Abstract
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.
Similar content being viewed by others
References
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 14:341–353
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 41:836–852
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 126:2718–2719
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 50:662–676
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 55:3558–3562
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 2:445–448
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 3:1412–1418
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 25:1605–1612
Meng EC, Pettersen EF, Couch GS, Huang CC, Ferrin TE (2006) Tools for integrated sequence-structure analysis with UCSF Chimera. BMC Bioinformatics 7:339
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 44:2544–2554
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 13:75–80
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 47:928–934
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 26:1668–1688
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 30:2785–2791
Mevik B-H, Wehrens R (2007) The pls package: principal component and partial least squares regression in R. J Stat Softw 18(2):1–24
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 8:1089–1094
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 2:293–302
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 336:569–578
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 31:455–461
R-Development-Core-Team R: a language and environment for statistical computing. http://www.R-project.org
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 52:1674–1685
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 12:9–20
Wesson L, Eisenberg D (1992) Atomic solvation parameters applied to molecular dynamics of proteins in solution. Protein Sci 1:227–235
Goodford PJ (1985) A computational procedure for determining energetically favorable binding sites on biologically important macromolecules. J Med Chem 28:849–857
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 110:5959–5967
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 54:718–727
Macarthur RD (2011) Clinical trial report: TMC278 (rilpivirine) versus efavirenz as initial therapy in treatment-naive, HIV-1-infected patients. Curr Infect Dis Rep 13:1–3
Acknowledgments
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.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Ballante, F., Musmuca, I., Marshall, G.R. et al. Comprehensive model of wild-type and mutant HIV-1 reverse transciptases. J Comput Aided Mol Des 26, 907–919 (2012). https://doi.org/10.1007/s10822-012-9586-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10822-012-9586-6