Bioinformatics Guided Antioxidant Property of Turmeric (Curcuma Longa)

Bioinformatics uses techniques from computer science and statistics to manage and analyze molecular data, aiding biological research. For example, the Protein Data Bank stores 3D structures of macromolecules, facilitating data access and submission ⁽¹⁾ Oxygen, known as the "Janus gas," plays a dual role in biology—critical for functions like oxidative phosphorylation to produce ATP, but also contributing to oxidative stress through reactive oxygen species (ROS). Antioxidants typically control ROS, but when their defense fails, oxidative stress can damage tissues. The term "oxidative stress" was first used in rubber chemistry in 1956 and expanded to cellular damage in 1985. ⁽²⁾ Antioxidants prevent oxidation, preserving food quality and delaying spoilage, especially in fats and oils.⁽³⁾ Turmeric (Curcuma longa), with curcumin as its active compound, has antiviral, anticancer, and cognitive benefits. It is widely used in medicine, cosmetics, and food, remaining nontoxic even at higher doses. ⁽⁴⁾

MATERIAL AND METHOD

Active Compounds in Turmeric: Data on curcuma longa active compounds was collected from the IMPPAT database, PubMed, Google Scholar, and PubChem for chemical information and SMILES structure. ⁽⁵⁾

Drug Likeness Screening: MolSoft software was used to evaluate the drug likeness score based on Lipinski’s Rule of Five, considering factors like lipophilicity, H-bond donors/acceptors, and molecular weight. ⁽⁶⁾

Target Prediction: SuperPred database was used to predict targets for turmeric compounds, selecting those with over 70% probability. ⁽⁷⁾

Oxidative Stress Targets: GeneCards database was queried for oxidative stress-related targets with a relevance score ≥5.0. ⁽⁸⁾

Common Targets Identification: Venny and Cytoscape were used to identify and visualize common targets between curcuma longa compounds and oxidative stress-relatedtargets. ⁽⁹⁾

Protein-Protein Interaction (PPI) Network Construction: STRING database created PPI networks for common targets, with Cytoscape used to identify core targets through network analysis. ⁽¹⁰⁾

Drug-Active Compound-Target-Pathway Network: KEGG pathways for oxidative stressrelated diseases were analyzed, and corresponding targets were imported into Cytoscape to visualize the network. ⁽¹¹⁾

Molecular Docking and Biological Activity Prediction: PASS Online predicted the biological activity of compounds, comparing them with known active/inactive structures to estimate their effectiveness. ⁽¹²⁾

Protein Structure Prediction and Quality Check: PROCHECK and ERRAT were used to assess the structural quality of predicted proteins using Ramachandran plots and error statistics.(13)

Protein and Ligand Preparation for Docking: Protein structures were obtained from the RCSB PDB, and ligands from PubChem, both in 3D formats, for use in docking simulations.(14)

In-Vitro Methodology:

Plant Material: Fresh turmeric rhizomes (Curcuma longa) were collected from Chikkodi Taluka, Belgaum District, Karnataka, India, and authenticated by Mrs. S. B. Patil, Associate Professor, GI Bagewadi College.

Chemicals and Reagents: Analytical grade chemicals were used, including DPPH, methanol, various reagents (e.g., Mayer's, Wagner's, Hager's), and potassium hydroxide pellets, sourced from Shree Enterprises. ⁽¹⁵⁾

Preliminary Phytochemical Evaluation: The crude extract was tested for phytoconstituents like carbohydrates, proteins, alkaloids, glycosides, terpenes, steroids, flavonoids, tannins, and saponins using standard tests. ⁽¹⁵⁾

Extraction Procedure: 100g of dried turmeric rhizome was ground, and the powder was macerated with 350ml of 90% ethanol for 3 hours, followed by percolation with 150ml of ethanol for 7 days, yielding a crimson-red extract stored in a dark, cool place. ⁽¹⁶⁾

Antioxidant Activity (DPPH ASSAY):

Sample Preparation: Three extract dilutions (250 µg/ml, 500 µg/ml, and 1000 µg/ml) were prepared in ethanol.

DPPH Assay: 200µl of extract was mixed with 1.8ml of 0.5mM DPPH ethanol solution, incubated for 30 minutes, and absorbance at 517nm was measured. Ascorbic acid was used as the standard. The inhibition percentage was calculated using the formula. ⁽¹⁷⁾

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RESULTS AND DISCUSSION

(In-Silico Network Pharmacology)

From 100 phytochemicals of Curcuma longa rhizomes analyzed via the IMPPAT Database, 13 compounds were selected based on drug-likeness scores from Molsoft: Thymol acetate, Bisabolone, Heptyl salicylate, Cyclocurcumin, Xanthorrhizol, 4-Carvomenthenol, 2-Hepten4- one, Carvacrol, Turmeronol A, Gamma-Terpineol, Procurcumadiol, Sesquisabinene, and Furanodienon. Their drug-likeness scores ranged from 0.00 to 0.52. Using Venny, common targets between the selected phytochemicals and disease targets were identified. Pathways linked to oxidative stress damage and phytochemical activity were also explored. These genes and targets were used to create a network using Cytoscape, highlighting the interactions between the compounds and their biological pathways.

Network pharmacology analysis identified key phytochemicals in Curcuma longa, including Furandieon, Xanthorrhizol, Thymol Acetate, Procurcumadiol, Bisabolone, Carvomenthenol, Gamma Terpineol, and Sesquisabinene. These compounds interact with target genes such as NFKB1, MAPK1, NOS3, and HSP90AA1, influencing antioxidant pathways.

Molecular Docking:

The molecular docking method is widely employed in the development of structure-based drugs. When it comes to drug discovery investigations, docking is critical. Docking is the process of selecting the best ligand-binding configuration that is both energetically and geometrically compatible with the protein binding site. Docking procedures. Begin by selecting a protein and a ligand molecule. Get the ligand and protein ready. In Protein, choose your docking location. Use the Protein Ligand Docking Tool to complete the process. By performing molecular Docking of ligands such as Furandieon, Xanthorrhizol, Thymol Acetate, Procurcumadiol, 1 Bisabolone, 4 Carvomenthenol, Gamma Terpineol and Sesquisabinene with target genes such as NFKB1, MAPK1, NOS3, HSP90AA1, PIK3R1, PRKCA, TLR4, PIK3CA we get binding affinities which is mentioned in Table 1

The maximum affinity of -7.6 kcal/mol indicates that the Furandieon bind to the MAPK target gene and shows the higher binding affinity.

Table2: Binding Affinity of Ascorbic Acid

Ramachandran Plots:

Plot is produced by the PROCHECK tool using an input of a protein file that has been simulated. It shows that the most preferred regions, additional allowed regions, generously allowed regions, and disallowed regions for the given protein are represented by residues with 90.073%, 91.257%, 91.835%, and 96.011%, respectively. No residue should be found in the banned or outlier zone, and no more than 2% of residues should be in the authorized region.it shown in figure 1,2,3,4.

Errat:

Based on statistics of non bonded interactions between various atom types computed by its sophisticated structural database. Figures 5,6,7, and 8 depict a graph between residues and error values as the ERRAT server's output. This input structure has a strong overall quality score of 90.073%, 91.257%, 91.835%, and 96.011%. The input structure should have a quality score of more than 95% if it has good resolution. With are solution of 2-3, the protein structure shows a score of more than 90%. In the ERRAT graph, regions with red and yellow colour represent the problematic part while the white colour represents the normal part in the structure. Residues with error values more than 95% and 99% can easily be identified from the plot analysis.

Antioxidant Activity of Fruit by DPPH Assay Method:

Test sample

At every concentration tested (250μg, 500μg, 1000μg), there was a noticeable radical scavenging activity. Ethanolic extract of Curcuma Longa Rhizhome at 250 μg/ml concentration shows free radical scavenging activity with 79.43% inhibition and at 500 μg/ml concentration shows free radical scavenging activity with 84.07% inhibition and at 1000 μg/ml concentration shows free radical scavenging activity with 89.79% inhibition.

Standard Drug: At every concentration tested (250μg,500 μg,1000 μg), there was a noticeable radical scavenging activity. Ethanol is used as a solvent. At 250 μg/ml concentration shows radical scavenging activity with 68.84% inhibition and at 500 μg/ml concentration shows radical scavenging activity with 72.42% and at 1000 μg/ml concentration shows radical scavenging activity with 79.90%.