Literature DB >> 31193691

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

Sejal P Gandhi1, Kiran B Lokhande2, Venkateswara K Swamy2, Rabindra K Nanda1, Sohan S Chitlange1.   

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.

Entities:  

Year:  2019        PMID： 31193691      PMCID： PMC6538924          DOI： 10.1016/j.dib.2019.103994

Source DB:  PubMed          Journal:  Data Brief        ISSN： 2352-3409


Specifications table 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.

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.

TargetPDB IDDescriptionResolutionR value freeR value work
Pancreatic Lipase1LPBThe 2.46 Å resolution structure of the pancreatic lipase colipase complex inhibited by a c11 alkyl phosphonate2.46 Å0.2850.183
Fat And Obesity Protein3LFMCrystal structure of the fat mass and obesity associated (FTO) protein reveals basis for its substrate specificity2.5 Å0.2850.239
Cannabinoid Receptor5TGZCrystal structure analysis of w35f/h207w mutant of human clic12.3 Å0.3060.240
Leptin1AX8Human obesity protein, leptin2.4 Å0.2830.185
SCH1 Protein4XWXCrystal structure of the PTB domain of SHC1.87 Å0.1910.168
Table 2

Uniprot ID and FASTA sequence of ghrelin and MCH1 receptor.

TargetUniProt IDFASTA sequence
Ghrelin receptorQ9UBU3MPSPGTVCSLLLLGMLWLDLAMAGSSFLSPEHQRVQQRKESKKPPAKLQPRALAGWLRPEDGGQAEGAEDELEVRFNAPFDVGIKLSGVQYQQHSQALGKFLQDILWEEAKEAPADK
MCH 1 receptorQ99705MSVGAMKKGVGRAVGLGGGSGCQATEEDPLPNCGACAPGQGGRRWRLPQPAWVEGSSARLWEQATGTGWMDLEASLLPTGPNASNTSDGPDNLTSAGSPPRTGSISYINIIMPSVFGTICLLGIIGNSTVIFAVVKKSKLHWCNNVPDIFIINLSVVDLLFLLGMPFMIHQLMGNGVWHFGETMCTLITAMDANSQFTSTYILTAMAIDRYLATVHPISSTKFRKPSVATLVICLLWALSFISITPVWLYARLIPFPGGAVGCGIRLPNPDTDLYWFTLYQFFLAFALPFVVITAAYVRILQRMTSSVAPASQRSIRLRTKRVTRTAIAICLVFFVCWAPYYVLQLTQLSISRPTLTFVYLYNAAISLGYANSCLNPFVYIVLCETFRKRLVLSVKPAAQGQLRAVSNAQTADEERTESKGT
Table 3

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

Sr. NoPosenameScoreInteracting ResiduesBond TypeBond Distance
1Niacin−27.2868SER 333HB2.01
ARG 265Pi-Pi Stacking5.21
HB1.79
Salt bridge3.08
LYS 239Salt bridge2.73
2Quercetin 3, 7 diglucoside−21.223LYS 239HB1.93
ASP 247HB1.93
ASP 257HB1.71
TRY 267HB1.98
THR 271HB2.70
LYS 268HB1.48
Pi cation5.10
ASP 249HB2.12
3Ascorbic acid−20.6315SER 333HB2.32
ASP 247HB2.06
ARG 265HB2.18
ASP 257HB2.17
ASP 249HB1.79
4Quercetin 3, 3′ diglucoside−20.3198SER 3332HB2.12,2.31
ASP 247HB2.34
ASP 331HB1.84
ARG 265HB2.20
ASP 257HB2.66
TYR 267HB2.2.3
ASN 88HB2.52
5Quercetin 3,4′ diglucoside−18.4448ASP 3312HB2.18, 2,07
ARG 265HB1.90
SER 333HB2.29
PHE 335Pi Pi stacking5.46
ASN 88HB2.61
68 nonynoic acid−17.7764LYS 239HB2.04
Salt Bridge3.91
ARG 265HB1.93
79 Decynoic acid−17.4676ARG 2652HB2.11, 1.85
LYS 239HB1.94
Salt bridge4.67
8Cyanidine 3, 5 diglucoside−15.6327ARG 265PI Pi stacking4.92
ASP 247HB1.91
ASP 257HB1.89
ASP 249HB2.11
Salt Bridge4.60
GLU 253HB2,29
SER 333HB2.39
LYS 268HB2.29
ASP 272HB2.08
9Riboflavin−15.3182ASP 249HB1.36
SER 333HB1.60
GLU 253HB2.16
LYS 268HB1.71
10Thiamine−14.7694ASP 249HB1.97
Salt Bridge4.85
ARG 265Pi Pi stacking4.94
LYS 268Pi cation2.86
ASP 247HB2.07
11Beta rosasterol−9.4736LYS 239HB2.16
ARG 265HB2.16
12Cyanidin 3-sophoroside-5-glucoside−8.2017ASP 2493HB1.46, 2.02, 1.91
ASP 272HB2.02
GLU 2532HB1.82, 1.81
13Methyl non-8-ynoate−7.2264LYS 239HB2.05
ARG 265HB1.94
14Methyl Dec-9-ynoate−5.9149LYS 239HB2.05
ARG 265HB1.94
15Methyl (E)-11-methoxy-9-oxononadec-10-enoate−4.9341SER 333HB2.14
ARG 265HB2.15
TRY 267HB2.14
LYS 268HB2.12
16Methyl malvalate−3.6439LYS 239HB2.05
ARG 265HB1.94
17Methyl 8-oxooctadec-9-ynoate−2.8512SER 333HB2.10
LYS 239HB1.89
ARG 265HB2.02
18Methyl Sterculate−1.1816ARG 265HB1.98
LYS 239HB2.06
19Campesterol1.5909GLU 253HB2.28
20Stigmasterol2.651ASP 249HB1.90
GLU 253HB2.12
21Beta sitosterol3.2084No interaction
22Orlistat0.1075ASP 249HB1.68
SER 333HB1.94
TYR 267HB2.23
Ar HB1.91

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.LigandScoreInteracting ResiduesBond TypeBond Distance
1Riboflavin−27.3248ARG 96HB1.62
SER 229HB2.07
GLU 234HB2.01
Ar HB2.35
2Niacin−21.5279ARG 322HB1.93
GLU 234HB1.84
ARG 96Pi-Pi Stacking4.26
3Thiamine−19.313TRY 108Pi-Pi Stacking4.78
HIP 231Pi-Pi Stacking3.68
Pi-Pi Stacking5.45
Pi Cation4.42
Pi Cation3.78
SER 229HB1.38
TYR 106HB2.00
4Ascorbic acid−16.8546ASP 233HB1.99
ARG 322HB2.45
ARG 96HB1.99
GLU 234HB1.90
5Cyanidine 3, 5 diglucoside−14.6454ARG 322Pi Cation5.23
HB1.52
TRY 106HB2.08
HB1.81
HIP 232HB1.82
GLU 234HB2.25
HIP 231Pi-Pi Stacking4.81
Pi-Pi Stacking5.43
VAL 94HB1.53
6Quercetin 3,4′ diglucoside−12.747VAL 94HB2.35
GLU 234HB2.00
HIP 232HB1.57
HB1.91
GLN 306HB2.18
HIP 231Pi Cation6.38
78 nonynoic acid−12.149ASN 205HB1.96
ARG 322HB2.05
Salt bridge3.78
ARG 96HB1.78
89 Decynoic acid−11.8069ARG 322HB1.97
GLU 234HB1.97
9Quercetin 3,3′ diglucoside−11.2637TYR 108Pi-Pi Stacking4.55
ARG 96HB2.66
VAL 94HB1.79
ALA 227HB2.24
GLU 234HB1.62
10Quercetin 3,7 diglucoside−7.7494GLU 234HB2.04
HB2.16
TYR 108Pi-Pi Stacking4.94
TYR 106HB2.29
ARG 322HB1.71
HIP 231Pi-Pi Stacking3.98
HIP 232HB2.06
11Methyl 8-oxooctadec-9-ynoate−6.5642HIP 232HB1.93
ARG 96HB1.52
12Methyl Dec-9-ynoate−4.8041ARG 96HB2.15
13Methyl non-8-ynoate−4.4543ARG 96HB2.15
14(9) Methyl (E)-11-methoxy-9-oxononadec-10-enoate−2.4721ARG 96HB2.15
15Beta rosasterol−1.029VAL 94HB1.78
16Methyl Sterculate0.5157ARG 96HB1.84
17Methyl malvalate0.7329ARG 96HB1.88
18Beta sitosterol1.2521ALA 227HB2.2
19Campesterol1.447ALA 227HB2.21
20Orlistat−7.2466ARG 322HB2.20
GLU 234HB2.04
HB1.66
HIP 232HB2.17

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.LigandScoreInteracting ResiduesBond TypeBond Distance
1Niacin−14.7132MET 103HB1.84
ASP 104HB2.07
2Thiamine−13.5476PHE 102Pi-Pi Stacking4.92
SER 383HB1.81
SER 123HB1.69
3Ascorbic acid−11.9942ASP 163HB2.30
TRP 356HB1.70
CYS 386HB1.86
SER 199HB2.44
ALA 162HB2.12
4Riboflavin−9.4202PHE 170Pi-Pi Stacking5.43
MET 103HB1.96
SER 383HB2.06
58 nonynoic acid−4.3902ASP 104HB1.84
69 Decynoic acid−3.8828ASP 104HB1.95
MET 103HB1.83
7Methyl 8-oxooctadec-9-ynoate−3.0906ASN 389HB2.66
TRP 356HB1.86
8Methyl non-8-ynoate−2.5398TRP 356HB1.95
9Methyl Dec-9-ynoate−2.4934TRP 356HB1.95
10(9) Methyl (E)-11-methoxy-9-oxononadec-10-enoate−2.1673TRP 356HB1.95
11Quercetin 3,3′ diglucoside−1.505SER 383HB2.61
TRP 356HB2.51
SER 390HB1.50
12Methyl malvalate−0.5677No Interaction
13Methyl Sterculate−0.2554ASN 389HB2.50
TRP 356HB1.85
14Quercetin 3,4′ diglucoside−0.201PHE 174Pi-Pi Stacking5.44
ASP 104HB2.14
15Campesterol3.5794No Interaction
16Beta rosasterol6.6198
17Beta sitosterol6.6198
18Orlistat−1.7877MET 103HB1.82
ASP 104HB2.09
SER 383HB1.65

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.LigandDock ScoreInteracting residuesBond TypeBond distance
1Riboflavin−18.4869GLN 134HB2.22
HB1.91
GLN 130HB2.12
HB1.72
ASP 40HB2.08
HB1.58
Ar HB2.21
ILE 21HB1.80
2Cyanidine 3, 5 diglucoside−13.4683ASP 40HB1.49
HB2.40
HB1.75
HB1.68
GLN 130HB1.87
HB1.83
GLN 134HB2.41
ILE 21HB1.86
HB1.53
3Thiamine−11.3807GLN 134HB2.21
ASP 40HB2.15
ILE 42HB1.84
4Ascorbic acid−11.1364GLY 44HB1.94
GLN 134HB1.98
HB2.20
5Quercetin 3,4′ diglucoside−10.9657GLY 44HB2.57
HB2.27
ASP 135HB2.15
GLN 130HB1.90
ASP 40HB2.05
LEU 39HB1.84
HB1.92
6Quercetin 3,3′ diglucoside−10.3108ASP 40HB2.29
SER 127HB1.60
7Quercetin 3,7 diglucoside−10.2723PHE 41Pi-Pi Stacking5.04
GLN 130HB1.97
ASP 40HB2.07
GLY 131HB1.56
GLY 44HB1.64
ASP 135HB1.56
8Niacin−9.3776ASP 40HB1.84
9Beta rosasterol−6.3064GLY 44HB1.82
10Cyanidin 3-sophoroside-5-glucoside−5.2426GLN 134HB1.84
ASP 135HB2.07
HB2.54
LEU 39HB1.84
GLN 130HB1.99
PHE 41HB1.84
HB1.91
11Campesterol−3.5982No interaction
12Stigmasterol−2.8915ASP 135HB2.22
GLY 44HB2.40
138 nonynoic acid0.1127OHE 41HB2.01
14Beta sitosterol0.4685ASP 135HB1.97
GLY 44HB2.48
159 Decynoic acid1.1976ASP 40HB1.88
PHE 41HB1.86
16Methyl non-8-ynoate2.0473PHE 41HB1.89
17Methyl Dec-9-ynoate2.8153PHE 41HB1.95
18Methyl 8-oxooctadec-9-ynoate5.4298PHE 41HB1.87
19(9) Methyl (E)-11-methoxy-9-oxononadec-10-enoate6.5759PHE 41HB1.83
20Methyl Sterculate6.9274PHE 41HB1.87
21Methyl malvalate8.0895PHE 41HB1.87
22Orlistat8.3009ASP 40HB1.71
GLU 134HB1.80
GLY 44HB1.99

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.LigandDock scoreInteracting residuesBond typeBond angle
1Riboflavin−13.553ARG 74Pi cation5.25
Pi Pi stacking4.72
ILE 150HB1.68
ALA 153HB1.84
SER 151HB1.95
HB2.19
2Niacin−11.0861PHE 198Pi-Pi Stacking4.93
3Ascorbic acid−8.3129ALA 153HB2.20
HB1.90
SER 151HB1.58
HB1.88
4Thiamine−8.2065ALA 153HB1.53
PHE 198Pi-Pi Stacking4.09
ILE 150HB1.73
5Quercetin 3,3′ diglucoside−6.2583GLY 195HB1.94
ALA 153HB1.58
ILE 191HB2.10
HB2.49
PHE 198Pi – Pi Stacking5.06
SER 151HB2.24
ILE 150HB1.61
HB1.75
6Cyanidine 3, 5′ diglucoside−4.9771GLU 199Salt Bridge2.92
PHE 198Pi Pi Stacking4.73
ALA 153HB2.15
HB2.17
SER 151HB1.84
WATERHB2.43
ILE 150HB1.81
HB1.77
7Campesterol−4.5453ALA 153HB1.92
8Beta sitosterol−1.7076ILE 191HB1.95
99 Decynoic acid−1.6636ARG 74Salt Bridge4.96
108 nonynoic acid−1.5286ARG 74Salt Bridge4.96
11Stigmasterol−1.0801No interaction
12Quercetin 3,4′ diglucoside0.3325GLY 155HB2.43
WATERHB2.17
ALA 153HB1.88
HB2.07
HB2.33
SER 151HB2.02
PHE 198Pi Pi Stacking5.32
GLY 195HB2.26
13Beta rosasterol0.3474No interaction
14Methyl non-8-ynoate0.477
15Methyl Dec-9-ynoate1.0452
16Quercetin 3,7 diglucoside2.2611WATERHB1.14
HB0.61
HB2.50
ILE 150HB1.76
HB1.66
SER 151HB1.76
ARG 74Pi Cation3.84
17Methyl 8-oxooctadec-9-ynoate3.4243No Interaction
18Methyl malvalate5.8575
19Methyl Sterculate6.8808
20(9) Methyl (E)-11-methoxy-9-oxononadec-10-enoate7.7443
21Cyanidin 3-sophoroside-5-glucoside8.5222
22Orlistat10.3508ALA 153HB1.86

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.LigandDock scoreInteracting residuesBond typeBond angle
1Niacin−11.1374ALA 53HB2.36
ASN 76HB1.85
2Ascorbic acid−7.2393PRO 49HB2.15
HB1.86
GLN 36HB1.77
ALA 77HB2.18
3Riboflavin−7.0131ALA 77HB2.40
GLN 36HB1.68
HB1.59
ASN 76HB1.62
4Thiamine−4.7344GLN 36HB1.77
ALA 77HB2.13
HIE 32Pi-Pi Stacking5.33
58 nonynoic acid1.9189ASN 76HB1.84
69 Decynoic acid2.7981ALA 77HB2.13
7Methyl non-8-ynoate2.9037No interaction
8Methyl Dec-9-ynoate4.0035
9Campesterol6.9115
10Methyl 8-oxooctadec-9-ynoate8.7284GLU 36HB2.20
ASN 76HB1.92
11Methyl malvalate11.8293ALA 77HB2.18
12Methyl Sterculate12.0917No interaction
13Beta sitosterol12.2015
14(9) Methyl (E)-11-methoxy-9-oxononadec-10-enoate13.2915
15Orlistat15.8166ASN 76HB1.65
ALA 77HB2.20

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.LigandDock scoreInteracting residuesBond typeBond angle
1Quercetin 3,3′ diglucoside−13.7266ASP 91HB1.96
HB1.74
GLY 80HB1.76
GLY 18HB2.00
SER 57HB1.73
2Riboflavin−12.9742GLY 18HB2.19
HB2.10
SER 87HB2.36
SER 57HB1.55
3Thiamine−9.527LEU 16HB1.82
HB1.90
GLU 54Salt Bridge4.99
HB1.90
4Quercetin 3,7 diglucoside−8.7967VAL 3HB2.02
LEU 76HB1.63
ACE 0HB2.10
GLU 80HB2.24
ASP 91HB2.35
5Cyanidine 3, 5′ diglucoside−8.3388LEU 76HB1.68
ACE 0HB1.63
HB1.51
VAL 3HB2.31
ASP 91HB1.55
HB1.58
SER 87HB1.76
GLY 18HB2.31
6Ascorbic acid−7.7733SER 57HB2.19
HB1.80
GLU 54HB1.76
HB1.65
7Cyanidin 3-sophoroside-5-glucoside−5.7144LEU 76HB1.70
ASP 91HB1.62
HB1.65
GLY 18HB1.73
ACE 0HB1.92
HB1.72
8Quercetin 3,4′ diglucoside−5.236GLY 15HB2.09
VAL 14HB2.35
SER 57HB1.74
SER 87HB2.20
ASP 91HB1.56
HB1.42
9Niacin−5.127VAL 3HB1.84
10Campesterol−0.843GLU 54HB2.16
11Stigmasterol−0.787GLU 54HB1.93
12Beta sitosterol1.849GLU 54HB2.00
13Beta rosasterol2.848No interaction
148 nonynoic acid3.749VAL 3HB1.84
159 Decynoic acid4.120VAL 3HB1.84
16Methyl Dec-9-ynoate5.030VAL 3HB1.89
17Methyl non-8-ynoate5.193VAL 3HB1.89
18(9) Methyl (E)-11-methoxy-9-oxononadec-10-enoate8.373TRP 61HB1.75
19Methyl 8-oxooctadec-9-ynoate8.384SER 2HB2.03
20VAL 3HB1.89
21Methyl malvalate11.003VAL 3HB1.89
22Methyl Sterculate11.447VAL 3HB1.89
23Orlistat11.712Glu 54HB2.09

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

1LPB interaction with Niacin.

Table summarizing details of targets selected. Uniprot ID and FASTA sequence of ghrelin and MCH1 receptor. Summary of docking analysis with pancreatic lipase (PDB ID 1LPB). 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. Summary of docking analysis with fat and obesity protein (PDB ID 3LFM). 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. Summary of docking analysis with cannabinoid receptor (PDB ID 3TGZ). 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. Summary of docking analysis with leptin (PDB ID 1AX8). 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. Summary of docking analysis with SCH1 protein (PDB ID 4XWX). 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. Summary of docking analysis with ghrelin. 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. Summary of docking analysis with MCH1. 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. 1LPB interaction with Niacin.

Experimental design, materials, and methods

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 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 −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.

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

1LPB interaction with Orlistat.

Fig. 3

3LFM interaction with Riboflavin.

Fig. 4

3LFM interaction with Orlistat.

Fig. 5

3TGZ interaction with Niacin.

Fig. 6

3TGZ interaction with Orlistat.

Fig. 7

1AX8 interaction with Riboflavin.

Fig. 8

1AX8 interaction with Orlistat.

Fig. 9

4XWX interaction with Riboflavin.

Fig. 10

4XWX interaction with Orlistat.

Fig. 11

Ghrelin interaction with Niacin.

Fig. 12

Ghrelin interaction with Orlistat.

Fig. 13

MCH1 interaction with Riboflavin.

Fig. 14

MCH1 interaction with Orlistat.

1LPB interaction with Orlistat. 3LFM interaction with Riboflavin. 3LFM interaction with Orlistat. 3TGZ interaction with Niacin. 3TGZ interaction with Orlistat. 1AX8 interaction with Riboflavin. 1AX8 interaction with Orlistat. 4XWX interaction with Riboflavin. 4XWX interaction with Orlistat. Ghrelin interaction with Niacin. Ghrelin interaction with Orlistat. MCH1 interaction with Riboflavin. MCH1 interaction with Orlistat.

Specifications table

Subject areaChemistry
More specific subject areaComputational chemistry
Type of dataTable, figure
How data was acquiredLigand based molecular docking using FlexX and Maestro software
Data formatRaw and analyzed
Experimental factorsPhytoconstituents structures downloaded from PubChem were subjected to Avogadro software for energy minimization.
Experimental featuresMinimized ligands structures were docked with seven selected protein structure using FlexX software.
Data source locationDepartment of bioinformatics, Dr. D. Y. Patil Biotechnology & Bioinformatics Institute Tathawade, Pune
Data accessibilityData is only with this article
Related research articleK. 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].
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.  Integrating network pharmacology analysis and pharmacodynamic evaluation for exploring the active components and molecular mechanism of moutan seed coat extract to improve cognitive impairment.

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