Chemistry Reference
In-Depth Information
112] Cavasotto CN, Phatak SS. Docking methods for structure- based library design. Methods
Mol. Biol., 2011, 685: 155-174.
[113] Amaro RE, Li WW. Emerging methods for ensemble-based virtual screening. Curr. Top.
Med. Chem., 2010, 10: 3-13.
[114] Cavasotto CN. Normal mode-based approaches in receptor ensemble docking. Methods
Mol. Biol. 2011, 2011, 51: 1604-1622.
[115] Cavasotto CN, Singh N. Docking and high throughput docking: successes and the
challenge of protein flexibility. Curr. Comput.Aided Drug Des., 2008, 4: 221-234.
[116] Spyrakis F, Bidon-Chanal, A, Barril X, Luque FJ. Protein flexibility and ligand recognition:
challenges for molecular modeling. Curr. Top. Med. Chem, 2011, 11: 192-210
[117] Cozzini P, Kellogg GE, Spyrakis F, Abraham DJ, Costantino G, Emerson A, Fanelli F,
Gohlke H, Kuhn LA. Morris, Morris GM, Orozco M, Pertinhez TA, Rizzi M, Sotriffer CA.
Target flexibility: an emerging consideration in drug discovery and design. J Med Chem.
2008,51: 6237-6255.
[118] Orozco M, Pertinhez TA, Rizzi M, Sotriffer CA. Target flexibility: an emerging
consideration in drug discovery and design. J. Med. Chem., 2008, 51: 6237-6255.
[119] Barillari C, Taylor J, Viner R, Essex JW. Classification of water molecules in protein
binding sites. J. Am. Chem. Soc., 2007, 129: 2577-2587.
[120] Marti-Renom MA, Stuart AC, Fiser A, Sanchez R, Melo F, Sali A. Comparative protein
structure modeling of genes and genomes. Annu. Rev. Biophys. Biomol. Struct., 2000, 29:
291-325.
[121] Canutescu AA, Shelenkov AA, Dunbrack RL Jr. A graphtheory algorithm for rapid protein
side-chain prediction. Protein Sci., 2003, 12: 2001-2014.
[122] Dunbrack RL, Karplus M. Backbone-dependent rotamer library for proteins. Application to
side-chain prediction. J. Mol. Biol., 1993, 230: 543-574.
[123] Sali A, Blundell TL. Comparative protein modeling by satisfaction of spatial restraints. J.
Mol. Biol. 1993, 234: 779-815.
[124] Evers A, Klebe G. Ligand-supported homology modeling of g-protein-coupled receptor
sites: models sufficient for successful virtual screening. Angew. Chem. Int Ed. Engl., 2004,
43: 248-251.
[125] Evers A, Klebe G. Successful virtual screening for a submicromolar antagonist of the
neurokinin-1 receptor based on a ligand-supported homology model. J. Med. Chem., 2004,
47: 5381-5392.
[126] Moro S, Deflorian F, Bacilieri M, Spalluto G. Ligand-based homology modeling as
attractive tool to inspect GPCR structural plasticity. Curr. Pharm. Des., 2006, 12: 2175-
2185.
[127] Sherman W, Day T, Jacobson MP, Friesner RA, Farid R. Novel procedure for modeling
ligand/receptor induced fit effects. J. Med. Chem., 2006, 49: 534-553.
[128] Orry AJW, Cavasotto CN. In: Ligand-docking-based homology model of the Melanin-
Concentrating Hormone 1 receptor, 231st Meeting of the American Chemical Society,
Atlanta, GA, 2006; Atlanta, GA, 2006.
[129] Diaz P, Phatak SS, Xu J, Astruc-Diaz F, Cavasotto CN, Naguib M. 6-Methoxy-N-alkyl
isatin acylhydrazone derivatives as a novel series of potent selective cannabinoid receptor 2
inverse agonists: Design, synthesis and binding mode prediction. J. Med. Chem., 2009, 52:
433-444.
