Medicinal Chemistry Research

, Volume 22, Issue 12, pp 5832–5843 | Cite as

The discovery of potential cyclin A/CDK2 inhibitors: a combination of 3D QSAR pharmacophore modeling, virtual screening, and molecular docking studies

Original Research

Abstract

Cyclin-dependent kinases are a family of enzymes that regulates the cell cycle process. They have been found to be novel targets for potential anti-cancer drugs. In the present study, a 3D pharmacophore model has been developed for cyclin A/CDK2 from its known inhibitors. The most reliable quantitative HypoGen model (Hypo1) consists of two hydrogen bond acceptors, one hydrogen bond donor and one hydrophobic feature. Hypo1, with a correlation coefficient of 0.98, a root mean square deviation of 0.84, a configuration cost of 16.25 and a cost difference of 102.93, showed a remarkable predictive power and has >90 % probability of representing a true correlation in the activity data. The model was validated using Fisher’s test at 95 % confidence level and test set prediction (r = 0.96). Hypo1 was then employed for virtual screening of Life Chemicals and NCI2003 databases of which multiple conformations were generated for each compound (596,030 compounds, 45,603,414 conformers). Hits were filtered according to the Lipinski, Ghose, and Veber’s rules. Following docking simulations, consensus scoring was used to determine the ligand poses that interact best with the protein binding site and to reduce number of false positives. 11 hits were ultimately selected as potent candidate leads. This work may help in the identification or design of novel anti-cancer drugs based on hits determined. The pharmacophore model obtained and validated in this study can be used as a three-dimensional query in searches for CDK2 inhibitors in additional compound databases.

Keywords

Cyclin A/CDK2 Pharmacophore Virtual screening Molecular docking Anti-cancer 

Supplementary material

44_2013_571_MOESM1_ESM.pdf (155 kb)
Supplementary material 1 (PDF 155 kb)

References

  1. Accelrys Software Inc. (2007) Discovery Studio Modeling Environment, San DiegoGoogle Scholar
  2. Arris CE, Boyle FT, Calvert AH, Curtin NJ, Endicott JA, Garman EF, Gibson AE, Golding BT, Grant S, Griffin RJ, Jewsbury P, Johnson LN, Lawrie AM, Newell DR, Noble MEM, Sausville EA, Schultz R, Yu W (2000) Identification of novel purine and pyrimidine cyclin-dependent kinase inhibitors with distinct molecular interactions and tumor cell growth inhibition profiles. J Med Chem 43:2797–2804CrossRefPubMedGoogle Scholar
  3. Beno BR, Langley DR (2010) MORPH: a new tool for ligand design. J Chem Inf Model 50:1159–1164CrossRefPubMedGoogle Scholar
  4. Bhattacharjee AK, Kyle DE, Vennerstrom JL, Milhous WK (2002) A 3D QSAR pharmacophore model and quantum chemical structure—activity analysis of chloroquine(CQ)-resistance reversal. J Chem Inf Comput Sci 42:1212–1220CrossRefPubMedGoogle Scholar
  5. Charifson PS (1997) Practical application of computer-aided drug design. Marcel Dekker, New YorkGoogle Scholar
  6. Debnath AK (2002) Pharmacophore mapping of a series of 2,4-diamino-5-deazapteridine inhibitors of Mycobacterium avium complex dihydrofolate reductase. J Med Chem 45:41–53CrossRefPubMedGoogle Scholar
  7. Ding Q, Jiang N, Wang K, Goelzer P, Yin X, Smith MA, Higgins BX, Chen Y, Xiang Q, Moliterni J, Kaplan G, Graves B, Lovey A, Fotouhi N (2006) Discovery of [4-amino-2-(1-methanesulfonylpiperidin-4-ylamino)pyrimidin-5-yl] (2,3-difluoro-6-methoxyphenyl)methanone (R547), a potent and selective cyclin-dependent kinase inhibitor with significant in vivo antitumor activity. J Med Chem 49:6549–6560CrossRefPubMedGoogle Scholar
  8. Ece A, Sevin F (2010) Exploring QSAR on 4-cyclohexylmethoxypyrimidines as antitumor agents for their inhibitory activity of cdk2. Lett Drug Des Discov 7:625–631CrossRefGoogle Scholar
  9. Fathalla OAE, Ismail MAH, Anwar MM, Abouzid KAM, Ramadan AAK (2013) Novel 2-thiopyrimidine derivatives as CDK2 inhibitors: molecular modeling, synthesis, and anti-tumor activity evaluation. Med Chem Res 22:659–673CrossRefGoogle Scholar
  10. Fisher R (1966) The design of experiments, chapter II, 8th edn. Hafner Publishing Co, New YorkGoogle Scholar
  11. Ghose AK, Viswanadhan VN, Wendoloski JJ (1999) Knowledge-based approach in designing combinatorial or medicinal chemistry libraries for drug discovery 1 A qualitative and quantitative characterization of known drug databases. J Comb Chem 1(1):55–68CrossRefPubMedGoogle Scholar
  12. Hecker EA, Duraiswami C, Andrea TA, Diller DJ (2002) Use of catalyst pharmacophore models for screening of large combinatorial libraries. J Chem Inf Comput Sci 42:1204–1211CrossRefPubMedGoogle Scholar
  13. Li H, Sutter J, Hoffmann R (2000) HypoGen: an automated system for generating predictive 3D pharmacophore models. In: Güner OF (ed) Pharmacophore perception, development, and use in drug design. La Jolla International University Line, California, pp 171–189Google Scholar
  14. Lipinski CA, Lombardo F, Dominy BW, Feeney PJ (1997) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 23:3–25CrossRefGoogle Scholar
  15. Lu S-H, Wu JW, Liu H-L, Zhao J-H, Liu K-T, Chuang C-K, Lin H-Y, Tsai W-B, Ho Y (2011) The discovery of potential acetylcholinesterase inhibitors: a combination of pharmacophore modeling, virtual screening, and molecular docking studies. J Biomed Sci 18:8PubMedCentralCrossRefPubMedGoogle Scholar
  16. Marchetti F, Sayle KL, Bentley J, Clegg W, Curtin NJ, Endicott JA, Golding BT, Griffin RJ, Haggerty K, Harrington RW, Mesguiche V, Newell DR, Noble MEM, Parsons RJ, Pratt DJ, Wang LZ, Hardcastle IR (2007) Structure-based design of 2-arylamino-4-cyclohexylmethoxy-5-nitroso-6-aminopyrimidine inhibitors of cyclin-dependent kinase 2. Org Biomol Chem 5:1577–1585CrossRefPubMedGoogle Scholar
  17. Marchetti F, Cano C, Curtin NJ, Golding BT, Griffin RJ, Haggerty K, Newell DR, Parsons RJ, Payne SL, Wang LZ, Hardcastle IR (2010) Structure-based design of 2-arylamino-4-cyclohexylmethoxy-5-nitroso-6-aminopyrimidine inhibitors of cyclin-dependent kinase. Org Biomol Chem 8:2397–2407CrossRefPubMedGoogle Scholar
  18. Marcu MG, Chadli A, Bouhouche I, Catelli N, Neckers LM (2000) The heat shock protein 90 antagonist novobiocin interacts with a previously unrecognized ATP-binding domain in the carboxyl terminus of the chaperone. J Biol Chem 275(47):37181–37186CrossRefPubMedGoogle Scholar
  19. Mascarenhasa NM, Ghoshal N (2008) An efficient tool for identifying inhibitors based on 3D-QSAR and docking using feature-shape pharmacophore of biologically active conformation—A case study with CDK2/CyclinA. Eur J Med Chem 43(12):2807–2818CrossRefGoogle Scholar
  20. Patrick GL (2005) An introduction to medicinal chemistry, 3rd edn. Oxford University Press, New York, pp 491–493Google Scholar
  21. Sakkiah S, Thangapandian S, John S, Kwon YJ, Lee KW (2010) 3D QSAR pharmacophore based virtual screening and molecular docking for identification of potential HSP90 inhibitors. Eur J Med Chem 45:2132–2140CrossRefPubMedGoogle Scholar
  22. Sayle KL, Bentley JF, Boyle TA, Calvert H, Cheng YZ, Curtin NJ, Endicott JA, Golding BT, Hardcastle IR, Jewsbury P, Mesguiche V, Newell DR, Noble MEM, Parsons RJ, Pratt DJ, Wang LZ, Griffin RJ (2003) Structure-based design of 2-arylamino-4-cyclohexylmethyl-5-nitroso-6-aminopyrimidine inhibitors of cyclin-dependent kinases 1 and 2. Bioorg Med Chem Lett 13:3079–3082CrossRefPubMedGoogle Scholar
  23. Sherr CJ (2000) The pezcoller lecture: cancer cell cycles revisited. Can Res 60:3689–3695Google Scholar
  24. Sielecki TM, Boylan JF, Benfield PA, Trainor GL (2000) Cyclin-dependent kinase inhibitors: useful targets in cell cycle regulation. J Med Chem 43(1):1–18CrossRefPubMedGoogle Scholar
  25. Taha MO, Qandil AM, Zaki DD, Murad AA (2005) Ligand-based assessment of factor Xa binding site flexibility via elaborate pharmacophore exploration and genetic algorithm-based QSAR modeling. Eur J Med Chem 40(7):701–727CrossRefPubMedGoogle Scholar
  26. Toba S, Srinivasan J, Maynard AJ, Sutter J (2006) Using pharmacophore models to gain insight into structural binding and virtual screening: an application study with CDK2 and human DHFR. J Chem Inf Model 46:728–735CrossRefPubMedGoogle Scholar
  27. Vadivelan S, Sinha BN, Irudayam SJ, Jagarlapudi SARP (2007) Virtual screening studies to design potent CDK2-Cyclin A inhibitors. J Chem Inf Model 47:1526–1535CrossRefPubMedGoogle Scholar
  28. Veber DF, Johnson SR, Cheng H-Y, Smith BR, Ward KW, Kopple KD (2002) Molecular properties that influence the oral bioavailability of drug candidates. J Med Chem 45:2615–2623CrossRefPubMedGoogle Scholar
  29. Zou J, Xie H-Z, Yang S-Y, Chen JJ, Ren J-X, Wein Y-Q (2008) Towards more accurate pharmacophore modeling: multicomplex-based comprehensive pharmacophore map and most-frequent-feature pharmacophore model of CDK2. J Mol Graph Model 27:430–438CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Department of ChemistryHacettepe UniversityBeytepe, AnkaraTurkey

Personalised recommendations