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Discovery of new druggable sites in the anti-cholesterol target HMG-CoA reductase by computational alanine scanning mutagenesis

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Abstract

The enzyme 3-hydroxy-3-methyl-glutaryl-CoA reductase (HMG-CoA-R) is the fundamental target for the treatment of hypercholesterolemia nowadays. The HMG-CoA-R clinical active site inhibitors (statins) are among the most widespread and profitable drugs ever sold but their side effects (myopathies, sometimes severe) still limit their use, which makes the finding of alternatives to statins a field of intense research. In this line, we address here a new strategy for inhibiting the homotetrameric HMG-CoA-R. The enzyme consists of a "dimer of dimers”, each dimer having two active sites. We pursue here the inhibition of enzyme oligomerization, through drug binding to the dimer interface. We have computationally mutated 232 interfacial residues by alanine and calculated the loss in binding free energy among the monomers that build up each dimer of the homotetramer. This led to the identification of the (ten) key residues for the formation of the active dimer (Glu528, Ile531, Met534, Tyr644, Glu665, Asn686, Lys692, Lys735, Met742, and Val863). The results show that these residues are located in two specific spots of the protein with a cleft shape, whose shape and size is favorable for small drug binding. It is expectable that small molecules specifically bound to these druggable pockets will have a major effect on the oligomerization of the protein or/and in active site formation. This paves the way for the discovery of new families of inhibitors of HMG-CoA-R.

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References

  1. Black DM (2003) Therapeutic targets in cardiovascular disease: a case for high-density lipoprotein cholesterol. Am J Cardiol 91:40E–43E

    Article  Google Scholar 

  2. Parks LW (1978) Metabolism of Sterols in Yeast. CRC Crit Rev Microbiol 6:301–341

    Article  CAS  Google Scholar 

  3. Goldstein JL, Brown MS (1990) Regulation of the mevalonate pathway. Nature 343:425–430

    Article  CAS  Google Scholar 

  4. Chappell J, Wolf F, Proulx J, Cuellar R, Saunders C (1995) Is the reaction catalyzed by 3-hydroxy-3-methylglutaryl coenzyme-a reductase a rate-limiting step for isoprenoid biosynthesis in plants. Plant Physiol 109:1337–1343

    CAS  Google Scholar 

  5. Istvan ES, Deisenhofer J (2001) Structural mechanism for statin inhibition of HMG-CoA reductase. Science 292:1160–1164

    Article  CAS  Google Scholar 

  6. Brown MS, Faust JR, Goldstein JL, Kaneko I, Endo A (1978) Induction of 3-hydroxy-3-methylglutaryl coenzyme-a reductase-activity in human fibroblasts incubated with compactin (Ml-236b), a competitive inhibitor of reductase. J Biol Chem 253:1121–1128

    CAS  Google Scholar 

  7. Singh N, Tamariz J, Chamorro G, Medina-Franco JL (2009) Inhibitors of HMG-CoA reductase: current and future prospects. Mini-Rev Med Chem 9:1272–1283

    Article  CAS  Google Scholar 

  8. Istvan E (2003) Statin inhibition of HMG-CoA reductase: a 3-dimensional view. Atheroscler Suppl 4:3–8

    Article  CAS  Google Scholar 

  9. Hermann M, Bogsrud MP, Molden E, Asberg A, Mohebi BU, Ose L, Retterstol K (2006) Exposure of atorvastatin is unchanged but lactone and acid metabolites are increased several-fold in patients with atorvastatin-induced myopathy. Clin Pharmacol Ther 79:532–539

    Article  CAS  Google Scholar 

  10. Golomb BA, Evans MA (2008) Statin adverse effects: a review of the literature and evidence for a mitochondrial mechanism. Am J Cardiovasc Drugs 8:373–418

    Article  CAS  Google Scholar 

  11. Skottheim IB, Gedde-Dahl A, Hejazifar S, Hoel K, Asbery A (2008) Statin induced myotoxicity: the lactone forms are more potent than the acid forms in human skeletal muscle cells in vitro. Eur J Pharm Sci 33:317–325

    Article  CAS  Google Scholar 

  12. Furberg CD, Pitt B (2001) Withdrawal of cerivastatin from the world market. Curr Control Trials Cardiovasc Med 2:205–207

    Article  Google Scholar 

  13. Graham DJ, Staffa JA, Shatin D, Andrade SE, Schech SD, La Grenade L, Gurwitz JH, Chan KA, Goodman MJ, Platt R (2004) Incidence of hospitalized rhabdomyolysis in patients treated with lipid-lowering drugs. JAMA-J Am Med Assoc 292:2585–2590

    Article  CAS  Google Scholar 

  14. Keskin O, Gursoy A, Ma B, Nussinov R (2008) Principles of protein-protein interactions: what are the preferred ways for proteins to interact? Chem Rev 108:1225–1244

    Article  CAS  Google Scholar 

  15. Arkin MR, Wells JA (2004) Small-molecule inhibitors of protein-protein interactions: progressing towards the dream. Nat Rev Drug Discov 3:301–317

    Article  CAS  Google Scholar 

  16. Chene P (2006) Drugs targeting protein-protein interactions. Chem Med Chem 1:400–411

    Article  CAS  Google Scholar 

  17. Wells JA, McClendon CL (2007) Reaching for high-hanging fruit in drug discovery at protein-protein interfaces. Nature 450:1001–1009

    Article  CAS  Google Scholar 

  18. Zinzalla G, Thurston DE (2009) Targeting protein-protein interactions for therapeutic intervention: a challenge for the future. Futur Med Chem 1:65–93

    Article  CAS  Google Scholar 

  19. Pieraccini S, Saladino G, Cappelletti G, Cartelli D, Francescato P, Speranza G, Manitto P, Sironi M (2009) In silico design of tubulin-targeted antimitotic peptides. Nat Chem 1:642–648

    Article  CAS  Google Scholar 

  20. Massova I, Kollman PA (1999) Computational alanine scanning to probe protein − protein interactions: a novel approach to evaluate binding free energies. J Am Chem Soc 121:8133–8143

    Article  CAS  Google Scholar 

  21. Kortemme T, Baker D (2002) A simple physical model for binding energy hot spots in protein-protein complexes. Proc Natl Acad Sci U S A 99:14116–14121

    Article  CAS  Google Scholar 

  22. Istvan ES, Deisenhofer J (2000) The structure of the catalytic portion of human HMG-CoA reductase. Biochim Biophys Acta 1529:9–18

    Article  CAS  Google Scholar 

  23. Ribeiro JV, Cerqueira NMFSA, Moreira IS, Fernandes PA, Ramos MJ (2012) CompASM: an Amber-VMD alanine scanning mutagenesis plug-in. Theor Chem Acc 131:1271

    Article  CAS  Google Scholar 

  24. Moreira IS, Fernandes PA, Ramos MJ (2007) Computational alanine scanning mutagenesis - an improved methodological approach. J Comput Chem 28:644–654

    Article  CAS  Google Scholar 

  25. Moreira IS, Fernandes PA, Ramos MJ (2007) Hot spots-a review of the protein-protein interface determinant amino-acid residues. Protein-Struct Funct Bioinforma 68:803–812

    Article  CAS  Google Scholar 

  26. Moreira IS, Fernandes PA, Ramos MJ (2007) Hot spot occlusion from bulk water: a comprehensive study of the complex between the lysozyme HEL and the antibody FVD1.3. J Phys Chem B 111:2697–2706

    Article  CAS  Google Scholar 

  27. Sousa SF, Tamames B, Fernandes PA, Ramos MJ (2011) Detailed atomistic analysis of the HIV-1 protease interface. J Phys Chem B 115:7045–7057

    Article  CAS  Google Scholar 

  28. Istvan ES, Deisenhofer J (2000) The structure of the catalytic portion of human HMG-CoA reductase. Biochim Biophys Acta Mol Cell Biol Lipids 1529:9–18

    Article  CAS  Google Scholar 

  29. Li H, Robertson AD, Jensen JH (2005) Very fast empirical prediction and rationalization of protein pK(a) values. Protein Struct Funct Bioinforma 61:704–721

    Article  CAS  Google Scholar 

  30. Olsson MHM, Sondergaard CR, Rostkowski M, Jensen JH (2011) PROPKA3: consistent treatment of internal and surface residues in empirical pK(a) predictions. J Chem Theory Comput 7:525–537

    Article  CAS  Google Scholar 

  31. Case DA, Darden TA, Cheatham, Simmerling CL, Wang J, Duke RE, Luo R, Merz KM, Pearlman DA, Crowley M, Walker RC, Zhang W, Wang B, Hayik S, Roitberg A, Seabra G, Wong KF, Paesani F, Wu X, Brozell S, Tsui V, Gohlke H, Yang L, Tan C, Mongan J, Hornak V, Cui G, Beroza P, Mathews DH, Schafmeister C, Ross WS, Kollman PA (2006) AMBER9, San Francisco

  32. Ryckaert JP, Ciccotti G, Berendsen HJC (1977) Numerical-integration of Cartesian equations of motion of a system with constraints-molecular-dynamics of N-alkanes. J Comput Phys 23:327–341

    Article  CAS  Google Scholar 

  33. Martins SA, Perez MAS, Moreira IS, Sousa SF, Ramos MJ, Fernandes PA (2013) Computational alanine scanning mutagenesis: MM-PBSA vs TI. J Chem Theory Comput 9:1311–1319

    Article  CAS  Google Scholar 

  34. Moreira IS, Fernandes PA, Ramos MJ (2006) Unraveling the importance of protein-protein interaction: application of a computational alanine-scanning mutagenesis to the study of the IgG1 streptococcal protein G (C2 fragment) complex. J Phys Chem B 110:10962–10969

    Article  CAS  Google Scholar 

  35. Moreira IS, Fernandes PA, Ramos MJ (2008) Protein-protein recognition: a computational mutagenesis study of the MDM2-P53 complex. Theor Chem Accounts 120:533–542

    Article  CAS  Google Scholar 

  36. Kollman PA, Massova I, Reyes C, Kuhn B, Huo S, Chong L, Lee M, Lee T, Duan Y, Wang W, Donini O, Cieplak P, Srinivasan J, Case DA, Cheatham TE 3rd (2000) Calculating structures and free energies of complex molecules: combining molecular mechanics and continuum models. Acc Chem Res 33:889–897

    Article  CAS  Google Scholar 

  37. Huo S, Massova I, Kollman PA (2002) Computational alanine scanning of the 1:1 human growth hormone-receptor complex. J Comput Chem 23:15–27

    Article  CAS  Google Scholar 

  38. Humphrey W, Dalke A, Schulten K (1996) VMD-visual molecular dynamics. J Mol Graph 14:33–38

    Article  CAS  Google Scholar 

  39. Ribeiro JV, Cerqueira NMFSA, Fernandes PA, Ramos MJ (2013) VOLAREA - a Bioinformatic tool to calculate the surface area and the volume of molecular systems. Chem Biol 82:743–755 doi: 10.1111/cbdd.12197

    Google Scholar 

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Acknowledgments

Diana Gesto is grateful to the Fundacao para a Ciencia e Technologia (FCT), Portugal for a PhD Grant (SFRH/BPD/44469/2008 - Mudar) and N.M.F.S.A. Cerqueira to program Ciência 2007. This work has been funded by FEDER/COMPETE and Fundação para a Ciência e a Tecnologia through grant n.º PTDC/QUI-QUI/102760/2008and grant no. PEst-C/EQB/LA0006/2011

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Correspondence to P. A. Fernandes.

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Gesto, D.S., Cerqueira, N.M.F.S.A., Ramos, M.J. et al. Discovery of new druggable sites in the anti-cholesterol target HMG-CoA reductase by computational alanine scanning mutagenesis. J Mol Model 20, 2178 (2014). https://doi.org/10.1007/s00894-014-2178-8

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  • DOI: https://doi.org/10.1007/s00894-014-2178-8

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