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. 2019 May 16;24:103994. doi: 10.1016/j.dib.2019.103994

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

Sejal P Gandhi a,∗, Kiran B Lokhande b, Venkateswara K Swamy b, Rabindra K Nanda a, Sohan S Chitlange a
PMCID: PMC6538924  PMID: 31193691

Abstract

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.

Specifications table

Subject area Chemistry
More specific subject area Computational chemistry
Type of data Table, figure
How data was acquired Ligand based molecular docking using FlexX and Maestro software
Data format Raw and analyzed
Experimental factors Phytoconstituents structures downloaded from PubChem were subjected to Avogadro software for energy minimization.
Experimental features Minimized ligands structures were docked with seven selected protein structure using FlexX software.
Data source location Department of bioinformatics, Dr. D. Y. Patil Biotechnology & Bioinformatics Institute Tathawade, Pune
Data accessibility Data is only with this article
Related research article K. H. Min, J. Yoo, H. Park, Computer-Aided Identification of Ligands for GPCR Anti-Obesity Targets, Curr Top Med Chem. 9 (2009) 539–553 [1].
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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 anti-obesity drug.

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1. 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 [1], [2], [3], [4], [5], [6], [7], [8]. 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. Table 3, Table 4, Table 5, Table 6, Table 7, Table 8, Table 9 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.

Table 1.

Table summarizing details of targets selected.

Target PDB ID Description Resolution R value free R value work
Pancreatic Lipase 1LPB The 2.46 Å resolution structure of the pancreatic lipase colipase complex inhibited by a c11 alkyl phosphonate 2.46 Å 0.285 0.183
Fat And Obesity Protein 3LFM Crystal structure of the fat mass and obesity associated (FTO) protein reveals basis for its substrate specificity 2.5 Å 0.285 0.239
Cannabinoid Receptor 5TGZ Crystal structure analysis of w35f/h207w mutant of human clic1 2.3 Å 0.306 0.240
Leptin 1AX8 Human obesity protein, leptin 2.4 Å 0.283 0.185
SCH1 Protein 4XWX Crystal structure of the PTB domain of SHC 1.87 Å 0.191 0.168
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Table 2.

Uniprot ID and FASTA sequence of ghrelin and MCH1 receptor.

Target UniProt ID FASTA sequence
Ghrelin receptor Q9UBU3 MPSPGTVCSLLLLGMLWLDLAMAGSSFLSPEHQRVQQRKESKKPPAKLQPRALAGWLRPEDGGQAEGAEDELEVRFNAPFDVGIKLSGVQYQQHSQALGKFLQDILWEEAKEAPADK
MCH 1 receptor Q99705 MSVGAMKKGVGRAVGLGGGSGCQATEEDPLPNCGACAPGQGGRRWRLPQPAWVEGSSARLWEQATGTGWMDLEASLLPTGPNASNTSDGPDNLTSAGSPPRTGSISYINIIMPSVFGTICLLGIIGNSTVIFAVVKKSKLHWCNNVPDIFIINLSVVDLLFLLGMPFMIHQLMGNGVWHFGETMCTLITAMDANSQFTSTYILTAMAIDRYLATVHPISSTKFRKPSVATLVICLLWALSFISITPVWLYARLIPFPGGAVGCGIRLPNPDTDLYWFTLYQFFLAFALPFVVITAAYVRILQRMTSSVAPASQRSIRLRTKRVTRTAIAICLVFFVCWAPYYVLQLTQLSISRPTLTFVYLYNAAISLGYANSCLNPFVYIVLCETFRKRLVLSVKPAAQGQLRAVSNAQTADEERTESKGT
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Table 3.

Summary of docking analysis with pancreatic lipase (PDB ID 1LPB).

Sr. No Posename Score Interacting Residues Bond Type Bond Distance
1 Niacin −27.2868 SER 333 HB 2.01
ARG 265 Pi-Pi Stacking 5.21
HB 1.79
Salt bridge 3.08
LYS 239 Salt bridge 2.73
2 Quercetin 3, 7 diglucoside −21.223 LYS 239 HB 1.93
ASP 247 HB 1.93
ASP 257 HB 1.71
TRY 267 HB 1.98
THR 271 HB 2.70
LYS 268 HB 1.48
Pi cation 5.10
ASP 249 HB 2.12
3 Ascorbic acid −20.6315 SER 333 HB 2.32
ASP 247 HB 2.06
ARG 265 HB 2.18
ASP 257 HB 2.17
ASP 249 HB 1.79
4 Quercetin 3, 3′ diglucoside −20.3198 SER 333 2HB 2.12,2.31
ASP 247 HB 2.34
ASP 331 HB 1.84
ARG 265 HB 2.20
ASP 257 HB 2.66
TYR 267 HB 2.2.3
ASN 88 HB 2.52
5 Quercetin 3,4′ diglucoside −18.4448 ASP 331 2HB 2.18, 2,07
ARG 265 HB 1.90
SER 333 HB 2.29
PHE 335 Pi Pi stacking 5.46
ASN 88 HB 2.61
6 8 nonynoic acid −17.7764 LYS 239 HB 2.04
Salt Bridge 3.91
ARG 265 HB 1.93
7 9 Decynoic acid −17.4676 ARG 265 2HB 2.11, 1.85
LYS 239 HB 1.94
Salt bridge 4.67
8 Cyanidine 3, 5 diglucoside −15.6327 ARG 265 PI Pi stacking 4.92
ASP 247 HB 1.91
ASP 257 HB 1.89
ASP 249 HB 2.11
Salt Bridge 4.60
GLU 253 HB 2,29
SER 333 HB 2.39
LYS 268 HB 2.29
ASP 272 HB 2.08
9 Riboflavin −15.3182 ASP 249 HB 1.36
SER 333 HB 1.60
GLU 253 HB 2.16
LYS 268 HB 1.71
10 Thiamine −14.7694 ASP 249 HB 1.97
Salt Bridge 4.85
ARG 265 Pi Pi stacking 4.94
LYS 268 Pi cation 2.86
ASP 247 HB 2.07
11 Beta rosasterol −9.4736 LYS 239 HB 2.16
ARG 265 HB 2.16
12 Cyanidin 3-sophoroside-5-glucoside −8.2017 ASP 249 3HB 1.46, 2.02, 1.91
ASP 272 HB 2.02
GLU 253 2HB 1.82, 1.81
13 Methyl non-8-ynoate −7.2264 LYS 239 HB 2.05
ARG 265 HB 1.94
14 Methyl Dec-9-ynoate −5.9149 LYS 239 HB 2.05
ARG 265 HB 1.94
15 Methyl (E)-11-methoxy-9-oxononadec-10-enoate −4.9341 SER 333 HB 2.14
ARG 265 HB 2.15
TRY 267 HB 2.14
LYS 268 HB 2.12
16 Methyl malvalate −3.6439 LYS 239 HB 2.05
ARG 265 HB 1.94
17 Methyl 8-oxooctadec-9-ynoate −2.8512 SER 333 HB 2.10
LYS 239 HB 1.89
ARG 265 HB 2.02
18 Methyl Sterculate −1.1816 ARG 265 HB 1.98
LYS 239 HB 2.06
19 Campesterol 1.5909 GLU 253 HB 2.28
20 Stigmasterol 2.651 ASP 249 HB 1.90
GLU 253 HB 2.12
21 Beta sitosterol 3.2084 No interaction
22 Orlistat 0.1075 ASP 249 HB 1.68
SER 333 HB 1.94
TYR 267 HB 2.23
Ar HB 1.91
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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 4.

Summary of docking analysis with fat and obesity protein (PDB ID 3LFM).

Sr. No. Ligand Score Interacting Residues Bond Type Bond Distance
1 Riboflavin −27.3248 ARG 96 HB 1.62
SER 229 HB 2.07
GLU 234 HB 2.01
Ar HB 2.35
2 Niacin −21.5279 ARG 322 HB 1.93
GLU 234 HB 1.84
ARG 96 Pi-Pi Stacking 4.26
3 Thiamine −19.313 TRY 108 Pi-Pi Stacking 4.78
HIP 231 Pi-Pi Stacking 3.68
Pi-Pi Stacking 5.45
Pi Cation 4.42
Pi Cation 3.78
SER 229 HB 1.38
TYR 106 HB 2.00
4 Ascorbic acid −16.8546 ASP 233 HB 1.99
ARG 322 HB 2.45
ARG 96 HB 1.99
GLU 234 HB 1.90
5 Cyanidine 3, 5 diglucoside −14.6454 ARG 322 Pi Cation 5.23
HB 1.52
TRY 106 HB 2.08
HB 1.81
HIP 232 HB 1.82
GLU 234 HB 2.25
HIP 231 Pi-Pi Stacking 4.81
Pi-Pi Stacking 5.43
VAL 94 HB 1.53
6 Quercetin 3,4′ diglucoside −12.747 VAL 94 HB 2.35
GLU 234 HB 2.00
HIP 232 HB 1.57
HB 1.91
GLN 306 HB 2.18
HIP 231 Pi Cation 6.38
7 8 nonynoic acid −12.149 ASN 205 HB 1.96
ARG 322 HB 2.05
Salt bridge 3.78
ARG 96 HB 1.78
8 9 Decynoic acid −11.8069 ARG 322 HB 1.97
GLU 234 HB 1.97
9 Quercetin 3,3′ diglucoside −11.2637 TYR 108 Pi-Pi Stacking 4.55
ARG 96 HB 2.66
VAL 94 HB 1.79
ALA 227 HB 2.24
GLU 234 HB 1.62
10 Quercetin 3,7 diglucoside −7.7494 GLU 234 HB 2.04
HB 2.16
TYR 108 Pi-Pi Stacking 4.94
TYR 106 HB 2.29
ARG 322 HB 1.71
HIP 231 Pi-Pi Stacking 3.98
HIP 232 HB 2.06
11 Methyl 8-oxooctadec-9-ynoate −6.5642 HIP 232 HB 1.93
ARG 96 HB 1.52
12 Methyl Dec-9-ynoate −4.8041 ARG 96 HB 2.15
13 Methyl non-8-ynoate −4.4543 ARG 96 HB 2.15
14 (9) Methyl (E)-11-methoxy-9-oxononadec-10-enoate −2.4721 ARG 96 HB 2.15
15 Beta rosasterol −1.029 VAL 94 HB 1.78
16 Methyl Sterculate 0.5157 ARG 96 HB 1.84
17 Methyl malvalate 0.7329 ARG 96 HB 1.88
18 Beta sitosterol 1.2521 ALA 227 HB 2.2
19 Campesterol 1.447 ALA 227 HB 2.21
20 Orlistat −7.2466 ARG 322 HB 2.20
GLU 234 HB 2.04
HB 1.66
HIP 232 HB 2.17
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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 5.

Summary of docking analysis with cannabinoid receptor (PDB ID 3TGZ).

Sr. No. Ligand Score Interacting Residues Bond Type Bond Distance
1 Niacin −14.7132 MET 103 HB 1.84
ASP 104 HB 2.07
2 Thiamine −13.5476 PHE 102 Pi-Pi Stacking 4.92
SER 383 HB 1.81
SER 123 HB 1.69
3 Ascorbic acid −11.9942 ASP 163 HB 2.30
TRP 356 HB 1.70
CYS 386 HB 1.86
SER 199 HB 2.44
ALA 162 HB 2.12
4 Riboflavin −9.4202 PHE 170 Pi-Pi Stacking 5.43
MET 103 HB 1.96
SER 383 HB 2.06
5 8 nonynoic acid −4.3902 ASP 104 HB 1.84
6 9 Decynoic acid −3.8828 ASP 104 HB 1.95
MET 103 HB 1.83
7 Methyl 8-oxooctadec-9-ynoate −3.0906 ASN 389 HB 2.66
TRP 356 HB 1.86
8 Methyl non-8-ynoate −2.5398 TRP 356 HB 1.95
9 Methyl Dec-9-ynoate −2.4934 TRP 356 HB 1.95
10 (9) Methyl (E)-11-methoxy-9-oxononadec-10-enoate −2.1673 TRP 356 HB 1.95
11 Quercetin 3,3′ diglucoside −1.505 SER 383 HB 2.61
TRP 356 HB 2.51
SER 390 HB 1.50
12 Methyl malvalate −0.5677 No Interaction
13 Methyl Sterculate −0.2554 ASN 389 HB 2.50
TRP 356 HB 1.85
14 Quercetin 3,4′ diglucoside −0.201 PHE 174 Pi-Pi Stacking 5.44
ASP 104 HB 2.14
15 Campesterol 3.5794 No Interaction
16 Beta rosasterol 6.6198
17 Beta sitosterol 6.6198
18 Orlistat −1.7877 MET 103 HB 1.82
ASP 104 HB 2.09
SER 383 HB 1.65
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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 6.

Summary of docking analysis with leptin (PDB ID 1AX8).

Sr. No. Ligand Dock Score Interacting residues Bond Type Bond distance
1 Riboflavin −18.4869 GLN 134 HB 2.22
HB 1.91
GLN 130 HB 2.12
HB 1.72
ASP 40 HB 2.08
HB 1.58
Ar HB 2.21
ILE 21 HB 1.80
2 Cyanidine 3, 5 diglucoside −13.4683 ASP 40 HB 1.49
HB 2.40
HB 1.75
HB 1.68
GLN 130 HB 1.87
HB 1.83
GLN 134 HB 2.41
ILE 21 HB 1.86
HB 1.53
3 Thiamine −11.3807 GLN 134 HB 2.21
ASP 40 HB 2.15
ILE 42 HB 1.84
4 Ascorbic acid −11.1364 GLY 44 HB 1.94
GLN 134 HB 1.98
HB 2.20
5 Quercetin 3,4′ diglucoside −10.9657 GLY 44 HB 2.57
HB 2.27
ASP 135 HB 2.15
GLN 130 HB 1.90
ASP 40 HB 2.05
LEU 39 HB 1.84
HB 1.92
6 Quercetin 3,3′ diglucoside −10.3108 ASP 40 HB 2.29
SER 127 HB 1.60
7 Quercetin 3,7 diglucoside −10.2723 PHE 41 Pi-Pi Stacking 5.04
GLN 130 HB 1.97
ASP 40 HB 2.07
GLY 131 HB 1.56
GLY 44 HB 1.64
ASP 135 HB 1.56
8 Niacin −9.3776 ASP 40 HB 1.84
9 Beta rosasterol −6.3064 GLY 44 HB 1.82
10 Cyanidin 3-sophoroside-5-glucoside −5.2426 GLN 134 HB 1.84
ASP 135 HB 2.07
HB 2.54
LEU 39 HB 1.84
GLN 130 HB 1.99
PHE 41 HB 1.84
HB 1.91
11 Campesterol −3.5982 No interaction
12 Stigmasterol −2.8915 ASP 135 HB 2.22
GLY 44 HB 2.40
13 8 nonynoic acid 0.1127 OHE 41 HB 2.01
14 Beta sitosterol 0.4685 ASP 135 HB 1.97
GLY 44 HB 2.48
15 9 Decynoic acid 1.1976 ASP 40 HB 1.88
PHE 41 HB 1.86
16 Methyl non-8-ynoate 2.0473 PHE 41 HB 1.89
17 Methyl Dec-9-ynoate 2.8153 PHE 41 HB 1.95
18 Methyl 8-oxooctadec-9-ynoate 5.4298 PHE 41 HB 1.87
19 (9) Methyl (E)-11-methoxy-9-oxononadec-10-enoate 6.5759 PHE 41 HB 1.83
20 Methyl Sterculate 6.9274 PHE 41 HB 1.87
21 Methyl malvalate 8.0895 PHE 41 HB 1.87
22 Orlistat 8.3009 ASP 40 HB 1.71
GLU 134 HB 1.80
GLY 44 HB 1.99
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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 7.

Summary of docking analysis with SCH1 protein (PDB ID 4XWX).

Sr. No. Ligand Dock score Interacting residues Bond type Bond angle
1 Riboflavin −13.553 ARG 74 Pi cation 5.25
Pi Pi stacking 4.72
ILE 150 HB 1.68
ALA 153 HB 1.84
SER 151 HB 1.95
HB 2.19
2 Niacin −11.0861 PHE 198 Pi-Pi Stacking 4.93
3 Ascorbic acid −8.3129 ALA 153 HB 2.20
HB 1.90
SER 151 HB 1.58
HB 1.88
4 Thiamine −8.2065 ALA 153 HB 1.53
PHE 198 Pi-Pi Stacking 4.09
ILE 150 HB 1.73
5 Quercetin 3,3′ diglucoside −6.2583 GLY 195 HB 1.94
ALA 153 HB 1.58
ILE 191 HB 2.10
HB 2.49
PHE 198 Pi – Pi Stacking 5.06
SER 151 HB 2.24
ILE 150 HB 1.61
HB 1.75
6 Cyanidine 3, 5′ diglucoside −4.9771 GLU 199 Salt Bridge 2.92
PHE 198 Pi Pi Stacking 4.73
ALA 153 HB 2.15
HB 2.17
SER 151 HB 1.84
WATER HB 2.43
ILE 150 HB 1.81
HB 1.77
7 Campesterol −4.5453 ALA 153 HB 1.92
8 Beta sitosterol −1.7076 ILE 191 HB 1.95
9 9 Decynoic acid −1.6636 ARG 74 Salt Bridge 4.96
10 8 nonynoic acid −1.5286 ARG 74 Salt Bridge 4.96
11 Stigmasterol −1.0801 No interaction
12 Quercetin 3,4′ diglucoside 0.3325 GLY 155 HB 2.43
WATER HB 2.17
ALA 153 HB 1.88
HB 2.07
HB 2.33
SER 151 HB 2.02
PHE 198 Pi Pi Stacking 5.32
GLY 195 HB 2.26
13 Beta rosasterol 0.3474 No interaction
14 Methyl non-8-ynoate 0.477
15 Methyl Dec-9-ynoate 1.0452
16 Quercetin 3,7 diglucoside 2.2611 WATER HB 1.14
HB 0.61
HB 2.50
ILE 150 HB 1.76
HB 1.66
SER 151 HB 1.76
ARG 74 Pi Cation 3.84
17 Methyl 8-oxooctadec-9-ynoate 3.4243 No Interaction
18 Methyl malvalate 5.8575
19 Methyl Sterculate 6.8808
20 (9) Methyl (E)-11-methoxy-9-oxononadec-10-enoate 7.7443
21 Cyanidin 3-sophoroside-5-glucoside 8.5222
22 Orlistat 10.3508 ALA 153 HB 1.86
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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 8.

Summary of docking analysis with ghrelin.

Sr. No. Ligand Dock score Interacting residues Bond type Bond angle
1 Niacin −11.1374 ALA 53 HB 2.36
ASN 76 HB 1.85
2 Ascorbic acid −7.2393 PRO 49 HB 2.15
HB 1.86
GLN 36 HB 1.77
ALA 77 HB 2.18
3 Riboflavin −7.0131 ALA 77 HB 2.40
GLN 36 HB 1.68
HB 1.59
ASN 76 HB 1.62
4 Thiamine −4.7344 GLN 36 HB 1.77
ALA 77 HB 2.13
HIE 32 Pi-Pi Stacking 5.33
5 8 nonynoic acid 1.9189 ASN 76 HB 1.84
6 9 Decynoic acid 2.7981 ALA 77 HB 2.13
7 Methyl non-8-ynoate 2.9037 No interaction
8 Methyl Dec-9-ynoate 4.0035
9 Campesterol 6.9115
10 Methyl 8-oxooctadec-9-ynoate 8.7284 GLU 36 HB 2.20
ASN 76 HB 1.92
11 Methyl malvalate 11.8293 ALA 77 HB 2.18
12 Methyl Sterculate 12.0917 No interaction
13 Beta sitosterol 12.2015
14 (9) Methyl (E)-11-methoxy-9-oxononadec-10-enoate 13.2915
15 Orlistat 15.8166 ASN 76 HB 1.65
ALA 77 HB 2.20
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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.

Sr. No. Ligand Dock score Interacting residues Bond type Bond angle
1 Quercetin 3,3′ diglucoside −13.7266 ASP 91 HB 1.96
HB 1.74
GLY 80 HB 1.76
GLY 18 HB 2.00
SER 57 HB 1.73
2 Riboflavin −12.9742 GLY 18 HB 2.19
HB 2.10
SER 87 HB 2.36
SER 57 HB 1.55
3 Thiamine −9.527 LEU 16 HB 1.82
HB 1.90
GLU 54 Salt Bridge 4.99
HB 1.90
4 Quercetin 3,7 diglucoside −8.7967 VAL 3 HB 2.02
LEU 76 HB 1.63
ACE 0 HB 2.10
GLU 80 HB 2.24
ASP 91 HB 2.35
5 Cyanidine 3, 5′ diglucoside −8.3388 LEU 76 HB 1.68
ACE 0 HB 1.63
HB 1.51
VAL 3 HB 2.31
ASP 91 HB 1.55
HB 1.58
SER 87 HB 1.76
GLY 18 HB 2.31
6 Ascorbic acid −7.7733 SER 57 HB 2.19
HB 1.80
GLU 54 HB 1.76
HB 1.65
7 Cyanidin 3-sophoroside-5-glucoside −5.7144 LEU 76 HB 1.70
ASP 91 HB 1.62
HB 1.65
GLY 18 HB 1.73
ACE 0 HB 1.92
HB 1.72
8 Quercetin 3,4′ diglucoside −5.236 GLY 15 HB 2.09
VAL 14 HB 2.35
SER 57 HB 1.74
SER 87 HB 2.20
ASP 91 HB 1.56
HB 1.42
9 Niacin −5.127 VAL 3 HB 1.84
10 Campesterol −0.843 GLU 54 HB 2.16
11 Stigmasterol −0.787 GLU 54 HB 1.93
12 Beta sitosterol 1.849 GLU 54 HB 2.00
13 Beta rosasterol 2.848 No interaction
14 8 nonynoic acid 3.749 VAL 3 HB 1.84
15 9 Decynoic acid 4.120 VAL 3 HB 1.84
16 Methyl Dec-9-ynoate 5.030 VAL 3 HB 1.89
17 Methyl non-8-ynoate 5.193 VAL 3 HB 1.89
18 (9) Methyl (E)-11-methoxy-9-oxononadec-10-enoate 8.373 TRP 61 HB 1.75
19 Methyl 8-oxooctadec-9-ynoate 8.384 SER 2 HB 2.03
20 VAL 3 HB 1.89
21 Methyl malvalate 11.003 VAL 3 HB 1.89
22 Methyl Sterculate 11.447 VAL 3 HB 1.89
23 Orlistat 11.712 Glu 54 HB 2.09
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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.

Fig. 1.

Fig. 1

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1LPB interaction with Niacin.

2. Experimental design, materials, and methods

2.1. 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.

2.2. 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 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.

2.3. 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 −5 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.

2.4. 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 software. The receptor ligand interactions were done using Maestro software. Interacting amino acid residue, bond type and bond distance were noted.

Data summarized in Table 3, Table 4, Table 5, Table 6, Table 7, Table 8, Table 9 and Fig. 1, Fig. 2, Fig. 3, Fig. 4, Fig. 5, Fig. 6, Fig. 7, Fig. 8, Fig. 9, Fig. 10, Fig. 11, Fig. 12, Fig. 13, Fig. 14.

Fig. 2.

Fig. 2

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1LPB interaction with Orlistat.

Fig. 3.

Fig. 3

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3LFM interaction with Riboflavin.

Fig. 4.

Fig. 4

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3LFM interaction with Orlistat.

Fig. 5.

Fig. 5

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3TGZ interaction with Niacin.

Fig. 6.

Fig. 6

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3TGZ interaction with Orlistat.

Fig. 7.

Fig. 7

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1AX8 interaction with Riboflavin.

Fig. 8.

Fig. 8

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1AX8 interaction with Orlistat.

Fig. 9.

Fig. 9

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4XWX interaction with Riboflavin.

Fig. 10.

Fig. 10

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4XWX interaction with Orlistat.

Fig. 11.

Fig. 11

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Ghrelin interaction with Niacin.

Fig. 12.

Fig. 12

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Ghrelin interaction with Orlistat.

Fig. 13.

Fig. 13

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MCH1 interaction with Riboflavin.

Fig. 14.

Fig. 14

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MCH1 interaction with Orlistat.

Acknowledgments

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

Footnotes

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

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