Theoretical study of GSK-3α: Neural networks QSAR studies for the design of new inhibitors using 2D descriptors

Molecular Diversity

Volumn 15, Issue 4, 2011, Pages 947-955

  García, Isela ^a   Fall, Yagamare ^a   García Mera, Xerardo ^b   Prado Prado, Francisco ^b  

^a UNIVERSITY OF VIGO   (Spain)

^b UNIVERSITY OF SANTIAGO DE COMPOSTELA   (Spain)

Author keywords

Artificial neural network; Fragment contribution; GSK 3 QSAR; Linear discriminant analysis; Linear neural network

Indexed keywords

AST 487; BARASERTIB; CANERTINIB; CHIR 265; CP 724714; DASATINIB; DORAMAPIMOD; GLYCOGEN SYNTHASE KINASE 3 INHIBITOR; GLYCOGEN SYNTHASE KINASE 3ALPHA; GLYCOGEN SYNTHASE KINASE 3ALPHA INHIBITOR; LINIFANIB; MOTESANIB; N [5 (5 TERT BUTYL 2 OXAZOLYLMETHYLTHIO) 2 THIAZOLYL]ISONIPECOTAMIDE; PELITINIB; PKT 787; SR 3677; UNCLASSIFIED DRUG;

ARTICLE; ARTIFICIAL NEURAL NETWORK; DISCRIMINANT ANALYSIS; PREDICTIVE VALUE; PRIORITY JOURNAL; STRUCTURE ACTIVITY RELATION; VALIDATION PROCESS;

DATABASES, FACTUAL; DISCRIMINANT ANALYSIS; DRUG DESIGN; GLYCOGEN SYNTHASE KINASE 3; MODELS, MOLECULAR; MOLECULAR CONFORMATION; NEURAL NETWORKS (COMPUTER); PROBABILITY; PROTEIN KINASE INHIBITORS; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP;

EID: 84855218909     PISSN: 13811991     EISSN: 1573501X     Source Type: Journal    
DOI: 10.1007/s11030-011-9325-2     Document Type: Article

References (45)

1. Olson RE (2000) Secretase inhibitors as therapeutics for Alzheimer's disease. Annu Rep Med Chem 35:31-40. doi:10.1016/S0065-7743(00)35005-9
2. Troussard AA, Tan C, Yoganathan TN, Dedhar S (1999) Cell- extracellular matrix interactions stimulate the AP-1 transcription factor in an integrin-linked kinase- and glycogen synthase kinase 3- dependent manner. Mol Cell Biol 19:7420-7427. doi:0270-7306/ 99/$04.0010 (Pubitemid 29493405)
3. Turenne GA, Price BD (2001) Glycogen synthase kinase3 beta phosphorylates serine 33 of p53 and activates p53's transcriptional activity. BMC Cell Biol 2:12-21. doi:10.1186/1471-2121-2-12 (Pubitemid 38597713)
4. Hoeflich KP, Luo J, Rubie EA, Tasao MS, Jin O, Woodgett JR (2000) Requirement for glycogen synthase kinase-3beta in cell survival and NF-kappaB activation. Nature 406:86-90. doi:10. 1038/35017574 (Pubitemid 30460216)
5. MacAulay K, Doble BW, Patel S, Hansotia T, Sinclair EM, Drucker DJ et al (2007) Glycogen synthase kinase 3alpha-specific regulation of murine hepatic glycogen metabolism. Cell Metab 6:329-337. doi:10.1016/j.cmet.2007.08.013 (Pubitemid 47450185)
6. Droucheau E, Primot A, Thomas V, Mattei D, Knockaert M, Richardson C et al (2004) Plasmodium falciparum glycogen synthase kinase-3: molecular model, expression, intracellular localisation and selective inhibitors. Biochim Biophys Acta 1697:181-196. doi:10.1016/j.bbapap.2003.11.023 (Pubitemid 38326771)
7. Andraos J (2008) Kinetic plasticity and the determination of products ratios for kinetic schemes leading to multiple products without rate laws: new methods based on directed graphs. Can J Chem 86:342-357. doi:10.1139/VO8-020 (Pubitemid 351536311)
8. Xie G, Mo Z (2011) Three 3D graphical representations of DNA primary sequences based on the classifications of DNA bases and their applications. J Theor Biol 269:123-130. doi:10.1016/j.jtbi. 2010.10.018
9. Wu ZC, Xiao X, Chou KC (2010) 2D-MH: A web-server for generating graphic representation of protein sequences based on the physicochemical properties of their constituent amino acids. J Theor Biol 267:29-34. doi:10.1016/j.jtbi.2010. 08.007
10. Todeschini R, Consonni V (2002) Handbook of molecular descriptors. Wiley-VCH, New York
11. Talete srl (ed) DRAGON for Windows (Software for Molecular Descriptor Calculations)
12. Prado-Prado FJ, Borges F, Perez-Montoto LG, Gonzalez-Diaz H (2009) Multi-target spectral moment: QSAR for antifungal drugs vs. different fungi species. Eur J Med Chem 44:4051-4056. doi:10.1016/j.ejmech.2009.04.040
13. Prado-Prado FJ, García-Mera X, González-Díaz H (2010) Multitarget spectral moment QSAR versus ANN for antiparasitic drugs against different parasite species. Bioorg Med Chem 18:2225-2231. doi:10.1016/j.bmc.2010.01.068
14. Mosier PD, Jurs PC (2002) QSAR/QSPR studies using probabilistic neural networks and generalized regression neural networks. J Chem Inf Comput Sci 42:1460-1470. doi:10.1021/ci020039i (Pubitemid 35468335)
15. War WA (2009) ChEMBL. An interview with John Overington, team leader, chemogenomics at the European Bioinformatics Institute Outstation of the European Molecular Biology Laboratory (EMBL-EBI). J Comp Aided Mol Des 23:195-208. doi:10.1007/ s10822-009-9260-9
16. Prado-Prado FJ, Ubeira FM, Borges F, Gonzalez-Diaz H (2010) Unified QSAR & network-based computational chemistry approach to antimicrobials. II. Multiple distance and triadic census analysis of antiparasitic drugs complex networks. J Comput Chem 31:164-173. doi:10.1002/jcc.21292
17. Chou KC (2011) Some remarks on protein attribute prediction and pseudo amino acid composition. J Theor Biol 273:236-247. doi:10. 1016/j.jtbi.2010.12. 024
18. Chou KC, Shen HB (2010) Plant-mPLoc: a top-down strategy to augment the power for predicting plant protein subcellular localization. PLoS One 5:e11335
19. Todeschini R, Consonni V (2000) Handbook of molecular descriptors. Wiley-VCH, Weinheim
20. Hill T, Lewicki P (2002) STATISTICS. Tulsa:StatSoft
21. Chou KC, Shen HB (2010) Cell-PLoc 2.0: an improved package of web-servers for predicting subcellular localization of proteins in various organisms. Nat Sci 2:1090-1103. doi:10.4236/ns.2010. 210136
22. KandaswamyKK, Chou KC, MartinetzT, Moller S, Suganthan PN, Sridharan S et al. (2011) AFP-Pred: a random forest approach for predicting antifreeze proteins from sequence-derived properties. J Theor Biol 270:56-62. doi:10.1016/j.jtbi.2010.10.037
23. Lin H, Ding H (2011) Predicting ion channels and their types by the dipeptide mode of pseudo amino acid composition. J Theor Biol 269:64-69. doi:10.1016/j.jtbi.2010.10.019
24. Zakeri P, Moshiri B, Sadeghi M (2011) Prediction of protein submitochondria locations based on data fusion of various features of sequences. J Theor Biol 269:208-216. doi:10.1016/j.jtbi.2010.10.026
25. Melagraki G, Afantitis A, Sarimveis H, Igglessi-Markopoulou O, Alexandridis A (2006) A novel RBF neural network training methodology to predict toxicity to Vibriof ischeri. Mol Divers 10:213-221. doi:10.1007/s11030-005- 9008-y (Pubitemid 44092210)
26. Prado-Prado FJ, Gonzalez-Diaz H, de la Vega OM, Ubeira FM, Chou KC (2008) Unified QSAR approach to antimicrobials. Part 3: first multi-tasking QSAR model for input-coded prediction, structural back-projection, and complex networks clustering of antiprotozoal compounds. BioorgMed Chem 16:5871-5880. doi:10.1016/j.bmc.2008.04.068 (Pubitemid 351766720)
27. Roy K, Mandal AS (2008) Development of linear and nonlinear predictive QSAR models and their external validation using molecular similarity principle for anti-HIV indolyl aryl sulfones. J Enzyme Inhib Med Chem 23:980-995. doi:10.1080/14756360701811379
28. Patra JC, Singh O (2009) Artificial neural networks-based approach to design ARIs usingQSARfor diabetes mellitus. JComp Chem 30:2494-2508. doi:10.1002/jcc.21240
29. Roy K, Mandal AS (2009) Predictive QSAR modeling of CCR5 antagonist piperidine derivatives using chemometric tools. J Enzyme Inhib Med Chem 24:205-223. doi:10.1080/14756360802051297
30. Gonzalez-Diaz H, Bonet I, Teran C, De Clerck E, Bello R, Garcia MM et al. (2007) ANN-QSAR model for selection of anticancer leads from structurally heterogeneous series of compounds. Eur J Med Chem 42:580-585. doi:10.1016/j.ejmech.2006.11.016 (Pubitemid 46719486)
31. Aguero-Chapin G, Varona-Santos J, de la Riva GA, Antunes A, Gonzalez-Villa T, Uriarte E et al. (2009) Alignment-free prediction of polygalacturonases with pseudofolding topological indices: experimental isolation from Cof f eaarabica and prediction of a new sequence. J Proteome Res 8:2122-2128. doi:10.1021/pr800867y
32. Fernandez M, Caballero J, Tundidor-Camba A (2006) Linear and nonlinear QSAR study of N-hydroxy-2-[(phenylsulfonyl) amino]acetamide derivatives as matrix metalloproteinase inhibitors. Bioorg Med Chem 14:4137-4150. doi:10.1016/j.bmc. 2006.01.072
33. Prado-Prado FJ, Uriarte E, Borges F, Gonzalez-Diaz H (2009) Multi-target spectral moments for QSAR and complex networks study of antibacterial drugs. Eur J Med Chem 44:4516-4521. doi:10.1016/j.ejmech.2009.06.018
34. Estrada E, Molina E (2006) Automatic extraction of structural alerts for predicting chromosome aberrations of organic compounds. J Mol Graph Model 25:275-288. doi:10.1016/j.jmgm.2006.01.002 (Pubitemid 44740067)
35. Estrada E, Uriarte E, Molina E, Simon-Manso Y, Milne GW (2006) An integrated in silico analysis of drug-binding to human serum albumin. J Chem Inf Model 46:2709-2724. doi:10. 1021/ci600274f (Pubitemid 46008140)
36. Johnson LN (2009) Protein kinase inhibitors: contributions from structure to clinical compounds. Quart Rev Biophys 42:1-40. doi:10.1017/S0033583508004745
37. Sushko I, Novotarskyi S, Körner R, Pandey AK, Cherkasov A, Li J et al (2010) Applicability domains for classification problems: benchmarking of distance to models for Ames mutagenicity set. J Chem Inf Model 50:2094-2111. doi:10.1021/ci100253r
38. Oberg T (2004) A QSAR for baseline toxicity: validation, domain of application, and prediction. Chem Res Toxicol 17:1630-1637. doi:10.1021/ tx0498253 (Pubitemid 39665555)
39. Gramatica P (2007) Principles of QSAR models validation: internal and external. QSAR Comb Sci 26:694-701. doi:10.1002/qsar.200610151 (Pubitemid 46932857)
40. Eriksson L, Jaworska J, Worth AP, Cronin MT, McDowell RM, Gramatica P (2003) Methods for reliability and uncertainty assessment and for applicability evaluations of classification and regression-based QSARs. Environ Health Perspect 111:1361-1375. doi:10.1289/ehp.5758 (Pubitemid 37021142)
41. Li J, Gramatica P (2009) The importance of molecular structures, endpoints' values, and predictivity parameters in QSAR research: QSAR analysis of a series of estrogen receptor binders. Mol Divers doi:10.1007/s11030-009- 9212-2
42. Gramatica P, Giani E, Papa E (2006) Statistical external validation and consensusmodeling: aQSPR case study for K(oc) prediction. J Mol Graph Model 25:755-766. doi:10.1016/j.jmgm.2006.06.005
43. Liu H, Papa E, Gramatica P (2006) QSAR prediction of estrogen activity for a large set of diverse chemicals under the guidance of OECD principles. Chem Res Toxicol 19:1540-1548. doi:10.1021/ tx0601509 (Pubitemid 44863497)
44. Papa E, Villa F, Gramatica P (2005) Statistically validated QSARs, based on theoretical descriptors, for modeling aquatic toxicity of organic chemicals in Pimephalespromelas (fathead minnow). J Chem Inf Model 45:1256-1266. doi:10.1021/ci050212l (Pubitemid 41476009)
45. Chou KC, Shen HB (2009) Recent advances in developing webservers for predicting protein attributes. Nat Sci 2:63-92. doi:10.4236/ns.2009.12011