Crowding Promotes the Switch from Hairpin to Pseudoknot Conformation in Human Telomerase RNA

Denesyuk, Natalia A.; Thirumalai, D.

doi:10.1021/ja2035128

Cited by 87 publications

(116 citation statements)

References 30 publications

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“…We chose this pseudoknot because it has two fairly long stems containing six and nine Watson-Crick base pairs. We had previously conducted simulations of the two stems at 15 • C in the limit of high ionic strength 25 and found that, for k r = 1.4 Å −2 and k φ = 4 rad −2 , the time averages of (r − r 0 ) 2 , (φ 1 − φ 1,0 ) 2 and (φ 2 − φ 2,0 ) 2 agreed well with the standard deviations computed from the NMR structure. The time averages were not very sensitive to a specific choice of U 0 ST .…”

Section: Stacking Interactionssupporting

confidence: 60%

Coarse-Grained Model for Predicting RNA Folding Thermodynamics

2013

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We present a thermodynamically robust coarse-grained model to simulate folding of RNA in monovalent salt solutions. The model includes stacking, hydrogen bond and electrostatic interactions as fundamental components in describing the stability of RNA structures. The stacking interactions are parametrized using a set of nucleotide-specific parameters, which were calibrated against the thermodynamic measurements for single-base stacks and base-pair stacks. All hydrogen bonds are assumed to have the same strength, regardless of their context in the RNA structure. The ionic buffer is modeled implicitly, using the concept of counterion condensation and the Debye-Hückel theory. The three adjustable parameters in the model were determined by fitting the experimental data for two RNA hairpins and a pseudoknot.A single set of parameters provides good agreement with thermodynamic data for the three RNA molecules over a wide range of temperatures and salt concentrations. In the process of calibrating the model, we establish the extent of counterion condensation onto the singlestranded RNA backbone. The reduced backbone charge is independent of the ionic strength and is 60% of the RNA bare charge at 37 • C. Our model can be used to predict the folding thermodynamics for any RNA molecule in the presence of monovalent ions.

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Section: Stacking Interactionssupporting

confidence: 60%

Coarse-Grained Model for Predicting RNA Folding Thermodynamics

2013

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“…Such extensions can include biopolymers in aqueous solutions, such as unfolded proteins, whose persistence lengths can be sensitive to excluded-volume interactions [6], and whose uncrowded size distributions can be independently computed [13]. For a single biopolymer in a crowded environment, our computational methods can be directly applied, given as input the requisite shape distribution [18,19,83]. Simulating solutions of multiple selfavoiding polymers would require incorporating polymerpolymer interactions [68,84].…”

Section: Discussionmentioning

confidence: 99%

Polymer crowding and shape distributions in polymer-nanoparticle mixtures

Lim

Denton

2014

The Journal of Chemical Physics

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Macromolecular crowding can influence polymer shapes, which is important for understanding the thermodynamic stability of polymer solutions and the structure and function of biopolymers (proteins, RNA, DNA) under confinement. We explore the influence of nanoparticle crowding on polymer shapes via Monte Carlo simulations and free-volume theory of a coarse-grained model of polymer-nanoparticle mixtures. Exploiting the geometry of random walks, we model polymer coils as effective penetrable ellipsoids, whose shapes fluctuate according to the probability distributions of the eigenvalues of the gyration tensor. Accounting for the entropic cost of a nanoparticle penetrating a larger polymer coil, we compute the crowding-induced shift in the shape distributions, radius of gyration, and asphericity of ideal polymers in a theta solvent. With increased nanoparticle crowding, we find that polymers become more compact (smaller, more spherical), in agreement with predictions of free-volume theory. Our approach can be easily extended to nonideal polymers in good solvents and used to model conformations of biopolymers in crowded environments.

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“…We use a variant of the coarse-grained SOP model (29,31), where each of the 176 residues in LZ26 is represented by a bead centered at the C α position (SI Text). The α-helical secondary structure is stabilized by interactions which mimic (i, i + 4) hydrogen bonding (32). We use residuedependent energies for tertiary interactions (30).…”

Section: Methodsmentioning

confidence: 99%

From mechanical folding trajectories to intrinsic energy landscapes of biopolymers

Hinczewski

Gebhardt

Rief

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

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In single-molecule laser optical tweezer (LOT) pulling experiments, a protein or RNA is juxtaposed between DNA handles that are attached to beads in optical traps. The LOT generates folding trajectories under force in terms of time-dependent changes in the distance between the beads. How to construct the full intrinsic folding landscape (without the handles and beads) from the measured time series is a major unsolved problem. By using rigorous theoretical methods-which account for fluctuations of the DNA handles, rotation of the optical beads, variations in applied tension due to finite trap stiffness, as well as environmental noise and limited bandwidth of the apparatus-we provide a tractable method to derive intrinsic free-energy profiles. We validate the method by showing that the exactly calculable intrinsic freeenergy profile for a generalized Rouse model, which mimics the two-state behavior in nucleic acid hairpins, can be accurately extracted from simulated time series in a LOT setup regardless of the stiffness of the handles. We next apply the approach to trajectories from coarse-grained LOT molecular simulations of a coiled-coil protein based on the GCN4 leucine zipper and obtain a free-energy landscape that is in quantitative agreement with simulations performed without the beads and handles. Finally, we extract the intrinsic free-energy landscape from experimental LOT measurements for the leucine zipper. T he energy landscape perspective has provided a conceptual framework to describe how RNA (1) and proteins (2-4) fold. Some of the key theoretical predictions (5, 6), have been confirmed by experiments (7). More refined comparisons require mapping the full folding landscape of biomolecules. Advances in laser optical tweezer (LOT) experiments have been used to obtain free-energy profiles as a function of the extension of biomolecules under tension (7-12).The usefulness of the LOT technique, however, hinges on the assumption that information about the fluctuating biomolecule can be accurately recovered from the raw experimental data, namely the time-dependent changes in the positions of the beads in the optical traps, attached to the biomolecule by doublestranded DNA (dsDNA) handles (Fig. 1). Thus, we only have access to the intrinsic folding landscape of the biomolecule (in the absence of handles and beads) indirectly through the bead-bead separation along the force direction. Many extraneous factors, such as fluctuations of the handles (13, 14), rotation of the beads, and the varying applied tension due to finite trap stiffness, can distort the intrinsic folding landscape. Moreover, the detectors and electronic systems used in the data collection have finite response times, leading to filtering of high-frequency components in the signal (15). Ad hoc attempts have been made to account for handle effects based on experimental estimates of stretched DNA properties, using techniques similar to image deconvolution (8,11,16). Theory has been used to extract free-energy information from nonequilibrium pullin...

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Crowding Promotes the Switch from Hairpin to Pseudoknot Conformation in Human Telomerase RNA

Cited by 87 publications

References 30 publications

Coarse-Grained Model for Predicting RNA Folding Thermodynamics

Coarse-Grained Model for Predicting RNA Folding Thermodynamics

Polymer crowding and shape distributions in polymer-nanoparticle mixtures

From mechanical folding trajectories to intrinsic energy landscapes of biopolymers

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