Application of ligand- and receptor-based approaches for prediction of the HIV-RT inhibitory activity of fullerene derivatives

  • Hayriye Yilmaz
  • Lucky Ahmed
  • Bakhtiyor Rasulev
  • Jerzy Leszczynski
Research Paper


Fullerene and its derivatives have potential to be utilized in many biomedical applications. In the present study, we investigated the role of fullerene derivatives as inhibitors of HIV-RT by combined protein–ligand docking approach and QSAR methods. The study shows the best predictive QSAR model that represents a two-variable model. It has a good ratio of the number of descriptors and predictive ability. The main contributions to the inhibitory activity are provided by signal JhetZ descriptor and μ (dipole moment, as a measure of the polarity of a compound). The developed GA-MLRA-based model demonstrates a good performance, confirmed by statistics \(\left( {R^{2}_{\text{training}} = 0.867,\;Q^{2} = 0.788,\;R^{2}_{\text{test}} = 0.902} \right).\) The structure–activity analysis of these fullerene analogues allowed us to design and suggest for synthesis a set of new potentially active fullerenes. Finally, the molecular docking analysis was carried out to understand the details of interactions between HIV-RT and fullerene-C60 derivatives.


Fullerenes Docking QSAR HIV-RT inhibition Biomedical applications 



This work was financially supported by The Scientific and Technological Research Council of Turkey (TÜBİTAK) and by NSF CREST Interdisciplinary Nanotoxicity Center NSF-CREST—Grant # HRD-0833178. The authors also thank the Extreme Science and Engineering Discovery Environment (XSEDE) for the award allocations (TG-DMR110088 and CHE140005) and the Mississippi Center for Supercomputer Research (Oxford, MS) for a generous allotment of a computer time. B.R. gratefully acknowledges support from the North Dakota State University Center for Computationally Assisted Science and Technology and the U.S. Department of Energy through Grant No. DE-SC0001717.

Compliance with ethical standards

Conflict of interest

The authors declare no conflict of interest.

Supplementary material

11051_2016_3429_MOESM1_ESM.docx (1 mb)
Supplementary material 1 (DOCX 1025 kb)


  1. Ahmed L, Rasulev B, Turabekova M, Leszczynska D, Leszczynski J (2013) Receptor and ligand-based study of fullerene analogues: comprehensive computational approach including quantum-chemical, QSAR and molecular docking simulations. Org Biomol Chem 11:5798–5808CrossRefGoogle Scholar
  2. Ardakani RB, Mirhosseini SM, Abadi FM (2014) Predicting critical micelle concentration by using stepwise—MLR and PLS as a variable selection mix method. Math Comput Chem 71:305–321Google Scholar
  3. Boutorine AS, Tokuyama H, Takasugi M, Isobe H, Nakamura E, Helen C (1994) Fullerene-oligonucleotide conjugates: photo-induced sequence-specific DNA cleavage. Angew Chem Int Ed Engl 33:2462–2465CrossRefGoogle Scholar
  4. Cook SM, Aker WG, Rasulev BF, Hwang HM, Leszczynski J, Jenkins JJ, Shockley V (2010) Choosing safe dispersing media for C60 fullerenes by using cytotoxicity tests on the bacterium Escherichia coli. J Hazard Mater 176:367–373CrossRefGoogle Scholar
  5. Da Ros T, Prato M, Novello F, Maggini M, Banfi E (1996) Easy access to water-soluble fullerene derivatives via 1,3-dipolar cycloadditions of azomethine ylides to C60. J Org Chem 61:9070–9072CrossRefGoogle Scholar
  6. Datar PA (2014) 2D-QSAR study of indolylpyrimidines derivative as antibacterial against Pseudomonas aeruginosa and Staphylococcus aureus: a comparative approach. J Comput 10:1155–1164Google Scholar
  7. De Oliveira DB, Gaudio AC (2003) BuildQSAR: a new computer program for QSAR analysis. Quant Struct Act Relat 19:599–604CrossRefGoogle Scholar
  8. Devillers J (1996) Genetic algorithms in molecular modeling. Academic Press, LondonGoogle Scholar
  9. DRAGON 5.0 (2004) Evaluation version
  10. Dugan LL, Turetsky DM, Du C, Lobner D, Wheeler M, Almli CR, Shen CK, Luh TY, Choi DW, Lin TS (1997) Carboxyfullerenes as neuroprotective agents. Proc Natl Acad Sci 94:9434–9439CrossRefGoogle Scholar
  11. Durdagi S, Papadopoulos MG, Papahatjis DP, Mavromoustakos T (2008a) Computational design of novel fullerene analogues as potential HIV-1 PR inhibitors: analysis of the binding interactions between fullerene inhibitors and HIV-1 PR residues using 3D QSAR, molecular docking and molecular dynamics simulations. Bioorg Med Chem 16:9957–9974CrossRefGoogle Scholar
  12. Durdagi S, Mavromoustakos T, Papadopoulos MG (2008b) 3D QSAR CoMFA/CoMSIA, molecular docking and molecular dynamics studies of fullerene-based HIV-1 PR inhibitors. Bioorg Med Chem Lett 23:6283–6289CrossRefGoogle Scholar
  13. Ewing TJ, Makino S, Skillman AG, Kuntz ID (2001) DOCK 4.0: search strategies for automated molecular docking of flexible molecule databases. J Comput Aided Mol Des 15:411–428CrossRefGoogle Scholar
  14. Fourches D, Pu D, Tassa C, Weissleder R, Shaw SY, Mumper RJ, Tropsha A (2010) Quantitative nanostructure—activity relationship modeling. ACS Nano 4:5703–5712CrossRefGoogle Scholar
  15. Friedman SH, DeCamp DL, Sijbesma RP, Srdanov G, Wudl F, Kenyon GL (1993) Inhibition of the HIV-1 protease by fullerene derivatives: model building studies and experimental verification. J Chem Soc 115:6506–6509CrossRefGoogle Scholar
  16. Frisch MJ et al. (2009) Gaussian 09, revision B.01. Wallingford CTGoogle Scholar
  17. Hansch C, Leo A (1995) Exploring QSAR, hydrophobic, electronic and steric constants. ACS, Washington, DCGoogle Scholar
  18. Hansch C, Leo A, Hoekman DH (1995) Exploring QSAR, fundamentals and application in chemistry and biology. ACS, Washington, DCGoogle Scholar
  19. Helguera AM, Comnbbbes RD, González MP, Cordeiro MN (2008) Applications of 2D descriptors in drug design: a DRAGON tale. Curr Top Med Chem 28:1628–1655CrossRefGoogle Scholar
  20. Huang YL, Shen CK, Luh TY, Yang HC, Hwang KC, Chou CK (1998) Blockage of apoptotic signaling of transforming growth factor- in human hepatoma cells by carboxyfullerene. Eur J Biochem 254:38–43CrossRefGoogle Scholar
  21. Huey R, Morris GM, Olson AJ, Goodsell DS (2007) A semiempirical free energy force field with charge-based desolvation. J Comput Chem 28:1145–1152CrossRefGoogle Scholar
  22. Hyperchem (hypercube) homepage. (2009)
  23. Jin H, Chen WQ, Tang XW, Chiang LY, Yang CY, Schloss JV, Wu JY (2000) Polyhydroxylated C60, fullerenols, as glutamate receptor antagonists and neuroprotective agents. J Neurosci Res 62:600–607CrossRefGoogle Scholar
  24. Jones G, Willett P, Glen RC, Leach AR, Taylor R (1997) Development and validation of a genetic algorithm for flexible docking. J Mol Biol 267:727–748CrossRefGoogle Scholar
  25. Kim JE, Lee MT (2003) Fullerene inhibits 3-amyloid peptide aggregation. Biochem Biophys Res Commun. 303:576–579CrossRefGoogle Scholar
  26. Manoel AM, Filho José Diogo LD, Gerd BR, Ricardo OF, Alfredo MS (2013) Sparkle/RM1 parameters for the semiempirical quantum chemical calculation of lanthanide complexes. RSC Adv 3:16747–16755CrossRefGoogle Scholar
  27. Marcorin GL, Da Ros T, Castellano S, Stefancich G, Bonin I, Miertus S, Prato M (2000) Design and synthesis of novel fullerene derivatives as potential HIV aspartic protease inhibitors. Org Lett 2:3955–3958CrossRefGoogle Scholar
  28. Mashino T, Shimotohno K, Ikegami N, Nishikawa D, Okuda K, Takahashi K, Nakamura S, Mochizuki M (2005) Human immunodeficiency virus-reverse transcriptase inhibition and hepatitis C virus RNA-dependent RNA polymerase inhibition activities of fullerene derivatives. Bioorg Med Chem Lett 15:1107–1109CrossRefGoogle Scholar
  29. Morris GM, Goodsell DS, Halliday RS, Huey R, Hart WE et al (1999) Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function. J Comput Chem 19:1639–1662CrossRefGoogle Scholar
  30. Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS, Olson AJ (2009) Autodock4 and AutoDockTools4: automated docking with selective receptor flexibility. J Comput Chem 16:2785–2791CrossRefGoogle Scholar
  31. Nakamura S, Mashino T (2009) Biological activities of water soluble fullerene derivatives. J Phys 159:1742–6596Google Scholar
  32. Ojha PK, Mitra I, Das RN, Roy K (2011) Further exploring rm2 metrics for validation of QSPR models. Chemom Intell Lab Syst 107:194–205CrossRefGoogle Scholar
  33. Park KH, Chhowalla M, Iqbal Z, Sesti F (2003) Single-walled carbon nanotubes are a new class of ion channel blockers. J Biol Chem 278:50212–50216CrossRefGoogle Scholar
  34. Petrova T, Rasulev B, Toropov A, Leszczynska D, Leszczynski J (2011) Improved model for fullerene C60 solubility in organic solvents based on quantum-chemical and topological descriptors. J Nanopart Res 13:1–13CrossRefGoogle Scholar
  35. Puzyn T, Leszczynski J, Cronin MTD (2010) Recent advances in QSAR studies. Springer, New YorkCrossRefGoogle Scholar
  36. Rarey M, Kramer B, Lengauer T, Klebe G (1996) A fast flexible docking method using an incremental construction algorithm. J Mol Biol 261:470–489CrossRefGoogle Scholar
  37. Rasulev BF, Saidkhodzhaev AI, Nazrullaev SS, Akhmedkhodzhaeva KS, Khushbaktova ZA, Leszczynski J (2007) Molecular modelling and QSAR analysis of the estrogenic activity of terpenoids isolated from Ferula plants. SAR QSAR Environ Res 18:663–673CrossRefGoogle Scholar
  38. Roy K, Mitra I, Kar S, Ojha PK, Das RN, Kabir H (2012) Comparative studies on some metrics for external validation of QSPR models. J Chem Inf Model 52:396–408CrossRefGoogle Scholar
  39. Schuster DI, Wilson SR, Schinazi RF (1996) Anti-human immunodeficiency virus activity and cytotoxicity of derivatized buckminster fullerenes. Bioorg Med Chem Lett 6:1253–1256CrossRefGoogle Scholar
  40. Selvaraj C, Tripathi SK, Reddy K, Singh SK (2011) Tool development for prediction of pIC50 values from the IC50 values—a pIC50 value calculator. Curr Trends Biotechnol Pharm 5:1104–1109Google Scholar
  41. Sijbesma R, Srdanov G, Wudl F, Castoro JA, Wilkins C, Friedman SH, DeCampDL Kenyon GL (1993) Synthesis of a fullerene derivative for the inhibition of HIV enzymes. J Am Chem Soc 115:6510–6512CrossRefGoogle Scholar
  42. Tkach AV, Yanamala N, Stanley S, Shurin MR, Shurin GV, Kisin ER, Murray AR, Pareso S, Khaliullin T, Kotchey GP, Castranova V, Mathur S, Fadeel B, Star A, Kagan VE, Shvedova AA (2013) Graphene oxide, but not fullerenes, targets immunoproteasomes and suppresses antigen presentation by dendritic cells. Small 9:1686–1690CrossRefGoogle Scholar
  43. Todeschini R, Consonni V (2000) Handbook of molecular descriptors. Wiley, WeinheimCrossRefGoogle Scholar
  44. Todeschini R, Consonni V, (2003) DRAGON software for the calculation of molecular descriptors., web version 3.0 for WindowsGoogle Scholar
  45. Toropov AA, Rasulev B, Leszczynska D, Leszczynski J (2007) Additive SMILES based optimal descriptors: QSPR modeling of fullerene C60 solubility in organic solvents. Chem Phys Lett 444:209–214CrossRefGoogle Scholar
  46. Toropov A, Rasulev B, Leszczynska D, Leszczynski J (2008) New approach to QSPR modeling of fullerene C60 solubility in organic solvents: an application of SMILES-based optimal descriptors. Med Chem Pharmacol Potential Fuller Carbon Nanotub 1:337–350CrossRefGoogle Scholar
  47. Trott O, Olson AJ (2010) AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization and multithreading. J Comput Chem 31:455–461Google Scholar
  48. Yilmaz H, Güzel Y, Önal Z, Altıparmak G, Özhan KŞ (2012) 4D-QSAR study of P56lck protein tyrosine kinase inhibitory activity of flavonoid derivatives using MCET method. J Bull Korean Chem Soc 32:4352–4360CrossRefGoogle Scholar
  49. Yilmaz H, Güzel Y, Boz M, Türkmenoğlu B (2014) Pharmacophore and functional group identification of 4,4′-dihydroxydiphenylmethane as bisphenol-A (BSA) derivatives. Trop J Pharma Res 13:117–129CrossRefGoogle Scholar
  50. Yilmaz H, Rasulev B, Leszczynski J (2015) Modeling the dispersibility of single walled carbon nanotubes in organic solvents by quantitative structure-activity relationship approach. Nanomaterials 5:778–791CrossRefGoogle Scholar
  51. Zhao Y, Truhlar DG (2008) The M06 suite of density functionals for main group thermochemistry, kinetics, noncovalent interactions, excited states, and transition elements: two new functionals and systematic testing of four M06 functionals and twelve other functionals. Theor Chem Acc 120:215–241CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  • Hayriye Yilmaz
    • 1
    • 2
  • Lucky Ahmed
    • 2
  • Bakhtiyor Rasulev
    • 2
    • 3
  • Jerzy Leszczynski
    • 2
  1. 1.Department of Biomedical Devices and Technology, Kayseri Vocational SchoolErciyes UniversityKayseriTurkey
  2. 2.Department of Chemistry and Biochemistry, Interdisciplinary Center for NanotoxicityJackson State UniversityJacksonUSA
  3. 3.Center for Computationally Assisted Science and Technology (CCAST)North Dakota State UniversityFargoUSA

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