QSAR study of ACK1 inhibitors by genetic algorithm-multiple linear regression (GA-MLR)

Journal of Saudi Chemical Society

Volumn 18, Issue 5, 2014, Pages 681-688

  Pourbasheer, Eslam ^a,b   Aalizadeh, Reza ^c   Ganjali, Mohammad Reza ^d   Norouzi, Parviz ^d   Shadmanesh, Javad ^c  

^a PAYAME NOOR UNIVERSITY   (Iran)

^b ISLAMIC AZAD UNIVERSITY   (Iran)

^c UNIVERSITY OF ATHENS   (Greece)

^d UNIVERSITY OF TEHRAN   (Iran)

Author keywords

ACK1; Genetic algorithm; Hierarchical clustering; Multiple linear regressions; QSAR

Indexed keywords

EID: 84908650825     PISSN: 13196103     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jscs.2014.01.010     Document Type: Article

References (60)

1. Manser E., Leung T., Salihuddin H., Tan L., Lim L. A non-receptor tyrosine kinase that inhibits the GTPase activity of p21cdc42. Nature 1993, 363:364-367.
2. Mahajan K., Coppola D., Challa S., Fang B., Chen Y.A., Zhu W., Lopez A.S., Koomen J., Engelman R.W., Rivera C., Muraoka-Cook R.S., Cheng J.Q., Schönbrunn E., Sebti S.M., Earp H.S., Mahajan N.P. Ack1 mediated AKT/PKB tyrosine 176 phosphorylation regulates its activation. PLoS One 2010, 5:e9646.
3. Mahajan K., Mahajan N.P. Shepherding AKT and androgen receptor by Ack1 tyrosine kinase. J. Cell. Phys. 2010, 224:327-333.
4. Mahajan N.P., Liu Y., Majumder S., Warren M.R., Parker C.E., Mohler J.L., Earp H.S., Whang Y.E. Activated Cdc42-associated kinase Ack1 promotes prostate cancer progression via androgen receptor tyrosine phosphorylation. Proc. Natl. Acad. Sci. U.S.A. 2007, 104:8438-8443.
5. Mahajan N.P., Whang Y.E., Mohler J.L., Earp H.S. Activated tyrosine kinase Ack1 promotes prostate tumorigenesis: role of Ack1 in polyubiquitination of tumor suppressor Wwox. Cancer Res. 2005, 65:10514-10523.
6. Mahajan K., Coppola D., Chen Y.A., Zhu W., Lawrence H.R., Lawrence N.J., Mahajan N.P. Ack1 tyrosine kinase activation correlates with pancreatic cancer progression. Am. J. Pathol. 2012, 180:1386-1393.
7. Nur-E-Kamal A., Zhang A., Keenan S.M., Wang X.I., Seraj J., Satoh T., Meiners S., Welsh W.J. Requirement of activated Cdc42-associated kinase for survival of v-Ras-transformed mammalian cells. Mol. Canc. Res. 2005, 3:297-305.
8. van der Horst E.H., Degenhardt Y.Y., Strelow A., Slavin A., Chinn L., Orf J., Rong M., Li S., See L.-H., Nguyen K.Q.C., Hoey T., Wesche H., Powers S. Metastatic properties and genomic amplification of the tyrosine kinase gene ACK1. Proc. Natl. Acad. Sci. USA 2005, 102:15901-15906.
9. Kato-Stankiewicz J., Ueda S., Kataoka T., Kaziro Y., Satoh T. Epidermal growth factor stimulation of the ACK1/Dbl pathway in a Cdc42 and Grb2-dependent manner. Biochem. Biophys. Res. Comm. 2001, 284:470-477.
10. Kato J., Kaziro Y., Satoh T. Activation of the guanine nucleotide exchange factor dbl following ACK1-dependent tyrosine phosphorylation. Biochem. Biophys. Res. Comm. 2000, 268:141-147.
11. Nur-E-Kamal M.S.A., Kamal J.M., Qureshi M.M., Maruta H. The CDC42-specific inhibitor derived from ACK-1 blocks v-Ha-Ras-induced transformation. Oncogene 1999, 18:7787-7793.
12. Fujita T. Qsar and Drug Design: New Developments and Applications 1995, 413-450. Elsevier, Amsterdam, Netherlands.
13. Hansch C., Fujita T. P-σ-π analysis. A method for the correlation of biological activity and chemical structure. J. Am. Chem. Soc. 1964, 86:1616-1626.
14. Hou T.J., Wang J.M., Liao N., Xu X.J. Applications of genetic algorithms on the structure-activity relationship analysis of some cinnamamides. J. Chem. Inf. Comp. Sci. 1999, 39:775-781.
15. Kubinyi H. 3D QSAR in Drug Design: Theory Methods and Applications 1993, Springer.
16. Lee D.L., Kollman P.A., Marsh F.J., Wolff M.E. Quantitative relations between steroid structure and binding to putative progesterone receptors. J. Med. Chem. 1977, 20:1139-1146.
17. Zhou Y.-X., Xu L., Wu Y.-P., Liu B.-L. A QSAR study of the antiallergic activities of substituted benzamides and their structures. Chemom. Intell. Lab. Syst. 1999, 45:95-100.
18. Pourbasheer E., Riahi S., Ganjali M.R., Norouzi P. Application of genetic algorithm-support vector machine (GA-SVM) for prediction of BK-channels activity. Eur. J. Med. Chem. 2009, 44:5023-5028.
19. Riahi S., Pourbasheer E., Ganjali M.R., Norouzi P. Investigation of different linear and nonlinear chemometric methods for modeling of retention index of essential oil components: concerns to support vector machine. J. Hazard. Mat. 2009, 166:853-859.
20. Beheshti A., Pourbasheer E., Nekoei M., Vahdani S. QSAR modeling of antimalarial activity of urea derivatives using genetic algorithm-multiple linear regressions. J. Saudi Chem. Soc. 2012, doi: 10.1016/j.jscs.2012.07.019.
21. Pourbasheer E., Aalizadeh R., Ganjali M.R., Norouzi P. QSAR study of α1β4 integrin inhibitors by GA-MLR and GA-SVM methods. Struct. Chem. 2013, 10.1007/s11224-013-0300-7.
22. Pourbasheer E., Aalizadeh R., Ganjali M., Norouzi P., Shadmanesh J., Methenitis C. QSAR study of Nav1.7 antagonists by multiple linear regression method based on genetic algorithm (GA-MLR). Med. Chem. 2013, 10.1007/s00044-013-0821-z.
23. Li W., Tang Y., Zheng Y.-L., Qiu Z.-B. Molecular modeling and 3D-QSAR studies of indolomorphinan derivatives as kappa opioid antagonists. Bioorg. Med. Chem. 2006, 14:601-610.
24. Habibi-Yangjeh A., Pourbasheer E., Danandeh-Jenagharad M. Prediction of basicity constants of various pyridines in aqueous solution using a principal component-genetic algorithm-artificial neural network. Monatsh. Chem. 2008, 139:1423-1431.
25. Habibi-Yangjeh A., Pourbasheer E., Danandeh-Jenagharad M. Application of principal component-genetic algorithm-artificial neural network for prediction acidity constant of various nitrogen-containing compounds in water. Monatsh. Chem. 2009, 140:15-27.
26. Draper N.R., Smith H. Applied Regression Analysis 1981, John Wiley & Sons Inc, New York.
27. Hocking R.R. A biometrics invited paper. The analysis and selection of variables in linear regression. Biometrics 1976, 32:1-49.
28. Shen Q., Lü Q.-Z., Jiang J.-H., Shen G.-L., Yu R.-Q. Quantitative structure-activity relationships (QSAR): studies of inhibitors of tyrosine kinase. Eur. J. Pharm. Sci. 2003, 20:63-71.
29. Holland J.H. Adaptation in Natural and Artificial Systems 1975, University of Michigan Press.
30. Afantitis A., Melagraki G., Sarimveis H., Koutentis P., Igglessi-Markopoulou O., Kollias G. A combined LS-SVM & MLR QSAR workflow for predicting the inhibition of CXCR3 receptor by quinazolinone analogs. Mol. Divers. 2010, 14:225-235.
31. Riahi S., Ganjali M.R., Pourbasheer E., Norouzi P. QSRR study of GC retention indices of essential-oil compounds by multiple inear regression with a genetic algorithm. Chromatographia 2008, 67:917-922.
32. Pourbasheer E., Riahi S., Ganjali M.R., Norouzi P. QSAR study on melanocortin-4 receptors by support vector machine. Eur. J. Med. Chem. 2010, 45:1087-1093.
33. Vapnik V. Statistical Learning Theory 1998, Wiley, New York.
34. Khajehsharifi H., Sadeghi M., Pourbasheer E. Spectrophotometric simultaneous determination of ceratine, creatinine, and uric acid in real samples by orthogonal signal correction-partial least squares regression. Monatsh. Chem. 2009, 140:685-691.
35. Jin M., Wang J., Kleinberg A., Kadalbajoo M., Siu K.W., Cooke A., Bittner M.A., Yao Y., Thelemann A., Ji Q., Bhagwat S., Mulvihill K.M., Rechka J.A., Pachter J.A., Crew A.P., Epstein D., Mulvihill M.J. Discovery of potent, selective and orally bioavailable imidazo[1,5-a]pyrazine derived ACK1 inhibitors. Bioorg. Med. Chem. Lett. 2013, 23:979-984.
36. HyperChem Molecular Modeling System 2002, Hypercube, Inc., Gainesville, FL. 7.03 ed.
37. Todeschini R., Consonni V., Mauri A., Pavan M. DRAGON. Software for the calculation of molecular descriptors, Talete srl 2005, Milan, Italy.
38. Leardi R., Boggia R., Terrile M. Genetic algorithms as a strategy for feature selection. J. Chemomet. 1992, 6:267-281.
39. Cartwright H.M. Applications of Artificial Intelligence in Chemistry 1993, Oxford University Press, Oxford.
40. Mathworks Genetic Algorithm and Direct Search Toolbox Users Guide 2005, The Mathworks Inc.
41. Jain A.K. Data clustering: 50 years beyond K-means. Pattern Recog. Lett. 2010, 31:651-666.
42. Jain A.K., Murty M.N., Flynn P.J. Data clustering: a review. ACM. Comp. Sur. 1999, 31:264-323.
43. Hartigan J.A. Clustering Algorithms 1975, John Wiley & Sons, Inc.
44. Dubes R., Jain A.K. Clustering techniques: The user's dilemma. Pattern Recog. 1976, 8:247-260.
45. Golbraikh A., Shen M., Xiao Z., Xiao Y.-D., Lee K.-H., Tropsha A. Rational selection of training and test sets for the development of validated QSAR models. J. Comput. Aided. Mol. Des. 2003, 17:241-253.
46. Gramatica P., Pilutti P., Papa E. Validated QSAR prediction of OH tropospheric degradation of VOCs: splitting into training-test sets and consensus modeling. J. Chem. Inf. Comp. Sci. 2004, 44:1794-1802.
47. Agrawal V.K., Khadikar P.V. QSAR prediction of toxicity of nitrobenzenes. Bioorg. Med. Chem. 2001, 9:3035-3040.
48. Tropsha A., Gramatica P., Gombar V.K. The importance of being earnest: validation is the absolute essential for successful application and interpretation of QSPR models. QSAR Comb. Sci. 2003, 22:69-77.
49. Eriksson L., Johansson E., Müller M., Wold S. On the selection of the training set in environmental QSAR analysis when compounds are clustered. J. Chemomet. 2000, 14:599-616.
50. Netzeva T.I., Worth A.P., Aldenberg T., Benigni R., Cronin M.T.D., Gramatica P., Jaworska J.S., Kahn S., Klopman G., Marchant C.A., Myatt G., Nikolova-Jeliazkova N., Patlewicz G.Y., Perkins R., Roberts D.W., Schultz T.W., Stanton D.T., Sandt J.J.M.v.d., Tong W., Veith G., Yang C. Current status of methods for defining the applicability domain of (Quantitative) structure-activity relationships: the report and recommendations of ECVAM workshop 52 Altern. Lab. Anim. 2005, 33:155-173.
51. Gramatica P., Consonni V., Todeschini R. QSAR study on the tropospheric degradation of organic compounds. Chemosphere 1999, 38:1371-1378.
52. Hall L.H., Kier L.B. Electrotopological State indices for atom types: a novel combination of electronic, topological, and valence state information. J. Chem. Inf. Compu. Sci. 1995, 35:1039-1045.
53. Huuskonen J.J., Livingstone D.J., Tetko I.V. Neural network modeling for estimation of partition coefficient based on atom-type electrotopological state indices. J. Chem. Inf. Comp. Sci. 2000, 40:947-955.
54. Randić M. Random walks and their diagnostic value for characterization of atomic environment. J. Comput. Chem. 1980, 1:386-399.
55. Ruecker G., Ruecker C. Counts of all walks as atomic and molecular descriptors. J. Chem. Inf. Comp. Sci. 1993, 33:683-695.
56. Gasteiger J., Sadowski J., Schuur J., Selzer P., Steinhauer L., Steinhauer V. Chemical information in 3D space. J. Chem. Inf. Comp. Sci. 1996, 36:1030-1037.
57. Todeschini R., Bettiol C., Giurin G., Gramatica P., Miana P., Argese E. Modeling and prediction by using WHIM descriptors in QSAR studies: submitochondrial particles (SMP) as toxicity blosensors of chlorophenols. Chemosphere 1996, 33:71-79.
58. Consonni V., Todeschini R., Pavan M. Structure/response correlations and similarity/diversity analysis by GETAWAY Descriptors. 1. Theory of the Novel 3D molecular descriptors. J. Chem. Inf. Comp. Sci. 2002, 42:682-692.
59. Consonni V., Todeschini R., Pavan M., Gramatica P. Structure/response correlations and similarity/diversity analysis by GETAWAY Descriptors. 2. Application of the Novel 3D molecular descriptors to QSAR/QSPR studies. J. Chem. Inf. Compu. Sci. 2002, 42:693-705.
60. Convard T., Dubost J.-P., Le Solleu H., Kummer E. SmilogP: A Program for a Fast Evaluation of Theoretical Log P from the Smiles Code of a Molecule. Quant. Struct.-Act. Relat. 1994, 13:34-37.