Identification of PPARγ Agonists from Natural Sources Using Different In Silico Approaches

Abstract

Peroxisome proliferator-activated receptor γ plays an important role in lipid and glucose homeostasis and is the target of many drug discovery investigations because of its role in diseases such as type 2 diabetes. Activation of peroxisome proliferator-activated receptor γ by agonists leads to a conformational change in the ligand-binding domain altering the transcription of several target genes involved in glucose and lipid metabolism, resulting in, for example, facilitation of glucose and lipid uptake and amelioration of insulin resistance, and other effects that are important in the treatment of type 2 diabetes. Peroxisome proliferator-activated receptor γ partial agonists are compounds with diminished agonist efficacy compared to full agonists; however, they maintain the antidiabetic effect of full agonists but do not induce the same magnitude of side effects. This mini-review gives a short introduction to in silico screening methods and recent research advances using computational approaches to identify peroxisome proliferator-activated receptor γ agonists, especially partial agonists, from natural sources and how these ligands bind to the peroxisome proliferator-activated receptor γ in order to better understand their biological effects.

Key words

PPARγ - natural products - 3D pharmacophores - in silico screening - molecular docking

• References

• 1 Bailey CJ, Day C. Traditional plant medicines as treatments for diabetes. Diabetes Care 1989; 12: 553-564
• 2 Newman DJ, Cragg GM. Natural products as sources of new drugs over the 30 years from 1981 to 2010. J Nat Prod 2012; 75: 311-335
• 3 Poulsen LC, Siersbæk M, Mandrup S. PPARs: fatty acid sensors controlling metabolism. Semin Cell Dev Biol 2012; 23: 631-639
• 4 Rosen ED, Spiegelman BM. Molecular regulation of adipogenesis. Annu Rev Cell Dev Biol 2000; 16: 145-171
• 5 Willson TM, Lambert MH, Kliewer SA. Peroxisome proliferator-activated receptor γ and metabolic disease. Annu Rev Biochem 2001; 70: 341-367
• 6 Choi JH, Banks AS, Estall JL, Kajimura S, Boström P, Laznik D, Ruas JL, Chalmers MJ, Kamenecka TM, Blüher M, Griffin PR, Spiegelman BM. Anti-diabetic drugs inhibit obesity-linked phosphorylation of PPARγ by Cdk5. Nature 2010; 466: 451-467
• 7 Shibuya A, Watanabe M, Fujita Y, Saigenji K, Kuwao S, Takahashi H, Takeuchi H. An autopsy case of troglitazone-induced fulminant hepatitis. Diabetes Care 1998; 21: 2140-2143
• 8 Berger JP, Petro AE, Macnaul KL, Kelly LJ, Zhang BB, Richards K, Elbrecht A, Johnson BA, Zhou G, Doebber TW, Biswas C, Parikh M, Sharma N, Tanen MR, Thompson GM, Ventre J, Adams AD, Mosley R, Surwit RS, Moller DE. Distinct properties and advantages of a novel peroxisome proliferator-activated protein γ selective modulator. Mol Endocrinol 2003; 17: 662-676
• 9 Ekins S, Mestres J, Testa B. In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling. Br J Pharmacol 2007; 152: 9-20
• 10 Reddy AS, Pati SP, Kumar PP, Pradeep HN, Sastry GN. Virtual screening in drug discovery – a computational perspective. Curr Protein Pept Sci 2007; 8: 329-351
• 11 Ekins S, Mestres J, Testa B. In silico pharmacology for drug discovery: applications to targets and beyond. Br J Pharmacol 2007; 152: 21-37
• 12 Wolber G, Rollinger JM. Virtual screening and target fishing for natural products using 3D pharmacophores. In: Jacoby E, editor Computational chemogenomics. Singapore: Pan Stanford Publishing; 2013: 117-139
• 13 Rollinger JM, Wolber G. Computational approaches for the discovery of natural lead structures. In: Tringali C, editor Bioactive compounds from natural sources, second edition. Natural products as lead compounds in drug discovery. Boca Raton, Florida, USA: CRC Press; 2012: 97-132
• 14 Ertl P, Schuffenhauer A. Cheminformatics analysis of natural products: lessons from nature inspiring the design of new drugs. Prog Drug Res 2008; 66: 217-235
• 15 Sarker SD, Nahar L. An introduction to natural products isolation. Methods Mol Biol; 2012; 864: 1-25
• 16 Schuster D, Wolber G. Identification of bioactive natural products by pharmacophore-based virtual screening. Curr Pharm Des 2010; 16: 1666-1681
• 17 Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE. The protein data bank. Nucleic Acids Res 2000; 28: 235-242
• 18 Lewis SN, Bassaganya-Riera J, Bevan DR. Virtual screening as a technique for PPAR modulator discovery. PPAR Res 2010; 2010: 861238
• 19 Anderson AC, Wright DL. The design and docking of virtual compound libraries to structures of drug targets. Curr Comput-Aided Drug Des 2005; 1: 103-127
• 20 Kitchen DB, Decornez H, Furr JR, Bajorath J. Docking and scoring in virtual screening for drug discovery: methods and applications. Nat Rev Drug Discov 2004; 3: 935-949
• 21 Halperin I, Ma B, Wolfson H, Nussinov R. Principles of docking: An overview of search algorithms and a guide to scoring functions. Proteins 2002; 47: 409-443
• 22 Perola E, Walters WP, Charifson PS. A detailed comparison of current docking and scoring methods on systems of pharmaceutical relevance. Proteins 2004; 56: 235-249
• 23 Wolber G, Langer T. LigandScout: 3-D pharmacophores derived from protein-bound ligands and their use as virtual screening filters. J Chem Inf Model 2005; 45: 160-169
• 24 Sousa SF, Ribeiro AJ, Coimbra JT, Neves RP, Martins SA, Moorthy NS, Fernandes PA, Ramos MJ. Protein-ligand docking in the new millennium – a retrospective of 10 years in the field. Curr Med Chem 2013; 20: 2296-2314
• 25 Warren GL, Andrews CW, Capelli AM, Clarke B, LaLonde J, Lambert MH, Lindvall M, Nevins N, Semus SF, Senger S, Tedesco G, Wall ID, Woolven JM, Peishoff CE, Head MS. A critical assessment of docking programs and scoring functions. J Med Chem 2006; 49: 5912-5931
• 26 Hawkins PCD, Skillman AG, Nicholls A. Comparison of shape-matching and docking as virtual screening tools. J Med Chem 2007; 50: 74-82
• 27 Chen L, Morrow JK, Tran HT, Phatak SS, Du-Cuny L, Zhang S. From laptop to benchtop to bedside: structure-based drug design on protein targets. Curr Pharm Des 2012; 18: 1217-1239
• 28 Seidel T, Ibis G, Bendix F, Wolber G. Strategies for 3D pharmacophore-based virtual screening. Drug Discov Today Technol 2010; 7: e221-e228
• 29 Venkatakrishnan AJ, Deupi X, Lebon G, Tate CG, Schertler GF, Babu MM. Molecular signatures of G-protein-coupled receptors. Nature 2013; 494: 185-194
• 30 Overington JP, Al-Lazikani B, Hopkins AL. How many drug targets are there?. Nat Rev Drug Discov 2006; 5: 993-996
• 31 Wolber G, Dornhofer AA, Langer T. Efficient overlay of small organic molecules using 3D pharmacophores. J Comput Aided Mol Des 2006; 20: 773-788
• 32 Krüger DM, Evers A. Comparison of structure- and ligand-based virtual screening protocols considering hit list complementarity and enrichment factors. ChemMedChem 2010; 5: 148-158
• 33 Klebe G. Virtual ligand screening: strategies, perspectives and limitations. Drug Discov Today 2006; 11: 580-594
• 34 Desvergne B, Wahli W. Peroxisome proliferator-activated receptors: nuclear control of metabolism. Endocr Rev 1999; 20: 649-688
• 35 Montanari R, Saccoccia F, Scotti E, Crestani M, Godio C, Gilardi F, Loiodice F, Fracchiolla G, Laghezza A, Tortorella P, Lavecchia A, Novellino E, Mazza F, Aschi M, Pochetti G. Crystal structure of the peroxisome proliferator-activated receptor γ (PPARγ) ligand binding domain complexed with a novel partial agonist: a new region of the hydrophobic pocket could be exploited for drug design. J Med Chem 2008; 51: 7768-7776
• 36 Bruning JB, Chalmers MJ, Prasad S, Busby SA, Kamenecka TM, He Y, Nettles KW, Griffin PR. Partial agonists activate PPARγ using a helix 12 independent mechanism. Structure 2007; 15: 1258-1271
• 37 Goebel M, Wolber G, Markt P, Staels B, Unger T, Kintscher U, Gust R. Characterization of new PPARγ agonists: benzimidazole derivatives-importance of positions 5 and 6, and computational studies on the binding mode. Bioorg Med Chem 2010; 18: 5885-5895
• 38 Markt P, Petersen RK, Flindt EN, Kristiansen K, Kirchmair J, Spitzer G, Distinto S, Schuster D, Wolber G, Laggner C, Langer T. Discovery of novel PPAR ligands by a virtual screening approach based on pharmacophore modeling, 3D shape, and electrostatic similarity screening. J Med Chem 2008; 51: 6303-6317
• 39 Guasch L, Sala E, Valls C, Blay M, Mulero M, Arola L, Pujadas G, Garcia-Vallve S. Structural insights for the design of new PPARgamma partial agonists with high binding affinity and low transactivation activity. J Comput Aided Mol Des 2011; 25: 717-728
• 40 Zoete V, Grosdidier A, Michielin O. Peroxisome proliferator-activated receptor structures: ligand specificity, molecular switch and interactions with regulators. Biochim Biophys Acta 2007; 1771: 915-925
• 41 Farce A, Renault N, Chavatte P. Structural insight into PPARγ ligands binding. Curr Med Chem 2009; 16: 1768-1789
• 42 Pochetti G, Godio C, Mitro N, Caruso D, Galmozzi A, Scurati S, Loiodice F, Fracchiolla G, Tortorella P, Laghezza A, Lavecchia A, Novellino E, Mazza F, Crestani M. Insights into the mechanism of partial agonism: crystal structures of the peroxisome proliferator-activated receptor γ ligand-binding domain in the complex with two enantiomeric ligands. J Biol Chem 2007; 282: 17314-17324
• 43 Gelman L, Feige JN, Desvergne B. Molecular basis of selective PPARγ modulation for the treatment of Type 2 diabetes. Biochim Biophys Acta 2007; 1771: 1094-1107
• 44 Lu IL, Huang CF, Peng YH, Lin YT, Hsieh HP, Chen CT, Lien TW, Lee HJ, Mahindroo N, Prakash E, Yueh A, Chen HY, Goparaju CMV, Chen X, Liao CC, Chao YS, Hsu JTA, Wu SY. Structure-based drug design of a novel family of PPARγ partial agonists: virtual screening, X-ray crystallography, and in vitro/in vivo biological activities. J Med Chem 2006; 49: 2703-2712
• 45 Choi JH, Banks AS, Kamenecka TM, Busby SA, Chalmers MJ, Kumar N, Kuruvilla DS, Shin Y, He Y, Bruning JB, Marciano DP, Cameron MD, Laznik D, Jurczak MJ, Schürer SC, Vidović D, Shulman GI, Spiegelman BM, Griffin PR. Antidiabetic actions of a non-agonist PPARγ ligand blocking Cdk5-mediated phosphorylation. Nature 2011; 477: 477-481
• 46 Hughes TS, Chalmers MJ, Novick S, Kuruvilla DS, Chang MR, Kamenecka TM, Rance M, Johnson BA, Burris TP, Griffin PR, Kojetin DJ. Ligand and receptor dynamics contribute to the mechanism of graded PPARγ agonism. Structure 2012; 20: 139-150
• 47 Hughes TS, Giri PK, Vera IMS, Marciano DP, Kuruvilla DS, Shin Y, Blayo AL, Kamenecka TM, Burris TP, Griffin PR, Kojetin DJ. An alternate binding site for PPARγ ligands. Nat Commun 2014; 5: 3571
• 48 Waku T, Shiraki T, Oyama T, Fujimoto Y, Maebara K, Kamiya N, Jingami H, Morikawa K. Structural insight into PPARγ activation through covalent modification with endogenous fatty acids. J Mol Biol 2009; 385: 188-199
• 49 Shiraki T, Kamiya N, Shiki S, Kodama TS, Kakizuka A, Jingami H. α,β-Unsaturated ketone is a core moiety of natural ligands for covalent binding to peroxisome proliferator-activated receptor γ . J Biol Chem 2005; 280: 14145-14153
• 50 Marles RJ, Farnsworth NR. Antidiabetic plants and their active constituents. Phytomed 1995; 2: 137-189
• 51 Penumetcha M, Santanam N. Nutraceuticals as ligands of PPARγ . PPAR Res 2012; 2012: 858352
• 52 Song MK, Roufogalis BD, Huang THW. Modulation of diabetic retinopathy pathophysiology by natural medicines through PPAR-γ-related pharmacology. Br J Pharmacol 2012; 165: 4-19
• 53 Huang THW, Kota BP, Razmovski V, Roufogalis BD. Herbal or natural medicines as modulators of peroxisome proliferator activated receptors and related nuclear receptors for therapy of metabolic syndrome. Basic Clin Pharmacol Toxicol 2005; 96: 3-14
• 54 Salam NK, Huang THW, Kota BP, Kim MS, Li Y, Hibbs DE. Novel PPAR-gamma agonists identified from a natural product library: a virtual screening, induced-fit docking and biological assay study. Chem Biol Drug Des 2008; 71: 57-70
• 55 Christensen KB, Petersen RK, Kristiansen K, Christensen LP. Identification of bioactive compounds from flowers of black elder (Sambucus nigra L.) that activate the human peroxisome proliferator-activated receptor (PPAR) γ . Phytother Res 2010; 24: S129-S132
• 56 Tanrikulu Y, Rau O, Schwarz O, Proschak E, Siems K, Muller-Kuhrt L, Schubert-Zsilavecz M, Schneider G. Structure-based pharmacophore screening for natural-product-derived PPARγ agonists. Chembiochem 2009; 10: 75-78
• 57 Rollinger JM, Haupt S, Stuppner H, Langer T. Combining ethnopharmacology and virtual screening for lead structure discovery: COX-inhibitors as application example. J Chem Inf Comput Sci 2004; 44: 480-488
• 58 Fakhrudin N, Ladurner A, Atanasov AG, Heiss EH, Baumgartner L, Markt P, Schuster D, Ellmerer EP, Wolber G, Rollinger JM, Stuppner H, Dirsch VM. Computer-aided discovery, validation, and mechanistic characterization of novel neolignan activators of peroxisome proliferator-activated receptor gamma. Mol Pharmacol 2010; 77: 559-566
• 59 Ehrman TM, Barlow DJ, Hylands PJ. Phytochemical databases of Chinese herbal constituents and bioactive plant compounds with known target specificities. J Chem Inf Model 2007; 47: 254-263
• 60 Irwin JJ, Sterling T, Mysinger MM, Bolstad ES, Coleman RG. ZINC: a free tool to discover chemistry for biology. J Chem Inf Model 2012; 52: 1757-1768
• 61 Petersen RK, Christensen KB, Assimopoulou AN, Fretté X, Papageorgiou VP, Kristiansen K, Kouskoumvekaki I. Pharmacophore-driven identification of PPARγ agonists from natural sources. J Comput Aided Mol Des 2011; 25: 107-116
• 62 Guasch L, Sala E, Mulero M, Valls C, Salvadó MJ, Pujadas G, Garcia-Vallvé S. Identification of PPARgamma partial agonists of natural origin (II): in silico prediction in natural extracts with known antidiabetic activity. PLoS ONE 2013; 8: e55889
• 63 Guasch L, Sala E, Castell-Auvi A, Cedo L, Liedl KR, Wolber G, Muehlbacher M, Mulero M, Pinent M, Ardevol A, Valls C, Pujadas G, Garcia-Vallve S. Identification of PPARgamma partial agonists of natural origin (I): development of a virtual screening procedure and in vitro validation. PLoS ONE 2012; 7: e50816
• 64 Goodsell DS, Olson AJ. Automated docking of substrates to proteins by simulated annealing. Proteins 1990; 8: 195-202
• 65 Ewing TJ, Makino S, Skillman AG, Kuntz ID. DOCK 4.0: search strategies for automated molecular docking of flexible molecule databases. J Comput Aided Mol Des 2001; 15: 411-428
• 66 Rarey M, Kramer B, Lengauer T, Klebe G. A fast flexible docking method using an incremental construction algorithm. J Mol Biol 1996; 261: 470-489
• 67 Halgren TA, Murphy RB, Friesner RA, Beard HS, Frye LL, Pollard WT, Banks JL. Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. J Med Chem 2004; 47: 1750-1759
• 68 Jones G, Willett P, Glen RC, Leach AR, Taylor R. Development and validation of a genetic algorithm for flexible docking. J Mol Biol 1997; 267: 727-748
• 69 Venkatachalam CM, Jiang X, Oldfield T, Waldman M. LigandFit: a novel method for the shape-directed rapid docking of ligands to protein active sites. J Mol Graph Model 2003; 21: 289-307
• 70 Meier R, Pippel M, Brandt F, Sippl W, Baldauf C. ParaDockS: a framework for molecular docking with population-based metaheuristics. J Chem Inf Model 2010; 50: 879-889
• 71 Jain AN. Surflex: fully automatic flexible molecular docking using a molecular similarity-based search engine. J Med Chem 2003; 46: 499-511
• 72 Mahindroo N, Peng YH, Lin CH, Tan UK, Prakash E, Lien TW, Lu IL, Lee HJ, Hsu JTA, Chen X, Liao CC, Lyu PC, Chao YS, Wu SY, Hsieh HP. Structural basis for the structure-activity relationships of peroxisome proliferator-activated receptor agonists. J Med Chem 2006; 49: 6421-6424
• 73 Jamali B, Bjørnsdottir I, Nordfang O, Hansen SH. Investigation of racemisation of the enantiomers of glitazone drug compounds at different pH using chiral HPLC and chiral CE. J Pharm Biomed Anal 2008; 46: 82-87
• 74 Zhang H, Xu X, Chen L, Chen J, Hu L, Jiang H, Shen X. Molecular determinants of magnolol targeting both RXRα and PPARγ . PLoS ONE 2011; 6: e28253