This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Molecular dynamics simulations will be performed on a signaling protein NtrC (Nitrogen regulatory protein C) to elucidate the free energy landscapes between its active and inactive states, and to gain insight on the conformational transition mechanism. Previously, our lab has shown by experiments that both conformations exist in solution, even in absence of phosphorylation [1, 2]. The protein is thus able to freely interconvert between the two states, and the phosphorylation simply shifts the equilibrium between the two population. Preliminary computational studies in our lab, mainly relying on Targeted Molecular Dynamics, have suggested a possible pathway for the conformational change [3]. In particular it has been found that the helix 4, the one that undergoes the largest conformational change and have been suggested to unfold in computational studies by other groups, is almost stable throughout the transition. The transition occurs through a number of intermediate metastable states stabilized by transient interactions among a few specific amino-acids. These transitional interactions, not present in either of the two end states, keep the free energy barriers low, allowing an interconversion timescale of the order of the high microsecond to millisecond. A set of experiments have been carried out to verify the reliability of the computational findings. In particular measurements on the unfolding of the helix 4 have shown that the helix has the same stability as the rest of the protein and the unfolding occurs on time scales too slow to account for the conformational transition. We have also measured the interconversion rate in mutants in which the transient interactions have been removed. These experimental results show that the stability of active and inactive states is not affected, while the barrier separating them is significantly increased by the destabilization of the transient metastable states. This reverberates in a slowing down of the interconversion rate, confirming the extreme importance of the metastable states for the correct functionality of the protein [4]. Stimulated by these promising results, we aim at understanding the mechanism of interconversion in its completeness, sampling extensively the ensemble of the most probable transition pathways. We will pursue this task combining minimum free energy pathway calculation techniques (in particular the string method [5]), algorithms for enhancing the exploration of the free energy landscape ( bias exchange metadynamics [6]), and schemes for efficiently sampling the space of the transition paths (transition path sampling [7]). We will rely on the start-up Roaming account mainly to perform tests on the different machines available at Teragrid to accurately evaluate the amount of needed resources and to devise an optimal strategy to achieve our goal. The calculations with the string method will be performed with NAMD. After having tested various possible trajectory durations, we will evaluate the performance of the MD runs, and, based on the performances, queue waiting times, and policies of the different facilities, we will decide the best scheme to optimize the efficiency of the calculation. As the result of string method is known to be in great measure affected by the choice of the initial guess of the pathways, we will combine this with other methods, in order to have reasonable initial guesses of possible alternative pathways to analyze. The standard metadynamics algorithm is both implemented in NAMD and available in GROMACS as an external plugin [8]. The metadynamics bias exchange scheme has been completely implemented by the developers of the methodology within Gromacs 3.3, modifying the facility for the replica exchange already in the code. This implementation takes advantage of the parallelization of Gromacs to perform the swap among the replicas, while the dynamical evolution of the single replicas is a single core process. An alternative would be preparing a set of scripts for performing the exchange indipendently from the MD runs. This will allow us to use the internal parallelization of the code for running the single metadynamics runs faster, but would require a large number of short independent runs. The more efficient choice will depend on the features of the facilities we will have access to, including the the queue times and the amount of resource we will access to. We will therefore need to perform tests on the different machines to select the optimal strategy. Once we will have collected a set of possible patways connecting the two basins, we want to accurately sample the ensemble of transition pathways, and for this we will use Transition Path Sampling (TPS) [7], which performs Monte Carlo sampling in the space of the reactive trajectories. We have performed preliminary calculations in CHARMM, and we are preparing scripts to run the algorithm in NAMD. We can guess from preliminary analysis that the free energy profile connecting the two relevant states is populated by a number of metastable states, that we hope to identify with the metadynamics calculations. For this reason, we will make use of the recently published version of the algorithm proposed by Rogal et Al [9], which is particularly suitable when multiple metastable states are present. The most challenging task in applying TPS will be to determine a minimal but complete set of order parameters that identify the stable or metastable states. To do this we will perform test runs and carefully analyze the resulting trajectories. Once we have obtained a satisfactory definition of the basins, we will perform a large number of runs, in order to improve the statistics of the sampled reactive trajectories. REFERENCES: [1] B. F. Volkman, D. Lipson, D. E.Wemmer, D. Kern, Two-state allosteric behavior in a single-domain signaling protein, Science 291(5512) (2001). [2] A. Gardino, D. Kern, Functional dynamics of response regulators using NMR relaxation techniques, Methods Enzymol 423 (2007). [3] M. Lei, J. Velos, A. Gardino, A. Kivenson, M. Karplus and D. Kern, Segmented transition pathways of the signaling protein NtrC, J Mol Biol (2009) submitted. [4] A. Gardino, J. Velos, M. Lei, A. Kivenson, C. F Liu, P. Steindel, E. Z. Eisenmesser, W. Labeikovsky, M. Wolf-Watz and D. Kern, Native-state energy landscape reveals activation pathway in a signaling protein, Nature (2009) submitted. [5] A. C. Pan, D. Sezer, B. Roux, Finding transition pathways using the string method with swarms of trajectories, J Phys Chem B 112 (11) (2008) 3432. [6] S. Piana, A. Laio, A Bias-Exchange Approach to Protein Folding, J. Phys. Chem. B, 2007, 111 (17). [7] P. G. Bolhuis, D. Chandler, C. Dellago, P. L. Geissler, Transition path sampling: Throwing ropes over rough mountain passes, in the dark, Ann Rev Phys Chem 53 (1) (2002). [8] http://merlino.mi.infn.it/~plumed/PLUMED/Home.html [9] J. Rogal, and P. G. Bolhuis, Multiple state transition path sampling, J. Chem. Phys. 129, 224107 (2008).