60 F245 was used for thin-layer chromatography (TLC). TLC plates were viewed under UV light at wavelengths of 254 nm and 366 nm. Further visualization was done by spraying with 10% sulfuric acid in MeOH solution followed by heating. All reagents were supplied by either Merck (Darmstadt, Germany) or Sigma (St. Louis, USA) chemical companies.
3.2.2 Plant Materials
Fresh bulbs and leaves of Scilla nervosa were purchased from the Berea muthi (herbal) market in Durban and were identified by the taxonomist, Mr. Edward Khathi of the School of Life Sciences, University of KwaZulu Natal (UKZN, Westville). A voucher specimen, 18272 02 1086000, was deposited in the ward herbarium in the School of Life Sciences.
3.2.3 Extraction, Isolation, and Purification
Plant material was air-dried and crushed. Crushed bulbs (300 g) and leaves (1 kg) were successively extracted using MeOH, ethyl acetate (EtOAc), and dichloromethane (DCM) by cold maceration on a shaker for 48 h at room temperature. Each crude extract was evaporated under reduced pressure to remove excess solvent and concentrated to give 5 g from DCM, 3 g from EtOAc, and 24 g from MeOH for the bulbs and 3 g from MeOH for the leaves. Extracts were subjected to column chromatography, using hexane and EtOAc gradient starting with 100% hexane that was stepped by 10 % to 100 % EtOAc. After that, 10 % MeOH was added to EtOAc. Fingerprinting using TLC was done for each of the chromatographic fractions.
Fractions that gave the same retention factor (Rf) were pooled together.
From the DCM extract of the bulbs (65 × 100 mL fractions), fractions 17 and 18 presented compound A1 (350 mg), a yellow and oily gum, fractions 23 and 24 presented compound A2
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(100 mg), while fraction 34 presented compound A3 (11 mg). Fraction 14 was purified to obtain compound A4 (10 mg). The MeOH extract of the leaves (30 × 100 mL fractions) resulted in the isolation of compound A1 from fraction 17, a mixture of compounds A1 and A5 from fraction 18 (6 mg), and compound A6 (5 mg) from fraction 19. From the EtOAc extract of the bulbs (50 × 100 mL fractions), compound 7 (20 mg), a yellow powder was precipitated out of fraction 25 using DCM, and compound A8 (12 mg), a yellow powder was obtained from fraction 28. Compound A9 (10 mg) was obtained as white flakes from fractions 41-44 of the DCM extract of the bulbs after cleaning with MeOH. Compound A10 was obtained as brown spikes from fraction 26 of the DCM extract of the bulbs, while compound A11, a brown powder, was obtained from fraction 31 of the same extract.
3.2.4 Evaluation of Antibacterial Activity
Antibacterial activity of the crude extracts (DCM and MeOH extracts of the bulbs, and MeOH extract of the leaves) and isolated phytocompounds (A1, A2, A4 and A7) was evaluated using the agar-well diffusion method (CLSI, 2012) against three Gram-positive bacteria (Bacillus subtilis ATCC 6653, methicillin-resistant Staphylococcus aureus ATCC 43000 and Mycobacterium smegmatis mc2 155) and four Gram-negative bacteria, (beta-lactam-resistant Escherichia coli ATCC 35218, multidrug-resistant Pseudomonas aeruginosa ATCC 27853, extended-spectrum beta-lactamase-producing Klebsiella pneumoniae ATCC 700603 and the quorum sensing indicator Chromobacterium violaceum ATCC 12472). Test samples were dissolved in MeOH to a final concentration of 20 mg mL-1 for the crude extracts and 10 mg mL-1 for the pure compounds. The wells were loaded with 25 µL and 50 µL of the test samples, respectively. Susceptibility or resistance to compounds tested was assigned based on the following zone diameter criteria: Susceptible (S) ≥ 15 mm, Intermediate (I) = 11–14 mm, and Resistant (R) ≤ 10 mm (Chenia, 2013). The criteria for assigning susceptibility or resistance to
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ampicillin was as follows: (S) ≥ 17 mm, (I) = 14–16 mm, (R) ≤ 13 mm, while those for tetracycline were: (S) ≥ 19 mm, (I) = 15–18 mm, (R) ≤ 14 mm (CLSI, 2012).
3.2.5 Molecular Docking Protocol
All calculations were performed with the Schrodinger molecular modeling suite (version 2019- 4) using the OPLS_2005 forcefield. The minimized 3D geometries of the ligands were generated with LigPrep (2019), and their protonation states were assigned at pH 7.0 2.0 using Epik (2019). The X-ray crystal structure of MraY (PDB ID: 6OYH) was retrieved from the RCSB database and processed with the Protein Preparation Wizard using the default workflow, including filling in the missing protein side chains and loops with Prime (2019). Subsequently, the ligand-receptor complexes were modeled with the induced fit docking protocol (Induced, 2019) using the standard protocol. The receptor grid was defined as the centroid of cognate ligand, and the first stage of glide docking was performed with a brief constrained refinement of the protein structure to a root mean square deviation of 0.18Å, followed by auto trimming of 3 residues within 5Å of the active site and B-factor > 40. The requisite implicit membrane for the Prime refinement stage was modeled by aligning the protein structure and loading the membrane coordinates from the OPM database (lomise). Then, glide redocking was performed using the extra-precision mode, and the best poses were selected based on docking score, glide emodel value, and IFD score. The binding affinity of the ligands to MraY was also estimated with Prime Molecular Mechanics and Generalized Born Surface Area (MM-GBSA) (prime) using the best poses as input structures and a Variable-dielectric Generalized Born (VSGB) model as implicit solvation model.
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