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Extracting ligands from receptors by reversed targeted molecular dynamics

Abstract

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.

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References

  1. Colizzi F, Perozzo R, Scapozza L, Recanatini M, Cavalli A (2010) J Am Chem Soc 132(21):7361

    CAS  Article  Google Scholar 

  2. Patel JS, Branduardi D, Masetti M, Rocchia W, Cavalli A (2011) J Chem Theory Comput 7(10):3368

    CAS  Article  Google Scholar 

  3. Gräter F, De Groot BL, Jiang H, Grubmüller H (2006) Structure 14(10):1567

    Article  Google Scholar 

  4. Lüdemann SK, Lounnas V, Wade RC (2000) J Mol Biol 303(5):797

    Article  Google Scholar 

  5. Lüdemann SK, Lounnas V, Wade RC (2000) J Mol Biol 303(5):813

    Article  Google Scholar 

  6. Winn PJ, Lüdemann SK, Gauges R, Lounnas V, Wade RC (2002) Proc Natl Acad Sci USA 99(8):5361

    CAS  Article  Google Scholar 

  7. Carlsson P, Burendahl S, Nilsson L (2006) Biophys J 91(9):3151

    CAS  Article  Google Scholar 

  8. Martínez L, Webb P, Polikarpov I, Skaf MS (2006) J Med Chem 49(1):23

    Article  Google Scholar 

  9. Vashisth H, Abrams CF (2008) Biophys J 95(9):4193

    Article  Google Scholar 

  10. Kingsley LJ, Lill MA (2014) J Comput Chem 35(24):1748

    CAS  Article  Google Scholar 

  11. Capelli AM, Costantino G (2014) J Chem Inf Model 54(11):3124

    CAS  Article  Google Scholar 

  12. Genest D, Garnier N, Arrault A, Marot C, Morin-Allory L, Genest M (2008) Eur Biophys J 37(4):369

    CAS  Article  Google Scholar 

  13. Peräkylä M (2009) Eur Biophys J 38(2):185

    Article  Google Scholar 

  14. Copeland RA, Pompliano DL, Meek TD (2006) Nat Rev Drug Discov 5(9):730

    CAS  Article  Google Scholar 

  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):517

    CAS  Article  Google Scholar 

  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):6102

    CAS  Article  Google Scholar 

  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 Francisco

    Google Scholar 

  18. Hornak V, Abel R, Okur A, Strockbine B, Roitberg A, Simmerling C (2006) Proteins Struct Funct Bioinfm 65(3):712

    CAS  Article  Google Scholar 

  19. Wang J, Wolf RM, Caldwell JW, Kollman PA, Case DA (2004) J Comput Chem 25(9):1157

    CAS  Article  Google Scholar 

  20. Wang J, Wang W, Kollman PA, Case DA (2006) J Mol Graph Model 25(2):247

    Article  Google Scholar 

  21. Jakalian A, Bush BL, Jack DB, Bayly CI (2000) J Comput Chem 21(2):132

    CAS  Article  Google Scholar 

  22. Jakalian A, Jack DB, Bayly CI (2002) J Comput Chem 23(16):1623

    CAS  Article  Google Scholar 

  23. Onufriev A, Bashford D, Case DA (2004) Proteins Struct Funct Bioinfm 55(2):383

    CAS  Article  Google Scholar 

  24. Roe DR, Cheatham TE III (2013) J Chem Theory Comput 9(7):3084

    CAS  Article  Google Scholar 

  25. Schrödinger LLC (2012) The PyMOL Molecular Graphics System, Version 1.5.0.5. Schrödinger, LLC

  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):1

    Google Scholar 

  27. Pearlstein RA, Sherman W, Abel R (2013) Proteins Struct Funct Bioinfm 81(9):1509

    CAS  Article  Google Scholar 

Download references

Acknowledgments

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.

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Correspondence to Romain M. Wolf.

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Wolf, R.M. Extracting ligands from receptors by reversed targeted molecular dynamics. J Comput Aided Mol Des 29, 1025–1034 (2015). https://doi.org/10.1007/s10822-015-9863-2

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  • DOI: https://doi.org/10.1007/s10822-015-9863-2

Keywords

  • Reversed targeted MD
  • Nuclear receptors
  • Ligand exit pathways