Methylation-targeted specificity of the DNA binding proteins R.DpnI and MeCP2 studied by molecular dynamics simulations

  • Siba Shanak
  • Ozlem Ulucan
  • Volkhard HelmsEmail author
Original Paper


DNA methylation plays a major role in organismal development and the regulation of gene expression. Methylation of cytosine bases and the cellular roles of methylated cytosine in eukaryotes are well established, as well as methylation of adenine bases in bacterial genomes. Still lacking, however, is a general mechanistic understanding, in structural and thermodynamic terms, of how proteins recognize methylated DNA. Toward this aim, we present the results of molecular dynamics simulations, alchemical free energy perturbation, and MM-PBSA calculations to explain the specificity of the R.DpnI enzyme from Streptococcus pneumonia in binding to adenine-methylated DNA with both its catalytic and winged-helix domains. We found that adenine-methylated DNA binds more favorably to the catalytic subunit of R.DpnI (−4 kcal mol−1) and to the winged-helix domain (−1.6 kcal mol−1) than non-methylated DNA. In particular, N6-adenine methylation is found to enthalpically stabilize binding to R.DpnI. In contrast, C5-cytosine methylation entropically favors complexation by the MBD domain of the human MeCP2 protein with almost no contribution of the binding enthalpy.


Restriction endonuclease DNA methylation m6A m5C Sequence specificity Binding free energy Conformational entropy Free energy perturbation MM-PBSA 



We thank Prof. Matthias Bochtler (Warsaw/Poland) for valuable discussions on mechanistic issues of the R.DpnI system and for early access to the crystallographic data. In addition, we thank Dr. Wei Gu for fruitful discussions and helpful suggestions concerning the free energy perturbation calculations.

Compliance with ethical standards

Funding sources

This work is embedded in the framework of the collaborative research center SFB 1027 funded by Deutsche Forschungsgemeinschaft (DFG). S.S. thanks the German Academic Exchange Service (DAAD) for a doctoral fellowship. O.U. was supported by DFG.

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  1. 1.
    Low DA, Weyand NJ, Mahan MJ (2001) Roles of DNA adenine methylation in regulating bacterial gene expression and virulence. Infect Immun 69(12):7197–7204CrossRefGoogle Scholar
  2. 2.
    Wu TP, Wang T, Seetin MG, Lai YQ, Zhu SJ, Lin KX, Liu YF, Byrum SD, Mackintosh SG, Zhong M, Tackett A, Wang GL, Hon LS, Fang G, Swenberg JA, Xiao AZ (2016) DNA methylation on N-6-adenine in mammalian embryonic stem cells. Nature 532(7599):329–333CrossRefGoogle Scholar
  3. 3.
    Siwek W, Czapinska H, Bochtler M, Bujnicki JM, Skowronek K (2012) Crystal structure and mechanism of action of the N6-methyladenine-dependent type IIM restriction endonuclease R.DpnI. Nucleic Acids Res 40(15):7563–7572CrossRefGoogle Scholar
  4. 4.
    Delacampa AG, Springhorn SS, Kale P, Lacks SA (1988) Proteins encoded by DpnI restriction gene cassette- hyperproduction and characterization of the DpnI endonuclease. J Biol Chem 263(29):14696–14702Google Scholar
  5. 5.
    Mierzejewska K, Siwek W, Czapinska H, Skowronek K, Bujnicki J, Bochtler M (2014) Structural basis of the methylation specificity of R.DpnI. Nucleic Acids Res 42:8745–8754CrossRefGoogle Scholar
  6. 6.
    Chahrour M, Jung SY, Shaw C, Zhou XB, Wong STC, Qin J, Zoghbi HY (2008) MeCP2, a key contributor to neurological disease, activates and represses transcription. Science 320(5880):1224–1229CrossRefGoogle Scholar
  7. 7.
    Chen WG, Chang Q, Lin YX, Meissner A, West AE, Griffith EC, Jaenisch R, Greenberg ME (2003) Derepression of BDNF transcription involves calcium-dependent phosphorylation of MeCP2. Science 302(5646):885–889CrossRefGoogle Scholar
  8. 8.
    Bekinschtein P, Cammarota M, Katche C, Slipczuk L, Rossato JI, Goldin A, Lzquierdo I, Medina JH (2008) BDNF is essential to promote persistence of long-term memory storage. Proc Natl Acad Sci USA 105(7):2711–2716CrossRefGoogle Scholar
  9. 9.
    Ho KL, McNae LW, Schmiedeberg L, Klose RJ, Bird AP, Walkinshaw MD (2008) MeCP2 binding to DNA depends upon hydration at methyl-CpG. Mol Cell 29(4):525–531CrossRefGoogle Scholar
  10. 10.
    Pabo CO, Sauer RT (1984) Protein-DNA recognition. Annu Rev Biochem 53:293–321CrossRefGoogle Scholar
  11. 11.
    Wecker K, Bonnet MC, Meurs EF, Delepierre M (2002) The role of the phosphorus BI-BII transition in protein-DNA recognition: the NF-kappa B complex. Nucleic Acids Res 30(20):4452–4459CrossRefGoogle Scholar
  12. 12.
    Ray BK, Dhar S, Henry C, Rich A, Ray A (2013) Epigenetic regulation by Z-DNA silencer function controls cancer-associated ADAM-12 expression in breast cancer: cross-talk between MeCP2 and NF1 transcription factor family. Cancer Res 73(2):736–744CrossRefGoogle Scholar
  13. 13.
    Madhumalar A, Bansal M (2005) Sequence preference for BI/II conformations in DNA: MD and crystal structure data analysis. J Biomol Struct Dyn 23(1):13–27CrossRefGoogle Scholar
  14. 14.
    Buck-Koehntop BA, Stanfield RL, Ekiert DC, Martinez-Yamout MA, Dyson HJ, Wilson IA, Wright PE (2012) Molecular basis for recognition of methylated and specific DNA sequences by the zinc finger protein Kaiso. Proc Natl Acad Sci USA 109(38):15229–15234CrossRefGoogle Scholar
  15. 15.
    Zou X, Ma W, Solov’yov IA, Chipot C, Schulten K (2012) Recognition of methylated DNA through methyl-CpG binding domain proteins. Nucleic Acids Res 40(6):2747–2758CrossRefGoogle Scholar
  16. 16.
    Schenkelberger M, Shanak S, Finkler M, Worst E, Noireaux V, Helms V, Ott A (2017) Expression regulation by a methyl-CpG binding domain in an E. coli based, cell-free TX-TL system. Phys Biol. doi: 10.1088/1478-3975/aa5d37 Google Scholar
  17. 17.
    Hess B, Kutzner C, van der Spoel D, Lindahl E (2008) GROMACS 4: Algorithms for highly efficient, load-balanced, and scalable molecular simulation. J Chem Theory Comput 4(3):435–447CrossRefGoogle Scholar
  18. 18.
    Foloppe N, MacKerell AD (2000) All-atom empirical force field for nucleic acids: I. Parameter optimization based on small molecule and condensed phase macromolecular target data. J Comput Chem 21(2):86–104CrossRefGoogle Scholar
  19. 19.
    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
  20. 20.
    Shanak S., Helms V. (2014) Hydration properties of natural and synthetic DNA sequences with methylated adenine or cytosine bases in the R.DpnI target and BDNF promoter studied by molecular dynamics simulations. J Chem Phys. p. 22D512Google Scholar
  21. 21.
    Darden T, York D, Pedersen L (1993) Particle Mesh Ewald- an n.log(n) method for Ewald sums in large systems. J Chem Phys 98(12):10089–10092CrossRefGoogle Scholar
  22. 22.
    Van Gunsteren WF, Berendsen HJC (1988) A leap-frog algorithm for stochastic dynamics. Mol Simul 1(3):173–185CrossRefGoogle Scholar
  23. 23.
    Bennett CH (1976) Efficient estimation of free-energy differences from Monte-Carlo data. J Comput Phys 22(2):245–268CrossRefGoogle Scholar
  24. 24.
    Pohorille A, Jarzynski C, Chipot C (2010) Good practices in free-energy calculations. J Phys Chem B 114(32):10235–10253CrossRefGoogle Scholar
  25. 25.
    Mobley DL, Chodera JD, Dill KA (2006) On the use of orientational restraints and symmetry corrections in alchemical free energy calculations. J Chem Phys 125(8):084902CrossRefGoogle Scholar
  26. 26.
    Hornak V, Simmerling C (2004) Development of softcore potential functions for overcoming steric barriers in molecular dynamics simulations. J Mol Graph Model 22(5):405–413CrossRefGoogle Scholar
  27. 27.
    Beutler TC, Mark AE, Vanschaik RC, Gerber PR, Vangunsteren WF (1994) Avoiding singularities and numerical instabilities in free-energy calculations based on molecular simulations. Chem Phys Lett 222(6):529–539CrossRefGoogle Scholar
  28. 28.
    Srinivasan J, Cheatham TE, Cieplak P, Kollman PA, Case DA (1998) Case, Continuum solvent studies of the stability of DNA, RNA, and phosphoramidate–DNA helices. J Am Chem Soc 120(37):9401–9409CrossRefGoogle Scholar
  29. 29.
    Baker NA, Sept D, Holst MJ, McCammon JA (2001) The adaptive multilevel finite element solution of the Poisson-Boltzmann equation on massively parallel computers. IBM J Res Dev 45(3–4):427–438CrossRefGoogle Scholar
  30. 30.
    Miller BR III, McGee TD Jr, Swails JM, Homeyer N, Gohlke H, Roitberg AE (2012) an efficient program for end-state free energy calculations. J Chem Theory Comput 8(9):3314–3321CrossRefGoogle Scholar
  31. 31.
    Crowley MF, Williamson MJ, Walker RC (2009) CHAMBER: comprehensive support for CHARMM force fields within the AMBER software. Int J Quantum Chem 109(15):3767–3772CrossRefGoogle Scholar
  32. 32.
    Humphrey W, Dalke A, Schulten K (1996) VMD: visual molecular dynamics. J Mol Graph Model 14(1):33–38CrossRefGoogle Scholar
  33. 33.
    Roe DR, Cheatham TE III (2013) PTRAJ and CPPTRAJ: software for processing and analysis of molecular dynamics trajectory data. J Chem Theory Comput 9(7):3084–3095CrossRefGoogle Scholar
  34. 34.
    Furini S, Barbini P, Domene C (2013) DNA-recognition process described by MD simulations of the lactose repressor protein on a specific and a non-specific DNA sequence. Nucleic Acids Res 41(7):3963–3972CrossRefGoogle Scholar
  35. 35.
    Schlitter J (1993) Estimation of absolute and relative entropies of macromolecules using the covariance matrix. Chem Phys Lett 215(6):617–621CrossRefGoogle Scholar
  36. 36.
    Hartmann B, Piazzola D, Lavery R (1993) BI-BII transitions in B-DNA. Nucleic Acids Res 21(3):561–568CrossRefGoogle Scholar
  37. 37.
    Pauling L (1992) The nature of chemical bond. J Chem Educ 69(7):519–521CrossRefGoogle Scholar
  38. 38.
    Lu XJ, Olson WK (2003) 3DNA: a software package for the analysis, rebuilding and visualization of three-dimensional nucleic acid structures. Nucleic Acids Res 31(17):5108–5121CrossRefGoogle Scholar
  39. 39.
    Liu Y, Toh H, Sasaki H, Zhang X, Cheng X (2012) An atomic model of Zfp57 recognition of CpG methylation within a specific DNA sequence. Genes Dev 26(21):2374–2379CrossRefGoogle Scholar
  40. 40.
    Rohs R, Jin X, West SM, Joshi R, Honig B, Mann RS (2010) Origins of specificity in protein-DNA recognition. Annu Rev Biochem 79(79):233–269CrossRefGoogle Scholar
  41. 41.
    Jen-Jacobson L, Engler LE, Jacobson LA (2000) Structural and thermodynamic strategies for site-specific DNA binding proteins. Structure 8(10):1015–1023CrossRefGoogle Scholar
  42. 42.
    Smith E, Jones ME, Drew PA (2009) Quantitation of DNA methylation by melt curve analysis. Bmc Cancer 9:123CrossRefGoogle Scholar
  43. 43.
    Lu X-J, Olson WK (2008) 3DNA: a versatile, integrated software system for the analysis, rebuilding and visualization of three-dimensional nucleic-acid structures. Nat Protoc 3(7):1213–1227CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.Center for BioinformaticsSaarland UniversitySaarbrückenGermany
  2. 2.Department of Biology and Biotechnology, Faculty of ScienceArab American UniversityJeninIsrael

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