Abstract
Body weight control is a mechanism finely regulated by several hormonal, metabolic, and nervous pathways. The leptin receptor (Ob-R) is crucial for energy homeostasis and regulation of food uptake. Leptin is a 16 kDa hormone that is mainly secreted by fat cells into the bloodstream, and under normal circumstances, circulating levels are proportionate to the fat body mass. Sensing of elevated leptin levels by the hypothalamic neurocircutry activates a negative feedback loop resulting in reduced food intake and increased energy expenditure. Decreased concentrations lead to opposite effects. Therefore rational design of leptin agonists constitute an appealing challenge in the battle against obesity. In this study, we performed protein-protein docking among the re-built crystal structure of leptin and leptin binding domain (LBD). The obtained complex was used as a starting point to carry out nanosecond-scale molecular dynamics simulations to characterize the key regions in terms of physical-chemical features involved in the protein-protein interaction (dynamic site mapping filtered by means multivariate analysis) and used to carry out a HTVS. The main goal of this study was to suggest guidelines for the rational drug design of new agonists of leptin. Identified hits could be a consistent starting point to carry out in vitro testing.
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References
Heshka J, Jones P (2001) A role for dietary fat in leptin receptor, Ob-Rb, function. Life Sci 69:987–1003
Fruhbeck G (2001) A heliocentric view of leptin. Proc Nutr Soc 60:301–318
Zhang FM, Basinski M, Beals J, Briggs S, Churgay L, Clawson D, Di Marchi R, Furman T, Hale J, Hsiung H, Schoner B, Smith D, Zhang XY, Wery J, Shevitz R (1997) Crystal structure of the obese leptin-E100. Nature 387:206–209
Tartaglia LA, Dembski M, Weng X, Deng N, Culpepper J, Devos R, Richards GJ, Campfield LA, Clark FT, Deeds J (1995) Identification and expression cloning of a leptin receptor, Ob-R. Cell 83:1263–1271
Fong TM, Huang RR, Tota MR, Mao C, Smith T, Varnerin J, Karpitskiy VV, Krause JE, van der Ploeg LH (1998) Localization of leptin binding domain in the leptin receptor. Mol Pharmacol 53:234–240
Sandowski Y, Raver N, Gussakovsky EE, Shochat S, Dym O, Livnah O, Rubinstein M, Krishna R, Gertler A (2002) Subcloning, expression, purification, and characterization of recombinant human leptin-binding domain. J Biol Chem 277:46304–46309
Iserentant H, Peelman F, Defeau D, Vandekerckhove J, Zabeau L, Tavernier J (2005) Mapping of the interface between leptin and the leptin receptor CRH2 domain. J Cell Sci 118:2519–2527
Peelman F, Van Beneden K, Zabeau L, Iserentant H, Ulrichts P, Defeau D, Verhee A, Catteeuw D, Elewaut D, Tavernier J (2004) Mapping of the leptin binding sites and design of a leptin antagonist. J Biol Chem 279:41038–41046
Peelman F, Iserentant H, De Smet AS, Vandekerckhove J, Zabeau L, Tavernier J (2006) Mapping of binding site III in the leptin receptor and modeling of a hexameric leptin-leptin receptor complex. J Biol Chem 281:15496–15504
Hiroike T, Higo J, Jingami H, Toh H (2000) Homology modeling of human leptin/leptin receptor complex. Biochem Biophys Res Commun 275:154–158
Zabeau L, Defeau D, Iserentant H, Vandekerckhove J, Peelman F, Tavernier J (2005) Leptin receptor activation depends on critical cysteine residues in its fibronectin type III subdomains. J Biol Chem 280:22632–22640
Zabeau L, Verhee A, Catteeuw D, Faes L, Seeuws S, Decruy T, Elewaut D, Peelman F, Tavernier J (2012) Selection of non-competitive leptin antagonists using a random nanobody-based approach. Biochem J 441:425–434
Carpenter B, Hemsworth G, Wu Z, Maamra M, Strasburger C, Ross R, Artymiuk P (2012) Structure of the human obesity receptor leptin-binding domain reveals the mechanism of leptin-antagonism by a monoclonal antibody. Structure 20:487–497
Tutone M, Lauria A, Almerico AM (2014) Leptin and the ob-receptor as anti-obesity target: recent in silico advances in the comprehension of the protein-protein interaction and rational drug design of anti-obesity lead compounds. Curr Pharm Des 20:136–145
Lauria A, Tutone M, Barone G, Almerico AM (2014) Multivariate analysis in the identification of biological targets for designed molecular structures: the BIOTA protocol. Eur J Med Chem. doi:10.1016/j.ejmech.2014.01.025
Lauria A, Terenzi A, Gentile C, Martorana A, Gennaro G, Barone G, Almerico AM (2014) In silico, spectroscopic, and biological insights on annelated pyrrolo[3,2-e]pyrimidines with antiproliferative activity. Lett Drug Des Discov 11:15–26
Almerico AM, Tutone M, Pantano L, Lauria A (2013) A3 adenosine receptor: Homology modeling and 3D-QSAR studies. J Mol Graph Mod 42:60–72
Lauria A, Abbate I, Patella C, Martorana A, Dattolo G, Almerico AM (2013) New annelated thieno[2,3-e][1,2,3]triazolo[1,5-a]pyrimidines, with potent anticancer activity, designed through VLAK protocol. Eur J Med Chem 62:416–424
Almerico AM, Tutone M, Pantano L, Lauria A (2012) Molecular dynamics studies on Mdm2 complexes: an analysis of the inhibitor influence. Biochem Biophys Res Comm 424:341–347
Almerico AM, Tutone M, Guarcello A, Lauria A (2012) In vitro and in silico studies of polycondensed diazine systems as anti-parasitic agents. Bioorg Med Chem Lett 22:1000–1004
Almerico AM, Tutone M, Lauria A (2012) Receptor-guided 3D-QSAR approach for the discovery of c-kit tyrosine kinase inhibitors. J Mol Model 18:2885–2895
Lauria A, Patella C, Abbate I, Martorana A, Almerico AM (2012) Lead optimization through VLAK protocol: new annelated pyrrolo-pyrimidine derivatives as antitumor agents. Eur J Med Chem 55:375–383
Lauria A, Tutone M, Almerico AM (2011) Virtual lock-and-key approach: the in silico revival of Fischer model by means of molecular descriptors. Eur J Med Chem 46:4274–4280
Almerico AM, Tutone M, Lauria A (2009) In-silico screening of new potential Bcl-2/Bcl-xl inhibitors as apoptosis modulators. J Mol Mod 15:349–355
Lauria A, Ippolito M, Almerico AM (2009) Combined use of PCA and QSAR/QSPR to predict the drugs mechanism of action. An application to the NCI ACAM Database. QSAR & Comb Sci 28:387–395
Lauria A, Ippolito M, Almerico AM (2009) Principal component analysis on molecular descriptors as an alternative point of view in the search of new Hsp90 inhibitors. J Comp Biol Chem 33:386–390
Almerico AM, Tutone M, Lauria A (2008) Docking and multivariate methods to explore HIV-1 drug-resistance: A comparative analysis. J Comp-Aided Mol Des 22:287–297
Magrane M (2011) UniProt Consortium, UniProt Knowledgebase: a hub of integrated protein data. Database (Oxford). doi:10.1093/database/bar009
Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE (2000) The Protein Data Bank. Nucleic Acids Res 28:235–242
Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ (1990) Basic local alignment search tool. J Mol Biol 215:403–410
Zhang Y (2008) I-TASSER server for protein 3D structure prediction. BMC Bioinforma 9:40–48
Benkert P, Tosatto SCE, Schomburg D (2008) QMEAN: a comprehensive scoring function for model quality assessment. Proteins: Struct Funct Bioinform 71:261–277
Benkert P, Biasini M, Schwede T (2011) Toward the estimation of the absolute quality of individual protein structure models. Bioinformatics 27:343–350
Benkert P, Künzli M, Schwede T (2009) QMEAN server for protein model quality estimation. Nucleic Acids Res 37:W510–W514
Rampage by Molprobity http://mordred.bioc.cam.ac.uk/~rapper/rampage.php
Tovchigrechko A, Vakser IA (2005) Development and testing of an automated approach to protein docking. Proteins 60:296–301
Schrödinger (2012) Desmond 2.2 Schrödinger, LLC, New York, NY
Schrödinger (2011) Sitemap 2.3, Schrödinger, LLC, New York, NY
Schrödinger (2012) Glide, version 5.7, Schrödinger, LLC, New York, NY
Irwin JJ, Shoichet BK (2005) ZINC-a free database of commercially available compounds for virtual screening. J Chem Inf Model 45:177–182
Haglund E, Sulkowska J, Zhao H, Gen-Sheng F, Jennings PA, Onuchic JN (2012) The unique cysteine knot regulates the pleiotropic hormone leptin. Plos One 7:e45654
Sherman W, Day T, Jacobson MP, Friesner RA, Farid R (2006) Novel procedure for modeling ligand⁄receptor induced fit effects. J Med Chem 49:534–553
Nabuurs SB, Wagener M, de Vlieg J (2007) A flexible approach to induced fit docking. J Med Chem 50:6507–6518
Corbeil CR, Englebienne P, Moitessier N (2007) Docking ligands into flexible and solvated macromolecules. 1. Development and validation of fitted 1.0. J Chem Inf Model 47:435–449
Rueda M, Bottegoni G, Abagyan R (2009) Consistent improvement of cross-docking results using binding site ensembles generated with elastic network normal modes. J Chem Inf Model 49:716–725
Rueda M, Bottegoni G, Abagyan R (2009) Recipes for the selection of experimental protein conformations for virtual screening. J Chem Inf Model 50:186–193
Osguthorpe DJ, Sherman W, Hagler AT (2012) Generation of receptor structural ensembles for virtual screening using binding site shape analysis and clustering. Chem Biol Drug Des 80:182–193
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Tutone, M., Pantano, L., Lauria, A. et al. Molecular dynamics, dynamic site mapping, and highthroughput virtual screening on leptin and the Ob receptor as anti-obesity target. J Mol Model 20, 2247 (2014). https://doi.org/10.1007/s00894-014-2247-z
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DOI: https://doi.org/10.1007/s00894-014-2247-z