Simulating RNA Folding Kinetics on Approximated Energy Landscapes

Tang, Xinyu; Thomas, Shawna; Tapia, Lydia; Giedroc, David P.; Amato, Nancy M.

doi:10.1016/j.jmb.2008.02.007

Cited by 52 publications

(51 citation statements)

References 37 publications

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“…We focus on the Turner model because its features are embedded in most available methods for prediction of MFE (Hofacker et al 1994;Mathews et al 1999a;Andronescu 2003) and suboptimal (Zuker 1989;Wuchty et al 1999) secondary structures, partition function calculation (McCaskill 1990), Bayesian statistical prediction approaches (Ding 2006), prediction of oligonucleotide affinity to nucleic acid targets (Mathews et al 1999b;Rehmsmeier et al 2004;Lu and Mathews 2008), and simulation of RNA folding kinetics (Flamm et al 2000;Xayaphoummine et al 2003;Tang et al 2008). Thus, improved parameters could immediately be used by all of these methods.…”

Section: Introductionmentioning

confidence: 99%

Computational approaches for RNA energy parameter estimation

Andronescu

Condon²,

Hoos³

et al. 2010

RNA

122

156

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Methods for efficient and accurate prediction of RNA structure are increasingly valuable, given the current rapid advances in understanding the diverse functions of RNA molecules in the cell. To enhance the accuracy of secondary structure predictions, we developed and refined optimization techniques for the estimation of energy parameters. We build on two previous approaches to RNA free-energy parameter estimation: (1) the Constraint Generation (CG) method, which iteratively generates constraints that enforce known structures to have energies lower than other structures for the same molecule; and (2) the Boltzmann Likelihood (BL) method, which infers a set of RNA free-energy parameters that maximize the conditional likelihood of a set of reference RNA structures. Here, we extend these approaches in two main ways: We propose (1) a max-margin extension of CG, and (2) a novel linear Gaussian Bayesian network that models feature relationships, which effectively makes use of sparse data by sharing statistical strength between parameters. We obtain significant improvements in the accuracy of RNA minimum free-energy pseudoknot-free secondary structure prediction when measured on a comprehensive set of 2518 RNA molecules with reference structures. Our parameters can be used in conjunction with software that predicts RNA secondary structures, RNA hybridization, or ensembles of structures. Our data, software, results, and parameter sets in various formats are freely available at http://www.cs.ubc.ca/labs/beta/Projects/RNA-Params.

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Section: Introductionmentioning

confidence: 99%

Computational approaches for RNA energy parameter estimation

Andronescu

Condon²,

Hoos³

et al. 2010

RNA

122

156

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show abstract

“…Further, although we are able to produce good results using a fairly simplistic energy model, a more complicated formulation, such as one including entropic forces, would clearly improve tFolder's analysis. More advanced heuristics also exist [13] that more efficiently extract folding pathway information, which could be applied to tFolder.…”

Section: Discussionmentioning

confidence: 99%

“…The resulting ordinary differential equation (ODE) system can be solved to predict and characterize RNA folding pathways. The method has since been improved to analyze the motion of large RNAs [13].…”

Section: Introductionmentioning

confidence: 99%

Efficient Traversal of Beta-Sheet Protein Folding Pathways Using Ensemble Models

Shenker

O'Donnell²,

Devadas³

et al. 2011

Lecture Notes in Computer Science

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Abstract. Molecular Dynamics (MD) simulations can now predict mstimescale folding processes of small proteins -however, this presently requires hundreds of thousands of CPU hours and is primarily applicable to short peptides with few long-range interactions. Larger and slowerfolding proteins, such as many with extended β-sheet structure, would require orders of magnitude more time and computing resources. Furthermore, when the objective is to determine only which folding events are necessary and limiting, atomistic detail MD simulations can prove unnecessary. Here, we introduce the program tFolder as an efficient method for modelling the folding process of large β-sheet proteins using sequence data alone. To do so, we extend existing ensemble β-sheet prediction techniques, which permitted only a fixed anti-parallel β-barrel shape, with a method that predicts arbitrary β-strand/β-strand orientations and strand-order permutations. By accounting for all partial and final structural states, we can then model the transition from random coil to native state as a Markov process, using a master equation to simulate population dynamics of folding over time. Thus, all putative folding pathways can be energetically scored, including which transitions present the greatest barriers. Since correct folding pathway prediction is likely determined by the accuracy of contact prediction, we demonstrate the accuracy of tFolder to be comparable with state-of-the-art methods designed specifically for the contact prediction problem alone. We validate our method for dynamics prediction by applying it to the folding pathway of the well-studied Protein G. With relatively very little computation time, tFolder is able to reveal critical features of the folding pathways which were only previously observed through time-consuming MD simulations and experimental studies. Such a result greatly expands the number of proteins whose folding pathways can be studied, while the algorithmic integration of ensemble prediction with Markovian dynamics can be applied to many other problems.Corresponding authors.

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“…We refer to a folding pathway from I to F with minimum barrier (taken over all possible folding pathways) as a min-barrier pathway. Several methods for computationally predicting nucleic acid folding pathways rely on energy barrier estimation [3,15]. Moreover, designed nucleic acid systems such as DNA strand displacement systems ensure that the desired folding pathways have low energy barriers, while undesired alternatives have high barriers.…”

Section: Introductionmentioning

confidence: 99%

On Low Energy Barrier Folding Pathways for Nucleic Acid Sequences

Mathieson

Condon

2015

Lecture Notes in Computer Science

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Abstract. Secondary structure folding pathways correspond to the execution of DNA programs such as DNA strand displacement systems. It is helpful to understand the full diversity of features that such pathways can have, when designing novel folding pathways. In this work, we show that properties of folding pathways over a 2-base strand (a strand with either A and T, or C and G, but not all four bases) may be quite different than those over a 4-base alphabet. Our main result is that, for a simple energy model in which each base pair contributes −1, 2-base sequences of length n always have a folding pathway of length O(n 3 ) with energy barrier at most 2. We provide an efficient algorithm for constructing such a pathway. In contrast, it is unknown whether minimum energy barrier pathways for 4-base sequences can be found efficiently, and such pathways can have barrier Θ(n). We also present several results that show how folding pathways with temporary and/or repeated base pairs can have lower energy barrier than pathways without such base pairs.

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Simulating RNA Folding Kinetics on Approximated Energy Landscapes

Cited by 52 publications

References 37 publications

Computational approaches for RNA energy parameter estimation

Computational approaches for RNA energy parameter estimation

Efficient Traversal of Beta-Sheet Protein Folding Pathways Using Ensemble Models

On Low Energy Barrier Folding Pathways for Nucleic Acid Sequences

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