Identification of potential Gly/NMDA receptor antagonists by cheminformatics approach: a combination of pharmacophore modelling, virtual screening and molecular docking studies

References

• H. Bräuner-Osborne, J. Egebjerg, E.O. Nielsen, U. Madsen, and P. Krogsgaard-Larsen, Ligands for glutamate receptors: Design and therapeutic prospects, J. Med. Chem. 43 (2000), pp. 2609–2645.
   PubMed Web of Science ®Google Scholar
• P. Paoletti and J. Neyton, NMDA receptor subunits: Function and pharmacology, Curr. Opin. Pharmacol. 7 (2007), pp. 39–47.
   PubMed Web of Science ®Google Scholar
• D.T. Monaghan, M.W. Irvine, B.M. Costa, G. Fang, and D.E. Jane, Pharmacological modulation of NMDA receptor activity and the advent of negative and positive allosteric modulators, Neurochem. Int. 61 (2012), pp. 581–592.
   PubMed Web of Science ®Google Scholar
• L.V. Kalia, S.K. Kalia, and M.W. Salter, NMDA receptors in clinical neurology: Excitatory times ahead, Lancet Neurol. 7 (2008), pp. 742–755.
   PubMed Web of Science ®Google Scholar
• K.B. Ruppa, D. King, and R.E. Olson, NMDA antagonists of GluN2B subtype and modulators of GluN2A, GluN2C, and GluN2D subtypes-recent results and developments, Annu. Rep. Med. Chem. 47 (2012), pp. 89–102.
   Web of Science ®Google Scholar
• R.M. Santangelo, T.M. Acker, S.S. Zimmerman, B.M. Katzman, K.L. Strong, S.F. Traynelis, and D.C. Liotta, Novel NMDA receptor modulators: An update, Expert Opin. Ther. Pat. 22 (2012), pp. 1337–1352.
   PubMed Web of Science ®Google Scholar
• J. Chen, S. Graham, F. Moroni, and R. Simon, A study of the dose dependency of a glycine receptor antagonist in focal ischemia, J. Pharmacol. Exp. Ther. 267 (1993), pp. 937–941.
   PubMed Web of Science ®Google Scholar
• S. Murata and K. Kawasaki, Common and uncommon behavioral effects of antagonists for different modulatory sites in the NMDA receptor/channel complex, Eur. J. Pharmacol. 239 (1993), pp. 9–15.
   PubMed Web of Science ®Google Scholar
• W. Koek and F.C. Colpaert, Selective blockade of N-methyl-D-aspartate (NMDA)-induced convulsions by NMDA antagonists and putative glycine antagonists: Relationship with phencyclidine-like behavioral effects, J. Pharmacol. Exp. Ther. 252 (1990), pp. 349–357.
   PubMed Web of Science ®Google Scholar
• W. Danysz and C.G. Parsons, Glycine and N-methyl-D-aspartate receptors: Physiological significance and possible therapeutic applications, Pharmacol. Rev. 50 (1998), pp. 597–664.
   PubMed Web of Science ®Google Scholar
• L.J. Bristow, K.L. Flatman, and P.H. Hutson, The atypical neuroleptic profile of the Glycine/N-methyl-D-aspartate receptor antagonist, L-701,324, in rodents, J. Pharmacol. Exp. Ther. 277 (1996), pp. 578–585.
   PubMed Web of Science ®Google Scholar
• P. Berger, K. Farrel, F. Sharp, and P. Skolnick, Drugs acting at the strychnine-insensitive glycine receptor do not induce HSP-70 protein in the cingulate cortex, Neurosci. Lett. 168 (1994), pp. 147–150.
   PubMed Web of Science ®Google Scholar
• C. Chiamulera, S. Costa, and A. Reggiani, Effect of NMDA and strychnine-insensitive glycine site antagonists on NMDA-mediated convulsions and learning, Psychopharmacology 102 (1990), pp. 551–552.
   PubMed Web of Science ®Google Scholar
• V.H. Masand, D.T. Mahajan, and T. Ben Hadda, R.D. Jawarkar, B.P. Bandgar, and H. Chauhan, Molecular docking and quantitative structure activity relationship (QSAR) analyses of indolylarylsulfones as HIV-1 non-nucleoside reverse transcriptase inhibitors, Med. Chem. Res. 23 (2014), pp. 427–425.
   Google Scholar
• PHASE, version 3.2, User Manual. Schrodinger Press, LLC, New York, 2005.
   Google Scholar
• S.L. Dixon, A.M. Smondyrev, E.H. Knoll, S.N. Rao, D.E. Shaw, and R.A. Friesner, PHASE: A new engine for pharmacophore perception, 3D-QSAR model development, and 3D database screening: 1. Methodology and preliminary results, J. Comput. Aided Mol. Des. 20 (2006), pp. 647–671.
   PubMed Web of Science ®Google Scholar
• M. Niu, K. Wang, C. Zhang, Y. Dong, G. Fida, X. Dong, and Y. Gu, The discovery of potential tubulin inhibitors: A combination of pharmacophore modelling, virtual screening, and molecular docking studies, J. Taiwan Inst. Chem. Eng. 45 (2014), pp. 2111–2121.
   Web of Science ®Google Scholar
• X. Chen, Y. Lin, M. Liu, and M.K. Gilson, The binding database: Data management and interface design, Bioinformatics 18 (2002), pp. 130–139.
   PubMed Web of Science ®Google Scholar
• T. Liu, Y. Lin, X. Wen, R.N. Jorrisen, and M.K. Gilson, Binding DB: A web-accessible database of experimentally determined protein-ligand binding affinities, Nucleic Acids Res. 35 (2007), pp. D198–D201.
   PubMed Web of Science ®Google Scholar
• A. Tropsha, P. Gramatica, and V.K. Gombar, The importance of being earnest: Validation is the absolute essential for successful application and interpretation of QSPR models, QSAR Comb. Sci. 22 (2003), pp. 69–77.
   Google Scholar
• D. Singh, M. Karthikeyan, P. Kirubakaran, and S. Nagamani, Pharmacophore filtering and 3D-QSAR in the discovery of new JAK2 inhibitors, J. Mol. Graphics Modell. 30 (2011), pp. 186–197.
   Google Scholar
• X. Pan, N. Tan, G. Zeng, H. Huang, and H. Yan, 3D-QSAR studies on ketoamides of human cathepsin K inhibitors based on two different alignment methods, Eur. J. Med. Chem. 45 (2010), pp. 667–681.
   PubMed Web of Science ®Google Scholar
• A. Golbraikh and A. Tropsha, Beware of q2, J. Mol. Graphics Modell. 20 (2002), pp. 269–276.
   Google Scholar
• M. Mysinger, M. Carchia, J. Irwin, and B. Shoichet, Directory of useful decoys, enhanced (DUD-E): Better ligands and decoys for better benchmarking, J. Med. Chem. 55 (2012), pp. 6582–6594.
   PubMed Web of Science ®Google Scholar
• A. Afantitis, G. Melagraki, H. Sarimveis, P.A. Koutentis, J. Markopoulos, and O. Igglessi-Markopoulou, A novel simple QSAR model for the prediction of anti-HIV activity using multiple linear regression analysis, Mol. Divers. 10 (2006), pp. 405–414.
   PubMed Web of Science ®Google Scholar
• N.S.H.N. Moorthy, M.J. Ramos, and P.A. Fernandes, hERG binding feature analysis of structurally diverse compounds by QSAR and fragmental analysis, RSC Adv. 1 (2011), pp. 1126–1136.
   Web of Science ®Google Scholar
• N.S.H.N. Moorthy, M.J. Ramos, and P.A. Fernandes, Structural analysis of α-glucosidase inhibitors by validated QSAR models using topological and hydrophobicity based descriptors, Chemom. Intell. Lab. Syst. 109 (2011), pp. 101–112.
   Web of Science ®Google Scholar
• OECD, Guidance Document on the Validation of (Quantitative) structure-Activity Relationships (Q)SARs Models, Environment Monographs, No. 02, OECD, 2007.
   Google Scholar
• G. Melagraki and A. Afantitis, Enalos KNIME nodes: Exploring corrosion inhibition of steel in acidic medium, Chemom. Intell. Lab. Syst. 123 (2013), pp. 9–14.
   Web of Science ®Google Scholar
• G. Melagraki and A. Afantitis, Enalos InSilicoNano platform: An online decision support tool for the design and virtual screening of nanoparticles, RSC Adv. 4 (2014), pp. 50713–50725.
   Web of Science ®Google Scholar
• K. Roy, S. Kar, and P. Ambure, On a simple approach for determining applicability domain of QSAR models, Chemom. Intell. Lab. Syst. 145 (2015), pp. 22–29.
   Web of Science ®Google Scholar
• K. Roy, S. Kar, and P. Ambure, Applicability domain using standardization approach; software available at http://dtclab.webs.com/software-tools and http://teqip.jdvu.ac.in/QSAR_Tools.
   Google Scholar
• N. Jatana, A. Sharma, and N. Latha, Pharmacophore modelling and virtual screening studies to design potential COMT inhibitors as new leads, J. Mol. Graphics Modell. 39 (2013), pp. 145–164.
   Google Scholar
• V.H. Masand, D.T. Mahajan, A.M. Alafeefy, and S.N.A. Bukhari, Optimization of antiproliferative activity of substituted phenyl 4-(2-oxoimidazolidin-1-yl) benzenesulfonates: QSAR and CoMFA analyses, Eur. J. Pharm. Sci. 77 (2015), pp. 230–237.
   PubMed Web of Science ®Google Scholar
• J.J. Irwin and B.K. Shoichet, ZINC-A free database of commercially available compounds for virtual screening, J. Chem. Inf. Model. 45 (2005), pp. 177–182.
   PubMed Web of Science ®Google Scholar
• H. Pajouhesh and G.R. Lenz, Medicinal chemical properties of successful central nervous system drugs, NeuroRx 2 (2005), pp. 541–553.
   PubMedGoogle Scholar
• QikProp, version 3.3, Schrödinger, LLC, New York, NY, 2010.
   Google Scholar
• Glide, version 5.5, Schrödinger, LLC, New York, NY, 2009.
   Google Scholar
• R. Yakar and E.D. Akten, Discovery of high affinity ligands for β-adrenergic receptor through pharmacophore based high-throughput virtual screening and docking, J. Mol. Graphics Modell. 53 (2014), pp. 148–160.
   Google Scholar
• H. Furukawa and E. Gouaux, Mechanisms of activation, inhibition and specificity: Crystal structures of the NMDA receptor NR1 ligand-binding core, EMBO J. 22 (2003), pp. 2873–2885.
   PubMed Web of Science ®Google Scholar
• LigPrep, version 2.3, Schrödinger, LLC, New York, NY, 2009.
   Google Scholar
• H. Zhong, L.M. Tran, and L. Jenna, Induced-fit docking studies of the active and inactive states of protein tyrosine kinases, J. Mol. Graphics Modell. 28 (2009), pp. 336–346.
   Google Scholar
• G.V. Dhoke, R.P. Gangwal, and A.T. Sangamwar, A combined ligand and structure based approach to design potent PPAR-alpha agonists, J. Mol. Struct. 2012 (1028), pp. 22–30.
   Google Scholar
• R.P. Gangwal, N.R. Das, K. Thanki, M.V. Damre, G.V. Dhoke, S.S. Sharma, and A.T. Sangamwar, Identification of p38α MAP kinase inhibitors by pharmacophore based virtual screening, J. Mol. Graphics Modell. 49 (2014), pp. 18–24.
   Google Scholar