Computational data of phytoconstituents from Hibiscus rosa-sinensis on various anti-obesity targets

Molecular docking analysis of twenty two phytoconstituents from Hibiscus rosa-sinensis, against seven targets of obesity like pancreatic lipase, fat and obesity protein (FTO protein), cannabinoid receptor, hormones as ghrelin, leptin and protein as SCH1 and MCH1 is detailed in this data article. Chemical structures of phytoconstituents were downloaded from PubChem and protein structures were retrieved from RCSB protein databank. Docking was performed using FlexX software Lead IT version 2.3.2; Bio Solved IT. Visualization and analysis was done by Schrodinger maestro software. The docking score and interactions with important amino acids were analyzed and compared with marketed drug, orlistat. The findings suggest exploitation of best ligands experimentally to develop novel anti-obesity agent.


Data
This dataset contains docking analysis of phytoconstituents of Hibiscus rosa-sinensis on different targets of obesity. Different secondary metabolites present in Hibiscus rosa-sinensis were selected. Chemical structures of selected phytoconstituents were taken from database and were subjected to energy minimization. Seven receptor structures were selected as potential targets of obesity [1e8]. Protein structures available in database were downloaded from RCSB protein databank. Table 1 gives details of the selected receptors. Two receptors model were prepared using I-TASSER server online. Table 2 summarizes FASTA sequence of Ghrelin and MCH1 receptor subjected to model preparation. Phytoconstituents were docked on the above targets to understand binding interactions. Tables 3e9 summarizes the dock score, bond distance and interacting amino acid residue of all phytoconstituents on seven different targets. Fig. 1-14 gives docked images of phytoconstituents with lowest dock score and standard drug orlistat with seven receptor proteins.

Ligand preparation
Twenty two phytoconstituents present in Hibiscus rosa-sinensis were selected. Structures of all phytoconstituents were downloaded from PubChem database. Orlistat (PubChem CID 3034010) only available synthetic drug was used as reference standard.

Energy minimization
All structures were subjected to energy minimization using Avogadro software where universal force field (UFF) and first order steepest descent algorithm were used. This gave energetically stable Specifications Value of the data Obesity declared as a disease by WHO and is the main cause of other many metabolic disorders which lead to mortality. Literature explains multiple mechanisms involved in energy uptake and energy consumption, the control of which can help in maintaining energy balance and thus keeping obesity at large. This article provides all dataset of protein structures to explore potential targets for obesity.
In-silico exploration of targets is the first step in drug design to understand the underlying mechanism of action of the identified drug molecule. Many herbal medicines and food supplements are found to be beneficial in reducing body weight, although mode of action and identification of marker phytoconstituents is still not explored. Docking of phytoconstituents to seven identified targets for obesity can pave a way towards identification of novel antiobesity drug.
conformations for the structures. Avogadro is free open source molecular builder software used for molecular modeling. It calculates the lowest energy conformation from the bond lengths and bond angles with smallest steric energy. Energy minimization helps in attaining structure conformation with lower delta G values which is considered close to biological system.

Retrieval of protein structure and preparation
Seven targets which play important role in maintaining energy balance of body and thus address obesity were selected. Protein structures of ligands were downloaded from the RCSB Protein Data Bank, database for 3D structures of large biological molecules, including proteins and nucleic acids. Downloaded protein structures were prepared X ray crystal structure of PDB ID 1LPB, 3LFM, 3TGZ, 1AX8, 4XWX for pancreatic lipase [2], FTO protein [3], cannabinoid receptor [4], hormones leptin [5] and protein SCH1 [6] respectively were selected. Data summarized in Table 1.
X-Ray crystal structure for Ghrelin [7] and MCH1 [8] receptor is not available in PDB databank so model protein structure was created using I-TASSER server online. FASTA sequence was taken from Uniprot ID of protein and submitted for model preparation. Table 2 summarizes FASTA sequence of Ghrelin and MCH1. Model was evaluated for C-score, TM score and RMSD. Model with C-score between À5 and 2, TM score greater than 0.5 were selected. Finalized model were validated on PROSA, Saves v5.0, Ramachandran plot and ProQ and then were used as receptors.

Molecular docking studies
Molecular docking techniques dock small molecules into the protein binding site. In order to understand how these ligands bind to the enzyme, docking analysis were performed using FlexX   Orlistat, as only standard drug used in market is used as standard reference for docking studies. Hence the docking result of orlistat in all tables is bold for ease of comparison.  Orlistat, as only standard drug used in market is used as standard reference for docking studies. Hence the docking result of orlistat in all tables is bold for ease of comparison.  Orlistat, as only standard drug used in market is used as standard reference for docking studies. Hence the docking result of orlistat in all tables is bold for ease of comparison.  Orlistat, as only standard drug used in market is used as standard reference for docking studies. Hence the docking result of orlistat in all tables is bold for ease of comparison.  Orlistat, as only standard drug used in market is used as standard reference for docking studies. Hence the docking result of orlistat in all tables is bold for ease of comparison.

Table 9
Summary of docking analysis with MCH1.            software. The receptor ligand interactions were done using Maestro software. Interacting amino acid residue, bond type and bond distance were noted. Data summarized in Tables 3e9 and Fig. 1e14.

Acknowledgments
The work was supported by Dr. D. Y. Patil Biotechnology & Bioinformatics Institute, Tathawade, Pune.

Transparency document
Transparency document associated with this article can be found in the online version at https:// doi.org/10.1016/j.dib.2019.103994.