|Year : 2019 | Volume
| Issue : 64 | Page : 280-287
Potential Cyclooxygenase (COX-2) enzyme inhibitors from Myrica nagi-from in-silico to in-vitro investigation
HP Prashanth Kumar1, Prachurjya Panda2, Prashantha Karunakar3, Kotikalapudi Shiksha2, Laxmi Singh2, Nijalingappa Ramesh1, Talambedu Usha4, Sushil Kumar Middha2
1 Department of Biotechnology, School of Applied Sciences, Reva University, Bengaluru, Karnataka, India
2 Department of Biochemistry and Biotechnology, Maharani Lakshmi Ammanni College for Women, Bengaluru, Karnataka, India
3 Department of Biotechnology, PES University, Bengaluru, Karnataka, India
4 Department of Biochemistry, Bangalore University, Bengaluru, Karnataka, India
|Date of Submission||31-Jan-2019|
|Date of Decision||06-Mar-2019|
|Date of Web Publication||23-Aug-2019|
Sushil Kumar Middha
Department of Biochemistry and Biotechnology, Maharani Lakshmi Ammanni College for Women, Bengaluru, Karnataka
Source of Support: None, Conflict of Interest: None
| Abstract|| |
Introduction: Myrica nagi Thunb. (family Myricaceae) are actinorhizal plants showing symbiotic interaction with Frankia. Inhibition of cyclooxygenase-2 (COX-2) enzyme is known to be significant in preventing inflammation and in therapeutics. Objectives: Our principal focus was to identify COX-2 enzyme inhibitors, safer and natural anti-inflammatory compounds from M. nagi. Protein–ligand interaction has a significant role in structure-based drug design. Materials and Methods: Sixty-eight phytochemicals were therefore screened and evaluated for their binding energies with COX-2. These phytoconstituents were screened and analyzed for drug Likeliness along with Lipinski's rule of five. The X-ray crystallographic structure of the target COX-2 (protein data bank [PDB] ID: 4PH9), obtained from PDB, was docked with PubChem structures of phytochemicals using AutoDock 4.2 that uses Lamarckian genetic algorithm. Further, myricetin was subjected to in vitro anti-inflammatory assay using RAW-264.7 cell lines and inhibitory concentration (IC50) value was also determined. Results: The myricetin, myricitrin, and corchoionoside-C inhibited COX-2 with − 6.52, −4.94, and − 4.94 Kcal/mol binding energies, respectively, comparable to ibuprofen. Eventually, bioactivity score and absorption distribution metabolism excretion-toxicity properties showed considerable biological activities as G protein-coupled receptor, nuclear receptor, protease inhibitor, and enzyme inhibitors for myricetin, myricitrin, and corchoionoside-C phytochemicals. Molecular docking revealed hydrophobic interactions followed by four, nine, and four numbers of hydrogen bonds between myricetin, myricitrin, and corchoionoside-C, respectively, within the binding site of COX-2. Flavonol myricetin showed 112 μg/mL as IC50 value when it was subjected to in vitro cytotoxicity assay. These results clearly demonstrated that myricetin, myricitrin, and corchoionoside-C could act as highly potential COX-2 inhibitors. Therefore, in silico and in vitro studies revealed that of three best phytochemicals, myricetin could be promising candidate.
Keywords: Anti-inflammatory, cyclooxygenase 2, docking, flavonoids, inhibitory concentration, intermolecular energy, myricetin
|How to cite this article:|
Prashanth Kumar H P, Panda P, Karunakar P, Shiksha K, Singh L, Ramesh N, Usha T, Middha SK. Potential Cyclooxygenase (COX-2) enzyme inhibitors from Myrica nagi-from in-silico to in-vitro investigation. Phcog Mag 2019;15, Suppl S2:280-7
|How to cite this URL:|
Prashanth Kumar H P, Panda P, Karunakar P, Shiksha K, Singh L, Ramesh N, Usha T, Middha SK. Potential Cyclooxygenase (COX-2) enzyme inhibitors from Myrica nagi-from in-silico to in-vitro investigation. Phcog Mag [serial online] 2019 [cited 2019 Dec 9];15, Suppl S2:280-7. Available from: http://www.phcog.com/text.asp?2019/15/64/280/265020
- The liquid chromatography–mass spectrometry analysis of the leaf extract of Myrica nagi revealed many phytochemicals and the ligand library of 68 compounds were generated from PubChem database
- The ligand library was subjected to drug likeliness with Molsoft tool and top 15 ligands were selected
- Shortlisted ligands were docked with cyclooxygenase-2 (COX-2) enzyme using AutoDock 4.2 tool and then subjected to absorption distribution metabolism excretion-toxicity and bioactivity studies to select the top three best ligands
- In silico studies of myricetin and myricitrin indicated anti-inflammatory effects on COX-2 enzyme.
Abbreviations used: COX-2: Cyclooxygenase-2; PDB: Protein data bank; LGA: Lamarckian genetic algorithm; ADMET: Absorption distribution metabolism excretion-toxicity; GPCR: G protein-coupled receptors; PI3K: Phosphoinositide 3-kinase; Akt/PKB: Akt/protein kinase B; JAK1: Janus kinase1; MAP: Mitogen-activated protein; MKK4: Mitogen-activated protein kinase-kinase 4; SMILES: Simplified molecular input line entry specification; GA: Genetic algorithm; LS: Local search; CHARMM: Chemistry at HARvard Macromolecular Mechanics; VDw: Van Der Waals; PDBQT: Protein data bank, partial charge, and atom type format; NMR: Nuclear magnetic resonance; GUI: Graphical user interface; IBP: Ibuprofen; BBB: Blood–brain barrier; RMSD: Root-mean-square deviation; RT: Reverse transcriptase; HIV: Human immunodeficiency virus; RLV: Rauscher murine leukemia virus.
| Introduction|| |
Myricaceae is a family of plants that are actinorhizal and having symbiotic interaction with actinomycete. Nodulation process has been observed in all species of Myrica as reported previously.,Myrica esculenta and Myrica nagi (called Katphala, Kafal, and box berry) are the two major species widely found in China, India, Nepal, and Pakistan. Ancient Ayurvedic medicine has extensively employed Myrica species for treating various disorders, such as bleeding piles, burns, gastrointestinal diseases, headaches, skin diseases, and typhoid. Furthermore, it is used as anti-inflammatory agent for asthma, bronchitis, fever, lung infection, dysentery, toothache, wounds, jaundice, catarrhal fever, cough, sore throat, ulcers, cardiac disorders, gonorrhea, diuresis, epilepsy, and paralysis and for regulating the menstrual cycle., The powdered leaves of Myrica species have traditional uses in various ailments.
Phytochemicals are most commonly found as polyphenolic compounds in food and beverages of plant origin. These are not only essential nutrients in plants, but also have numerous activities to avert several diseases.,,,, They have been structurally classified into different classes, namely phenolic acids, saponins, alkaloids, carotenoids, flavonoids, lignans, terpenes, phytosterols, triterpenoids, and xanthophylls.
Of these, flavonoids are the most extensively investigated molecules and are recorded to have diversified pharmacological activities such as antioxidant, antidiabetic, antiallergic, anti-inflammatory, and anticancer activity.,
The flavonoids are further classified into seven subclasses, namely flavanols, flavones, anthocyanins, isoflavones, flavanones, flavonols, and flavanonols, based on their structures. Flavonoids, at molecular level, are found to regulate various molecules including interleukins, proteins, and different enzymes. Flavonoids can bind directly to some protein kinases, namely phosphoinositide 3-kinase, Akt/protein kinase B, Janus kinase 1, mitogen-activated protein kinase kinase4, and Raf1. They then modify their phosphorylation site to regulate multiple cell signaling pathways. The flavonols are one such group that has different compounds such as quercetin, kaempferol, myricetin, fisetin, and morin, exhibiting beneficial effects such as anti-inflammatory, antioxidants, antiallergic, antiviral, as well as anticancer activity.,
Inflammation is one of the pathological features of many diseases and is complicated during pathogenesis of various disorders, such as jaundice, cough, ulcers, and aging. Cyclooxygenase enzyme (COX) exists in two different isoforms, COX-1 and COX-2. It is an endogenous enzyme that catalyze the transformation of arachidonic acid to prostaglandins and thromboxanes. COX-2 enzyme is inducible and gets expressed only on inflammatory stimulus.,
Molecular docking simulation works in a two-step process that starts with compiling various ligand conformations in the identified binding site of the receptor. Then, ranking of these according to their binding pose energies for each individual molecule is done. Diverse types of commercially available tools such as GOLD, FLEX, AutoDock, HEX, and Argus Lab are extensively employed for evaluating docking binding energies of ligand-receptor conformations. The recognition of the most apt-binding conformations is performed by probing the large conformational space, reporting various binding sites and then meticulously predicting the interaction energy correlated with the respective binding poses. This process is carried out iteratively until obtaining a solution with minimum energy. Molecular docking is the most frequently used approach in drug design because of its competence to predict highly significant, accurate, and different conformations of ligands within the target binding site. Hence, it can be employed to study interaction between phytochemicals and COX-2 enzyme.
Raw-264.7 cells are predominantly used to determine the cytotoxicity and anti-inflammatory activity of secondary metabolites. Previously, our laboratory has reported that compounds of M. nagi could be good COX-2 enzyme inhibitors hypothetically. The study was continued using preclinical laboratory to understand the anti-inflammatory activity of the same using crude extract. However, there is a serious need to understand the mechanism of inhibition involving M. nagi compounds. This study was therefore initiated or aimed to understand the specific compound involved for its anti-inflammatory activity by in silico and in vitro mean.
| Materials and Methods|| |
Selection of target protein
The protein structure of COX-2 (Protein Data Bank [PDB] ID: 4PH9) was obtained from the Research Collaboratory for Structural Bioinformatics (RCSB) PDB (http://www.rcsb.org/pdb/) with X-ray diffraction resolution of 1.81 Š, belonging to Mus musculus.
Phytochemical dataset generation
The data obtained from our previous study  and other laboratory  on M. nagi were used to build the dataset of 68 phytochemicals [Figure 1]. The chemical structures and canonical Simplified Molecular Input Line Entry Specification notation of the molecules used in the present study were obtained from NCBI PubChem database for further analysis.
Drug likeliness prediction by Molsoft software
Drug likeliness is defined as an intricate balance of different molecular properties and structural features that determines if the test molecule is similar to the known drugs. Lipinski's rule of five is a thumb rule to evaluate drug likeliness. Lipinski's rules state that an ideal drug molecule need to possesses not >500 Da molecular weight, not >5 hydrogen bond donors, not >10 hydrogen bond acceptors, and LogP not >5. Drug likeliness of the ligands was predicted using “Molsoft” (http://www.molsoft.com) that screens the molecule based on Lipinski's rule. This tool distinguishes drug-like and nondrug-like properties and shows probability scores for a molecule along with the graph depicting drug likeliness.
Preparation of receptor
The native crystal structure of ibuprofen (IBP) bound COX-2 enzyme was retrieved from RCSB PDB  (PDB ID: 4PH9). The retrieved PDB file was cleaned to restrict small molecules and hetero atoms manually to obtain the protein for further docking studies., PDBSum was availed to understand the binding site region with which the ligand can bind to the COX-2 enzyme. After this process, the ligands and COX-2 enzyme were docked.
Molecular docking simulation
Docking is a virtual screening process of ligand dataset and predicting the agonists and antagonists based on some scoring functions. AutoDock  was used for docking of ligands with COX-2 enzyme. AutoDock works on Lamarkian genetic algorithm (LGA), which is a blend of genetic algorithm and adaptive local search that identifies the conformational space of the ligand and enzyme for proper interaction.
Before docking process, the ligand molecules were prepared using the “prepare ligand” module. Enzyme–ligand interactions were refined by molecular dynamics using Chemistry at HARvard Macromolecular Mechanics (CHARMM) in Biovia discovery suite. Force fields are energies applied on each particle and their positional relationships, indicating energies between the atoms. The form of the potential energy function CHARMM uses the same equation indicated as per our previous studies.
In the equation, kb is the bond force constant and b – b0 is the distance of atom displaced from the equilibrium. The next term in the equation is bond angles where kθ is the angle force constant and θ – θ0 denotes the angle between three bonded atoms from equilibrium.
The third term in the equation represents dihedrals (a. k. a. torsion angles) where kϕ implies dihedral force constant, n is function multiplicity, ϕ indicate dihedral angle and δ is phase shift.
The fourth expression represents impropers, which shows out of plane bending, where kω denotes force constant and ω – ω0 shows out of plane angle.
The Urey-Bradley element contains the next term, where KU displays respective force constant and U represents the harmonic potential of distance between 1 and 3 atoms. Nonbonded synergies between pairs of atoms (i, j) are shown by the last two expressions.
By denotation, the non-bonded forces are only applied to atom pairs apportioned by at least three bonds. The Van Der Waals energy is determined with the help of standard 12–6 Lennard-Jones (L J) potential and with Coulombic potential's electrostatic energy. In the equation mentioned above, Lennard-Jones potential is given by Rminij and is not the minimum of the potential, while L J potential crosses the x-axis (i.e., where the potential is zero). To run the molecular dynamics simulations, the parameter file has all the specified terms in the energy function (i.e., the equation denoted above).
AutoDock was performed in four steps:
- Coordinate files preparation, where PDB, partial charge, and atom type format (PDBQT) files from traditional PDB files
- Precalculation of atomic grid affinities of the ligands and embedding of protein in a three-dimensional (3D) grid
- Docking of ligands that is performed using one of the most efficient method, namely LGA
- Result analysis is performed by clustering the solutions of COX-2 enzyme and ligands interaction. [Figure 2] depicts the graphical representation of different methods or approaches used in our study.
Prediction of bioactivity for the selected ligands
Molinspiration was used to determine bioactivity of the selected ligands. It analyzes physicochemical molecular descriptors for a given ligand; such as, ion channel modulator, G-protein coupled receptors ligand, kinase inhibitor, nuclear receptor ligand, and enzyme inhibitor (http://www.molinspiration.com).
Pharmacokinetics and toxicity analysis
Ligands that inhibit the receptors cannot be extrapolated as good drug candidate, unless the ligand has good bioavailability and desirable duration of action. For the identification of possible effects of ligand molecules in virtual, the absorption distribution metabolism excretion-toxicity (ADMET) properties of the compounds were inferred using an online freeware, AdmetSAR (http://http://lmmd.ecust.edu.cn/admetsar1).
Determination of cell cytotoxicity
For adherent cells, 100–200 μl of desired cell suspension (RAW-264.7) in a 96-well plate at a cell density (25,000–50,000 cells per well), without the test agent were seeded and allowed the cells to adhere to the plate for 24 h.
The plate was incubated for the required period at 37°C in a 5% CO2 atmosphere. After 24 h, the growth medium was removed. 100 μl of freshly prepared five different concentrations (300 μg, 100 μg, 10 μg, 1 μg, and 0.1 μg) of purified myricetin were seeded in 96-well plate in triplicate.
After the incubation, plates were removed from the incubator and 10% of 3-(4, 5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) reagent was added to the total volume. This volume was maintained as same as the volume used while determining optimum cell density. The plate was wrapped with aluminum foil to avoid exposure to light and re-incubated for 2–4 h. For adherent cells, aspiration of the culture medium without disturbing the monolayer was carried out. Then, solubilization solution was added in an amount equal to the culture volume and was stirred gently in a gyratory shaker to enhance dissolution. The absorbance was read using spectrostar Nano ELISA Plate reader at 570 nm and inhibitory concentration (IC50) value was carried out.
| Results and Discussion|| |
Structure of the target protein
RCSB is a global repository for 3D structure of macromolecules (proteins and DNA) and their complexes, resolved by X-ray crystallography, NMR spectroscopy, and cryoelectron microscopy., In the current study, the X-ray crystallographic structure of COX-2 enzyme  bound with IBP was retrieved from the PDB with PDB ID: 4PH9 and has been exploited as the therapeutic target.
Drug likeliness of phytochemicals
During the initial part of the study, we hypothesized drug likeliness for all the 68 phytochemicals using Molsoft tool. Majority of ligand molecules showed positive for drug likeliness and did not deviate much with Lipinski's rule. Threshold values >−1.05 [Table 1] were considered significant and 15 ligands were selected for further analysis. Biomolecular properties of these phytochemicals were also investigated which is essential for rational drug design.
|Table 1: Top 15 phytochemicals selected based on drug-likeness score in Molsoft tool|
Click here to view
Virtual screening and docking
Docking explicitly with AutoDock is useful in various situations in rational drug discovery, namely screening thousands of phytochemicals or ligand molecules against specific target protein, screening a small set of ligand molecules against multiple protein molecules, active site sampling, and ligand–protein interaction prediction.
In the current study, docking simulation was carried out using AutoDock 4.2. The docking process is performed using one of the most efficient method namely LGA. AutoDock 4.2 is frame worked to use an extended PDB format, namely PDBQT, for coordinate files. In AutoDock 4.2, force field is applied differently on aromatic and aliphatic carbon atoms and different for polar and nonpolar atoms. PDBQT files also have information on torsional degrees of freedom for all the atoms of protein. The graphical user interface for AutoDock is used for creating PDBQT files from traditional PDB files.
Selected receptor (COX-2) was prepared for docking process with the help of different modules present in AutoDock. Commercially available IBP was used as standard in the current study. During the AutoGrid process, the protein is embedded in a 3D grid; affinity grids are estimated for each type of atom in the ligand; such as, carbon, hydrogen, oxygen, and nitrogen and also for electrostatic and de-solvation potentials. The energetics of a specific ligand pose is calculated using the values obtained from the grids.
AutoDock was run multiple times to obtain different docked conformations or poses. The conformations obtained after docking are sorted based on the order of increasing energy (kcal/mol) and the lowest energy (kcal/mol) conformation is seen in the first frame [Table 2].
The threshold value was decided with respect to binding energies (kcal/mol) of different receptor-ligand complexes. The best docked ligand poses of 15 out of 68 phytochemicals [Figure 1] were then ranked according to their binding energies with COX-2 enzyme. The current study revealed that the IBP showed the binding energy of −7.99 kcal/mol. [Table 2] indicates top 10 ligands exhibiting docking energy values above −4.90 kcal/mol. Hence, these ligand molecules were selected for further investigation as they exhibited good drug likeliness and ranked based on high to low docking energy scores: α-Selinene > β-sitosterol > α-cadinol > β-sitosterol-β-D-glucopyranoside > 3-cyclohexanedione, butyl-2-methyl- > myricetin > nerolidol >myricanol > myricitrin > corchoionoside-C >myricanone. α-Selinene has emerged as most prospective drug candidate that can be further evaluated and enforced against COX-2 enzyme.
In addition, one need not trust completely the docking predictions. Instead, one should evaluate if the generated docked conformations are realistic or not. That is, there are no negative or positive charged groups nearby to aliphatic or neutral regions. α-Selinene was ranked first and thirteenth based on binding score and drug likeliness score, respectively, and it did not have any residues interacting within the active site of the receptor. However, β-sitosterol, α-cadinol and β-sitosterol-β-d-glucopyranoside, 3-cyclohexanedione, 2-butyl-2-methyl-, and myricanone had good binding scores as well as drug likeliness scores. However, it did not display much impressive bioactivity [Table 3] and ADMET properties [Table 4]. Hence, it has been eliminated from further analysis.
|Table 3: Bioactivity of selected ligand molecules using molinspiration tool|
Click here to view
|Table 4: Hypothetical absorption distribution metabolism excretion-toxicity properties of selected ligands|
Click here to view
Absorption distribution metabolism excretion-toxicity analysis
The ADMET parameters were evaluated using knowledge-based online server AdmetSAR. Myricetin and myricitrin are predicted to display high human intestinal absorption with a probability of 0.96 and 0.90, while corchoionoside-C displayed poor intestinal absorption. Blood–brain barrier (BBB) is a regulatory system that separates the brain from the direct contact of blood in the circulatory system, thus safeguarding the brain from unwanted particles. All the three molecules were predicted to be BBB negative, assuring that its administration is safe for the brain. These ligands also showed to be noncarcinogenic with some amount of oral, AMES, and rat toxicity.
Hydrogen bonding and electrostatic interactions are considerable driving forces and act in placing the ligand molecule in unanticipated binding pose. There are various metrics for determining whether docked conformations for a given ligand are “good” or “bad.” These can be explored and explained by RMSD, free energy of binding (kcal/mol) and many more. One can also look for the presence or absence of different potential interactions to understand the binding mechanism.
Molecular docking simulation
Molecular docking simulation revealed the interacting residues in the binding site of COX-2 enzyme [Table 5]. For IBP, residues such as ARG121 and TYR356 provided only three hydrogen bonds; α-selinene, which showed the second best binding energy next to standard drug IBP, did not have any interaction residues. However, myricetin showed interactions with HIS90, GLN193, MET523, and SER531 providing four hydrogen bonds. Another flavonoid myricitrin [Figure 3] interacted with HIS90, ARG121, TYR356, TYR386, ILE518, PHE519, and SER531 with nine hydrogen bonds. However, Corchoionside-C provides four H-bonds by interacting with residues such as ARG 121, TYR 356, and VAL 524. [Figure 4] shows the ligplot and residues interacting in the binding site of COX-2 enzyme for the selected ligands. These interactions clearly display that the molecules interact with conserved amino acids in COX-2 binding pocket and hence, apprehend to inhibit the functionality of the target enzyme.
|Table 5: Number of H-bonds and its distances in best selected molecule based on their energies|
Click here to view
|Figure 3: Surface view of cyclooxygenase-2 (Protein Data Bank ID: 4PH9) with myricitrin in the binding site|
Click here to view
|Figure 4: Ligplot of myricetin (a), myricitrin (b) and corchoionoside-C (c) and residues interacting in the binding site for myricetin (a. 1), myricitrin (b. 1) and corchoionoside-C (c. 1) of cyclooxygenase-2 enzyme for the best selected ligands along with the standard ibuprofen (d and d. 1)|
Click here to view
Determination of cell cytotoxicity by 3-(4, 5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay
IC50 value of myricetin was carried out using MTT cytotoxicity assay to obtain percentage of cell viability and also percentage of inhibitory concentration (%IC) for each dilution. As the concentration of drug increases the %IC of RAW-264.7 cells also increased from 6.5% to 64.6%, respectively. The IC50 value of myricetin was found to be 102 μg/mL.
| Conclusion|| |
Myricetin occurs throughout the plant kingdom as it is synthesized primarily by members of the families Anacardiaceae,Myricaceae,Primulaceae,Polygonaceae, and Pinaceae.
Myricetin, a flavanol (subclass of flavonoids) derived from M. nagi leaves, has lot of pharmacological activities, such as antimicrobial, antioxidant, and anticancerous. It is extensively studied in vitro for their various properties, namely, anti-inflammatory, anti-angiogenesis, enzyme activation, anti-proliferation activity, anti-invasiveness, and induction of apoptosis. In vitro studies have also shown that myricetin regulates cancer initiation and development. Myricetin showed antiviral and antibacterial properties against various organisms. D'Souza et al. proved myricetin to be very active at a concentration of 30 μg/mL against Klebsiella pneumoniae, Proteus mirabilis, Pseudomonas aeruginosa, and Shigella flexineri. Antitubercular activity was evaluated exposing myricetin to Mycobacterium tuberculosis. Astructure–activity relationship proved that the responsible factor for this is hydroxyl groups present in the structure of myricetin. Myricetin showed strong inhibition of reverse transcriptase from human immunodeficiency virus, and Rauscher murine leukemia virus. Furthermore, the flavonoid inhibited DNA polymerase I and DNA polymerase-α.
Myricetin fulfills all the drug likeliness and ADMET properties and emerges as one of the most potent anti-inflammatory agents in our analysis. The myricitrin exhibits good binding ability with poor bioactivity which can be modulated in in vitro and in vivo studies.
Overall, we conclude that the myricetin possess the desired properties to be the potent anti-inflammatory drug against human COX-2 enzyme for the treatment of acute and chronic inflammation process. Cytotoxicity activity of myricetin from M. nagi using MTT assay showed IC50 value as 102 μg/mL proving its potential as probable anti-inflammatory drug candidate.
We thank STAR College Scheme, Government of India, BT-Finishing School, GOK, and MLACW Management, for providing the necessary facility. We would like to acknowledge DBT-BIF Facility, Maharani Lakshmi Ammanni College for Women, Bengaluru, India, for rendering support for this study. The author(s) acknowledges the encouragement received from Sapthagiri College of Engineering and Smt. K. Naganjana Devi for editing the manuscript.
Financial support and sponsorship
This work was supported by DBT-BIF Facility, Maharani Lakshmi Ammanni College for Women, Bengaluru, India.
Conflicts of interest
There are no conflicts of interest.
| References|| |
Franche C, Normand P, Pawlowski K, Louis ST, Didier B. An update on research on Frankia
and Actinorhizal plants on the occasion of the 18th
meeting of the Frankia
-Actinorhizal plants symbiosis. Symbiosis 2016;70:1-4.
Tisa LS, Oshone R, Sarkar I, Gtari M. Genomic approaches toward understanding the actinorhizal symbiosis: An update on the status of Frankia
genomes. Symbiosis 2016;70:5-16.
Silva BJ, Seca AM, Barreto Mdo C, Pinto DC. Recent breakthroughs in the antioxidant and anti-inflammatory effects of Morella and Myrica
species. Int J Mol Sci 2015;16:17160-80.
Middha SK, Goyal AK, Faizan SA, Sanghamitra N, Basistha BC, Usha T. In silico
based combinatorial pharmacophore modelling and docking studies of GSK-3β and GK inhibitors of Hippophae
. J Biosci 2013;38:805-14.
Preeti P, Harendra K, Joshi DD, Kharkwal H. Investigations on Myrica nagi
leaves: phytochemical screening and physicochemical evaluation. World J Pharm Pharm Sci 2013;2:2867-73.
Rao BN. Bioactive phytochemicals in Indian foods and their potential in health promotion and disease prevention. Asia Pac J Clin Nutr 2003;12:9-22.
Hamuel JD, Human I, Bennade S, Ndakidemi P. Phytochemicals as chemotherapeutic agents and antioxidants: Possible solution to the control of antibiotic resistant verocytotoxin producing bacteria. J Med Plant Res 2009;3:839-48.
Goyal AK, Middha SK, Usha T, Sen A. Ameliorating reactive oxygen species-induced in vitro
lipid peroxidation in liver, carbohydrate and DNA damage by Dendrocalamus hamiltonii
different leaf extracts Chiang Mai. J Sci 2016;43:80-8.
Usha T, Middha SK, Narzary D, Brahma BK, Goyal AK. In silico
and in vivo
based evaluation of traditional antidiabetic herb Hodgsonia heteroclite
. Bangladesh J Pharmacol 2017a; 12:165-6.
Kamala A, Middha SK, Sindhura HS, Gopinath C, Karigar CS. In vitro
antioxidant potentials of Cyperus rotundus L rhizome extracts and their phytochemical analysis. Phcog Mag 2018; 14: 261-7.
] [Full text]
Chahar MK, Sharma N, Dobhal MP, Joshi YC. Flavonoids: A versatile source of anticancer drugs. Pharmacogn Rev 2011;5:1-2.
Kumar S, Pandey AK. Chemistry and biological activities of flavonoids: An overview. ScientificWorldJournal 2013;2013:162750.
Hou DX, Kumamoto T. Flavonoids as protein kinase inhibitors for cancer chemoprevention: Direct binding and molecular modeling. Antioxid Redox Signal 2010;13:691-719.
Jiang Q, Ames BN. Gamma-tocopherol, but not alpha-tocopherol, decreases proinflammatory eicosanoids and inflammation damage in rats. FASEB J 2003;17:816-22.
Middha SK, Lokesh P, Goyal AK, Prashanth HP, Bhardwaj A, Kamal R. In silico
exploration of cyclooxygenase inhibitory activity of natural compounds found in Myrica nagi
using LC-MS. Symbiosis 2016a; 70:169-78.
Usha T, Middha SK, Bhattacharya M, Lokesh P, Goyal AK. Rosmarinic acid, a new polyphenol from Baccaurea ramiflora
lour. leaf: A probable compound for its anti-inflammatory activity. Antioxidants (Basel) 2014;3:830-42.
Smith WL, DeWitt DL, Garavito RM. Cyclooxygenases: Structural, cellular, and molecular biology. Annu Rev Biochem 2000;69:145-82.
Seyedi SS, Shukri M, Hassandarvish P, Oo A, Shankar EM, Abubakar S, et al
. Computational approach towards exploring potential anti-Chikungunya activity of selected flavonoids. Sci Rep 2016;6:26368.
Lounnas V, Ritschel T, Kelder J, McGuire R, Bywater RP, Foloppe N. Current progress in structure-based rational drug design marks a new mindset in drug discovery. Comput Struct Biotechnol J 2013;5:e201302011.
Ferreira LG, Dos Santos RN, Oliva G, Andricopulo AD. Molecular docking and structure-based drug design strategies. Molecules 2015;20:13384-421.
Usha T, Shanmugarajan D, Goyal AK, Kumar CS, Middha SK. Recent updates on computer-aided drug discovery: Time for a paradigm shift. Curr Top Med Chem 2017;17:3296-307.
Erel SB, Karaalp C, Bedir E, Kaehlig H, Glasl S, Khan S, et al.
Secondary metabolites of centaurea calolepis and evaluation of cnicin for anti-inflammatory, antioxidant, and cytotoxic activities. Pharm Biol 2011;49:840-9.
Middha SK, Usha T, Babu D, Misra AK, Lokesh P, Goyal AK. Evaluation of antioxidative, analgesic and anti-inflammatory activities of methanolic extract of Myrica nagi
leaves-an animal model approach. Symbiosis 2016b; 70:179-84.
Ranjbar MM, Assadolahi V, Yazdani M, Nikaein D, Rashidieh B. Virtual dual inhibition of COX-2/5-LOX enzymes based on binding properties of alpha-amyrins, the anti-inflammatory compound as a promising anti-cancer drug. EXCLI J 2016;15:238-45.
Kar P, Dey P, Misra A, Chaudhuri T, Sen A. Phytometabolomic fingerprinting of selected actinorhizal fruits popularly consumed in North-East India. Symbiosis 2016;70:159-68.
Lipinski CA. Lead- and drug-like compounds: The rule-of-five revolution. Drug Discov Today Technol 2004;1:337-41.
Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, et al.
The protein data bank. Nucleic Acids Res 2000;28:235-42.
Kalim M, Khan A, Baig MH, Arif JM, Lohani M, Jamal F. Efficacy of natural inhibitors against PKC: An in silico
approach to combat cancer. Recent Res Sci Technol 2011;3:90-3.
Usha T, Tripathi P, Pande V, Middha SK. Molecular docking and quantum mechanical studies on pelargonidin-3-glucoside as renoprotective ACE inhibitor. ISRN Comput Biol 2013;2013:428378.
Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS, et al.
AutoDock4 and autoDockTools4: Automated docking with selective receptor flexibility. J Comput Chem 2009;30:2785-91.
Brooks BR, Brooks CL 3rd
, Mackerell AD Jr., Nilsson L, Petrella RJ, Roux B. CHARMM: The biomolecular simulation program. J Comput Chem 2009;30:1545-614.
Usha T, Middha SK, Goyal AK, Karthik M, Manoj D, Faizan S, et al.
Molecular docking studies of anti-cancerous candidates in Hippophae rhamnoides
and Hippophae salicifolia
. J Biomed Res 2014;28:406-15.
Cheng F, Li W, Zhou Y, Shen J, Wu Z, Liu G, et al.
AdmetSAR: A comprehensive source and free tool for assessment of chemical ADMET properties. J Chem Inf Model 2012;52:3099-105.
Rose PW, Prlić A, Altunkaya A, Bi C, Bradley AR, Christie CH, et al.
The RCSB protein data bank: Integrative view of protein, gene and 3D structural information. Nucleic Acids Res 2017;45:D271-81.
Dhanjal JK, Sreenidhi AK, Bafna K, Katiyar SP, Goyal S, Grover A, et al.
Computational structure-based de novo
design of hypothetical inhibitors against the anti-inflammatory target COX-2. PLoS One 2015;10:e0134691.
Rizvi SM, Shakil S, Haneef M. A simple click by click protocol to perform docking: AutoDock 4.2 made easy for non-bioinformaticians. EXCLI J 2013;12:831-57.
Forli S, Huey R, Pique ME, Sanner MF, Goodsell DS, Olson AJ. Computational protein-ligand docking and virtual drug screening with the autoDock suite. Nat Protoc 2016;11:905-19.
Huang N, Shoichet BK, Irwin JJ. Benchmarking sets for molecular docking. J Med Chem 2006;49:6789-801.
Umadevi I, Daniel M, Sabnis SD. Chemotaxonomic studies on some members of Anacardiaceae
. Proc Plant Sci 1988;98:205-8.
Lau-Cam CA, Chan HH. Flavonoids from Comptonia peregrine
. Phytochem 1973;12:1829.
Chua LS, Latiff NA, Lee SY, Lee CT, Sarmidi MR, Aziz RA. Flavonoids and phenolic acids from Labisia pumila
(Kacip Fatimah). Food Chem 2011;127:1186-92.
Abd El-Kader AM, El-Readi MZ, Ahmed AS, Nafady AM, Wink M, Ibraheim ZZ. Polyphenols from aerial parts of Polygonum bellardii
and their biological activities. Pharm Biol 2013;51:1026-34.
Hergert HL. The flavonoids of lodgepole pine bark. J Org Chem 1956;21:534-7.
Bahuguna P, Kharkwal H, Kharwal H, Datt JD. Myrica nagi
: A review on active constituents, biological and therapeutic effects. Int J Pharm Pharm Sci 2012;4:38-42.
D'Souza L, Wahidulla S, Devi P. Antibacterial phenolics from the mangrove Lumnitzera racemosa
. Indian J Mar Sci 2010;39:294-8.
Yadav AK, Thakur J, Prakash O, Khan F, Saikia D, Gupta MM. Screening of flavonoids for antitubercular activity and their structure-activity relationships. Med Chem Res 2013;22:2706-16.
Ono K, Nakane H, Fukushima M, Chermann JC, Barré-Sinoussi F. Differential inhibitory effects of various flavonoids on the activities of reverse transcriptase and cellular DNA and RNA polymerases. Eur J Biochem 1990;190:469-76.
[Figure 1], [Figure 2], [Figure 3], [Figure 4]
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5]