Simplified molecular input line entry system-based: QSAR modelling for MAP kinase-interacting protein kinase (MNK1)

SAR and QSAR in Environmental Research

Volumn 26, Issue 5, 2015, Pages 343-361

  Begum, S ^a   Achary, P Ganga Raju ^a  

^a SIKSHA O ANUSANDHAN DEEMED TO BE UNIVERSITY   (India)

Author keywords

mitogen activated protein kinase (MAPK); optimal descriptor; pIC50; QSAR; SMILES

Indexed keywords

BROMINE COMPOUNDS; CHLORINE COMPOUNDS; ENZYMES; MOLECULAR GRAPHICS; MONTE CARLO METHODS;

ENTRY SYSTEM; MAP KINASE; MITOGEN-ACTIVATED PROTEIN KINASE; OPTIMAL DESCRIPTOR; PIC50; PROTEIN KINASE; QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIP; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP MODELING; SIMPLIFIED MOLECULAR INPUT LINE ENTRY SYSTEM;

COMPUTATIONAL CHEMISTRY;

PROTEIN SERINE THREONINE KINASE; PYRAZOLE DERIVATIVE; PYRIMIDINE DERIVATIVE; PYRROLIDINE DERIVATIVE;

ANTAGONISTS AND INHIBITORS; CHEMICAL STRUCTURE; CHEMISTRY; MONTE CARLO METHOD; QUANTITATIVE STRUCTURE ACTIVITY RELATION;

MODELS, MOLECULAR; MONTE CARLO METHOD; PROTEIN-SERINE-THREONINE KINASES; PYRAZOLES; PYRIMIDINES; PYRROLIDINES; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP;

EID: 84929653763     PISSN: 1062936X     EISSN: 1029046X     Source Type: Journal    
DOI: 10.1080/1062936X.2015.1039577     Document Type: Article

References (26)

1. G.Manning, D.B.Whyte, R.Martinez, T.Hunter, and S.Sudarsanam, The protein kinase complement of the human genome, Science 298 (2002), pp. 1912–1934.
2. G.Pearson, F.Robinson, T.Beers Gibson, B.E.Xu, M.Karandikar, K.Berman, and M.H.Cobb, Mitogen-activated protein (MAP) kinase pathways: Rregulation and physiological function, Endocr. Rev. 22 (2001), pp. 153–183.
3. K.Rajalingam, R.Schreck, U.R.Rapp, and Š.Albert, Ras oncogenes and their downstream targets, Bba-Mol. Cell. Res. 1773 (2007), pp. 1177–1195.
4. S.S.L.Judith, Development of anticancer drugs targeting the MAP kinase pathway, Oncogene 19 (2000), pp. 6594–6599.
5. H.W.Chang, F.S.Chung, and C.N.Yang, Molecular modeling of p38α Mitogen-activated protein kinase inhibitors through 3D-QSAR and molecular dynamics simulations, J. Chem. Inf. Modell., 53 (2013), pp. 1775–1786.
6. Coral 1.5, 2010; software available at http://www.insilico.eu/coral.
7. P.G.R.Achary, Simplified molecular input line entry system-based optimal descriptors: QSAR modelling for voltage-gated potassium channel subunit Kv7.2, SAR QSAR Environ. Res. 25 (2014), pp. 73–90.
8. P.G.R.Achary, QSPR modelling of dielectric constants ofπ-conjugated organic compounds by means of the CORAL software, SAR QSAR Environ. Res. 25 (2014), pp. 507–552.
9. A.Worachartcheewan, P.Mandi, V.Prachayasittikul, A.P.Toropova, A.A.Toropov, and C.Nantasenamat, Large-scale QSAR study of aromatase inhibitors using SMILES-based descriptors, Chemom. Intell. Lab. Syst. 138 (2014), pp. 120–126.
10. K.Nesměrák, A.A.Toropov, A.P.Toropova, P.Kohoutova, and K.Waisser, SMILES-based quantitative structure-property relationships for half-wave potential of N-benzylsalicylthioamides, Eur. J. Med. Chem. 67 (2013), pp. 111–114.
11. A.P.Toropova, A.A.Toropov, E.Benfenati, G.Gini, D.Leszczynska, and J.Leszczynski, CORAL: Quantitative structure - activity relationship models for estimating toxicity of organic compounds in rats, J. Comput. Chem. 32 (2011), pp. 2727–2733.
12. X.Chen, Y.Lin, M.Liu, and M.K.Gilson, The binding database: Data management and interface design, Bioinformatics 18 (2002), pp. 130–139.
13. T.Liu, Y.Lin, X.Wen, R.N.Jorrisen, and M.K.Gilson, BindingDB: A web-accessible database of experimentally determined protein-ligand binding affinities, Nucleic Acids Res. 35 (2007), pp. D198–D201.
14. A.A.Toropov, A.P.Toropova, and E.Benfenati, SMILES-based optimal descriptors: QSAR modeling of carcinogenicity by balance of correlations with ideal slopes, Eur. J. Med. Chem. 45 (2010), pp. 3581–3587.
15. A.P.Toropova, A.A.Toropov, A.Lombardo, A.Roncaglioni, E.Benfenati, and G.Gini, A new bioconcentration factor model based on SMILES and indices of presence of atoms, Eur. J. Med. Chem. 45 (2010), pp. 4399–4402.
16. A.P.Toropova, A.A.Toropov, E.Benfenati, D.Leszczynska, and J.Leszczynski, QSAR modeling of measured binding affinity for fullerene-based HIV-1 PR inhibitors by CORAL, J. Math. Chem. 48 (2010), pp. 959–987.
17. A.P.Toropova, A.A.Toropov, E.Benfenati, and G.Gini, QSAR modelling toxicity toward rats of inorganic substances by means of CORAL, Cent. Eur. J. Chem. 9 (2011), pp. 75–85.
18. A.P.Toropova, A.A.Toropov, E.Benfenati, and G.Gini, Co-evolutions of correlations for QSAR of toxicity of organometallic and inorganic substances: An unexpected good prediction based on a model that seems untrustworthy, Chemom. Intell. Lab. Syst. 105 (2011), pp. 215–219.
19. A.P.Toropova, A.A.Toropov, E.Benfenati, G.Gini, D.Leszczynska, and J.Leszczynski, CORAL: QSPR models for solubility of [C60] and [C70] fullerene derivatives, Mol. Diver. 15 (2011), pp. 249–256.
20. A.P.Toropova, A.A.Toropov, R.G.Diaza, E.Benfenati, and G.Gini, Analysis of the co-evolutions of correlations as a tool for QSAR-modeling of carcinogenicity: An unexpected good prediction based on a model that seems untrustworthy, Cent. Eur. J. Chem. 9 (2011), pp. 165–174.
21. P.P.Roy and K.Roy, QSAR studies of CYP2D6 inhibitor aryloxypropanolamines using 2D and 3D descriptors, Chem. Biol. Drug Des. 73 (2009), pp. 442–455.
22. P.K.Ojha and K.Roy, Comparative QSARs for antimalarial endochins: Importance of descriptor-thinning and noise reduction prior to feature selection, Chemom. Intell. Lab. Syst. 109 (2011), pp. 146–161.
23. A.Golbraikh and A.Tropsh, Beware of q2!, J. Mol. Graphics Modell. 20 (2002), pp. 269–276.
24. 2 metrics for validation of QSPR models, Chemom. Intell. Lab. Syst. 107 (2011), pp. 194–205.
25. S.Sharma, B.Bagchi, S.Mukhopadhyay, and A.K.Bothra, 2D QSAR studies of several potent aminopyridine, anilinopyrimidine and pyridine carboxamide-based JNK inhibitors, Indian J. Pharm. Sci. 73 (2011), pp. 165–170.
26. T.E.S.T. (version 4.1), Toxicity Estimation Software Tool; software available freely at http://www.epa.gov/nrmrl/std/qsar/qsar.html#TEST.