Journal of Computer-Aided Molecular Design

, Volume 26, Issue 8, pp 907–919 | Cite as

Comprehensive model of wild-type and mutant HIV-1 reverse transciptases

  • Flavio Ballante
  • Ira Musmuca
  • Garland R. Marshall
  • Rino Ragno


An enhanced version of COMBINE that uses both ligand-based and structure-based alignment of ligands has been used to build a comprehensive 3-D QSAR model of wild-type HIV-1 reverse transcriptase and drug-resistant mutants. The COMBINEr model focused on 7 different RT enzymes complexed with just two HIV-RT inhibitors, niverapine (NVP) and efavirenz (EFV); therefore, 14 inhibitor/enzyme complexes comprised the training set. An external test set of chiral 2-(alkyl/aryl)amino-6-benzylpyrimidin-4(3H)-ones (DABOs) was used to test predictability. The COMBINEr model MC4, although developed using only two inhibitors, predicted the experimental activities of the test set with an acceptable average absolute error of prediction (0.89 pK i). Most notably, the model was able to correctly predict the right eudismic ratio for two R/S pairs of DABO derivatives. The enhanced COMBINEr approach was developed using only software freely available to academics.


3-D–QSAR HIV-1 reverse transciptase Drug resistance NNRTI RT mutants Molecular modeling COMBINEr DABO inhibitors PLS 



The authors thank the research group (Rotili et al. [5]) of Prof. Antonello Mai for sharing their data about the separation and activity of their DABO derivatives prior to publication. In addition, Garland R. Marshall acknowledges financial support from the Dipartimento di Chimica e Tecnologie del Farmaco, Facoltà di Farmacia e Medicina, Sapienza Università di Roma, which made his visiting professorship in Rome feasible.


  1. 1.
    Lozano JJ, Pastor M, Cruciani G, Gaedt K, Centeno NB, Gago F, Sanz F (2000) 3-D–QSAR methods on the basis of ligand-receptor complexes. Application of COMBINE and GRID/GOLPE methodologies to a series of CYP1A2 ligands. J Comput Aided Mol Des 14:341–353CrossRefGoogle Scholar
  2. 2.
    Perez C, Pastor M, Ortiz AR, Gago F (1998) Comparative binding energy analysis of HIV-1 protease inhibitors: incorporation of solvent effects and validation as a powerful tool in receptor-based drug design. J Med Chem 41:836–852CrossRefGoogle Scholar
  3. 3.
    Rodriguez-Barrios F, Gago F (2004) Chemometrical identification of mutations in HIV-1 reverse transcriptase conferring resistance or enhanced sensitivity to arylsulfonylbenzonitriles. J Am Chem Soc 126:2718–2719CrossRefGoogle Scholar
  4. 4.
    Musmuca I, Caroli A, Mai A, Kaushik-Basu N, Arora P, Ragno R (2010) Combining 3-D quantitative structure-activity relationship with ligand based and structure based alignment procedures for in silico screening of new hepatitis C virus NS5B polymerase inhibitors. J Chem Inform Model 50:662–676CrossRefGoogle Scholar
  5. 5.
    Rotili D, Samuele A, Tarantino D, Ragno R, Musmuca I, Ballante F, Botta G, Morera L, Pierini M, Cirilli R, Nawrozkij MB, Gonzalez E, Clotet B, Artico M, Este JA, Maga G, Mai A (2012) 2-(Alkyl/aryl)amino-6-benzylpyrimidin-4(3H)-ones as inhibitors of wild-type and mutant HIV-1: enantioselectivity studies. J Med Chem 55:3558–3562CrossRefGoogle Scholar
  6. 6.
    Cancio R, Mai A, Rotili D, Artico M, Sbardella G, Clotet-Codina I, Este JA, Crespan E, Zanoli S, Hubscher U, Spadari S, Maga G (2007) Slow-, tight-binding HIV-1 reverse transcriptase non-nucleoside inhibitors highly active against drug-resistant mutants. ChemMedChem 2:445–448CrossRefGoogle Scholar
  7. 7.
    Samuele A, Facchini M, Rotili D, Mai A, Artico M, Armand-Ugon M, Este JA, Maga G (2008) Substrate-induced stable enzyme-inhibitor complex formation allows tight binding of novel 2-aminopyrimidin-4(3H)-ones to drug-resistant HIV-1 reverse transcriptase mutants. ChemMedChem 3:1412–1418CrossRefGoogle Scholar
  8. 8.
    Pettersen EF, Goddard TD, Huang CC, Couch GS, Greenblatt DM, Meng EC, Ferrin TE (2004) UCSF Chimera—a visualization system for exploratory research and analysis. J Comput Chem 25:1605–1612CrossRefGoogle Scholar
  9. 9.
    Meng EC, Pettersen EF, Couch GS, Huang CC, Ferrin TE (2006) Tools for integrated sequence-structure analysis with UCSF Chimera. BMC Bioinformatics 7:339CrossRefGoogle Scholar
  10. 10.
    Mai A, Sbardella G, Artico M, Ragno R, Massa S, Novellino E, Greco G, Lavecchia A, Musiu C, La Colla M, Murgioni C, La Colla P, Loddo R (2001) Structure-based design, synthesis, and biological evaluation of conformationally restricted novel 2-alkylthio-6-[1-(2,6-difluorophenyl)alkyl]-3,4-dihydro-5-alkylpyrimidin-4 (3H)-ones as non-nucleoside inhibitors of HIV-1 reverse transcriptase. J Med Chem 44:2544–2554CrossRefGoogle Scholar
  11. 11.
    Quaglia M, Mai A, Sbardella G, Artico M, Ragno R, Massa S, del Piano D, Setzu G, Doratiotto S, Cotichini V (2001) Chiral resolution and molecular modeling investigation of rac-2-cyclopentylthio-6-[1-(2,6-difluorophenyl)ethyl]-3,4-dihydro-5-methyl pyrimidin-4(3H)-one (MC-1047), a potent anti-HIV-1 reverse transcriptase agent of the DABO class. Chirality 13:75–80CrossRefGoogle Scholar
  12. 12.
    Ragno R, Mai A, Sbardella G, Artico M, Massa S, Musiu C, Mura M, Marturana F, Cadeddu A, La Colla P (2004) Computer-aided design, synthesis, and anti-HIV-1 activity in vitro of 2-alkylamino-6-[1-(2,6-difluorophenyl)alkyl]-3,4-dihydro-5-alkylpyrimidin-4(3H)-ones as novel potent non-nucleoside reverse transcriptase inhibitors, also active against the Y181C variant. J Med Chem 47:928–934CrossRefGoogle Scholar
  13. 13.
    Case DA, Cheatham TE III, Darden T, Gohlke H, Luo R, Merz KM Jr, Onufriev A, Simmerling C, Wang B, Woods RJ (2005) The Amber biomolecular simulation programs. J Comput Chem 26:1668–1688CrossRefGoogle Scholar
  14. 14.
    Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS, Olson AJ (2009) AutoDock and AutoDockTools: automated docking with selective receptor flexibility. J Comput Chem 30:2785–2791CrossRefGoogle Scholar
  15. 15.
    Mevik B-H, Wehrens R (2007) The pls package: principal component and partial least squares regression in R. J Stat Softw 18(2):1–24Google Scholar
  16. 16.
    Ren J, Milton J, Weaver KL, Short SA, Stuart DI, Stammers DK (2000) Structural basis for the resilience of efavirenz (DMP-266) to drug resistance mutations in HIV-1 reverse transcriptase. Structure 8:1089–1094CrossRefGoogle Scholar
  17. 17.
    Ren J, Esnouf R, Garman E, Somers D, Ross C, Kirby I, Keeling J, Darby G, Jones Y, Stuart D et al (1995) High resolution structures of HIV-1 RT from four RT-inhibitor complexes. Nat Struct Biol 2:293–302CrossRefGoogle Scholar
  18. 18.
    Ren J, Nichols CE, Chamberlain PP, Weaver KL, Short SA, Stammers DK (2004) Crystal structures of HIV-1 reverse transcriptases mutated at codons 100, 106 and 108 and mechanisms of resistance to non-nucleoside inhibitors. J Mol Biol 336:569–578CrossRefGoogle Scholar
  19. 19.
    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
  20. 20.
    R-Development-Core-Team R: a language and environment for statistical computing.
  21. 21.
    Ballante F, Ragno R (2012) 3-D QSAutogrid/R: an alternative procedure to build 3-D QSAR models. Methodologies and applications. J Chem Inf Model 52:1674–1685Google Scholar
  22. 22.
    Baroni M, Costantino G, Cruciani G, Riganelli D, Valigi R, Clementi S (1993) Generating optimal linear PLS estimations (GOLPE): an advanced chemometric tool for handling 3-D–QSAR problems. Quant Struct Activ Relatsh 12:9–20CrossRefGoogle Scholar
  23. 23.
    Wesson L, Eisenberg D (1992) Atomic solvation parameters applied to molecular dynamics of proteins in solution. Protein Sci 1:227–235CrossRefGoogle Scholar
  24. 24.
    Goodford PJ (1985) A computational procedure for determining energetically favorable binding sites on biologically important macromolecules. J Med Chem 28:849–857CrossRefGoogle Scholar
  25. 25.
    Cramer RD, Patterson DE, Bunce JD (1988) Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins. J Am Chem Soc 110:5959–5967CrossRefGoogle Scholar
  26. 26.
    Azijn H, Tirry I, Vingerhoets J, de Bethune MP, Kraus G, Boven K, Jochmans D, Van Craenenbroeck E, Picchio G, Rimsky LT (2010) TMC278, a next-generation nonnucleoside reverse transcriptase inhibitor (NNRTI), active against wild-type and NNRTI-resistant HIV-1. Antimicrob Agents Chemother 54:718–727CrossRefGoogle Scholar
  27. 27.
    Macarthur RD (2011) Clinical trial report: TMC278 (rilpivirine) versus efavirenz as initial therapy in treatment-naive, HIV-1-infected patients. Curr Infect Dis Rep 13:1–3CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  • Flavio Ballante
    • 1
  • Ira Musmuca
    • 1
  • Garland R. Marshall
    • 1
    • 2
  • Rino Ragno
    • 1
  1. 1.Rome Center for Molecular Design, Dipartimento di Chimica e Tecnologie del FarmacoSapienza Università di RomaRomeItaly
  2. 2.Department of Biochemistry and Molecular BiophysicsWashington University School of MedicineSt. LouisUSA

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