Journal of Molecular Modeling

, Volume 19, Issue 3, pp 1167–1177 | Cite as

Molecular dynamics and free energy studies of chirality specificity effects on aminobenzo[a]quinolizine inhibitors binding to DPP-IV

  • Cui Wei
  • Liang Desheng
  • Gao Jian
  • Luo Fang
  • Geng Lingling
  • Ji Mingjuan
Original Paper

Abstract

The aminobenzo[a]quinolizines were investigated as a novel class of DPP-IV inhibitors. The stereochemistry of this class plays an important role in the bioactivity. In this study, the mechanisms of how different configuration of three chiral centers of this class influences the binding affinity were investigated by molecular dynamics simulations, free energy decomposition analysis. The S configuration for chiral center 3* is decisive for isomers to maintain high bioactivity; the chirality effect of chiral center 2* on the binding affinity is largely dependent, while the S configuration for chiral center 2* is preferable to R configuration for the bioactivity gain; the effect of chiral center 11b* on the binding affinity is insignificant. The chirality specificity for three chiral centers is responsible for distinction of two van der Waals contacts with Tyr547 and Phe357, and of H-bonding interactions with Arg125 and Glu206. Particularly, the Arg125 to act as a bridge in the H-bonding network contributes to stable H-bonding interactions of isomer in DPP-IV active site.

Figure

The S configuration for chiral center 3* is decisive for high bioactivity; the chirality effect of chiral center 2* on binding affinity is largely dependent, while the S configuration for 2* is preferable to R for bioactivity gain; the chirality specificity for chiral center 11b* to binding affinity is insignificant.

Keywords

Aminobenzo[a]quinolizine inhibitors Chirality specificity DPP-IV MM/GBSA Molecular dynamics simulations 

Supplementary material

894_2012_1653_MOESM1_ESM.doc (272 kb)
ESM 1(DOC 272 kb)

References

  1. 1.
    Hussain A, Claussen B, Ramachandran A, Williams R (2007) Prevention of type 2 diabetes: a review. Diabetes Res Clin Pract 76(3):317–326CrossRefGoogle Scholar
  2. 2.
    Nathan DM, Buse JB, Davidson MB, Ferrannini E, Holman RR, Sherwin R, Zinman B (2009) Medical management of hyperglycemia in type 2 diabetes: a consensus algorithm for the initiation and adjustment of therapy: a consensus statement of the American Diabetes Association and the European Association for the Study of Diabetes. Diabetes Care 32(1):193–203CrossRefGoogle Scholar
  3. 3.
    Ahrrén B (2009) DPP-4 inhibitors. Insulin 4(1):15–31CrossRefGoogle Scholar
  4. 4.
    Havale SH, Pal M (2009) Medicinal chemistry approaches to the inhibition of dipeptidyl peptidase-4 for the treatment of type 2 diabetes. Bioorg Med Chem 17(5):1783–1802CrossRefGoogle Scholar
  5. 5.
    Kshirsagar AD, Aggarwal AS, Harle UN, Deshpande AD (2011) DPP IV inhibitors: successes, failures and future prospects. Diabetes Metab Syndr Clin Res Rev 5(2):105–112CrossRefGoogle Scholar
  6. 6.
    Holst JJ, Deacon CF (2004) Glucagon-like peptide 1 and inhibitors of dipeptidyl peptidase IV in the treatment of type 2 diabetes mellitus. Curr Opin Pharmacol 4(6):589–596CrossRefGoogle Scholar
  7. 7.
    Deacon CF (2004) Therapeutic strategies based on glucagon-like peptide 1. Diabetes 53(9):2181–2189CrossRefGoogle Scholar
  8. 8.
    Thornberry NA, Gallwitz B (2009) Mechanism of action of inhibitors of dipeptidyl-peptidase-4 (DPP-4). Best Pract Res Clin Endocrinol Metab 23(4):479–486CrossRefGoogle Scholar
  9. 9.
    Kim D, Wang L, Beconi M, Eiermann GJ, Fisher MH, He H, Hickey GJ, Kowalchick JE, Leiting B, Lyons K, Marsilio F, McCann ME, Patel RA, Petrov A, Scapin G, Patel SB, Roy RS, Wu JK, Wyvratt MJ, Zhang BB, Zhu L, Thornberry NA, Weber AE (2005) (2R)-4-oxo-4-[3-(trifluoromethyl)-5,6-dihydro[1, 2, 4]triazolo[4,3-a]pyrazin-7(8H)- yl]-1-(2,4,5-trifluorophenyl)butan-2-amine: a potent, orally active dipeptidyl peptidase IV inhibitor for the treatment of type 2 diabetes. J Med Chem 48(1):141–151CrossRefGoogle Scholar
  10. 10.
    Deacon CF (2007) Dipeptidyl peptidase 4 inhibition with sitagliptin: a new therapy for type 2 diabetes. Expert Opin Investig Drugs 16(4):533–545CrossRefGoogle Scholar
  11. 11.
    Kim SJ, Nian C, Doudet DJ, McIntosh CH (2008) Inhibition of dipeptidyl peptidase IV with sitagliptin (MK0431) prolongs islet graft survival in streptozotocin-induced diabetic mice. Diabetes 57(5):1331–1339CrossRefGoogle Scholar
  12. 12.
    Ahren B (2006) Vildagliptin: an inhibitor of dipeptidyl peptidase-4 with antidiabetic properties. Expert Opin Investig Drugs 15(4):431–442CrossRefGoogle Scholar
  13. 13.
    Villhauer EB, Brinkman JA, Naderi GB, Burkey BF, Dunning BE, Prasad K, Mangold BL, Russell ME, Hughes TE (2003) 1-[[(3-hydroxy-1-adamantyl)amino]acetyl]-2-cyano-(S)-pyrrolidine: a potent, selective, and orally bioavailable dipeptidyl peptidase IV inhibitor with antihyperglycemic properties. J Med Chem 46(13):2774–2789CrossRefGoogle Scholar
  14. 14.
    Feng J, Zhang Z, Wallace MB, Stafford JA, Kaldor SW, Kassel DB, Navre M, Shi L, Skene RJ, Asakawa T, Takeuchi K, Xu R, Webb DR, Gwaltney SL 2nd (2007) Discovery of alogliptin: a potent, selective, bioavailable, and efficacious inhibitor of dipeptidyl peptidase IV. J Med Chem 50(10):2297–2300CrossRefGoogle Scholar
  15. 15.
    Pratley RE (2009) Alogliptin: a new, highly selective dipeptidyl peptidase-4 inhibitor for the treatment of type 2 diabetes. Expert Opin Pharmacother 10(3):503–512CrossRefGoogle Scholar
  16. 16.
    Boehringer M, Fischer H, Hennig M, Hunziker D, Huwyler J, Kuhn B, Loeffler BM, Luebbers T, Mattei P, Narquizian R, Sebokova E, Sprecher U, Wessel HP (2010) Aryl- and heteroaryl-substituted aminobenzo[a]quinolizines as dipeptidyl peptidase IV inhibitors. Bioorg Med Chem Lett 20(3):1106–1108CrossRefGoogle Scholar
  17. 17.
    Mattei P, Boehringer M, Di Giorgio P, Fischer H, Hennig M, Huwyler J, Kocer B, Kuhn B, Loeffler BM, Macdonald A, Narquizian R, Rauber E, Sebokova E, Sprecher U (2010) Discovery of carmegliptin: a potent and long-acting dipeptidyl peptidase IV inhibitor for the treatment of type 2 diabetes. Bioorg Med Chem Lett 20(3):1109–1113CrossRefGoogle Scholar
  18. 18.
    Smith RH Jr, Jorgensen WL, Tirado-Rives J, Lamb ML, Janssen PA, Michejda CJ, Kroeger Smith MB (1998) Prediction of binding affinities for TIBO inhibitors of HIV-1 reverse transcriptase using Monte Carlo simulations in a linear response method. J Med Chem 41(26):5272–5286CrossRefGoogle Scholar
  19. 19.
    Sham YY, Chu ZT, Tao H, Warshel A (2000) Examining methods for calculations of binding free energies: LRA, LIE, PDLD-LRA, and PDLD/S-LRA calculations of ligands binding to an HIV protease. Proteins 39(4):393–407CrossRefGoogle Scholar
  20. 20.
    Kollman P (1993) Free energy calculations: applications to chemical and biochemical phenomena. Chem Rev 93(7):2395–2417CrossRefGoogle Scholar
  21. 21.
    DiCapua FM, Beveridge DL (1989) Free energy via molecular simulation: applications to chemical and biomolecular systems. Annu Rev Biophys Biophys Chem 18:431–492CrossRefGoogle Scholar
  22. 22.
    Wang J, Hou T, Xu X (2006) Recent advances in free energy calculations with a combination of molecular mechanics and continuum models. Curr Comput Aided Drug Des 2(3):287–306CrossRefGoogle Scholar
  23. 23.
    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 (2000) Calculating structures and free energies of complex moleculesy molecular mechanics and continuum models. Acc Chem Res 33(12):889–897CrossRefGoogle Scholar
  24. 24.
    Gohlke H, Case DA (2004) Converging free energy estimates: MM-PB(GB)SA studies on the protein–protein complex Ras–Raf. J Comput Chem 25(2):238–250CrossRefGoogle Scholar
  25. 25.
    Hou T, McLaughlin W, Lu B, Chen K, Wang W (2006) Prediction of binding affinities between the human amphiphysin-1 SH3 domain and its peptide ligands using homology modeling, molecular dynamics and molecular field analysis. J Proteome Res 5(1):32–43CrossRefGoogle Scholar
  26. 26.
    Chen Q, Cui W, Cheng Y, Zhang F, Ji M (2011) Studying the mechanism that enables paullones to selectively inhibit glycogen synthase kinase 3 rather than cyclin-dependent kinase 5 by molecular dynamics simulations and free-energy calculations. J Mol Model 17(4):795–803CrossRefGoogle Scholar
  27. 27.
    SYBYL molecular simulation package. (2004) http://www.sybyl.com
  28. 28.
    Case DA, Cheatham TE, Darden T, Gohlke H, Luo R, Merz KM, Onufriev A, Simmerling C, Wang B, Woods RJ (2005) The Amber biomolecular simulation programs. J Comput Chem 26(16):1668–1688CrossRefGoogle Scholar
  29. 29.
    Duan Y, Wu C, Chowdhury S, Lee MC, Xiong G, Zhang W, Yang R, Cieplak P, Luo R, Lee T, Caldwell J, Wang J, Kollman P (2003) A point-charge force field for molecular mechanics simulations of proteins based on condensed-phase quantum mechanical calculations. J Comput Chem 24(16):1999–2012CrossRefGoogle Scholar
  30. 30.
    Wang J, Wolf RM, Caldwell JW, Kollman PA, Case DA (2004) Development and testing of a general amber force field. J Comput Chem 25(9):1157–1174CrossRefGoogle Scholar
  31. 31.
    Frisch MJ, Trucks GW, Schlegel HB, Scuseria GE, Robb MA, Cheeseman JR, Montgomery JA, Vreven T, Kudin KN, Burant JC, Millam JM, Iyengar SS, Tomasi J, Barone V, Mennucci B, Cossi M, Scalmani G, Rega N, Petersson GA, Nakatsuji H, Hada M, Ehara M, Toyota K, Fukuda R, Hasegawa J, Ishida M, Nakajima T, Honda Y, Kitao O, Nakai H, Klene M, Li X, Knox JE, Hratchian HP, Cross JB, Bakken V, Adamo C, Jaramillo J, Gomperts R, Stratmann RE, Yazyev O, Austin AJ, Cammi R, Pomelli C, Ochterski JW, Ayala PY, Morokuma K, Voth GA, Salvador P, Dannenberg JJ, Zakrzewski VG, Dapprich S, Daniels AD, Strain MC, Farkas O, Malick DK, Rabuck AD,Raghavachari K, Foresman JB, Ortiz JV, Cui Q, Baboul AG, Clifford S, Cioslowski J, Stefanov BB, Liu G, Liashenko A, Piskorz P, Komaromi I, Martin RL, Fox DJ, Keith T, Laham A, Peng CY, Nanayakkara A, Challacombe M, Gill PMW, Johnson B, Chen W, Wong MW, Gonzalez C, Pople JA (2003) Gaussian 03, revision C.02Google Scholar
  32. 32.
    Bayly CI, Cieplak P, Cornell W, Kollman PA (2003) A well-behaved electrostatic potential based method using charge restraints for deriving atomic charges: the RESP model. J Phys Chem 97:10269–10280CrossRefGoogle Scholar
  33. 33.
    Jorgensen WL, Chandrasekhar J, Madura JD, Impey RW, Klein ML (1983) Comparison of simple potential functions for simulating liquid water. J Chem Phys 79:926–935CrossRefGoogle Scholar
  34. 34.
    Berendsen HJC, Postma JPM, van Gunsteren WF, DiNola A, Haak JR (1984) Molecular dynamics with coupling to an external bath. J Chem Phys 81:3684–3690CrossRefGoogle Scholar
  35. 35.
    Ryckaert JP, Ciccotti G, Berendsen HJC (1977) Numerical integration of the cartesian equations of motion of a system with constraints: molecular dynamics of n-alkanes. J Comput Phys 23:327–341CrossRefGoogle Scholar
  36. 36.
    Darden T, York D, Pedersen L (1993) Particle mesh Ewald: an N [center-dot] log(N) method for Ewald sums in large systems. J Chem Phys 98:10089–10092CrossRefGoogle Scholar
  37. 37.
    Onufriev A, Bashford D, Case DA (2004) Exploring protein native states and large-scale conformational changes with a modified generalized born model. Proteins 55(383–394)Google Scholar
  38. 38.
    Weiser J, Shenkin PS, Still WC (1999) Approximate atomic surfaces from linear combinations of pairwise overlaps (LCPO). J Comput Chem 20:217–230CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Cui Wei
    • 1
  • Liang Desheng
    • 1
  • Gao Jian
    • 1
  • Luo Fang
    • 1
  • Geng Lingling
    • 1
  • Ji Mingjuan
    • 1
  1. 1.College of Chemistry and Chemical EngineeringGraduate University of the Chinese Academy of SciencesBeijingPeople’s Republic of China

Personalised recommendations