Journal of Computer-Aided Molecular Design

, Volume 29, Issue 11, pp 1025–1034 | Cite as

Extracting ligands from receptors by reversed targeted molecular dynamics

  • Romain M. Wolf


Short targeted MD trajectories are used to expel ligands from binding sites. The expulsion is governed by a linear increase of the target RMSD value, growing from zero to an arbitrary chosen final RMSD that forces the ligand to a selected distance outside of the receptor. The RMSD lag (i.e., the difference between the imposed and the actual RMSD) can be used to follow barriers encountered by the ligand during its way out of the receptor. The force constant used for the targeted MD can transform the RMSD lag into a strain energy. Integration of the (time-dependent) strain energy over time yields a value with the dimensions of “action” (i.e, energy multiplied by time) and can serve as a measure for the overall effort required to extract the ligand from its binding site. Possibilities to compare (numerically and graphically) the randomly detected exit pathways are discussed. As an example, the method is tested on the exit of bisphenol A from the human estrogen-related receptor \(\gamma\) and of GW0072 from the peroxysome proliferator activated receptor.


Reversed targeted MD Nuclear receptors Ligand exit pathways 



The author thanks Anna Vulpetti and Rainer Wilcken for useful suggestions and for successfully testing the simulation protocols described here on various projects, and Richard Lewis for support and constructive comments.

Supplementary material (490 kb)
Supplementary material 1 (pdf 491KB)


  1. 1.
    Colizzi F, Perozzo R, Scapozza L, Recanatini M, Cavalli A (2010) J Am Chem Soc 132(21):7361CrossRefGoogle Scholar
  2. 2.
    Patel JS, Branduardi D, Masetti M, Rocchia W, Cavalli A (2011) J Chem Theory Comput 7(10):3368CrossRefGoogle Scholar
  3. 3.
    Gräter F, De Groot BL, Jiang H, Grubmüller H (2006) Structure 14(10):1567CrossRefGoogle Scholar
  4. 4.
    Lüdemann SK, Lounnas V, Wade RC (2000) J Mol Biol 303(5):797CrossRefGoogle Scholar
  5. 5.
    Lüdemann SK, Lounnas V, Wade RC (2000) J Mol Biol 303(5):813CrossRefGoogle Scholar
  6. 6.
    Winn PJ, Lüdemann SK, Gauges R, Lounnas V, Wade RC (2002) Proc Natl Acad Sci USA 99(8):5361CrossRefGoogle Scholar
  7. 7.
    Carlsson P, Burendahl S, Nilsson L (2006) Biophys J 91(9):3151CrossRefGoogle Scholar
  8. 8.
    Martínez L, Webb P, Polikarpov I, Skaf MS (2006) J Med Chem 49(1):23CrossRefGoogle Scholar
  9. 9.
    Vashisth H, Abrams CF (2008) Biophys J 95(9):4193CrossRefGoogle Scholar
  10. 10.
    Kingsley LJ, Lill MA (2014) J Comput Chem 35(24):1748CrossRefGoogle Scholar
  11. 11.
    Capelli AM, Costantino G (2014) J Chem Inf Model 54(11):3124CrossRefGoogle Scholar
  12. 12.
    Genest D, Garnier N, Arrault A, Marot C, Morin-Allory L, Genest M (2008) Eur Biophys J 37(4):369CrossRefGoogle Scholar
  13. 13.
    Peräkylä M (2009) Eur Biophys J 38(2):185CrossRefGoogle Scholar
  14. 14.
    Copeland RA, Pompliano DL, Meek TD (2006) Nat Rev Drug Discov 5(9):730CrossRefGoogle Scholar
  15. 15.
    Matsushima A, Kakuta Y, Teramoto T, Koshiba T, Liu X, Okada H, Tokunaga T, Kawabata Si, Kimura M, Shimohigashi Y (2007) J Biochem 142(4):517CrossRefGoogle Scholar
  16. 16.
    Oberfield JL, Collins JL, Holmes CP, Goreham DM, Cooper JP, Cobb JE, Lenhard JM, Hull-Ryde EA, Mohr CP, Blanchard SG, Parks DJ, Moore LB, Lehmann JM, Plunket K, Miller AB, Milburn MV, Kliewer SA, Willson TM (1999) Proc Natl Acad Sci USA 96(11):6102CrossRefGoogle Scholar
  17. 17.
    Case DA, Babin V, Berryman JT, Betz RM, Cai Q, Cerutti DS, Cheatham TE III, Darden TA, Duke RE, Gohlke H, Goetz AW, Gusarov S, Homeyer N, Janowski P, Kaus J, Kolossváry I, Kovalenko A, Lee TS, LeGrand S, Luchko T, Luo R, Madej B, Merz KM, Paesani F, Roe DR, Roitberg A, Sagui C, Salomon-Ferrer R, Seabra G, Simmerling CL, Smith W, Swails J, Walker RC, Wang J, Wolf RM, Wu X, Kollman P (2014) Amber 14. University of California, San FranciscoGoogle Scholar
  18. 18.
    Hornak V, Abel R, Okur A, Strockbine B, Roitberg A, Simmerling C (2006) Proteins Struct Funct Bioinfm 65(3):712CrossRefGoogle Scholar
  19. 19.
    Wang J, Wolf RM, Caldwell JW, Kollman PA, Case DA (2004) J Comput Chem 25(9):1157CrossRefGoogle Scholar
  20. 20.
    Wang J, Wang W, Kollman PA, Case DA (2006) J Mol Graph Model 25(2):247CrossRefGoogle Scholar
  21. 21.
    Jakalian A, Bush BL, Jack DB, Bayly CI (2000) J Comput Chem 21(2):132CrossRefGoogle Scholar
  22. 22.
    Jakalian A, Jack DB, Bayly CI (2002) J Comput Chem 23(16):1623CrossRefGoogle Scholar
  23. 23.
    Onufriev A, Bashford D, Case DA (2004) Proteins Struct Funct Bioinfm 55(2):383CrossRefGoogle Scholar
  24. 24.
    Roe DR, Cheatham TE III (2013) J Chem Theory Comput 9(7):3084CrossRefGoogle Scholar
  25. 25.
    Schrödinger LLC (2012) The PyMOL Molecular Graphics System, Version Schrödinger, LLCGoogle Scholar
  26. 26.
    Chovancova E, Pavelka A, Benes P, Strnad O, Brezovsky J, Kozlikova B, Gora A, Sustr V, Klvana M, Medek P, Biermannova L, Sochor J, Damborský J (2012) PLoS Comput Biol 8(10):1Google Scholar
  27. 27.
    Pearlstein RA, Sherman W, Abel R (2013) Proteins Struct Funct Bioinfm 81(9):1509CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Novartis Institutes for Biomedical Research, NIBRNovartis Pharma AGBaselSwitzerland

Personalised recommendations