All solvents, used for preparation of crude extractions, were of HPLC grades In order of increasing polarities, these were Chloroform (99.9%, Sigma-Aldrich, LiChrosolv, Malaysia), Ethyl acetate, Acetone (99.5% Chemiz, Malaysia), Ethanol, Methanol (99.8%, ChemAR, Systerm, Malaysia) and double distilled Milli-Q Type 1 water (MilliporeMerck, Germany). Solvents used for LC-MS and GC-MS were of MS grades.
The yellow variety fruits of N. lappaceum L. were purchased from local marketplace, Bandar Sunway, Selangor, Malaysia.
Six clinical isolates, used in the study, were obtained from the Department of Biological Sciences, Sunway University, Malaysia. These were Streptococcus pyogenes (ATCC-49399), Bacillus subtilis (ATCC-11774), methicillin-resistant Staphylococcus aureus (MRSA) (MTCC-381123), Pseudomonas aeruginosa (ATCC-10145), Klebsiella pneumoniae (ATCC-700603) and Salmonella enterica (ATCC-14028). All strains were tested to be multidrug resistant74.
Preparation of crude extracts.
Epicarp crude extracts were prepared following the method of Do et al.75 using the solvents mentioned earlier for the direct extracts. For the sequential method of extraction, the mentioned solvents were used in order of increasing polarity viz chloroform<ethyl acetate<acetone<ethanol<methanol<water. In both the cases, essentially, the peels of N. lappaceum were removed from the fruit and washed thoroughly with running, followed by, distilled water to remove contaminants and thereafter dried using freeze-dryer. Dried peels were ground into fine powder using an electric grinder. To produce different fractions of crude extracts, 10 g of powder was extracted in 100 mL of selected solvents. The solution was mixed thoroughly by using incubator shaker (Yihder LM-530D Incubator Shaker, Taiwan) for 24 h. To separate supernatant, the solution was centrifuged (Eppendorf 5810 R Centrifuge, Germany) at 4000 rpm for 10 min at 4 °C to completely eliminate the leftover fine sediments. The solvent extracts were concentrated using Rotary evaporator, and further with vacuum concentrator until a viscous extract was obtained. All extracts were stored at 4°C for future experiments.
Potential in-vitro antibacterial activities of yellow rambutan fruit epicarp extracts.
Disc Diffusion assay.
Seed culture of the tested pathogen was consistently swabbed on agar plate. Sterilized blank paper discs were separately impregnated by different concentration of extracts (250 to 2000µg/ml) and placed on agar plate. The plates were incubated at 37ºC for 16h. The antibacterial activity was noted by measuring the diameter of inhibition zone. Gentamicin (10µg/disc) was used as positive control while DMSO (<1%) was kept as negative control. All the experiments had technical triplicates and were performed twice to render two biological replicates.
Broth Dilution Assay.
A broth micro-dilution method was used to evaluate the minimum inhibitory concentration (MIC) values of crude extracts using Clinical & Laboratory Standards Institute (CLSI) procedures. Each extract (5μL) was added into the wells of a 96 well plate comprising 105 CFU/mL bacterial cells. The 96 well plates were incubated at 37°C for 16 h. Final concentrations ranged from 250 to 2000 µg/mL. Three controls comprising, gentamicin 10µg/mL (positive control), DMSO <1% (solvent control) and bacterial inoculum (negative control) were included in each test. The lowest concentration of the tested extract showing inhibitory effect against the pathogens, recorded via the Microplate reader (TECAN, Infinite-M200-PRO), was taken as the MIC value. All tests, having technical triplicates, were confirmed twice. Both the fractions of ethyl acetate and acetone extracts gave promising results with which all chromatographic analyses were carried out.
Exploration of chemical constituents through chromatographic analyses.
High performance Liquid Chromatography (HPLC).
Ethyl acetate and acetone extracts were used as samples for qualitative phytochemical screening via HPLC using Agilent-1260 infinity system, according to the reported method of Zeb76. Briefly, one-gram sample extract was mixed in methanol and water (1:1; 20 mL; v/v) and heated at 70˚C for 1 hour in water bath. This was centrifuged at 4000 rpm for 10 minutes and 2 mL of the supernatant was filtered into HPLC vials through Whatman filter paper. The separation was performed via Agilent-Zorbax-Eclipse column (XDB-C18). Column gradients system comprised solvent B and C. Solvent B consisted of deionized water: methanol: acetic acid having a ratio of 180: 100: 20; v/v while solvent C had deionized water: methanol: acetic acid in the ratio of 80: 900: 20; v/v. Gradient system was started by solvent B for 100%, 85%, 50% and 30% at 0, 5, 20 and 25 minutes followed by solvent C (100%) from 30-40 minutes. Elution occurred after 25 minutes. The ultraviolet array detector (UVAD) was set at 280 nm for the antioxidants analysis and chromatogram were documented using retention times. UV spectra of compounds and accessible standards along with quantification was carried out by taking the percent peak area. Quantification of the antioxidants was measured by formula:
Cx= Sample concentration; As= Standard peak area; Ax= Sample peak area; Cs= Standard concentration (0.09 µg/ml).
Liquid Chromatography and Mass Spectrometry (LC-MS).
A mixture of standards and new metabolites found in the ethyl acetate and acetone fractions were analyzed via LC-MS exactly as per the method reported by Yap et al.77. In order to eradicate systematic errors, reference solution was used with the two ions, having m/z of 121.0508 and 92266.0097, being selected for mass calibration. Finally, the mass spectra for the compounds present in ethyl acetate (EA) and acetone (AC) fractions were run against the database of NIST (National Institutes of Standard and Technology, Gaithersburg, MD, USA) Mass Spectral Search Program-2009 version 2 for the documentation of homologous compounds over Agilent Mass-Hunter Qualitative Analysis B.05.00 software.
Gas chromatography–mass spectrometry (GC-MS).
Ethyl acetate and Acetone fractions were subjected to gas chromatography-mass spectrometry (GC-MS) analysis, using Agilent technologies model 7890B GC System coupled with Pegasus HT High Throughput TOFMS (Leco Corp., MI, USA). An aliquot of an extract of 1ml was injected to the GC-MS apparatus. Next, Agilent J&W HP-5MS (phenyl methyl siloxane, length 30 m, Dia. 0.32 mm, Film, 0.25µm) analytic column was used to separate components under an inert atmosphere of helium (1.5 mL/min). Other standardized parameters utilized during the process: oven temperature of 80°C (2 min) was increased to a temperature of 300°C at the rate of 3°C/min, solvent delay time was 5 min, inlet line temperature was 225°C, and ion source temperature was 250°C. Mass spectra were taken at 70 eV and acquisition mode-scan was 20-1000 amu while sixty-four (64) minutes was the GC run time. The interpretation of mass spectrum and documentation of phytochemicals present in the fractions were achieved via the database of NIST libraries.
Virtual Screening of Chemical determinants from chromatographic analyses.
In silico Protein Model Generation.
S. aureus (Sa) and P. aeruginosa (Pa) were chosen as gram-positive and gram-negative bacterial representatives for computational analyses of DnaK protein binding. 3D structures of DnaK proteins, from the aforesaid species, were generated via homology modelling using MODELLER version 9.2478. DnaK has two conformations, namely, the open or ATP-bound and the closed or ADP-bound conformation65. Herein, we focused on the open conformation of DnaK, to identify potential competitive inhibitors of ATP in order to prevent proper functioning of DnaK protein.
The protein sequences of Sa and Pa DnaK were obtained from UniProtKB with accession IDs of Q2FXZ2 and A6VCL8 (UniProt Consortium, 2019), respectively. To search for suitable homology modelling templates, both NCBI BLASTp and the MODELLER in-built build_profile.py were utilized78,79. For Pa DnaK (PaD), the templates were full-length ATP-bound E. coli (Ec) DnaK protein structures (PDB ID: 5NRO, Chain: A, Query Coverage (QC): 94%, Percent Identity (PI): 79.50%, Resolution (R): 3.25 Å; PDB ID: 4JNE, Chain: A, QC: 94%, PI: 78.80%, R: 1.96 Å; and PDB ID: 4B9Q, Chain: A, QC: 94%, PI: 77.96%, R: 2.40 Å). For Sa DnaK (SaD), besides the aforementioned Ec DnaK (EcD) models, one additional template, from Geobacillus kaustophilus DnaK protein (PDB ID: 2V7Y, Chain: A), was selected due to the high percentage of sequence identity expected as per the gram positive character of S. aureus and G. kaustophilus. As this template structure was in closed conformation and we were only interested in the open conformation, only the Nucleotide Binding Domain (NBD, residues 1 to 350 in template model) which does not differ much in both conformations, were taken into consideration for homology modelling, and the remaining C-terminal residues modelling were guided by the Ec models to shape an open conformation. Therefore, the templates for SaD were (PDB ID: 2V7Y, Chain: A, Template Residues: 1-350, QC: 57%, PI: 83.19%, R: 2.37 Å; PDB ID: 5NRO, Chain: A, QC: 93%, PI: 56.19%, R: 3.25 Å; PDB ID: 4JNE, Chain: A, QC: 92%, PI: 55.54%, R: 1.96 Å; and PDB ID: 4B9Q, Chain: A, QC: 94%, PI: 55.43%, R: 2.40 Å). 5 homology models were generated for each protein of SaD and PaD, and the models with lowest DOPE (discrete optimized protein energy) scores were selected for downstream virtual screening for both. The SaD and PaD homology models were validated via Swiss-Model Structure Assessment and SAVES v5.0 servers80 (Table S7).
Druggable Pocket Validation.
To validate the druggability of the ATP docking pocket, we have conducted ligand binding site prediction using P2Rank from PrankWeb server81. P2Rank predicts the chemical druggability on protein solvent-accessible surface via a non-templated machine learning approach. The ATP binding pocket was predicted to be druggable and ranked first in both cases of SaD and PaD (Table S8; Fig. S8). These pockets from SaD and PaD were further considered to be targeted for virtual screening.
Molecular Docking with Chemical Determinants.
POAP pipeline, Samdani & Vetrivel82 was followed for an in silico virtual screening of the chemical compounds obtained through different chromatographic separation. SMILES notations of these compounds were obtained and their 3D models (in mol2 format) were generated through POAP Ligand Preparation pipeline. To this end, Chimera was utilized to generate physiological protonation states of ligands, and PDBQT files were prepared83. Ligand optimizations were carried out via POAP Ligand Preparation pipeline utilizing MMFF94 force field, being optimized for drug-like organic molecules and molecular docking84. Out of the 50 conformers, generated for each ligand through Weighted Rotor Search approach, only the best conformers were retained. Finally, the ligands were subjected to energy minimization for 5000 steps by the conjugate algorithm.
The macromolecule receptors, pertaining to the SaD and PaD proteins, were prepared using AutoDockTools. AutoDock 4.2, aided by POAP pipeline, was utilized for the virtual screening process85. For AutoDock parameters, 100 generations of Lamarckian Genetic Algorithm were set for each protein-ligand complex. To fit in the previously predicted pocket, docking grids were adjusted into squares of 24 Å with x, y, z coordinates of 17.647, 75.43, 27.766, and 18.069, 74.299, 28.532, for SaD and PaD, respectively. For the silicon-containing compound among the set of ligands, molecular docking was separately carried out with AD4.1_bound parameter file, obtained from AutoDock, wherein parameters for silicon atoms (Rii=4.3; eii=0.402) were added86.
Pharmacological Properties Screening.
Pharmacological properties, encompassing pharmacokinetics, drug likeness, and molecular information for each chemical compound, were predicted using SwissADME87.
Molecular Dynamics Simulation.
Ensuing virtual and pharmacological screenings, potential drug candidates were rationally selected to undergo molecular dynamics (MD) simulation via GROMACS version 2019.388. CHARMM36 force field (Version July, 2020), along with TIP3P water model, was utilized for macromolecule processing89. Avogadro software was utilized for mol2 format conversion and complete protonation (protonation of non-polar atoms)90. The Perl script, sort_mol2_bonds.pl, written by Justin Lemkul was utilized for bond order arrangements in ligand mol2 files. Then, topologies of the ligand models were generated through CGenFF server, and a python script (cgenff_charmm2gmx.py) was utilized to convert topologies for CHARMM to GROMACS91. Solvation was carried out in a dodecahedron box ranged 1.0 Å from the protein-ligand complex. The system was then being ionized to achieve electrostatic neutralization. Subsequently, the system was subjected to energy minimization via steepest descent algorithm until convergence at maximum force of less than 1000 kJ mol-1 nm-1 (Fig. S9). Potential energy shifts of the systems were monitored herein.
Equilibration of the systems were carried out via NVT and NPT ensembles for 50000 steps (100ps), with temperature, pressure, and density shifts being monitored therein. Subsequently, production MD simulations were carried out for 5000000 steps (10ns) to observe protein-ligand interactions. RMSD (Root Mean Square Deviation) values of ligands and receptors, number of hydrogen bonds between ligands and receptors, and ligand-receptor interaction energies (Coulombic interaction energies and Lennard-Jones energies) were computed throughout the MD simulations. Total interaction energies were computed, and errors were estimated via error propagation by addition.