|Year : 2023 | Volume
| Issue : 1 | Page : 55-64
Similarity and docking analysis of bioactive compounds from Adansonia digitata L. against vascular endothelial growth factor receptor—An in silico approach
Shikha Sharma, Vinay Janardan Shukla
Department of Pharmaceutical Chemistry, IPGT & RA, Gujarat Ayurved University, Jamnagar, Gujarat, India
|Date of Submission||23-Sep-2021|
|Date of Acceptance||21-Nov-2022|
|Date of Web Publication||30-Dec-2022|
Dr. Shikha Sharma
Department of Pharmaceutical Chemistry, ITRA, Gujarat Ayurved University, Jamnagar 361008, Gujarat
Source of Support: None, Conflict of Interest: None
BACKGROUND: The development of novel anticancer medications has been the most pressing necessity in recent years because cancer is one of the leading causes of death worldwide. For more than 50 years, natural remedies have been acknowledged as powerful in the fight against many illnesses, including cancer. So in the current study, different similarity-based approaches were used to discover whether the bioactive present in Adansonia digitata has anticancer potential through similarity analysis and in silico docking study. METHODS: The chemotype similarity searching was done using KNIME using Tanimoto and dice (substructure) similarity measures against the molecules present in the CHEMBEL database having activity on the HepG2 cell line. After that, docking was performed against vascular endothelial growth factor receptor 2, which is responsible for hepatic cancer, using Pyrx AutoDock wizard with MGL tools 1.5.6 by using a genetic algorithm, and visualization was done using UCSF chimera. RESULTS: The study has shown that the bioactives present in Adansonia digitata have similarity with the anticancer molecules having activity against the HepG2 cell lines and the docking study revealed that the binding energy ranges from -0.72 to -10.32 (Kcal/mol). Smaller binding energies represent the stronger interaction of the molecules. CONCLUSION: Adansonia digitata has bioactives that may be an effective inhibitor against the vascular endothelial growth factor receptor 2 and may possess anticancer properties.
Keywords: Adansonia, docking, receptor, similarity
|How to cite this article:|
Sharma S, Shukla VJ. Similarity and docking analysis of bioactive compounds from Adansonia digitata L. against vascular endothelial growth factor receptor—An in silico approach. J Drug Res Ayurvedic Sci 2023;8:55-64
|How to cite this URL:|
Sharma S, Shukla VJ. Similarity and docking analysis of bioactive compounds from Adansonia digitata L. against vascular endothelial growth factor receptor—An in silico approach. J Drug Res Ayurvedic Sci [serial online] 2023 [cited 2023 Jan 27];8:55-64. Available from: http://www.jdrasccras.com/text.asp?2023/8/1/55/366296
| Introduction|| |
The drug discovery and development process aims to make available new pharmacological interventions to prevent, treat, mitigate, or cure disease safely and effectively. It is a slow, complex, multidisciplinary, costly process and an integral part of contemporary drug research. The entrance into the force of REACH regulation in June 2007 boosted the interest in the field of in silico methodologies. Its goal is to avoid the unnecessary testing (especially animal testing) and to provide the registrants with several tools to pursue this objective, including the promotion of alternative test methods, such as in vitro and in silico methodologies like virtual screening methods. Virtual screening methods are divided into ligand-based virtual screening (LBVS) and structure-based virtual screening. Ligand-based approaches use structure-activity data from known actives to develop models, such as similarity searching, machine learning methods including quantitative-structure activity relationship, and ligand-based pharmacophore models. “Similarity” refers to resemblance, likeness, sameness, and agreement. The concept of similarity searching was applied to numerous applications and domains, such as pattern recognition, chemistry, and cheminformatics. It is closely related to the similar property principle, whereby structurally similar molecules are likely to have similar properties. The similarity coefficient plays an important role in the context of similarity search, as it is used to quantify the resemblance between the two structural representations. Most chemical similarity algorithms use topological two-dimensional (2D) or three-dimensional (3D) features. The basic idea behind all the software and algorithms is that if two ligands share a similar structure, they will have similar bioactivity. On the other hand, structure-based methods use the biological target’s 3D structure. In this case, candidate molecules are docked in the binding site and ranked based on their predicted binding affinity or complementarily.
Adansonia digitata, the baobab, is the most widespread tree species of the genus Adansonia. The scientific name Adansonia refers to the French explorer and botanist, Michel Adanson (1727–1806), who observed a specimen in 1749 on the island of Sor Senegal. The species name digitata (hand-like) indicates the shape of the leaves. This is a most remarkably shaped tree with a vast, swollen trunk that tapers suddenly and sends out several thick, horizontal tranches. The leaves are large and smooth, and digitate with five leaflets radiating from a central point. Flowers are large and hang like balls of pale-green suede before the creamy white petals burst open, and the fruit is gourd-like and has a spongy, acid pulp containing many blackish, kidney-shaped seeds surrounded by tough fibers. This plant is considered a source of antioxidants because of the presence of a high amount of flavonoid and phenolic compounds. It may have the potential to act as an anticancer as most of the plants having antioxidant potential are reported to have good anticancer activity. So, in the present study, an attempt was made to study whether the bioactive of the plant Adansonia digitata has anticancer potential using in silico similarity analysis and docking studies. These techniques are used to detect the chemical and biological activity relationship of compounds and to predict the therapeutic activity of plants based on active constituents present in the plant. It also supports the research paradigm’s animal replacement, refinement, and reduction (3Rs). Traditional methods have several drawbacks like these need the large amounts of test agents for in vivo testing, poor predictability of in vivo animal and in vitro models, a lack of reliable high-throughput in vitro assays, and several animals sacrificed during the animal studies. However, the usage of animal models is often subject to ethical considerations. As stated in the Science editorial: The FDA is also working to replace animal testing with a combination of in silico eventually and in vitro approaches. Therefore, alternative methods are being developed to reduce the requirement of animals in testing. Hence, it can be used to predict biological activity before the commencement of in vivo studies.
| Materials and Methods|| |
The reported structure of molecules present in the plant Adansonia digitata (shown in [Table 1]) is downloaded from the PubChem database into SDF (Structure-Data File) format. Some molecules are drawn by using ACD ChemSketch and saved in Mol format, and later on, converted into SDF format using open babel. These molecules represent the chemotype of the plant. Mol and SDF formats provide information for 2D or 3D structures (e.g., connectivity of atoms) and data annotation.
|Table 1: The molecule name, molecule code, type of molecule, and IUPAC name of reported molecules in Adansonia digitata|
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Lipinski screening of the molecules
All compounds are evaluated for Lipinski screening to determine their oral bioavailability using KNIME nodes (TeachOpenCADD workflow).
Similarity searching for anticancer activity
A similar study is done against the HepG2 cell line. HepG2 is the most frequently used liver cancer cell line responsible for hepatocellular carcinoma (HCC), the primary cancer of the liver. A similarity study is carried out using KNIME nodes in the following steps.
Data acquisition from ChEMBL
Information on compound structure, bioactivity, and associated targets is organized in databases such as ChEMBL. To get the target information, the data node is connected to the ChEMBL database by using Uniprot-ID CHEMBL395 (HepG2 cell line—hepatic cancer). Bioactivity data (IC50) and compound (SMILES) data from ChEMBL ID are downloaded and filtered using a row filter to remove missing values and duplicates. After that, entries are filtered with IC50 in molar units with exact measurements. Then all molar units were converted to nM and bioactivity data were converted from the IC50 values to pIC50 because the low IC50 values are difficult to read. A total of 6786 molecules having activity on the HepG2 cell line (hepatic cancer) were retrieved from the ChEMBL database.
Ligand-based screening for calculating compound similarity
Similarity study was done against the 6786 similar anticancer molecules as mentioned above and retrieved from the ChEMBL database against hepatic cell lines using MACCS and Morgan fingerprints and Tanimoto and dice similarity measures.
The docking was performed by the following steps:
Preparation of protein
Three-dimensional structure of the vascular endothelial growth factor receptor 2 (IVR2) was retrieved from the Research Collaboratory for Structural Bioinformatics protein data bank in PDB format; afterward, the retrieved structure was preprocessed. Protein retrieved from the protein data bank was opened with a word document and removed hetero atoms; then energy minimization was performed by using spdb viewer, and visualization was done by using UCSF chimera 1.11.2. Ramachandran plot was generated by using V life mds software.
Preparation of ligand
The compounds were added hydrogen, and energy was minimized with the Universal Force Field force field using the conjugate-gradient algorithm by open babel in PyRX. All structures were saved as PDB file format for input to pyrx 0.8 and then converted into protein data bank (Q) partial charge & (T) atom type (PDBQT) file format for input into the AutoDock version. Later, all lead molecules were converted into Auto Dock PDBQT format.
Receptor grid generation
It searches for favorable interactions between ligand molecules and a receptor molecule. The receptor grid was set up and generated from the auto grid panel. The grid dimension has grid center X-38.886, Y-24.986, Z-13.490, and no. of points X-122, Y-160, Z-128, and grid spacing is 0.3765A°.
Ligand was docked against the protein, and the interactions were analyzed by using pyrx 0.8. For the docking of ligands into active protein sites and to estimate the binding affinities of docked compounds, an advanced molecular docking program AutoDock Vina (4) was used in this study. All computational studies were carried out using pyrx AutoDock wizard with MGL tools 1.5.6, by using the genetic algorithm. The scoring function gives a score based on the best-docked ligand complex that is picked out.
AutoDock 4 writes out the coordinates of the atoms in the ligand. Each docked conformation is written in PDBQT format in the DLG (docking log file). UCSF chimera 1.11.2 is used to visualize the complexity of both the receptor and all the docked ligands.
| Observation and Results|| |
The molecule name, code, and IUPAC name of each of the molecule is shown in [Table 1].
All the molecules are passed through the Lipinski filter. This rule is an algorithm consisting of four rules in which many of the cutoff numbers are five or multiples of five, thus originating the rule’s name “Lipinski rule of 5.” To be drug-like, a candidate should have to pass all the rules. The main aim of Lipinski screening is to check the oral bioavailability of the molecules. In short, the substance should have a comparatively low molecular weight (MW), be relatively nonpolar, and partition between an aqueous and a particular lipid phase in favor of the lipid phase while simultaneously possessing perceptible solubility. However, some molecules do not obey the rules but are found to be better drugs with good approval. Out of 40 molecules, 15 molecules (M1, M6, M7, M8, M10, M15, M23, M25, M28, M29, M30, M31, M35, M37, and M38) have entirely passed the Lipinski rule of five by following all the rules as shown in [Table 2], and all other molecules have shown deviation by deviating the rules by 1, 2, or 3 rules.
The Lipinski filter shows that the molecules having a MW less than 400 Da and SlogP less than five have hydrogen bond acceptor (HBA) less than 10, while the molecules having MW more than 400 Da and SlogP more than five have HBA more than 10, whereas in relationship with hydrogen bond donor, most of the molecules have HBD less than five concerning MW less than 400 Da and SlogP less than five. Only some of the molecules having higher weight and SlogP less than five have shown HBD of more than 10 as shown in [Figure 1] (3D scatter plot, box plot).
|Figure 1: (A) 3D scatter plot of MW, SlogP, and HBA; (B) 3D scatter plot of MW, SlogP, and hydrogen bond donor|
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Similarity searching for anticancer activity
Similarity searching using KNIME
ChEMBL is a chemical database of bioactive molecules. A total of 6786 molecules having activity on the HepG2 Cell line (hepatic cancer) were retrieved from the ChEMBL database. Similarity searching was done using KNIME using Tanimoto and dice (substructure) similarity measures. Similarity study carried out using MACCS fingerprint using both Tanimoto and dice similarity has shown that most of the molecules have shown a similarity range of 0.8–0.9, whereas similarity using Morgan fingerprint using both Tanimoto and dice similarity has shown a similarity range of 0.5–0.6. However, some of the molecules have shown similarity more than this range, but they are lesser in number as shown in the scatter plot in [Figure 2].
|Figure 2: (A) Scatter plot of similarity using MACC fingerprint and Tanimoto similarity; (B) scatter plot of similarity using Morgan fingerprint and Tanimoto similarity; (C) scatter plot of similarity using MACC fingerprint and dice similarity; (D) scatter plot of similarity using Morgan fingerprint and dice similarity|
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Docking is performed against vascular endothelial growth factor receptor 2 (1VR2) responsible for hepatobiliary cancer. Ramachandran’s plot analysis for the target is shown in [Table 3] and [Figure 3].
|Table 3: Ramachandran plot of vascular endothelial growth factor receptor 2|
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|Figure 3: Ramachandran plot of vascular endothelial growth factor receptor 2|
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Results of molecular docking are shown in [Table 4] and interactions are shown in [Figure 4].
|Table 4: Result of molecular docking study of molecules present in Adansonia digitata with vascular endothelial growth factor receptor 2|
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|Figure 4: Docking complex of molecules with vascular endothelial growth factor receptor|
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| Discussion|| |
HCC, the primary cancer of the liver, is derived from hepatocytes. HCC is aggressive cancer that occurs in the setting of chronic liver disease and cirrhosis that frequently presents in advanced stages. Primary liver cancer, predominantly hepatoblastoma and HCC, is one of the most common solid tumors. HepG2 is the most frequently used liver cancer cell line. So, the similarity is done on HepG2 cell line. Chemical similarity searching using molecular fingerprints is one of the mainstay methods in the chemoinformatics field and continues to be one of the most popular approaches for LBVS. According to a similarity study using KNIME, one triterpenoid type of molecule and three flavonoid type of molecules have an approximate similarity range of 1; four triterpenoids, one steroid, and two polyphenolic types of moieties have more than 0.9 range; three triterpenoids, one steroid, two polyphenols, two flavonoids, and one coumarin type of the molecule have more than 0.8 range; and three flavonoid and one fatty acid type of molecule have shown similarity range of 0.7. So, it can be concluded that most of the flavonoid and triterpenoid types of molecules are similar to the HepG2 cell lines molecules.
Molecular docking studies were done to determine the strength of the interactions and to find out the best orientation of the interaction between molecules present in Adansonia digitata with vascular endothelial growth factor receptor 2, which would form a complex with protein with minimum energy. All the molecules have shown inhibition against vascular endothelial growth factor receptor 2 responsible for hepatobiliary cancer. All the molecules have shown binding energy ranges from -0.72 to -10.32 (Kcal/mol) against vascular endothelial growth factor receptor 2. Vascular endothelial growth factor receptor (VEGFR) belongs to tyrosine kinase receptors. These transduce extracellular signals into the cell through a series of specific phosphorylation events, starting with the activation of RAS, which in turn activates serine-threonine kinases of the RAF-family member called RAS/RAF/MEK/ERK extracellular signal-regulated kinase pathway. The RAS/RAF/MEK/ERK pathway is one of the most significant cellular signaling cascades in developing and maintaining liver cancer. Activated RAF phosphorylates MEK kinases, which activate ERK. Once activated, ERK enters the nucleus to act as a regulator of gene expression of various proteins for life processes, such as cell cycle progression, apoptosis, extracellular matrix remodeling, cellular motility angiogenesis, and drug resistance.
| Conclusions|| |
Cheminformatics is the domain that deals with chemical information, and both similarity and distance coefficient have played an important role in matching, searching, and classifying chemical information. The advance in computational science has given rise to many new possibilities for understanding the difference and similarities between molecules. The study has also shown that the chemotype of the plant A. digitata is similar to the anticancer molecules. According to the similarity principle, structurally similar molecules likely will have similar properties. The molecular docking results revealed that the bioactive present in Adansonia digitata could be an effective inhibitor against the vascular endothelial growth factor receptor 2. Therefore, the plant may possess anticancer activity. However, in vitro and in vivo studies are needed to be performed on animals, or validation in wet lab studies is required for further confirmation and exploration of the mechanism of action.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
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[Figure 1], [Figure 2], [Figure 3], [Figure 4]
[Table 1], [Table 2], [Table 3], [Table 4]