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In-Silico Analysis of Secondary Metabolites That Modulates Enzymes of Cholesterol Target

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In-Silico Analysis of Secondary Metabolites That Modulates Enzymes of Cholesterol Target In-Silico Analysis of Secondary Metabolites that Modulates Enzymes of Cholesterol Target Rishab Marahatha1, Saroj Basnet2, Bibek Raj Bhattarai1, Prakriti Budhathoki1, Babita Aryal1, Bikash Adhikari1, Ganesh Lamichhane1, Darbin Kumar Poudel1, and Niranjan Parajuli1 Table 1S. Details about molecular docking platform Subject Pharmaceutical Science Specific subject area Interdisciplinary fields include organic chemistry, biochemistry, and biology. Drug design and discovery from plant sources. Type of data Tables and Figures How data were acquired MOE 2009 and GOLD V 4.0.1 Data format Raw and Analysed Parameters for data collection Gold Fitness score, energetic values, and interactions of the protein with the ligand. Description of data collection The protein was collected from the RCSB protein bank. The secondary metabolite structures were obtained from the PubChem online database. The docking was done using GOLD software. Data source location https://www.rcsb.org/, https://pubchem.ncbi.nlm.nih.gov/ Data accessibility PDB files of the chosen enzyme targets are publically available at https://www.rcsb.org/ Tables, and Figures of the docking are accessible in the article. Value of the Data ● The screening procedure enables the researchers to rapidly identify active natural compounds that can modulate a particular biochemical pathway. ● The screening results help to study the interaction/role of active metabolites in a particular biochemical process at the cellular level and provide preliminary ideas for drug design development ● By using this in-silico docking data, novel synthetic analogs with improved bioactivity and minimized side effects can be developed against these targets, and research time can be minimized considerably. ● We select these 16 metabolites because these are abundant in nature and well explored. Among these metabolites, the compounds which show the best affinity for various targets are shortlisted. ● The data is also useful for research scholars who do not have sufficient software and hardware requirements that are not affordable by them. ● Research scholars, researchers in pharmaceutical chemistry, Medicinal Chemistry, Drug Design Industry can benefit from the data. Table 2S. List of Targets showing the PDB ID, resolution and description of the proteins selected for docking with complexed inhibitor PDB ID Resolution Description (Å) 1N5X 2.8 Crystal Structure of Xanthine Oxidase from Bovine Milk [1] 1HWK 2.22 Crystal structure of human HMG-CoA Reductase with Atorvastatin [2] Table 3S. Active Site residues of HMG-CoA Reductase and Xanthine Oxidase PDB ID Name of the Organism Active Site Residues 1HWK Homo sapiens Arg B568,Ala B856, Ser B565, Leu B853, Leu B562, Ser B852, Gly B560, Asn B755, Lys A692, Ser A661, Cys B561, Glu B559, His B752, Asp A690, Ala B751, Asn A686, Arg A690, Ala B751, Asn A686, Arg A590, Ser A684, Lys B735,Lys A691, Leu B857, Val A683 1N5X Bos taurus Ala 1079, Phe 1009, Arg 880, Ser 1008, Thr 1010, Ser 876, Phe 914, Val 1011, Phe 649, Leu 1014, Leu 648, Pro 1076, Lys 771, Phe 1013, Asn 768, Leu 873, Glu 802,Phe 798, Gln 767, Asp 872, Met 1038 Table 4S. Molecular Properties of standard compounds and selected secondary metabolites Compound Name Vdw Elec Weight logP TPSA Donar Acceptor Allopurinol 10.54 -21.031 136.11 -0.187 70.14 2 3 Amentoflavone (1) 88.370 -12.116 538.464 4.820 173.980 6 8 Atorvastatin 78.69 -40.826 557.64 5.245 114.62 3 5 Febuxostat 33.8 -8.336 315.37 2.389 86.04 0 4 Ganoderic acid DM (15) 72.216 -11.591 468.678 6.931 71.440 1 4 Ganoderic acid η (16) 81.76 -15.528 531.66 1.479 155.19 4 8 Ganoleucoin K (11) 85.021 --20.835 670.752 0.730 215.30 2 11 Ganoleucoin T (14) 84.62 -2.034 597.76 3.56 148.87 2 8 Ganoleucoin Y (13) 98.04 -31.164 672.76 0.521 218.46 3 11 Ganoleucoin Z (12) 101 -29.294 670.75 0.73 215.3 2 11 Ganomycin I (9) 32.44 -7.947 342.43 5.191 66.76 2 3 Hydroxychavicol (7) 19.804 -2.997 150.177 1.826 40.46 2 2 Isoquercitrin (5) 76.126 -24.94 463.37 -0.292 209.43 7 11 Lovastatin 42.59 -11.604 404.54 4.196 72.83 1 3 Neotaiwanensol B (6) 46.12 -3.304 298.33 3.555 80.92 4 4 Pravastatin 52.7 -39.373 423.52 1.106 127.12 3 6 Probenecid 28.09 1.256 284.35 0.861 77.51 0 4 Riparsaponin (8) 87.136 21.061 620.868 3.791 139.840 6 8 Selgin (4) 55.268 -31.384 316.265 2.134 116.450 4 6 Simvastatin 47.09 -11.683 418.57 4.586 72.83 1 3 Topiroxostat 33.07 3.108 248.24 1.8 91.14 1 5 n-octadecanyl-O-훂-D- 55.396 10.940 594.783 1.889 178.530 7 11 glucopyranosyl(6’→1”)- O-훂-D-glucopyranoside (10) 6- Gingerol (2) 35.09 -10.246 294.39 3.234 66.76 2 4 6- Paradol (3) 33.14 -6.873 278.39 4.263 46.53 1 3 Where: Vdw = Van der waals energy, Elect = Electrostatic energy, logP =Partition coefficient, TPSA =Total polar surface area Table 5S. Inhibition of HMG-CoA reductase by secondary metabolites Natural Source Secondary metabolite IC50 value Reference Ficus virens n-octadecanyl-O-α-D- 0.164 μM [3] Bark glucopyranosyl(6’→1”)-O-α-D- glucopyranoside (10)* Ganoderma Ganoleucoin Z (12)* 8.68 ± 0.96 μM [4] leucocontextum Ganoleucoin Y (13)* 9.72 ± 0.91μM Ganoleucoin T (14)* 10.3 ± 1.78 μM Ganoderma Ganoderic acid DM (15) 9.5 ± 1.5 μM [5] leucocontextum Ganoleucoin K (11) 10.7 ± 2.9 μM Fruit Ganoderiol J 12.6 ± 2.7 μM Ganoderma Ganomycin I (9) 12.3 ± 1.7 μM [6] leucocontextum Ganomycin B 29.3 ± 2.5 μM Fruit Ganomycin C 45.2 ± 7.1 μM Fornicin B 56.9 ± 12.1 μM Ganoderma lucidum Ganomycin I 14.3 ± 1.5 μM [7] Fruit Ganoderenic acid K 16.5 ± 2.4 μM Ganoderic acid ɳ (16) 29.8 ± 1.5 μM Ganomycin B 30.3 ± 1.5 μM Ganoderma Lucidum 15-hydroxy-ganoderic acid S 21.7 μM [8] 7-oxo-ganoderic acid Z 22.3 μM Vitis vinifera Vitisin B 23.9 ± 5.0 μM [9] Stem bark Vitisin A 42 ± 3.1 μM γ-Viniferin 232.6 ± 30.9 μM Ampelopcin-A 294 ± 9.5 μM Rosa damascena Roxyloside 47.1 μM [10] Quercetin gentiobioside 50.6 μM Afzelin 80.1 μM Isoquercitrin 80.6 μM Note: Some IC50 values are adjusted in terms of molarity to make the comparison convenient; *Already docked molecules Current medication: Atorvastatin, Simvastatin, Pravastatin, Lovastatin Table 6S. Inhibition of Xanthine Oxidase by secondary metabolites Natural Source Secondary Metabolites IC50 value References Homonoia riparia Lour Riparsaponin (8) 0.011 μM [11] Semecarpus anacardium Amentoflavone (1) 0.092 μM [12] Chrysanthemum sinense Diosmetin* 0.13 μM [13] Acacetin* 0.16 μM Chrysoeriol 0.19 μM Eupafolin 0.20 μM Selgin (4) 0.22 μM Apigenin 0.36 μM Jaceidin 1.15 μM Luteolin* 1.24 μM 4,5-O-dicaffeoylquinic acid methyl ester 2.31 μM Piper nudibaccatum Neotaiwanesol B (6) 0.28 μM [14] Hydroxychavicol (7)* 0.38μM Flavones (class) Isorhamnetin* 0.4 μM [15] Kaempferol* 0.67 μM Myricetin* 1.27 μM Rutin* 46.8 μM Genistein* 83 μM Perilla frutescens Apigenin* 0.44 μM [16] Methyl rosamarinate 26.59 μM Vinyl caffeate 31.26 μM Rosamarinic acid* 91.72 μM Caffeic acid* 121.22 μM Dihydrochalcone (Class) Phloretin* 0.66 μM [15] Coffee beans Pyrogallol* 0.73 µM [17] Stauntonia brachyanthera Isoquercitrin (5) 1.6 μM [18] 3β,20α,24-trihydroxy-29-norolean-12-en-28- 5.22 μM oic-acid-24-O-β-L-fucopyranosyl- (1→ 2)-6-O- acetyl-β-D-glucopyranoside Blumea balsamifera Luteolin* 2.38 μM [19] Quercetin* 2.92 μM Tamarixetin* 3.16 μM 5,7,3’,5’-Tetrahydroxy flavone 32.14 μM Rhamnetin* 36.09 μM Blumeatin 53.21 μM Dihydroquercetin-4’-methyl ether 58.46 μM Luteolin-7-methyl ether 42.19 μM Lagerstroemi speciosa Valoneic acid 2.5 μM [20] Ellagic acid * 71.5 μM Toona sinensis 1,2,3,4,6-Penta-O-galloyl-β-D-glucopyranose 2.8 μM [21] Rabdosia japonica Hara 3,4-Dihydroxy phenyl acetic acid 3.5 ± 0.5 μM [22] Centaurea virgata Lam. Hispidulin 4.88 μM [23] Amentotaxus formosana (+) Sugiol 6.8 ± 0.4 μM [24] Zea mays L. Ferulic acid* 8.2 ± 0.3 µM [25] Momordica charantia Esculetin* 8.2 μM [26] Taiwacin A 24.3 ± 3.4 μM Salviae miltiorrhizae Lithospermic acid* 9.65 µM [27] Zingiber officinale 6-Gingerol (2)* 10.5 μM [28] 6-Paradol (3) 12.4 μM 6-Shogaol* 15.2 μM Herpetospermum penduculosum Cucurbitacin E 10.16 ± 0.21 μM [29] Neocucurbitacin D 15.27 ± 0.29 μM Cucurbitacin B* 18.41 ± 0.34 μM Veratrum taliense Veraphenol 11 μM [30] Piceid 66.1 μM Isorhapontin 70 μM Mulberroside E 78.4 μM Resveratol* 96.7 μM Pueraria lobata Genistein* 11.18 μM [31] Diadzin 12.75 μM Daidzein* 57.03 μM Puerarin 73.96 μM Pistacia integerrima Quercetin-3-O-훃-D-glucopyranoside 12.389 μM [32] Kaempferol-3-O-훃-D-glucopyranoside 26.199 μM Carallia brachiate Carallidin 12.9 μM [33] Rhodiola crenulata 4’-Hydroxy acetophenone 15.62 ± 1.19 μM [34] Epicatechin-(4β,8)-epicatechin gallate 24.24 ±1.8 μM Citrus aurantium Hesperetin* 16.48 μM [35] Nobiletin 107.51 μM Piper betel 4-Allyl-1,3-hydroxybenzene (hydroxychavicol) 16.7 μM [36] Radix Salviae Danshenxinkun B 17.45 ± 2.1μM [27] Paulownia catalpifolia Paucatalinone N 20.3 μM [37] Paucatalinone L 29.6 μM Anthocyanidin (Class) Pelargonidin 21.9 μM [15] Peonidin 26.0 μM Cyanidin 27.8 μM Apigenidin 29.1 μM Delphinidin 52.4 μM Palhinhaea ceruna Apigenin-4‘-O-(2‘ ‘-O-p-coumaroyl)-β-d- 23.95 μM [38] glucopyranoside Centaurium erythraea Caulerpenyne 26.92 μM [39] Sinapic acid 147.4 μM Artocarpus communis Artonol A 43.3 ± 8.1 μM [40] Cyclogera communin 73.3 ± 19.1 μM Morus alba L.
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