This subproject is one of many research subprojects utilizing theresources provided by a Center grant funded by NIH/NCRR. The subproject andinvestigator (PI) may have received primary funding from another NIH source,and thus could be represented in other CRISP entries. The institution listed isfor the Center, which is not necessarily the institution for the investigator.The binding of the Ets-1 transcription factor to DNA is characterized by a unique conformational transition. While many sequence specific DNA-binding proteins fold upon binding their targets, the Ets-1 protein unfolds upon binding. We will use large-scale molecular dynamics simulations to gain insights into the interactions responsible for this distinct behavior. Introduction: Ets-1 plays an important role in developmental processes and is also involved in cancer and viral infections [1]. The protein is a monomer that binds the 5GGA(A/T)3 DNA consensus sequence through the winged helix-turn-helix motif of the highly conserved ETS domain. This ETS domain is flanked by inhibitory helices, which act as an autoinhibitory conformational switch [2]. In solution the HI-1 inhibitory region of the switch is folded into a helix (although the helix is labile and exchange with an unfolded state takes place on the millisecond - microsecond time scale) [3]. Binding of Ets 1 to the target DNA sequence unfolds the HI-1 inhibitory helix [4], while refolding of the helix leads to the dissociation of DNA [2,3,5]. No other DNA-binding protein unfolds upon binding [4,6-9]; moreover, the unfolding behavior of Ets-1 seems contrary to expectations based on the binding thermodynamics of sequence specific DNA-binding proteins [9]. Proposed Research: Insights into the interaction network leading to the conformational behavior can be obtained from large-scale molecular dynamics (MD) simulations. In these simulations, the system is propagated by Newtonian dynamics in femtosecond time steps; providing very detailed information on all atomic positions, velocities and interactions [10]. We will perform unbiased MD simulations of Ets-1 in solution and bound to DNA, in the folded and unfolded states. Comparison of the cross-correlation matrices of atomic fluctuations in these simulations will quantify the coupling of motion between pairs of residues [11]. Combined with structural data from the simulations, insights into the interactions responsible for the motion can be obtained. The role of water molecules in mediating these interactions can be assessed from the structural analyses as well. Justification of Request: The large computational costs require a high performance computing environment to complete the project. The simulations will be performed with the widely used CHARMM package [12]. Using a single 2.8 GHz Intel Xeon EM64T processor, CHARMM takes 2330 CPU seconds to simulate 1 picosecond of the solvated Ets-1 system of ~40,000 atoms, and 4670 CPU seconds for 1 picosecond of the solvated Ets-1 DNA system of ~65,000 atoms. Using these timings and an effiency of about 70% when using 32 processors, a simulation protocol consisting of 0.4 nanoseconds heating followed by 5 nanoseconds of MD for Ets-1 in solution and bound to DNA, in the folded and unfolded states will require ~2x(4670+2330)/3600x5.4x103x(1/0.7) = 30,000 CPU hours total. Storage of the trajectory takes 0.91 Mb and 0.52 Mb per frame for the DNA bound and solution state system, respectively. Saving the trajectory every 0.5 picosecond will require ~2x(0.91+0.52)x5.0x103/0.5 = 28,600 Mb; in addition, approximately 0.5-1.0 Gb will be needed for restart files, analysis files, etc. References: 1. B. Wasylyk, S. L. Hahn, and A. Giovane, Eur. J. Biochem. 211 7-18 (1993). 2. M. A. Pufall, and B. J. Graves, Annu. Rev. Cell. Dev. Biol. 18 421-462 (2002). 3. G. M. Lee et.al., J. Biol. Chem. 280, 7088-7099 (2005). 4. J. M. Petersen et.al., Science 269, 1866-1869 (1995). 5. M. A. Pufall et.al., Science 309, 142-145 (2005). 6. R. S. Spolar, and M. T. Record Jr., Science 263, 777-784 (1994). 7. P. E. Wright, and H. J. Dyson, J. Mol. Biol. 293 321-331 (1999). 8. P. Tompa, TIBS 27, 527-533 (2002). 9. H. J. Dyson, and P. E. Wright, Curr. Opin. Struct. Biol. 12, 54-60 (2002). 10. D. Frenkel, and B. Smit, Understanding Molecular Simulation, Academic Press, 2002. 11. T. Ichiye, and M. Karplus, Proteins 11, 205-217 (1991). 12. B.R. Brooks et.al., J. Comp. Chem. 4, 187-217 (1983).
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