In Silico Study of Selected Phytoconstituents of Cajanus Cajan Peel

Pigeon pea (Cajanus cajan), commonly known as toor dal, is a leguminous plant belonging to the Fabaceae family and is widely cultivated in tropical and semi-arid regions [1,2]. It is typically grown as an annual crop and can reach a height of 3–10 feet, characterized by deep root systems, slender leaves, and yellow to reddish flowers [3]. The pods contain 2–9 seeds and serve as an important dietary source in countries such as India, Africa, and Asia. The seeds are nutritionally rich, containing 21–28% protein along with essential vitamins and minerals [4-7]. However, raw seeds contain antinutritional factors such as tannins and trypsin inhibitors, which are significantly reduced upon cooking.

The pods are flat, fibrous, and measure approximately 5–9 cm in length, enclosing hard-coated seeds of varying colors. The peel (pod husk), often discarded as agricultural waste, is fibrous and leathery upon drying, with colors ranging from pale green to purplish brown. Despite being underutilized, the peel represents a potential source of bioactive compounds [8-11].

Phytoconstituents are naturally occurring bioactive compounds synthesized by plants for protection against environmental stress. These compounds exhibit significant pharmacological activities [12,13]. Major classes include phenolics (flavonoids and tannins) known for antioxidant activity, terpenoids with aromatic and antimicrobial properties, alkaloids widely used in therapeutic agents, glycosides with diverse pharmacological actions, and organosulfur compounds with notable biological effects.

In recent years, in silico studies have emerged as powerful tools in drug discovery. These computational approaches utilize models and simulations to analyze biological and chemical systems, predict molecular interactions, and guide experimental studies while reducing time, cost, and animal testing [14]. In silico methods include structure-based approaches such as molecular docking, ligand-based approaches like QSAR, sequence-based analysis, systems biology, and simulation-based techniques such as molecular dynamics.

Molecular docking is a widely used computational technique that predicts the binding orientation and affinity between a ligand and a target protein. It is based on models such as lock-and-key, induced fit, and conformational ensemble [15]. Despite challenges such as protein flexibility and scoring function limitations, docking remains essential in lead identification.

ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) prediction is another critical component of in silico studies, enabling early evaluation of pharmacokinetic and safety profiles [16]. Tools such as SwissADME, pkCSM, ADMETlab, and PASS Online are commonly used for this purpose.

1. MATERIALS AND METHOD:
   1. Collection of Phytoconstituents:

Phytoconstituents of Cajanus Cajan were collected from published research articles and databases such as PubMed, PubChem, and Google Scholar. Reported compounds and their chemical structures were compiled, and structures were downloaded from PubChem in SDF format.

1. 2. Preparation of Ligands:

2D structures obtained from PubChem

Converted to 3D using ChemDraw/Chem3D

Energy minimized using MMFF94 force field

Saved in PDB format for docking

1. 3. Protein Selection and Preparation:

Target protein structures were retrieved from the Protein Data Bank (PDB). Selected proteins were prepared for docking by removing unwanted molecules and optimizing structures.

1. 4. Molecular Docking:

Docking performed using SwissDock

Protein and ligands converted to suitable format

Active site identified and grid set

Binding interactions and energies recorded

Compounds with lowest binding energy considered most active

1. 5. ADMET Analysis:

ADMET properties were predicted using tools like SwissADME, pkCSM, and admetSAR.

Parameters analyzed include:

Absorption: (GI absorption, Lipinski’s rule)

Distribution: (BBB permeability)

Metabolism: (CYP interaction)

Excretion: Toxicity (AMES, hepatotoxicity)

Compounds meeting drug-likeness criteria were identified as potential leads.

2. RESULTS:

3.1 Swiss ADME:

The predicted ADME properties of the phytoconstituents are as provided below:

• Most of the compounds are not permeable through the BBB except the cpmpound-2
• GI absorption of the compound 2,3,5 is very high and compound 1&4 is low
• All the compounds are not metabolized by all the inhibitors
• Log kp values are in range ( -5.36 to -8.59)
• All the compounds have same bioavailability score of 0.55

All the summarized values of Swiss ADME are represented in the table below:

Table-1 Molecular Details of Selected Compounds

3.2 Molecular Docking:

Molecular docking studies reveal the interaction between proteins and ligands based upon the binding affinity the activity of the compounds was identified. In our project work, we used 15 proteins and 5 ligands i.e. phytoconstituents of Cajanus Cajan when we compared all the 3 compounds, compound 2&3 have most binding affinity

3.2.1 Docking of ligand o-Methylisourea hydrogen sulfate:

1.with protein Casein Kinase II:

Figure-1 Compound-1 Docking with protein Casein Kinase II

2.With protein Bovine Serum Albumin:

Figure-2 Compound-1 Docking with protein Bovine Serum Albumin

3.With protein Lysozyme:

Figure-3 Compound-1 Docking with protein Lysozyme

3.2.2 Docking of 1,N-dimethyl -N- Propargyl-2-phenylethylmine:

1.With protein Human Serum Albumin:

Figure-4 Compound-2 Docking with protein Human Serum Albumin

2.With protein Acetylcholinesterase:

Figure-5 Compound-2 Docking with protein Acetylcholinesterase

3.With protein Bovine Serum Albumin:

Figure-6 Compound-2 Docking with protein Bovine Serum Albumin

3.2.3 Docking of 4,6-Dimethyl-3,5-Dioxo-2,3,4,5-Tetrahydro-1,2,4-Triazine:

1.With protein Urease:

Figure-7 Compound-3 Docking with protein Urease

2.Dihydro Folate Reductase:

Figure-8 Compound-3 Docking with protein Dihydro Folate Reductase

3.With protein Arginase:

Figure-9 Compound-3 Docking with protein Arginase

3.2.4 Docking of N-[3-Methylaminopropyl] Aziridine:

1.With protein Diamine Oxidase:

Figure-10 Compound-4 Docking with protein Diamine Oxidase

2.With protein Monoamine Oxidase:

Figure-11 Compound-4 Docking with protein Monoamine Oxidase

3.With protein Dehydrogenase:

Figure-12 Compound-4 Docking with protein Dehydrogenase

3.2.5 Docking of 1,3-Propanediamine:

1.With protein Diamine Oxidase:

Figure-13 Compound-5 Docking with protein Diamine Oxidase

2.With protein Monoamine Oxidase:

Figure-14 Compound-5 Docking with protein Monoamine Oxidase

3.With protein Polyamide Oxidase:

Figure-15 Compound-5 Docking with protein Polyamine Oxidase

3.3 Toxicology Study:

The In Silico toxicology evaluation was carried out to predict the safety profile of the selected phytoconstituents. Compound 2,3,4 are more toxic as they’re showing active toxicity at 4 sites and compound 1 at only one site Computational toxicology prediction tools were used to assess various toxicity parameters such as Hepatotoxicity, mutagenicity, carcinogenicity, cytotoxicity and acute oral toxicity.

Table-2 Toxic activity of the compounds