Int J App Pharm, Vol 16, Issue 5, 2024, 309-325Original Article
COMPUTATIONAL ASSESSMENT OF UNDARIA PINNATIFIDA AND MORINGA OLEIFERA COMPOUNDS AS ANTI-OBESITY AGENTS
C. SAI KALYANI YOGINI1, CHITTA SURESH KUMAR2, C. M. ANURADHA3, C. H. M. KUMARI CHITTURI*
1Department of Bio Medical Sciences, Assistant Professor, School of Health Sciences, the Apollo University, Chittoor-517127, Andhra Pradesh, India. 2,3Department of Biochemistry, Srikrishnadevaraya University, Ananthapuram-515003, India. *Department of Applied Microbiology and Biochemistry, Sri Padmavati Mahila Vishvavidyalayam, (Women’s University), Tirupati, Andhra Pradesh, India
*Corresponding author: C. H. M. Kumari Chitturi; *Email: chandi2222002@yahoo.co.in
Received: 15 Mar 2024, Revised and Accepted: 29 Jun 2024
ABSTRACT
Objective: The objective of this topic is to discuss the potential of using bioactive substances of Undaria Pinnatifida Ethanolic Extract of (UPEE) and Moringa Oleifera Methanolic Extract of (MOME) extracts as pharmacological agents and inhibitors of Peroxisome Proliferator-Activated Receptor gamma (PPAR-γ), Fat Mass and Obesity-Associated (FTO), Resistin and leptin to counter obesity.
Methods: The study uses Gas Chromatography-Mass Spectrometry (GC-MS) and Fourier-Transform InfraRed (FTIR) Spectroscopy techniques to identify the bioactive components of these extracts and evaluates their efficacy through in silico assessments and molecular docking analysis.
Results: Analysis of docking results revealed that ligand interaction with FTO (ID: 3LFM) docking complex showed good binding affinity, binding oreintation, pharmocological properties. Hence, the best ligands were proposed as the best antagonist to block PPAR-γ, FTO, Resistin and leptin, which plays major role in the drug devolopment pathways.
Conclusion: UPEE and MOME extracts acts as pharmacological agents for anti-obesity genes. PPAR-γ-4CI5 has a best docking score (-7.716 kcal/mol), as a result. As a result, the standard was recommended as the best antagonist to block the key enzyme involved in the drug development pathways.
Keywords: Obesity, Undaria pinnatifida, Moringa oleifera, PPAR γ, Molecular docking
© 2024 The Authors. Published by Innovare Academic Sciences Pvt Ltd. This is an open access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/)
DOI: https://dx.doi.org/10.22159/ijap.2024v16i5.50867 Journal homepage: https://innovareacademics.in/journals/index.php/ijap
INTRODUCTION
Obesity, characterized by excess fat accumulation, affects about half of the global population, as reported by the World Health Organization (WHO) in 2016 [1]. Contributing factors include genetic predisposition, energy imbalance, and excessive calorie intake [2]. It is associated with various degenerative diseases such as type 2 diabetes, cardiovascular conditions, and certain cancers [3]. The primary approach to combat obesity involves medications like orlistat, which inhibit pancreatic lipase expression, leading to delayed fat breakdown and subsequent loss through faeces [4, 5]. However, some anti-obesity medications have had limitations due to side effects [6, 7]. Fucoxanthin, a unique allenic carotenoid, has shown promise in inhibiting lipogenic enzyme activities and improving insulin sensitivity [8]. Natural substances like Undaria pinnatifida contain bioactive compounds with antioxidant, antitumor, anti-hypertensive, hypolipidemic, and immunoregulatory properties [9, 10]. Scientific literature has extensively demonstrated the beneficial effects of Moringa oleifera, a nutritious herb and miracle tree. It has been shown to improve lipid profiles, reduce body weight, enhance insulin signalling, and regulate satiety hormones like ghrelin and leptin [11]. Moringa oleifera is rich in various nutrients, including carbohydrates, amino acids, minerals like potassium and calcium, vitamins, oils, isothiocyanates, and phenolics such as Rutin, p-Coumaric acid, Chlorogenic acid, Kaempferol, and Quercetin [12, 13]. Moreover, it possesses therapeutic properties, including anticancer, antioxidant, anti-obesity, anti-ulcer, antiepileptic, antipyretic, and anti-inflammatory effects [14].
In the field of bioactive medication interactions, kinetic assessments and in silico molecular docking analyses are commonly employed techniques [15-17]. Additionally, in silico analytical methods like semi-empirical quantum mechanics and cheminformatics tools such as pre-Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) properties can accurately predict chemical properties and drug-like behaviour, aiding in drug development processes [18].
MATERIALS AND METHODS
FTIR analysis
One of the most useful instruments for identifying the many kinds of chemical bonds (also known as functional groups) that are present in compounds is the FTIR, following standard procedure [34]. The wavelength of light absorbed indicates the nature of the chemical bond, as can be seen in the annotated spectrum. Dried powders of different solvent extracts of every plant material were employed for FTIR analysis. 100 mg of Potassium Bromide (KBr) pellet and 10 mg of dried extract powder were combined to create translucent sample discs. FTIR was filled with a powdered sample of each plant specimen.
GC-MS analysis
The process of extracting UPEE and MOME was taken Department of Science and Technology (DST) Consolidation of University Research for Innovation and Excellence (CURIE) Sri Padmavathi Mahila Vishwa Vidyalayam (SPMVV) (Women’s University), Tirupati, a standard GC-MS model to analyse a whole plant extract (methanolic) [19, 35]. The Agilent 7890 GC-MS apparatus and Flame Ionisation Detector (FID) detector were used and ran for 35 min in total. The instrument used was the Joel Accu Time of Flight (TOF) Analyzer for Mass Spectroscopy (MS). The resolution is 6000 and the mass range is 10-2000 amu. Splitless injection (20:80-8-200-5M-8-260-10M-10-280-HP5-ETOH) of 1.0 l of the sample in methanol was used for GC-MS analysis. The gas chromatograph, a Hewlett Packard 6890 Unites States of America (USA), was fitted with a cross-linked 5 percent phenyl methyl Siloxane Hewlett Packard (5%-phenyl)-methylpolysiloxanephasecapillary column (length 30 mm x internal diameter 0).
GC-MS operating conditions
The initial column temperature was 35 °C, with a 3-minute hold time. The temperature was set to rise at an 8 °C/min rate, with a final temperature of 280 °C. 1l of the sample was injected into the port, vaporized, and moved down the column with helium as the carrier gas at a flow rate of 1 ml/min. At 70 eV, the MS Spectrum was captured. Following the separation in the column, the components were identified and analysed further using the FID. The compounds were identified by comparing the spectrum of unknown compounds to the spectrum of known compounds in the National Institute of Standards and Technology (NIST) mass spectral 2.0 structural library to determine their names, molecular weight, and structure.
In silico analysis of anti-obesity proteins
Retrieval and visualization of structure of anti-obesity proteins (PPAR-γ, α-Glucosidase, FTO, Resistin, Leptin)
The 3-dimensional atomic coordinates of a protein determine its tertiary structure [18]. Proteins can perform a variety of tasks, from molecular recognition to catalysis, and these tasks call for a precise tertiary structure. The primary structure of the biomolecules, or the order of the amino acids, is important for determining tertiary structure. Databases for protein tertiary structures such Protein Data Bank (PDB) sum, PDB and Molecular Modelling Database (MMDB), etc. The PDB sum provides a summary of all macromolecular structures that have been stored in the PDB, together with schematic diagrams of the molecules that make up each structure and their relationships. Other domains include links to literature, similar sequences, information about compounds bound to structure, and other domains are also included.
The 3-dimensional structural information of big biological molecules like proteins and nucleic acids is stored in the PDB. Global biochemists and biologists contribute the data, which is commonly obtained using X-ray crystallography or Nuclear Magnetic Resonance (NMR) spectroscopy. The data are available online. PDB is the most important source for structural biology information. About 15% of the structures were established using protein NMR while a small number were even determined by cryo-electron microscopy. X-ray diffraction is used to determine many structures. The protein structure's 3-dimensional coordinate file can be downloaded from PDB and viewed using programmes like PyMOL, Rasmol, Cn3 dimensional, Swiss protein database viewer, and molscript, among others.
The crystal structures of PPAR-γ (ID: 4CI5), α-Glucosidase (7P2Z) FTO (ID: 3LFM), Resistin (1LV6), Leptin (ID: 1AX8), were acquired from the PDB and visualised using Maestro v13.0 both biological macromolecules like proteins and tiny molecules can be rendered in stunning 3-dimensional visuals.
Virtual screening
Screening is virtually the main application of docking software. A computational method called virtual screening is used to explore databases of small molecules for those structures that are most likely to be therapeutic targets for protein receptors or enzymes [20, 21]. Virtual screening often comes in two Flavors: docking, or screening based on structure, and ligand-based virtual screening, or screening based on active compounds as templates. A technique called protein-ligand docking sometimes referred to as structure-based virtual screening, and involves comparing ligands to protein targets and then using a scoring function to ascertain whether the ligand will bind to the protein with a high affinity [22].
Virtual screening is a complementary method to experimental Standard Precision (SP) that analyses enormous databases of chemical compounds using computers to identify potential drug candidates SP. As part of the process of developing novel drugs, SP technology enables the testing of hundreds to millions of molecules for activity against a new target system. One of the main goals of the drug discovery process is to identify novel chemical compounds that have a high probability of binding to the target protein and causing the intended biological effect. Structure-based virtual screening involves a number of phases.
Compiling ligand dataset
UPEE and MOME were analysed using GC-MS, and bioactive substances like fucoxanthin, fucosterol, and fucoidan were found. Octahydro-1H-1,3,2-benzodiazaphosphole, 2-(3-Methylphenoxy) 1,4,10,13,16-diazacyclooctadecane, and 2-oxide Benzaldehyde, Thiofene, D-Threonine, N, N'-Ethylenebis(N-nitroacetamide), Methoxyacetic anhydride acid and 13-Pentano-1,4,10-trioxa-7,13-diazacyclopentadecane have significant therapeutic potential. Consequently, the existence of these phytochemicals may be to blame for the plant's medicinal properties.
Fig. 1: Ligands retrieved from PubChem
The primary ligand sources for applications involving virtual screening are small molecule databases. To achieve the best results in computational docking research, the numerous chemicals present in small molecule databases must be simplified and optimised.
By incorporating tautomeric, stereochemical, and ionization variants, energy minimization, and customizable filters, LigPrep creates completely personalized ligand libraries by going much beyond straightforward 2-dimensional to 3-dimensional structural translations. That are suited for subsequent computational investigations. LigPrep can extend stereoisomers, ring conformations, tautomeric and ionisation states, and ionisation states to generate a wide range of chemical and structural diversity from a single input structure. Epik is a programme for generating the likely ionised and tautomerized structures within a specified pH range, as well as for predicting the pKa values of the ionizable groups in ligands.
Both physically important descriptors and qualities that are important for medicinal applications are predicted by the analogues UPEE and MOME. It includes primary descriptors, physiochemical characteristics, and a thorough examination of absorption. Drug design must consider distribution, metabolism, excision, and toxicity prediction. The UPEE and MOME principles for the last batch of promising leads against PPARG γ (ID: 4CI5), glucosidase (ID: 7P2Z), FTO (ID: 3LFM), resistin (ID: 1LV6), and leptin (ID: 1AX8).
Protein preparation
The three-dimensional arrangement of PPAR-γ or (ID: 4CI5), α-Glucosidase (ID: 7P2Z), FTO (ID: 3LFM), Resistin (ID: 1LV6), Leptin (ID: 1AX8), The Maestro (13) protein preparation procedure was used to pre-process adiponectin (ID: 4D04) (Schrodinger LLC, 2022). After adding all the hydrogens, the impact molecular mechanics engine and Optimised Potentials for Liquid Simulations (OPLS) 2005 force field were used to minimise the hydrogens (Schrodinger LLC, 2022). The heavy atoms were minimised by turning off the hydrogen torsion parameters, allowing the hydrogens to rotate freely. The maximum Root mean Square Deviation (RMSD) was set at 0.30Å.
Computational docking using Schrodinger software
A simulation method called "molecular docking" can be used to predict the shape of a complex between a tiny chemical and a protein receptor. It is also known as a simulation technique in which the location of a ligand in an anticipated or predetermined binding site of the receptor molecule is approximated.
The docking and scoring calculations were performed Glide v6.0, available in Schrodinger 2022. From the High-Throughput Virtual Screening (HTVS) mode, which effectively enriches millions of compound libraries, to the SP, which consistently docks tens to hundreds of thousands of ligands with high accuracy, to the Extra Precision (XP) mode, which further eliminates false positives through more extensive sampling and advanced scoring, leading to even higher enrichment, Glide offers the full range of speed vs. accuracy options. Glide approximates a comprehensive systematic search of the conformational, orientation, and positional space of the docked ligand.
For the constructed protein, a receptor grid was created so that different ligand configurations would bind within the anticipated binding region during docking. The grid in Glide was created using the OPLS 2005 force field with the default values of the van der Waals scaling factor of 1.00 and the charge cutoff of 0.25. The centroid of the binding site residues served as the centre of a cubic box that was created to a specified size. For the docking experiments, the bounding box was set to 10 Å x 10 Å x 10Å.
The ligand dataset (prepared ligands and published inhibitors) was docked into the grid of structure using Glide. Glide SP mode was applied for the prepared ligands along with published inhibitors in PPAR-γ (ID: 4CI5), α-Glucosidase (ID: 7P2Z), FTO (ID: 3LFM), Resistin (ID: 1LV6), Leptin (ID: 1AX8), Adiponectin (ID: 4D04). The lead molecules with least docking score were ranked and evaluated for ADMET properties using QikProp. The obtained docking score of leads were compared with existing inhibitors.
Orlistat with anti-obesity proteins
The anti-obesity proteins PPAR-γ (ID: 4CI5), α-Glucosidase (ID: 7K9N), FTO (ID: 3LFM), Resistin (ID: 1RFX), Leptin (ID: 3V6O) were aimed at molecular docking for virtual ligand screening with Orlistat.
RESULTS
FTIR analysis of Undaria pinnatifida
An analysis of algal biomass using Fourier infrared spectroscopy showed the presence of phenols, OH groups, phenols, carboxylic acids, halogens, methyl groups vinyl compounds, amides, alkenes and alkyl halide organic compound groups.
Table 1: Compounds from FTIR analysis of U. Pinnatifida
| Peak values | Functional groups |
| 3942.55-3687.45 | O-H Stretching |
| 3583.62 | Phenols |
| 3288.63 | C-H |
| 3251.23 | carboxylic acid |
| 2329.51 | C-N |
| 928.1 | C=C alkene |
| 500 | halogens |
| 421.33 | Alkyl halides |
Fig. 2: FTIR analysis of UPEE
FTIR analysis of MOME
An analysis of algal biomass using Fourier infrared spectroscopy showed the presence of phenols, aldehyde alkanes, amines, methyl groups, vinyl compounds, amides, hydroxyl (OH) groups and carboxylic acids organic compound groups.
GC-MS analysis of Undariapinnatifida
Moringa oleifera leaves were subjected to phytochemical and GC-MS profiling, which indicated the presence of bioactive compounds such as fucoxanthin, pentaborane, D-Threonine, 13-Pentano-1,4,10-trioxa-7,13-diazacyclopentadecane, propanoic acid and hydroxypropanoic acid possessing significant therapeutic qualities. Therefore, the plant's medicinal properties may be due to the presence of these phytochemicals.
Table 2: Compounds from FTIR analysis of Moringa oleifera
| Peak values | Functional groups |
| 3634.77 | O-H Stretching |
| 3556.36 | Phenols |
| 3494.47 | C-H stretching |
| 3437.34 | N-H |
| 3309.13 | carboxylic acid |
| 1636.43 | C=C alkene |
| 516.35 | halogens |
Fig. 3: FTIR analysis of MOME
Table 3: GC-MS analysis for bioactive components in UPEE
| R. time | Area % | Compund name |
| 0.066 | 81.09 | 2-(3-Methylphenoxy)octahydro-1H-1,3,2-benzodiazaphosphole 2-oxide |
| 0.875 | 0.43 | Hydrochloric Acid |
| 1.24 | 0.27 | Hexaborane |
| 27.77 | 0.14 | Benzaldehyde, |
| 28.385 | 1.24 | Fucoxanthin |
| 28.678 | 1.67 | Fucoidan |
| 29.103 | 2.29 | Fucosterol |
| 1.363 | 0.13 | benzene |
| 1.058 | 0.7 | 1,3-Difluoro-2-propanol |
Fig. 4: GC-MS analysis of UPEE
GC-MS analysis of Moringa oleifera
The phytochemical compounds of MOME were identified based on mass spectra by comparing them with spectrum known components stored in NIST WILEY library. The active compounds in the extract of with their Retention Time (RT), area percentage were Pentaborane (1.112 retention time and 10.55% peak area), 4,7,13-Trioxa-1,10-diazacyclopentadecane (1.246 retention time and 82.37 % peak area), D-Threonine(1.511 retention time and 6.77% peak area), N, N'-Ethylenebis(N-nitro acetamide) (10.338 retention time and 0.13% peak area), Fucosterol (29.103 retention time and 2.29 % peak area) and Acetic acid, methoxy-, anhydride (10.82 retention time and 0.04 % peak area). Hence, the presence of these phytochemicals could be responsible for the therapeutic effects.
Table 4: Compounds from GC-MS analysis of Moringa oleifera
| R. time | Area % | Compund name |
| 1.112 | 10.55 | Pentaborane (9) |
| 1.246 | 82.37 | 4,7,13-Trioxa-1,10-diazacyclopentadecane |
| 1.511 | 6.77 | D-Threonine |
| 10.338 | 0.13 | N,N'-Ethylenebis(N-nitroacetamide) |
| 29.103 | 2.29 | Fucosterol |
| 10.82 | 0.04 | Acetic acid, methoxy-, anhydride |
Fig. 5: GC-MS analysis of MOME
In silico analysis of anti-obesity proteins
PPAR-γ (PDB ID: 4CI5)
The co-crystal structure of PPAR-γ (ID: 4CI5) is retrieved and visualized by using the visualization tool PyMOL. The active site residues which are involved in bonding with ligand 2-methyl-2-[4-[2-[4-[(E)-phenyldiazenyl] phenoxy] ethyl]phenoxy]propanoic acid PDB site records.
Fig. 6: PPAR-γ (ID: 4CI5)
Computational docking using schrodinger
A receptor grid of 10Å x 10Å x 10Å was generated around active site residues fun initial docking of 13 ligand molecules docked with significant docking score. The best lead fucosterol (lead-1) showed the lowest docking showed score of -6.709kcal/mol (figure).
Fig. 7: Molecular interactions of PPAR-γ (PDB ID: 4CI5) with fucosterol (5281328)
Lcu 229 formed two hydeogen bonds with ligand. The active residues such as The active site residues such as His-121, Tyr-127, Glu-205, Lcu-229, Ser-289, Ser-299, Glu-295, Ser-299, His-323, Ilu-326, Icc-440, His-449, Tyr-473 formed in molecular interactions were also well correlated with the active site residues of native co-crystal structure of PPAR-γ (PDB ID: 4CI5) and the residues Phe-226, Pro-227, Ala-292, Tyr-327, Met-329, Leu-333, His-449, Leu-453, Tyr-473 were around the active site (within 4 A˚) found to be present involed in van der Waals interactions with ligand.
Analysis of docking results revealed that ligand interaction (Aminocyclitol) with protein PPAR-γ docking complex showed good binding affinity, binding orientation, pharmocological properties. The formation of hydrogen bond along with the residues involed in van der Waals interctions were also highly important for the stability of the docking complex. Hence, lead-1with better docking score (6.709kcal/mol). Hence, the ligands were proposed as the best antagonist to block PPAR-γ of, which plays major role in drug development.
Table 5: H-bond donor and acceptor and rotatable bonds and docking score with PPAR-γ with ligands
| S. No. | Title | Entry name | Donor HB | Accpt HB | glide rotatable bonds | docking score |
| 1 | 5281328 | Fucosterol | 1 | 1.7 | 6 | -6.709 |
| 2 | 1.3E+08 | Fucoidan | 2 | 9.1 | 4 | -6.503 |
| 3 | 11989 | Benzothiazole | 0 | 1.5 | 1 | -6.111 |
| 4 | 72805 | TT7 | 2 | 9.8 | 0 | -6.024 |
| 5 | 69435 | Threonine | 3 | 3.7 | 3 | -5.895 |
| 6 | 123435 | Trioxadiazacyclopentadecane | 2 | 8.1 | 0 | -5.813 |
| 7 | 240 | Benzaldehyde | 0 | 2 | 1 | -5.751 |
| 8 | 1032 | Propionic acid | 1 | 2 | 1 | -5.269 |
| 9 | 8030 | Thiofene | 0 | 0 | 0 | -4.521 |
| 10 | 88095 | Methoxyacetic_anhydride | 0 | 7.9 | 6 | -3.697 |
| 11 | 1.02E+08 | Ethylenebisacetamide | 4 | 9.4 | 15 | -2.219 |
*Hydrogen bond donor (<5), *Hydrogen bond acceptor (<10), *Number of rotatable bonds, *Docking score.
Table 6: ADMET properties as verified by using QikProp (Schrodinger 13) of PPAR-γ with ligands
| S. No. | Title | Entry name | Mol MW | QPlog Po/w |
QPlog S |
QPP Caco |
QPlog BB |
QPP MDCK |
QPlog Kp |
QPlog Khsa |
Percent Human oral absorption |
Rule of five |
Rule of three |
| 1 | 5281328 | Fucosterol | 412.698 | 7.522 | -8.722 | 3405.278 | -0.292 | 1860.133 | -1.718 | 2.071 | 100 | 1 | 1 |
| 2 | 1.3E+08 | Fucoidan | 256.27 | -0.047 | -1.192 | 100.862 | -0.959 | 53.808 | -3.752 | -1.126 | 62.533 | 0 | 0 |
| 3 | 11989 | Benzothioazole | 181.27 | 3.031 | -2.735 | 6656.771 | 0.574 | 10000 | -1.097 | -0.074 | 100 | 0 | 0 |
| 4 | 72805 | TT7 | 262.348 | 0.099 | 2 | 402.712 | 0.098 | 226.545 | -4.275 | -1.265 | 74.148 | 0 | 0 |
| 5 | 69435 | Threonine | 119.12 | -3.351 | 0.054 | 10.81 | -0.639 | 5.218 | -6.624 | -1.026 | 25.826 | 0 | 1 |
| 6 | 123435 | Trioxadiaza cyclopentadecane |
218.295 | -0.235 | -0.015 | 286.124 | 1.068 | 156.57 | -6.292 | -0.528 | 69.536 | 0 | 0 |
| 7 | 240 | Benzaldehyde | 106.124 | 1.479 | -1.116 | 1830.242 | -0.08 | 950.803 | -2.121 | -0.657 | 94 | 0 | 0 |
| 8 | 1032 | Proionicacid | 74.079 | 0.606 | 0.284 | 239.776 | -0.284 | 134.41 | -3.405 | -0.962 | 73.087 | 0 | 0 |
| 9 | 8030 | Thiofene | 84.136 | 1.815 | -1.449 | 9906.038 | 0.192 | 10000 | -0.841 | -0.374 | 100 | 0 | 0 |
| 10 | 88095 | Methoxyacetic _anhydride |
162.142 | -1.384 | 1.316 | 1124.064 | -0.516 | 561.37 | -2.972 | -1.949 | 73.443 | 0 | 0 |
| 11 | 1.02E+08 | Ethylenebis acetamide |
296.399 | -1.022 | -0.349 | 30.277 | -2.246 | 103.075 | -4.17 | -1.585 | 47.473 | 0 | 0 |
*Molecular weight (<500 Da), *Predicted octanol/water partition-efficient log P (acceptable range-2-6.5), *Predicted aqueous solubility test: S Mol/l(acceptable range6.5-0.5), *Apparent Caco2 permeability nm/sec(<25 poor and>500 high), *prediction of brain/blood; (acceptable range-3.0-1.2),*Predicted apparent MDCK cell permeability in nm/s (<25 poor and>500 high),*log Kp for skin permeability (Kp in cm/hr), *log Khsa serum protein binding (-1.5-1.5), Lipinski rule of five violation (max is 4), Jorgenson rule of 3 violation (max is 3), *Percentage of Human oral absorption(<25%is poor and>80% is high), ADMET PROPERTIES: absorption, distribution, metabolism, excretion, toxicity.
| PPAR-γ (ID: 4CI5) 2D and 3-dimensional molecular Interaction with obtained lead molecules | |||
| PMID-5281328_Fucosterol | PMID-129532628_ Fucoidan | ||
| PMID-11989_2-(methylthio) benzothiazole | PMID-72805_ 1,4,10,13Tetraoxa-. 7,16-diazacyclooctadecane | ||
| PMID-69435_ D-threonine | PMID-1234351,4,10-trioxa-7,13 diazacyclopentadecane | ||
| PMID-240_ benzaldehyde | PMID-1032_ propionic acid | ||
| PMID-88095_Methoxyacetic anhydride | PMID-101922872_Ethylenebisacetamide | ||
Fig. 8: Molecular interactions of PPAR-γ (PDB ID: 4CI5) with ligands
Alpha glucosidase (AG) (PDB ID: 7K9N)
The co-crystal structure of AG (ID: 7K9N) and visualized by using the visualization tool PyMOL. The active site residues which are involved in bonding with ligand W9G (Hydroxy Methyl methoxynonyl amino cyclohexane-tetrol) from the PDB site records.
Computational docking using schrodinger
A receptor grid of 10Å x 10Å x 10Å was generated around active site residues AG. Initial docking of 13 ligand molecules catalase docked with significant docking score. The best lead, Trioxadiazacyclopentadecane showed the lowest docking showed score of-6.466kcal/mol (fig).
ASP-640 created one salt bridge and one hydrogen bond, interaction with ligand. The active site residues such as Asp-34, Asp-305, Trp-423, Asp-451, Trp-525, Asp-564, Phe-571, Arg-624, Asp-640, Phe-673, Phe-674, His-698, His-700 formed molecular interactions were also well correlated with the active site residues of native co-crystal structure of AG and the residues Phe-307, Tyr-318, Trp-423, Ile-452, Ile-488, Trp-525, Trp-562, Met-565, Ser-569, Asn-572, Bal-576, Trp-637 were around the active site (within 4 A˚) found to be present involed in van der Waals interactions with ligand.
Analysis of docking results revealed that ligand interaction with protein AG docking complex showed good binding affinity, binding orientation, pharmocological properties. The formation of hydrogen bonds along with the residues involved in van der Waals interactions were also highly important for the stability of the docking complex. Hence, lead-1 with a better docking score (-6.466kcal/mol). Hence, the best ligands are the best antagonist to block AG of antagonistsplays a major role in drug development.
Fig. 9: AG (ID: 7K9N)
Fig. 10: Molecular interactions of AG (PDB ID: 7K9N)
Table 7: H-bond donor and acceptor and rotatable bonds and docking score with AG with ligands
| Title | Entry name | Donor HB | Accpt HB | Glide rotatable bonds | Docking score |
| 123435 | Trioxadiazacyclopentadecane | 2 | 8.1 | 0 | -6.466 |
| 69435 | Threonine | 3 | 3.7 | 3 | -5.736 |
| 240 | Benzaldehyde | 0 | 2 | 1 | -5.308 |
| 72805 | TT7 | 2 | 9.8 | 0 | -5.229 |
| 11989 | Benzothioazole | 0 | 1.5 | 1 | -5.103 |
| 5281328 | Fucosterol | 1 | 1.7 | 6 | -4.496 |
| 8030 | Thiofene | 0 | 0 | 0 | -4.244 |
| 1.3E+08 | Fucoidan | 2 | 9.1 | 4 | -3.667 |
| 1.02E+08 | Ethylenebisacetamide | 4 | 9.4 | 15 | -2.888 |
| 88095 | Methoxyacetic_anhydride | 0 | 7.9 | 6 | -2.344 |
| 1032 | Proionicacid | 1 | 2 | 1 | -1.322 |
*Hydrogen bond donor (<5), *Hydrogen bond acceptor (<10), *Number of rotatable bonds, *Docking score.
Table 8: ADMET properties of AG with ligands
| S. No. | Title | Entry name | mol MW | QPlog Po/w |
QPlog S |
QPP Caco |
QPlog BB |
QPP MDCK |
QPlog Kp |
QPlog Khsa |
Percent human oral absorption |
Rule of five |
Rule of three |
| 1 | 123435 | Trioxadiazacyclo pentadecane |
218.295 | -0.235 | -0.015 | 286.124 | 1.068 | 156.57 | -6.292 | -0.528 | 69.536 | 0 | 0 |
| 2 | 69435 | Threonine | 119.12 | -3.351 | 0.054 | 10.81 | -0.639 | 5.218 | -6.624 | -1.026 | 25.826 | 0 | 1 |
| 3 | 240 | Benzaldehyde | 106.124 | 1.479 | -1.116 | 1830.242 | -0.08 | 950.803 | -2.121 | -0.657 | 94 | 0 | 0 |
| 4 | 72805 | TT7 | 262.348 | 0.099 | 2 | 402.712 | 0.098 | 226.545 | -4.275 | -1.265 | 74.148 | 0 | 0 |
| 5 | 11989 | Benzothioazole | 181.27 | 3.031 | -2.735 | 6656.771 | 0.574 | 10000 | -1.097 | -0.074 | 100 | 0 | 0 |
| 6 | 5281328 | Fucosterol | 412.698 | 7.522 | -8.722 | 3405.278 | -0.292 | 1860.133 | -1.718 | 2.071 | 100 | 1 | 1 |
| 7 | 8030 | Thiofene | 84.136 | 1.815 | -1.449 | 9906.038 | 0.192 | 10000 | -0.841 | -0.374 | 100 | 0 | 0 |
| 8 | 1.3E+08 | Fucoidan | 256.27 | -0.047 | -1.192 | 100.862 | -0.959 | 53.808 | -3.752 | -1.126 | 62.533 | 0 | 0 |
| 9 | 1.02E+08 | Ethylenebis acetamide |
296.399 | -1.022 | -0.349 | 30.277 | -2.246 | 103.075 | -4.17 | -1.585 | 47.473 | 0 | 0 |
| 10 | 88095 | Methoxyacetic_ anhydride |
162.142 | -1.384 | 1.316 | 1124.064 | -0.516 | 561.37 | -2.972 | -1.949 | 73.443 | 0 | 0 |
| 11 | 1032 | Proionicacid | 74.079 | 0.606 | 0.284 | 239.776 | -0.284 | 134.41 | -3.405 | -0.962 | 73.087 | 0 | 0 |
*Molecular weight (<500 Da), *Predicted octanol/water partition-efficient log P (acceptable range-2-6.5), *Predicted aqueous solubility test: S Mol/l(acceptable range6.5-0.5), *Apparent Caco2 permeability nm/sec(<25 poor and>500 high), *prediction of brain/blood; (acceptable range-3.0-1.2),*Predicted apparent MDCK cell permeability in nm/s (<25 poor and>500 high),*log Kp for skin permeability (Kp in cm/hr), *log Khsa serum protein binding (-1.5-1.5), Lipinski rule of five violation (max is 4), Jorgenson rule of 3 violation (max is 3), *Percentage of Human oral absorption(<25%is poor and>80% is high), ADMET properties.
| AG (PDB ID: 7K9N) 2D and3-dimensional molecular Interaction with obtained lead molecules | |||
| PMID-1234351,4,10-Trioxa | PMID-69435_ D-Threonine 7,13-Diazacyclopentadecane | ||
| PMID-240_ Benzaldehyde | PMID-72805_ 1,4,10,13-Tetraoxa-7,16-Diazacyclooctadecane | ||
| PMID-11989_2-(Methylthio) Benzothiazole | PMID-5281328_Fucosterol | ||
| PMID-8030_Thiofene | PMID-129532628_ Fucoidan | ||
| PMID-101922872_Ethylenebisacetamide | PMID-88095_methoxyacetic anhydride | ||
| PMID-1032_ propionic acid | |||
Fig. 11: Molecular interactions of AG (PDB ID: 7K9N) with ligands
FTO (ID: 3LFM)
The co-crystal structure of FTO (ID: 3LFM) and visualized by using the visualization tool PyMOL. The active site residues which are involved in bonding with ligand 3Metyl thymidine from PDB site records.
Computational docking using schrodinger
A receptor grid of 10Å x 10Å x 10Å was generated around active site residues FTO (ID: 3LFM). Initial docking of 13 ligand molecules catalase docked with significant docking score. The best lead (Benzothioazole) showed lowest docking showed score of-5.566kcal/mol (Figure).
Fig. 12: FTO (ID: 3LFM)
Fig. 13: Molecular interactions of FTO (ID: 3LFM)
Arg-108 Tyr-108 formed one hydrogen bond ASP-152 formed Pi-Pi staking interaction with ligand. Residues from the active site, includingArg-96, Tyr-108, Asn-205, Ser-229, His-231, His-232, Glu-234, Arg-322 formed in molecular interactions were also well correlated with the residuals from the active sites of native co-crystal FTO and the residues Ile-85, Leu90, Leu-91, Thr-92, Pro-93, Val94, Leu109, Met-212, Leu-215, Lys-216, Met-226, Ala-227, Val-228 were around the active site (within 4 A˚) found to be present involed in van der Waals interactions with ligand.
Analysis of docking results revealed that ligand interaction with FTO (ID: 3LFM) docking complex showed good binding affinity, binding orientation, pharmocological properties. The formation of hydrogen bond along with the residues involved in van der Waals interactions were also highly important for the stability of the docking complex. Hence, lead-1 with better docking score (-5.566kcal/mol). Hence, the best ligands were proposed as the best antagonist to block FTO, which plays major role in the drug development pathways.
Resistin (ID: 1RFX)
The co-crystal structure of Resistin (ID: 1RFX) and visualized by using the visualization tool PyMOL. The active site residues which are involved in bonding with ligand Polyethylene glycol (Di Hydro ethyl) ether) from the PDB site records.
Table 9: H-bond donor and acceptor and rotatable bonds and docking score with FTO with ligands
| S. No. | Title ID | Title | Donor HB | Accpt HB | glide rotatable bonds | docking score |
| 1 | 11989 | Benzothioazole | 0 | 1.5 | 1 | -5.566 |
| 2 | 240 | Benzaldehyde | 0 | 2 | 1 | -5.164 |
| 3 | 123435 | Trioxadiazacyclopentadecane | 2 | 8.1 | 0 | -5.115 |
| 4 | 72805 | TT | 2 | 9.8 | 0 | -4.926 |
| 5 | 8030 | Thiofene | 0 | 0 | 0 | -4.155 |
| 6 | 1.3E+08 | Fucoidan | 2 | 9.1 | 4 | -4.095 |
| 7 | 5281328 | Fucosterol | 1 | 1.7 | 6 | -3.734 |
| 8 | 69435 | Threonine | 3 | 3.7 | 3 | -3.191 |
| 9 | 1032 | Proionicacid | 1 | 2 | 1 | -3.092 |
| 10 | 88095 | Methoxyacetic_anhydride | 0 | 7.9 | 6 | -3.082 |
| 11 | 1.02E+08 | Ethylenebisacetamide | 4 | 9.4 | 15 | -0.876 |
*Hydrogen bond donor (<5), *Hydrogen bond acceptor (<10), *Number of rotatable bonds, *Docking score.
Table 10: ADMET properties of Fat mass and obesity-associated (FTO) with Ligands
| S. No. | Title ID | mol. MW | Q Plog Pw |
Q Plog S |
Q Plog BB |
QPP MDCK |
Q Plog Kp |
Q Plog khsa |
Q Plog khsa |
Rule of five |
Rule of three |
| 1 | 11989 | 181.27 | 3.331 | -2.735 | 0.574 | 10000 | -1.097 | -0.074 | -0.074 | 0 | 0 |
| 2 | 240 | 106.124 | 4.126 | -1.116 | -0.08 | 950.803 | -2.121 | -0.657 | -0.657 | 0 | 0 |
| 3 | 123435 | 218.295 | 11.986 | -0.015 | 1.068 | 156.57 | -6.292 | -0.528 | -0.528 | 0 | 0 |
| 4 | 72805 | 262.348 | 9.787 | 2 | 0.098 | 226.545 | -4.275 | -1.265 | -1.265 | 0 | 0 |
| 5 | 8030 | 84.136 | 1.647 | -1.449 | 0.192 | 10000 | -0.841 | -0.374 | -0.374 | 0 | 0 |
| 6 | 1.3E+08 | 256.27 | 12.586 | -1.192 | -0.959 | 53.808 | -3.752 | -1.126 | -1.126 | 0 | 0 |
| 7 | 5281328 | 412.698 | 3.96 | -8.722 | -0.292 | 1860.133 | -1.718 | 2.071 | 2.071 | 1 | 1 |
| 8 | 69435 | 119.12 | 8.848 | 0.054 | -0.639 | 5.218 | -6.624 | -1.026 | -1.026 | 0 | 1 |
| 9 | 1032 | 74.079 | 4.261 | 0.284 | -0.284 | 134.41 | -3.405 | -0.962 | -0.962 | 0 | 0 |
| 10 | 88095 | 162.142 | 8.217 | 1.316 | -0.516 | 561.37 | -2.972 | -1.949 | -1.949 | 0 | 0 |
| 11 | 1.02E+08 | 296.399 | 19.619 | -0.349 | -2.246 | 103.075 | -4.17 | -1.585 | -1.585 | 0 | 0 |
*Molecular weight (<500 Da), *Predicted octanol/water partition-efficient log P (acceptable range-2-6.5), *Predicted aqueous solubility test: S Mol/l(acceptable range6.5-0.5), *Apparent Caco2 permeability nm/sec(<25 poor and>500 high), *prediction of brain/blood; (acceptable range-3.0-1.2),*Predicted apparent MDCK cell permeability in nm/s (<25 poor and>500 high),*log Kp for skin permeability (Kp in cm/hr), *log Khsa serum protein binding (-1.5-1.5), Lipinski rule of five violation (max is 4), Jorgenson rule of 3 violation (max is 3), *Percentage of Human oral absorption (<25% is poor and >80% is high), ADMET PROPERTIEST: absorption, distribution, metabolism, excretion, toxicity.
| FTO (ID: 3LFM) 2D and 3-dimensional molecular interaction with obtained lead molecules | |||
| PMID-11989_2-(Methylthio) Benzothiazole | PMID-240_ Benzaldehyde | ||
| PMID-123435_ 1,4,10-Trioxa-Diazacyclooctadecane | PMID-72805 7,167,13-Diazacyclopentadecane | ||
| PMID-8030_Thiofene | PMID-129532628_ Fucoidan | ||
| PMID-5281328_Fucosterol | PMID-69435_ D-Threonine | ||
| PMID-1032_ propionic acid | PMID-88095_methoxyacetic anhydride | ||
| PMID-101922872_Ethylenebisacetamide | |||
Fig. 14: Molecular interactions of fat mass and obesity associated (FTO) (PDB ID: 3LFM)
Fig. 15: Resistin (ID: 1RFX)
Computational docking using schrodinger
A receptor grid of 10Å x 10Å x 10Å was generated around the active site residues catalase. Initial docking of 13 ligand molecules catalase docked with significant docking score. The best lead (Benzothioazole) showed lowest docking showed score of -5.322kcal/mol (Figure).
Gln-C: 76 one hydrogen bond interaction with leading ligand. The active site residues such as Arg-B: 41, Trp-C: 65, Asp-C: 66, Ile-C: 67, Asp-B: 81, Trp-B: 82 formed in molecular interactions were also well correlated with the active site residues of native co-crystal structure of Resistin (ID: 1RFX) and the residues Trp-B: 36, Thr-B: 37, Ser-B: 39, Ser-B: 40, Arg-C: 68, Glu-C: 69, GlU-C: 70, Arg-b: 86 were around the active site (within 4 A˚) found to be present involed in van der Waals interactions with ligand.
Analysis of docking results revealed that ligand interaction with Resistin (ID: 1RFX) docking complex showed good binding affinity, binding orientation, pharmocological properties. The formation of hydrogen bond along with the residues involved in van der Waals interactions were also highly important for the stability of the docking complex. Hence, lead-1 with better docking score (-5.322). Hence, the best ligands were proposed as the best antagonist to block Resistin (ID: 1RFX) of, which plays major role in the drug development pathways.
Fig. 16: Molecular interactions of resistin (ID: 1RFX)
Table 11: Docking score of Resistin (ID: 1RFX) with ligands
| S. No. | Title ID | Title | Donor HB | Accpt HB | glide rotatable bonds | docking score |
| 1 | 11989 | Benzothiazole | 0 | 1.5 | 1 | -5.322 |
| 2 | 240 | Benzaldehyde | 0 | 2 | 1 | -4.98 |
| 3 | 72805 | TT | 2 | 9.8 | 0 | -4.601 |
| 4 | 123435 | Trioxadiazacyclopentadecane | 2 | 8.1 | 0 | -4.393 |
| 5 | 8030 | Thiofene | 0 | 0 | 0 | -4.346 |
| 6 | 1.3E+08 | Fucoidan | 2 | 9.1 | 4 | -3.818 |
| 7 | 69435 | Threonine | 3 | 3.7 | 3 | -3.485 |
| 8 | 88095 | Methoxyacetic_anhydride | 0 | 7.9 | 6 | -3.339 |
| 9 | 1032 | Proionicacid | 1 | 2 | 1 | -2.235 |
| 10 | 5281328 | Fucosterol | 1 | 1.7 | 6 | -1.814 |
| 11 | 1.02E+08 | Ethylenebisacetamide | 4 | 9.4 | 15 | -0.31 |
*Hydrogen bond donor (<5), *Hydrogen bond acceptor (<10), *Number of rotatable bonds, *Docking score.
Table 12: H-bond donor and acceptor and rotatable bonds and docking score with resist in (ID: 1RFX) with ligands
| S. No | Title ID | mol MW | QPlog Po/w |
QPlog S |
QPP Caco |
QPlog BB |
QPP MDCK |
QPlog Kp |
QPlog Khsa |
Percent Human oral absorption |
Rule of five |
Rule of three |
| 1 | 11989 | 181.27 | 3.031 | -2.735 | 6656.771 | 0.574 | 10000 | -1.097 | -0.074 | 100 | 0 | 0 |
| 2 | 240 | 106.124 | 1.479 | -1.116 | 1830.242 | -0.08 | 950.803 | -2.121 | -0.657 | 94 | 0 | 0 |
| 3 | 72805 | 262.348 | 0.099 | 2 | 402.712 | 0.098 | 226.545 | -4.275 | -1.265 | 74.148 | 0 | 0 |
| 4 | 123435 | 218.295 | -0.235 | -0.015 | 286.124 | 1.068 | 156.57 | -6.292 | -0.528 | 69.536 | 0 | 0 |
| 5 | 8030 | 84.136 | 1.815 | -1.449 | 9906.038 | 0.192 | 10000 | -0.841 | -0.374 | 100 | 0 | 0 |
| 6 | 1.3E+08 | 256.27 | -0.047 | -1.192 | 100.862 | -0.959 | 53.808 | -3.752 | -1.126 | 62.533 | 0 | 0 |
| 7 | 69435 | 119.12 | -3.351 | 0.054 | 10.81 | -0.639 | 5.218 | -6.624 | -1.026 | 25.826 | 0 | 1 |
| 8 | 88095 | 162.142 | -1.384 | 1.316 | 1124.064 | -0.516 | 561.37 | -2.972 | -1.949 | 73.443 | 0 | 0 |
| 9 | 1032 | 74.079 | 0.606 | 0.284 | 239.776 | -0.284 | 134.41 | -3.405 | -0.962 | 73.087 | 0 | 0 |
| 10 | 5281328 | 412.698 | 7.522 | -8.722 | 3405.278 | -0.292 | 1860.133 | -1.718 | 2.071 | 100 | 1 | 1 |
| 11 | 1.02E+08 | 296.399 | -1.022 | -0.349 | 30.277 | -2.246 | 103.075 | -4.17 | -1.585 | 47.473 | 0 | 0 |
*Molecular weight (<500 Da), *Predicted octanol/water partition coefficient log P (acceptable range-2-6.5), *Predicted aqueous solubility test: S Mol/l(acceptable range6.5-0.5), *Apparent Caco2 permeability nm/sec(<25 poor and>500 high), *prediction of brain/blood; (acceptable range-3.0-1.2),*Predicted apparent MDCK cell permeability in nm/s (<25 poor and>500 high),*log Kp for skin permeability (Kp in cm/hr), *log Khsa serum protein binding (-1.5-1.5), Lipinski rule of five violation (max is 4), Jorgenson rule of 3 violation (max is 3), *Percentage of Human oral absorption(<25%is poor and>80% is high), ADMET PROPERTIES: absorption, distribution, metabolism, excretion, toxicity.
| Resist in (ID: 1RFX) 2D and3-dimensional molecular Interaction with obtained lead molecules | |||
| PMID-11989_2-(Methylthio) Benzothiazole | PMID-240_ Benzaldehyde | ||
| PMID-72805_ 1,4,10,13-7,13-Diazacyclopentadecane | PMID-1234351,4,10-Trioxa Tetraoxa-7,16-Diazacyclooctadecane | ||
| PMID-8030_Thiofene | PMID-129532628_ Fucoidan | ||
| PMID-69435_ D-Threonine | PMID-88095_Methoxyacetic anhydride | ||
| PMID-1032_ propionic acid | PMID-5281328_fucosterol | ||
| PMID-101922872_Ethylenebisacetamide | |||
Fig. 17: Molecular interactions of resist in (ID: 1RFX) with ligands
Leptin (ID: 3V6O)
The co-crystal structure of Leptin (ID: 3V6O) and visualized by using the visualization tool PyMOL. The active site residues which are involved in bonding with ligand N acetylglucosamine (NAG) (2 cetamide-2-deoxy-beta-beta-D-Glycopyranose) from the protein database (PDB) site records.
Computational docking using schrodinger
A receptor grid of 10Å x 10Å x 10Å was generated around the active site residues catalase. Initial docking of 13 ligand molecules catalase docked with significant docking score. The best lead (2-(Methylthio) Benzothiazole) showed the lowest docking showed score of-5.903kcal/mol (Figure).
Fig. 18: Leptin (ID: 3V6O)
Fig. 19: Molecular interactions of leptin (ID: 3V6O)
The active site residues such as Pro-10, Thr-24, Thr-28, Cys-90, Thr-99, The-100, Ser-102, Gly-103, Thr-104 formed in molecular interactions were also well correlated with the active site residues of native Leptin (ID: 3V6O) and the Lys-11, cys-25, Lys-26, Ala-27, Thr-28, Gln-29, Val-31, Cys-19, Gln-92, Trp-94, Asn-95, Thr-96, Pro-97, Gly-101 were around the active site (within 4 A˚) found to be present involed in van der Waals interactions with ligand.
Analysis of docking results revealed that ligand interaction Leptin (ID: 3V6O) docking complex showed good binding affinity, binding oreintation, pharmocological properties. The formation of hydrogen bond along with the residues involed in van der Waals interctions were also highly important for the stability of the docking complex. Hence, lead-1with better docking score (-5.903kcal/mol). Hence, the best ligands were proposed as the best antagonist to block Leptin (ID: 3V6O) of, which plays a major role in the drug development pathways.
Table 13: Docking score of leptin (ID: 3V6O) with ligands
| S. No. | Title | Donor HB | Accpt HB | Glide rotatable bonds | Docking score |
| 1 | 11989 | 0 | 1.5 | 1 | -5.903 |
| 2 | 240 | 0 | 2 | 1 | -5.013 |
| 3 | 129532628 | 2 | 9.1 | 4 | -4.949 |
| 4 | 5281328 | 1 | 1.7 | 6 | -4.78 |
| 5 | 8030 | 0 | 0 | 0 | -4.527 |
| 6 | 69435 | 3 | 3.7 | 3 | -4.515 |
| 7 | 72805 | 2 | 9.8 | 0 | -4.292 |
| 8 | 123435 | 2 | 8.1 | 0 | -4.171 |
| 9 | 5281239 | 1 | 7.45 | 14 | -3.63 |
| 10 | 88095 | 0 | 7.9 | 6 | -3.21 |
| 11 | 1032 | 1 | 2 | 1 | -2.47 |
| 12 | 101922872 | 4 | 9.4 | 15 | -1.328 |
*Hydrogen bond donor (<5), *Hydrogen bond acceptor (<10), *Number of rotatable bonds, *Docking score.
Table 14: H-bond donor and acceptor and rotatable bonds and docking score with leptin with ligands
| S. No. | Title | Q Plog Po/w | QP log S | QPP Caco | QP log BB | QPP mdck | Q Plog Kp | Q Plog Khsa | Percent human oral absorption | Rule of five | Rule of three |
| 1 | 11989 | 3.031 | -2.735 | 6656.771 | 0.574 | 10000 | -1.097 | -0.074 | 100 | 0 | 0 |
| 2 | 240 | 1.479 | -1.116 | 1830.242 | -0.08 | 950.803 | -2.121 | -0.657 | 94 | 0 | 0 |
| 3 | 129532628 | -0.047 | -1.192 | 100.862 | -0.959 | 53.808 | -3.752 | -1.126 | 62.533 | 0 | 0 |
| 4 | 5281328 | 7.522 | -8.722 | 3405.278 | -0.292 | 1860.133 | -1.718 | 2.071 | 100 | 1 | 1 |
| 5 | 8030 | 1.815 | -1.449 | 9906.038 | 0.192 | 10000 | -0.841 | -0.374 | 100 | 0 | 0 |
| 6 | 69435 | -3.351 | 0.054 | 10.81 | -0.639 | 5.218 | -6.624 | -1.026 | 25.826 | 0 | 1 |
| 7 | 72805 | 0.099 | 2 | 402.712 | 0.098 | 226.545 | -4.275 | -1.265 | 74.148 | 0 | 0 |
| 8 | 123435 | -0.235 | -0.015 | 286.124 | 1.068 | 156.57 | -6.292 | -0.528 | 69.536 | 0 | 0 |
| 9 | 5281239 | 9.739 | -12.081 | 395.845 | -2.829 | 181.686 | -1.725 | 2.441 | 100 | 2 | 2 |
| 10 | 88095 | -1.384 | 1.316 | 1124.064 | -0.516 | 561.37 | -2.972 | -1.949 | 73.443 | 0 | 0 |
| 11 | 1032 | 0.606 | 0.284 | 239.776 | -0.284 | 134.41 | -3.405 | -0.962 | 73.087 | 0 | 0 |
*Molecular weight (<500 Da), *Predicted octanol/water partition co-efficient log P (acceptable range-2-6.5), *Predicted aqueous solubility test: S Mol/l(acceptable range6.5-0.5), *Apparent Caco2 permeability nm/sec(<25 poor and>500 high), *prediction of brain/blood; (acceptable range-3.0-1.2),*Predicted apparent MDCK cell permeability in nm/s (<25 poor and>500 high),*log Kp for skin permeability (Kp in cm/hr), *log Khas serum protein binding (-1.5-1.5), Lipinski rule of five violation (max is 4), Jorgenson rule of 3 violation (max is 3), *Percentage of Human oral absorption(<25%is poor and>80% is high ADMET PROPERTIES: absorption, distribution, metabolism, excretion, toxicity.
Orlistat with anti-obesity proteins
Around the active site residues, a 10Å x 10Å x 10Å receptor grid was created for anti-obesity proteins. Initial docking of orlistat with anti-obesity proteins docked with significant docking score. The best lead PPAR-γ (ID: 4CI5) showed the lowest docking showed score of-7.716 kcal/mol (Figure).
PPAR-γ (ID: 4CI5) with His-194, Ser-201, Arg-201, Asn213, Trp-303, Gln-442, forming 1 hydrogen bond, and His-305 forming 2 hydrogen bonds. Additionally, Arg-203 forms one Pi-cation interaction and two salt bridges, while Lys-237 forms one salt bridge. Moreover, His-305 forms one salt bridge, and Phe-446 forms a Pi-Pi interaction with the ligand, FTO (ID: 3LFM), with Lys-216 forming one hydrogen bond. Leptin (PDB ID: 3V6O) interacts with Trh-24 and Lys26, forming two hydrogen bonds. However, Resistin (ID: 1RFX) shows no interactions.
Analysis of docking results revealed that ligand interaction Orlistat with PPAR-γ (ID: 4CI5), α-Glucosidase (ID: 7K9N), FTO (ID: 3LFM), Resistin (ID: 1RFX), Leptin (ID: 3V6O) docking complex showed good binding affinity, binding orientation, pharmocological properties. The formation of hydrogen bond along with the residues involved in van der Waals interactions were also highly important for the stability of the docking complex. Hence, the lead PPAR-γ (ID: 4CI5) with a better docking score (-7.716 kcal/mol). Hence, the Standard was proposed as the best antagonist to block aminoglycoside 6'-N-acetyltransferase (Aac(6')-lb), which plays a major role in the drug development pathways.
| Leptin (ID: 3V6O) 2D and3-dimensional molecular interaction with obtained lead molecules | |||
| PMID-11989_2-(methylthio) benzothiazole | PMID-240_ benzaldehyde | ||
| PMID-129532628_ Fucoidan | PMID-5281328_Fucosterol | ||
| PMID-8030_Thiofene | PMID-69435_ D-Threonine | ||
| PMID-72805_ 1,4,10,13-Diazacyclopentadecane | PMID-1234351,4,10-Trioxa-Tetraoxa-7,16-Diazacyclooctadecane | ||
| PMID-5281239_Fucoxanthin | PMID-88095_Methoxyacetic anhydride | ||
| PMID-1032_ Propionic Acid | PMID-101922872_Ethylenebisacetamide | ||
Fig. 20: Molecular interactions of leptin (PDB ID: 3V6O) with ligands
Fig. 21: Molecular interactions of orlistat with PPAR-γ (ID: 4CI5)
Table 15: ADMET properties of antioxidant and anti-obesity proteins with orlistat
| Standard ligand | Protein name | Mol MW | Q Plog Po/w |
Q Plog S |
QPP Caco |
Q Plog BB |
QPP MDCK |
Q Plog Kp | Q Plog Khsa |
Percent Human oral absorption |
Rule of five |
Rule of three |
3034010_ Orlistat_ Standard |
4CI5 7K9N 3LFM 1RFX 3V6O |
495.741 | 6.096 | -7.182 | 153.403 | -2.868 | 131.94 | -2.375 | 0.845 | 88.803 | 1 | 1 |
*Molecular weight (<500 Da), *Predicted octanol/water partition coefficient log P (acceptable range-2-6.5), *Predicted aqueous solubility test: S Mol/l(acceptable range6.5-0.5), *Apparent Caco2 permeability nm/sec(<25 poor and>500 high), *prediction of brain/blood; (acceptable range-3.0-1.2),*Predicted apparent MDCK cell permeability in nm/s (<25 poor and>500 high),*log Kp for skin permeability (Kp in cm/hr), *log Khsa serum protein binding (-1.5-1.5), Lipinski rule of five violation (max is 4), Jorgenson rule of 3 violation (max is 3), *Percentage of Human oral absorption (<25%is poor and>80% is high), ADMET properties.
Table 16: Docking score of orlistat with anti-obesity genes
| Protein name | Docking score |
| 4CI5 | -7.716 |
| 1LFM | -2.661 |
| 3V6O | -4.419 |
| 7K9N | -2.801 |
*Hydrogen bond donor (<5), * Hydrogen bond acceptor (<10), *Number of rotatable bonds*
| a) Interactions of Orlistat with antioxidant and anti-obesity genes | |||
| 3034010_Orlistat with 7K9N 3034010_Orlistat with 3LFM | |||
| 3034010_Orlistat with 3V6O 3034010_Orlistat with 4CI5 | |||
Fig. 22: Molecular interactions of orlistat with anti-obesity genes
DISCUSSION
Natural compounds play a pivotal role in therapeutic applications due to negligible adverse effects. Therefore, scientific knowledge towards acute oral toxicity study is much needed, which will not only help identify the range and concentration of dose that could be used subsequently but also to reveal the possible clinical signs elicited by the substances under investigation. In UPEE and MOME have good bio active compounds. The result was compared with reference anti-obesity drug, orlistat. Strong binding affinities are shown by the study's results with important targets related to obesity, such as PPAR-γ (ID: 4CI5), (ID: 3LFM), Resistin (ID: 1RFX), Leptin (ID: 3V6O) as well as with the UPEE and MOME.
Carboxyl, sulfhydryl, and OH functional groups the primary seaweed constituents and those derived from polysaccharides are involved in the uptake of seaweed [23]. UPEE has numerous bioactive properties such as antioxidant and anticancer activities [24]. Fucoidan isolated from brown seaweeds UPEE showed interesting antitumor activity due to its various biological activities [25]. Brown algae Barbus barbus as a natural source of bioactive substances (chlorophylls and carotenoids) [26]. In present study, Fourier infrared spectroscopy examination of UPEE revealed the presence of phenols, OH groups, phenols, carboxylic acids, halogens, methyl groups vinyl compounds, amides, alkenes, and alkyl halide organic compound groups. Fourier infrared spectroscopy examination of in herbal medicine analysis [27]. Main structure of herbal materials adds the complexity of FTIR spectra interpretation. About the spectral behaviour of homogenized herbal and herbal extracts [28]. MOME revealed the presence of phenols, aldehyde alkanes, amines, methyl groups vinyl compounds, amides, OH groups and carboxylic acids organic compound groups.
GC-MS analysis of Undaria pinnatifida revealed the presence of bioactive compounds such as fucosterol, 1,3, Difluro-2-propanol, benzaldehyde, hexaborane and 2-(3-Methylphenoxy) octahydro-1H-1,3,2-benzodiazaphosphole 2-oxide the important medicinal properties. Glycoprotein isolated from UPEE and tested for antioxidant activities using an in vitro digestion model [29, 30]. GC-MS analysis of cinnamon bark indicated the presence of trans-cinnamaldehyde [31]. Hence, the presence of these compounds was responsible for the therapeutic effects. The phytochemical and GC-MS profiling of moringa oleifera revealed the presence of bioactive compounds such as pentaborane, D-Threonine,13-pentano-1,4,10-trioxa-7,13-diazacyclopentadecane, propanoic acid and hydroxy propanoic acid with important medicinal properties. Hence, the presence of these phytochemicals could be responsible for the therapeutic effects.
1,4,10-Trioxa-7,13-diazacyclopentadecane_1234352-methylthio)benzothiazole, benzaldehyde, di methyl pentathiophene, D-threonine, fucoxanthin, fucosterol, fucoidan, thiophene, hexaborane, methoxyacetic_anhydride, NN ethylene bis acetamide,1,4,10,13-tetraoxa-7,16-diazacyclooctadecane ligands were docked anti-obesity proteins among all 13 ligands PPAR-γ (PDB ID: 4CI5) with fucosterol (-6.709kcal/mol), α-glucosidase (PDB ID: 7K9N) with trioxadiazacyclopentadecane (-6.466kcal/mol), FTO, (PDB ID: 3LFM)with bbenzothiazole (-5.566kcal/mol), Resistin(PDB ID: 1RFX) with benzothiazole (-5.322kcal/mol), leptin (PDB ID: 3V6O) with 2-methylthio benzothiazole (-5.903 kcal/mol) showed best docking score.
The docking findings analysis showed that the interaction of the ligand Orlistat standard with the PPAR-γ (ID: 4CI5), α-Glucosidase (ID: 7K9N), FTO (ID: 3LFM), Resistin(ID: 1RFX), Leptin(ID: 3V6O)demonstrated good binding affinity, binding organisation, and pharmacological characteristics. For the docking complex to remain stable, hydrogen bond formation and residues involved in van der Waals interactions were both crucial. Lead PPAR-γ has the best docking score (-7.716 kcal/mol), as a result. As a result, the standard was recommended as the best antagonist to block the key enzyme involved in the drug development pathways, aminoglycoside 6'-N-acetyltransferase (Aac(6')-lb).
CONCLUSION
In this study, we investigated the anti-obesity potential of bioactive components from MOME and UPEE using in silico methodologies. Using molecular docking investigations and virtual screening, we found intriguing drugs that have robust interactions with important targets related to obesity, such as PPAR-γ (ID: 4CI5), Leptin (ID: 3V6O) and FTO (ID: 3LFM). These results point to the possibility of additional experimental validation. Although computational approaches provide insightful information, they are only the first stage in the drug discovery process. Bioactivity, toxicology, and pharmacokinetic assays are only a few of the rigorous experiments needed to determine these drugs' genuine therapeutic value. The benefits of combining traditional knowledge with computational methods highlight the value of multidisciplinary study. The potential of UPEE and MOME as sources of bioactive chemicals that combat obesity is highlighted by this research, which aids in the hunt for novel therapeutic agents. Translating these findings into workable remedies to the obesity pandemic will require cooperation among experts in different domains.
FUNDING
Nil
AUTHORS CONTRIBUTIONS
All authors have contributed equally
CONFLICT OF INTERESTS
Declared none
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