The inhibitory activities (pIC50) of N2 and O6 substituted guanine derivatives as cyclin-dependent kinase 2 (CDK2) inhibitors have been successfully modeled using calculated molecular descriptors. Two linear (MLR) and nonlinear (ANN) methods were utilized for construction of models to predict the pIC50 activities of those compounds. The QSAR models were validated by cross-validation (leave-one-out) as well as application of the models for prediction of pIC50 of external set compounds. Also, the models were validated by calculation of statistical parameters and Y-randomization test. Two methods provided accurate predictions, although more accurate results were obtained by ANN model. The mean-squared errors (MSEs) for validation and test sets of MLR are 0.065, 0.069 and of ANN are 0.017 and 0.063, respectively. 1. Introduction The cyclin-dependent kinases (CDKs) are a class of enzymes which play a fundamental role in cell cycle regulation [1, 2]. Particularly as their name suggests CDKs activation partially depends on the binding of another class of proteins named cyclins, for example, cyclins of the D family complex with CDK4 and CDK6 during G1 phase, cyclin E with CDK2 in late G1, cyclin A with CDK2 in S phase, and cyclin B with CDK1 (also known as cdc2) in late G2/M. Then, aberrant CDK control and consequent loss of cell cycle check point function have been directly linked to the molecular pathology of cancer [3]. It is well known that phosphorylation in a conserved threonine residue of the CDK subunit is required for its complete activation. This task is performed by the CDK activating kinase. These proteins properly regulate the cell cycle progress and DNA synthesis only as an active complex (T160pCDK/cyclin) [4]. Overall, the activity of the CDK/cyclin complex can be depleted by at least two different mechanisms that contain the phosphorylation of the CDK subunit at the inhibitory sites or the binding of the specialized natural inhibitors known as CDK inhibitors. In the first mechanism, the amino acid residue Y15 and to a lesser extend T14 (in CDK2) are phosphorylated by human Wee 1 Hu [5]. This inhibitory phosphorylation is independent of previous cyclin binding [6]. The second mechanism involves the binding of natural CDK inhibitors. Four major mammalian CDK inhibitors have been discovered: P21 (CIP1/WAF190) and P27 (KIP1) inactive CDK2 and CDK4 cyclin complexes by binding to them. The two other inhibitors are and that are specific for CDK4 and CDK6. They inhibit the formation of the active cyclin complexes by binding to the inactive CDK, and they
References
[1]
C. Norbury and P. Nurse, “Animal cell cycles and their control,” Annual Review of Biochemistry, vol. 61, pp. 441–470, 1992.
[2]
D. O. Morgan, “Principles of CDK regulation,” Nature, vol. 374, no. 6518, pp. 131–134, 1995.
[3]
M. Hall and G. Peters, “Genetic alterations of cyclins, cyclin-dependent kinases, and Cdk inhibitors in human cancer,” Advances in Cancer Research, vol. 68, pp. 67–108, 1996.
[4]
T. M. Sielecki, J. F. Boylan, P. A. Benfield, and G. L. Trainor, “Cyclin-dependent kinase inhibitors: useful targets in cell cycle regulation,” Journal of Medicinal Chemistry, vol. 43, no. 1, pp. 1–18, 2000.
[5]
N. Watanabe, M. Broome, and T. Hunter, “Regulation of the human WEE1Hu CDK tyrosine 15-kinase during the cell cycle,” EMBO Journal, vol. 14, no. 9, pp. 1878–1891, 1995.
[6]
K. Coulonval, L. Bockstaele, S. Paternot, and P. P. Roger, “Phosphorylations of cyclin-dependent kinase 2 revisited using two-dimensional gel electrophoresis,” Journal of Biological Chemistry, vol. 278, no. 52, pp. 52052–52060, 2003.
[7]
N. P. Pavletich, “Mechanisms of cyclin-dependent kinase regulation: structures of Cdks, their cyclin activators, and Cip and INK4 inhibitors,” Journal of Molecular Biology, vol. 287, pp. 821–828, 1999.
[8]
M. D. Losiewicz, B. A. Carlson, G. Kaur, E. A. Sausville, and P. J. Worland, “Potent inhibition of Cdc2 kinase activity by the flavonoid L86-8275,” Biochemical and Biophysical Research Communications, vol. 201, no. 2, pp. 589–595, 1994.
[9]
A. M. Senderowicz and E. A. Sausville, “Preclinical and clinical development of cyclin-dependent kinase modulators,” Journal of the National Cancer Institute, vol. 92, no. 5, pp. 376–387, 2000.
[10]
I. R. Hardcastle, B. T. Golding, and R. J. Griffin, “Designing inhibitors of cyclin-dependent kinases,” Annual Review of Pharmacology and Toxicology, vol. 42, pp. 325–348, 2002.
[11]
M. Knockaert, P. Greengard, and L. Meijer, “Pharmacological inhibitors of cyclin-dependent kinases,” Trends in Pharmacological Sciences, vol. 23, no. 9, pp. 417–425, 2002.
[12]
P. L. Toogood, “Progress toward the development of agents to modulate the cell cycle,” Current Opinion in Chemical Biology, vol. 6, pp. 472–478, 2002.
[13]
G. I. Shapiro, “Preclinical and clinical development of the cyclin-dependent kinase inhibitor flavopiridol,” Clinical Cancer Research, vol. 10, no. 12, pp. 4270s–4275s, 2004.
[14]
M. Vieth, R. E. Higgs, D. H. Robertson, M. Shapiro, E. A. Gragg, and H. Hemmerle, “Kinomics—structural biology and chemogenomics of kinase inhibitors and targets,” Biochimica et Biophysica Acta, vol. 1697, no. 1-2, pp. 243–257, 2004.
[15]
M. Fernandez, A. Tundidor-Camba, and J. Caballero, “Modeling of cyclin-dependent kinase inhibition by 1H-pyrazolo[3,4-d]pyrimidine derivatives using artificial neural network ensembles,” Journal of Chemical Information and Modeling, vol. 45, no. 6, pp. 1884–1895, 2005.
[16]
M. P. Gonzalez, J. Caballero, A. M. Helguera, M. Garriga, G. Gonzalez, and M. Fernandez, “2D autocorrelation modelling of the inhibitory activity of cytokinin-derived cyclin-dependent kinase inhibitors,” Bulletin of Mathematical Biology, vol. 68, no. 4, pp. 735–751, 2006.
[17]
H. Dureja and A. K. Madan, “Topochemical models for prediction of cyclin-dependent kinase 2 inhibitory activity of indole-2-ones,” Journal of Molecular Modeling, vol. 11, no. 6, pp. 525–531, 2005.
[18]
J. Z. Li, H. X. Liu, X. J. Yao, M. C. Liu, Z. D. Hu, and B. T. Fan, “Structure-activity relationship study of oxindole-based inhibitors of cyclin-dependent kinases based on least-squares support vector machines,” Analytica Chimica Acta, vol. 581, pp. 333–342, 2007.
[19]
S. Samanta, B. Debnath, A. Basu, S. Gayen, K. Srikanth, and T. Jha, “Exploring QSAR on 3-aminopyrazoles as antitumor agents for their inhibitory activity of CDK2/cyclin A,” European Journal of Medicinal Chemistry, vol. 41, no. 10, pp. 1190–1195, 2006.
[20]
M. Goodarzi and M. P. Freitas, “Predicting boiling points of aliphatic alcohols through multivariate image analysis applied to quantitative structure-property relationships,” Journal of Physical Chemistry A, vol. 112, no. 44, pp. 11263–11265, 2008.
[21]
M. Goodarzi and M. P. Freitas, “Augmented three-mode MIA-QSAR modeling for a series of anti-HIV-1 compounds,” QSAR and Combinatorial Science, vol. 27, no. 9, pp. 1092–1097, 2008.
[22]
M. Goodarzi, T. Goodarzi, and N. Ghasemi, “Spectrophotometric simultaneous determination of manganese(ii) and iron(ii) in pharmaceutical by orthogonal signal correction-partial least squares,” Annali di Chimica, vol. 97, no. 5-6, pp. 303–312, 2007.
[23]
N. Goudarzi, M. H. Fatemi, and A. Samadi-Maybodi, “Quantitative structure-properties relationship study of the 29Si-NMR chemical shifts of some silicate species,” Spectroscopy Letters, vol. 42, no. 4, pp. 186–193, 2009.
[24]
N. Goudarzi and M. Goodarzi, “Prediction of the logarithmic of partition coefficients (log P) of some organic compounds byleast square-support vector machine (LS-SVM),” Molecular Physics, vol. 106, pp. 2525–2535, 2008.
[25]
N. Goudarzi and M. Goodarzi, “Prediction of the acidic dissociation constant (pKa) of some organic compounds using linear and nonlinear QSPR methods,” Molecular Physics, vol. 107, no. 14, pp. 1495–1503, 2009.
[26]
N. Goudarzi and M. Goodarzi, “Prediction of the vapor pressure of some halogenated methyl-phenyl ether (anisole) compounds using linear and nonlinear QSPR methods,” Molecular Physics, vol. 107, no. 15, pp. 1615–1620, 2009.
[27]
N. Goudarzi and M. Goodarzi, “QSPR models for prediction of half wave potentials of some chlorinated organic compounds using SR-PLS and GA-PLS methods,” Molecular Physics, vol. 107, pp. 1739–1744, 2009.
[28]
Z. Elmi, K. Faez, M. Goodarzi, and N. Goudarzi, “Feature selection method based on fuzzy entropy for regression in QSAR studies,” Molecular Physics, vol. 107, no. 17, pp. 1787–1798, 2009.
[29]
N. Goudarzi, M. Goodarzi, M. C. U. Araujo, and R. K. H. Galvao, “QSPR modeling of soil sorption coefficients (KOC) of pesticides usingSPA-ANN and SPA-MLR,” Journal of Agricultural and Food Chemistry, vol. 57, pp. 7153–7158, 2009.
[30]
N. Goudarzi, M. Goodarzi, and M. Arab Chamjangali, “Prediction of inhibition effect of some aliphatic and aromatic organic compounds using QSAR method,” Journal of Environmental Chemistry and Ecotoxicology, vol. 2, pp. 47–50, 2010.
[31]
N. Trinajstic, Chemical Graph Theory, CRC Press, Boca Raton, Fla, USA, 1992.
[32]
A. R. Katritzky, V. S. Lobanov, and M. Karelson, “QSPR: the correlation and quantitative prediction of chemical and physical properties from structure,” Chemical Society Reviews, vol. 24, no. 4, pp. 279–287, 1995.
[33]
N. R. Draper and H. Smith, Applied Regression Analysis, Wiley Series in Probability and Statistics, New York, NY, USA, 1998.
[34]
J. Zupan and J. Gasteiger, Neural Networks in Chemistry and Drug Design, Wiley-VCH, Weinheim, Germany, 1999.
[35]
N. K. Bose and P. Liang, Neural Networks, Fundamentals, McGraw-Hill, New York, NY, USA, 1996.
[36]
J. H. Alzate-Morales, J. Caballero, A. Vergara Jague, and F. D. Gonzalez, “Insights into the structural basis of N2 and O6 substituted guanine derivatives as cyclin-dependent kinase 2 (CDK2) inhibitors: prediction of the binding modes and potency of the inhibitors by docking and ONIOM calculations,” Journal of Chemical Information and Modeling, vol. 49, no. 4, pp. 886–899, 2009.
