Predicting the bioactive conformations of macrocycles: a molecular dynamics-based docking procedure with DynaDock

  • Ilke Ugur
  • Maja Schroft
  • Antoine Marion
  • Manuel Glaser
  • Iris AntesEmail author
Original Paper


Macrocyclic compounds are of growing interest as a new class of therapeutics, especially as inhibitors binding to protein–protein interfaces. As molecular modeling is a well-established complimentary tool in modern drug design, the number of attempts to develop reliable docking strategies and algorithms to accurately predict the binding mode of macrocycles is rising continuously. Standard molecular docking approaches need to be adapted to this application, as a comprehensive yet efficient sampling of all ring conformations of the macrocycle is necessary. To overcome this issue, we designed a molecular dynamics-based docking protocol for macrocycles, in which the challenging sampling step is addressed by conventional molecular dynamics (750 ns) simulations performed at moderately high temperature (370 K). Consecutive flexible docking with the DynaDock approach based on multiple, pre-sampled ring conformations yields highly accurate poses with ligand RMSD values lower than 1.8 Å. We further investigated the value of molecular dynamics-based complex stability estimations for pose selection and discuss its applicability in combination with standard binding free energy estimations for assessing the quality of poses in future blind docking studies.


Macrocyclic compounds Protein–ligand docking Drug design Conformational sampling Molecular dynamics DynaDock 



Financial support from Deutsche Forschungsgemeinschaft (SFB 1035/A10 and CIPSM) is gratefully acknowledged.

Author contributions

I.U. and M.S. performed the computational studies. A.M. provided guidelines and established parameters of the macrocyles. M.G. gave advice regarding the DynaDock and MMGBSA calculations. I.A. and I.U. designed and supervised the study. All the authors contributed to the writing of the manuscript.

Supplementary material

894_2019_4077_MOESM1_ESM.pdf (3.8 mb)
ESM 1 (PDF 3840 kb)


  1. 1.
    Driggers EM, Hale SP, Lee J, Terrett NK (2008) The exploration of macrocycles for drug discovery--an underexploited structural class. Nat Rev Drug Discov 7(7):608–624CrossRefGoogle Scholar
  2. 2.
    Heinis C, discovery D (2014) Tools and rules for macrocycles. Nat Chem Biol 10(9):696–698PubMedCrossRefGoogle Scholar
  3. 3.
    Villar EA, Beglov D, Chennamadhavuni S, Porco Jr JA, Kozakov D, Vajda S, Whitty A (2014) How proteins bind macrocycles. Nat Chem Biol 10(9):723–731PubMedPubMedCentralCrossRefGoogle Scholar
  4. 4.
    Mallinson J, Collins I (2012) Macrocycles in new drug discovery. Future Med Chem 4(11):1409–1438PubMedCrossRefGoogle Scholar
  5. 5.
    Tapeinou A, Matsoukas MT, Simal C, Tselios T (2015) Review cyclic peptides on a merry-go-round; towards drug design. Biopolymers 104(5):453–461PubMedCrossRefGoogle Scholar
  6. 6.
    Salvador-Reyes LA, Luesch H (2015) Biological targets and mechanisms of action of natural products from marine cyanobacteria. Nat Prod Rep 32(3):478–503PubMedPubMedCentralCrossRefGoogle Scholar
  7. 7.
    Marsault E, Peterson ML (2011) Macrocycles are great cycles: applications, opportunities, and challenges of synthetic macrocycles in drug discovery. J Med Chem 54(7):1961–2004PubMedCrossRefGoogle Scholar
  8. 8.
    Luther A, Moehle K, Chevalier E, Dale G, Obrecht D (2017) Protein epitope mimetic macrocycles as biopharmaceuticals. Curr Opin Chem Biol 38:45–51PubMedCrossRefGoogle Scholar
  9. 9.
    Morrison C (2018) Constrained peptides’ time to shine? Nat Rev Drug Discov 17(8):531–533PubMedCrossRefGoogle Scholar
  10. 10.
    Joo SH (2012) Cyclic peptides as therapeutic agents and biochemical tools. Biomol Ther 20(1):19CrossRefGoogle Scholar
  11. 11.
    Horton DA, Bourne GT, Smythe ML (2002) Exploring privileged structures: the combinatorial synthesis of cyclic peptides. J Comput Aided Mol Des 16(5–6):415–431PubMedCrossRefGoogle Scholar
  12. 12.
    Ciemny M, Kurcinski M, Kamel K, Kolinski A, Alam N, Schueler-Furman O, Kmiecik S (2018) Protein–peptide docking: opportunities and challenges. Drug Discov Today 123(8):1530–1537Google Scholar
  13. 13.
    Watts KS, Dalal P, Tebben AJ, Cheney DL, Shelley JC (2014) Macrocycle conformational sampling with MacroModel. J Chem Inf Model 54(10):2680–2696PubMedCrossRefGoogle Scholar
  14. 14.
    Labute P (2010) LowModeMD--implicit low-mode velocity filtering applied to conformational search of macrocycles and protein loops. J Chem Inf Model 50(5):792–800PubMedCrossRefGoogle Scholar
  15. 15.
    Allen SE, Dokholyan NV, Bowers AA (2016) Dynamic docking of conformationally constrained macrocycles: methods and applications. ACS Chem. Biol. 11(1):10–24PubMedCrossRefGoogle Scholar
  16. 16.
    Castro-Alvarez A, Costa AM, Vilarrasa J (2017) The performance of several docking programs at reproducing protein-macrolide-like crystal structures. Molecules 22(1).pii: E136Google Scholar
  17. 17.
    McHugh SM, Rogers JR, Solomon SA, Yu H, Lin YS (2016) Computational methods to design cyclic peptides. Curr Opin Chem Biol 34:95–102PubMedCrossRefGoogle Scholar
  18. 18.
    Anighoro A, de la Vega de León A, Bajorath J (2016) Predicting bioactive conformations and binding modes of macrocycles. J Comput Aided Mol Des 30(10):841–849Google Scholar
  19. 19.
    Chen IJ, Foloppe N (2013) Tackling the conformational sampling of larger flexible compounds and macrocycles in pharmacology and drug discovery. Bioorg Med Chem 21(24):7898–7920PubMedCrossRefGoogle Scholar
  20. 20.
    Forli S, Botta M (2007) Lennard-Jones potential and dummy atom settings to overcome the AUTODOCK limitation in treating flexible ring systems. J Chem Inf Model 47(4):1481–1492PubMedCrossRefGoogle Scholar
  21. 21.
    Alogheli H, Olanders G, Schaal W, Brandt P, Karlen A (2017) Docking of macrocycles: comparing rigid and flexible docking in glide. J Chem Inf Model 57(2):190–202PubMedCrossRefGoogle Scholar
  22. 22.
    Sindhikara D, Spronk SA, Day T, Borrelli K, Cheney DL, Posy SL (2017) Improving accuracy, diversity, and speed with prime macrocycle conformational sampling. J Chem Inf Model 57(8):1881–1894PubMedCrossRefGoogle Scholar
  23. 23.
    Coutsias EA, Lexa KW, Wester MJ, Pollock SN, Jacobson MP (2016) Exhaustive conformational sampling of complex fused ring macrocycles using inverse kinematics. J Chem Theory Comput 12(9):4674–4687PubMedPubMedCentralCrossRefGoogle Scholar
  24. 24.
    Kamenik AS, Lessel U, Fuchs JE, Fox T, Liedl KR (2018) Peptidic macrocycles — conformational sampling and thermodynamic characterization. J Chem Inf Model 58(5):982–992PubMedPubMedCentralCrossRefGoogle Scholar
  25. 25.
    Antes I (2010) DynaDock: a new molecular dynamics-based algorithm for protein-peptide docking including receptor flexibility. Proteins 78(5):1084–1104PubMedCrossRefGoogle Scholar
  26. 26.
    Gross S, Nguyen F, Bierschenk M, Sohmen D, Menzel T, Antes I, Wilson DN, Bach T (2013) Amythiamicin D and related thiopeptides as inhibitors of the bacterial elongation factor EF-Tu: modification of the amino acid at carbon atom C2 of ring C dramatically influences activity. ChemMedChem 8(12):1954–1962PubMedCrossRefGoogle Scholar
  27. 27.
    Marcinowski M, Rosam M, Seitz C, Elferich J, Behnke J, Bello C, Feige MJ, Becker CF, Antes I, Buchner J (2013) Conformational selection in substrate recognition by Hsp70 chaperones. J Mol Biol 425(3):466–474PubMedCrossRefGoogle Scholar
  28. 28.
    Schneider M, Rosam M, Glaser M, Patronov A, Shah H, Back KC, Daake MA, Buchner J, Antes I (2016) BiPPred: combined sequence- and structure-based prediction of peptide binding to the Hsp70 chaperone BiP. Proteins 84(10):1390–1407PubMedCrossRefGoogle Scholar
  29. 29.
    Case DA, Berryman JT, Betz RM, Cerutti DS, Cheatham 3rd TE, Darden TA, Duke RE, Giese TJ, Gohlke H, Götz AW, Homeyer N, Izadi S, Janowski P, Kaus J, Kovalenko A, Lee TS, LeGrand S, Li P, Luchko T, Luo R, Madej B, Merz KM, Monard G, Needham P, Nguyen H, Nguyen HT, Omelyan I, Onufriev A, Roe DR, Roitberg A, Salomon-Ferrer R, Simmerling CL, Smith W, Swails J, Walker RC, Wang J, Wolf RM, Wu X, York DM, Kollman PA (2015) AMBER. University of California, San FranciscoGoogle Scholar
  30. 30.
    Jorgensen WL, Chandrasekhar J, Madura JD, Impey RW, Klein ML (1983) Comparison of simple potential functions for simulating liquid water. J Chem Phys 79(2):926–935CrossRefGoogle Scholar
  31. 31.
    Hanwell MD, Curtis DE, Lonie DC, Vandermeersch T, Zurek E, Hutchison GR (2012) Avogadro: an advanced semantic chemical editor, visualization, and analysis platform. J Cheminform 4(1):17PubMedPubMedCentralCrossRefGoogle Scholar
  32. 32.
    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):1999–2012PubMedCrossRefGoogle Scholar
  33. 33.
    Meagher KL, Redman LT, Carlson HA (2003) Development of polyphosphate parameters for use with the AMBER force field. J Comput Chem 24(9):1016–1025PubMedCrossRefGoogle Scholar
  34. 34.
    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
  35. 35.
    Frisch MJ, Trucks GW, Schlegel HB, Scuseria GE, Robb MA, Cheeseman JR, Scalmani G, Barone V, Mennucci B, Petersson GA et al (2009) Gaussian 09, revision D. 01. Gaussian, Inc., WallingfordGoogle Scholar
  36. 36.
    Bayly CI, Cieplak P, Cornell WD, Kollman PA (1993) A well-behaved electrostatic potential based method using charge restraints for deriving atomic charges: the RESP model. J Phys Chem 97(40):10269–10280CrossRefGoogle Scholar
  37. 37.
    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(3):327–341CrossRefGoogle Scholar
  38. 38.
    Essmann U, Perera L, Berkowitz ML, Darden T, Lee H, Pedersen LG (1995) A smooth particle mesh Ewald method. J Chem Phys 103(19):8577–8593CrossRefGoogle Scholar
  39. 39.
    Götz AW, Williamson MJ, Xu D, Poole D, Le Grand S, Walker RC (2012) Routine microsecond molecular dynamics simulations with AMBER on GPUs. 1. Generalized born. J Chem Theory Comput 8(5):1542–1555PubMedPubMedCentralCrossRefGoogle Scholar
  40. 40.
    Salomon-Ferrer R, Götz AW, Poole D, Le Grand S, Walker RC (2013) Routine microsecond molecular dynamics simulations with AMBER on GPUs. 2. Explicit solvent particle mesh Ewald. J Chem Theory Comput 9(9):3878–3888PubMedCrossRefGoogle Scholar
  41. 41.
    Miller 3rd BR, McGee Jr TD, Swails JM, Homeyer N, Gohlke H, Roitberg AE (2012) MMPBSA.Py: an efficient program for end-state free energy calculations. J Chem Theory Comput 8(9):3314–3321PubMedCrossRefGoogle Scholar
  42. 42.
    Onufriev A, Bashford D, Case DA (2004) Exploring protein native states and large-scale conformational changes with a modified generalized born model. Proteins 55(2):383–394PubMedCrossRefGoogle Scholar
  43. 43.
    Srinivasan J, Trevathan MW, Beroza P, Case DA (1999) Application of a pairwise generalized born model to proteins and nucleic acids: inclusion of salt effects. Theor Chem Accounts 101(6):426–434CrossRefGoogle Scholar
  44. 44.
    Kolář M, Fanfrlík J, Lepšík M, Forti F, Luque FJ, Hobza P (2013) Assessing the accuracy and performance of implicit solvent models for drug molecules: conformational ensemble approaches. J Phys Chem B 117(19):5950–5962PubMedCrossRefGoogle Scholar
  45. 45.
    Merten C, Li F, Bravo-Rodriguez K, Sanchez-Garcia E, Xu Y, Sander W (2014) Solvent-induced conformational changes in cyclic peptides: a vibrational circular dichroism study. Phys Chem Chem Phys 16(12):5627–5633PubMedCrossRefGoogle Scholar
  46. 46.
    Jain AN (2008) Bias, reporting, and sharing: computational evaluations of docking methods. J Comput Aided Mol Des 22(3–4):201–212PubMedCrossRefGoogle Scholar
  47. 47.
    Cho AE, Guallar V, Berne BJ, Friesner R (2005) Importance of accurate charges in molecular docking: quantum mechanical/molecular mechanical (QM/MM) approach. J Comput Chem 26(9):915–931PubMedPubMedCentralCrossRefGoogle Scholar
  48. 48.
    Nordqvist A, O’Mahony G, Friden-Saxin M, Fredenwall M, Hogner A, Granberg KL, Aagaard A, Backstrom S, Gunnarsson A, Kaminski T, Xue Y, Dellsen A, Hansson E, Hansson P, Ivarsson I, Karlsson U, Bamberg K, Hermansson M, Georgsson J, Lindmark B, Edman K (2017) Structure-based drug design of mineralocorticoid receptor antagonists to explore oxosteroid receptor selectivity. ChemMedChem 12(1):50–65PubMedCrossRefGoogle Scholar
  49. 49.
    Zapf CW, Bloom JD, McBean JL, Dushin RG, Nittoli T, Otteng M, Ingalls C, Golas JM, Liu H, Lucas J, Boschelli F, Hu Y, Vogan E, Levin JI (2011) Macrocyclic lactams as potent Hsp90 inhibitors with excellent tumor exposure and extended biomarker activity. Bioorg Med Chem Lett 21(11):3411–3416PubMedCrossRefGoogle Scholar
  50. 50.
    Delfosse V, Grimaldi M, Cavailles V, Balaguer P, Bourguet W (2014) Structural and functional profiling of environmental ligands for estrogen receptors. Environ Health Perspect 122(12):1306–1313PubMedPubMedCentralCrossRefGoogle Scholar
  51. 51.
    Hamajima Y, Nagae T, Watanabe N, Ohmae E, Kato-Yamada Y, Kato C (2016) Pressure adaptation of 3-isopropylmalate dehydrogenase from an extremely piezophilic bacterium is attributed to a single amino acid substitution. Extremophiles 20(2):177–186PubMedCrossRefGoogle Scholar
  52. 52.
    Kettle JG, Alwan H, Bista M, Breed J, Davies NL, Eckersley K, Fillery S, Foote KM, Goodwin L, Jones DR, Kack H, Lau A, Nissink JW, Read J, Scott JS, Taylor B, Walker G, Wissler L, Wylot M (2016) Potent and selective inhibitors of MTH1 probe its role in Cancer cell survival. J Med Chem 59(6):2346–2361PubMedCrossRefGoogle Scholar
  53. 53.
    Morton WM, Ayscough KR, McLaughlin PJ (2000) Latrunculin alters the actin-monomer subunit interface to prevent polymerization. Nat Cell Biol 2(6):376–378PubMedCrossRefGoogle Scholar
  54. 54.
    Nair UB, Joel PB, Wan Q, Lowey S, Rould MA, Trybus KM (2008) Crystal structures of monomeric actin bound to cytochalasin D. J Mol Biol 384(4):848–864PubMedPubMedCentralCrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Center for Integrated Protein Science at the Department for BiosciencesTechnische Universität MünchenFreisingGermany
  2. 2.Department of ChemistryMiddle East Technical UniversityAnkaraTurkey

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