Predicting RNA folding thermodynamics with a reduced chain representation model

Cao, Song; Chen, Shi‐Jie

doi:10.1261/rna.2109105

Cited by 128 publications

(200 citation statements)

References 44 publications

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“…16,20 Here we consider two effective virtual bonds that connect atoms P-C 4 and atoms C 4 -P (Fig. 2), and their torsion angles along the backbone, θ and η.…”

Section: B Virtual Bond Representation and Discrete K-state Modelmentioning

confidence: 99%

“…12 An important recent advance is the development of a method for calculating loop entropy based on a virtual bond representation of RNA backbone. 16 This method considers excluded volume and is based on the enumeration of all possible self-avoiding walks on a diamond lattice with fixed ends at the stem terminus of an RNA structure. 16 For loops up to length of 9, the calculated loop entropy of hairpins, bulges, and internal loops has excellent agreement with the experimental results.…”

Section: Introductionmentioning

confidence: 99%

“…16 This method considers excluded volume and is based on the enumeration of all possible self-avoiding walks on a diamond lattice with fixed ends at the stem terminus of an RNA structure. 16 For loops up to length of 9, the calculated loop entropy of hairpins, bulges, and internal loops has excellent agreement with the experimental results. However, for loops of longer length, enumeration becomes infeasible due to the exponentially increasing size of conformational space.…”

Section: Introductionmentioning

confidence: 99%

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Discrete state model and accurate estimation of loop entropy of RNA secondary structures

Jian

Lin

Chen

et al. 2008

The Journal of Chemical Physics

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Conformational entropy makes important contribution to the stability and folding of RNA molecule, but it is challenging to either measure or compute conformational entropy associated with long loops. We develop optimized discrete k-state models of RNA backbone based on known RNA structures for computing entropy of loops, which are modeled as self-avoiding walks. To estimate entropy of hairpin, bulge, internal loop, and multibranch loop of long length (up to 50), we develop an efficient sampling method based on the sequential Monte Carlo principle. Our method considers excluded volume effect. It is general and can be applied to calculating entropy of loops with longer length and arbitrary complexity. For loops of short length, our results are in good agreement with a recent theoretical model and experimental measurement. For long loops, our estimated entropy of hairpin loops is in excellent agreement with the Jacobson-Stockmayer extrapolation model. However, for bulge loops and more complex secondary structures such as internal and multibranch loops, we find that the Jacobson-Stockmayer extrapolation model has large errors. Based on estimated entropy, we have developed empirical formulae for accurate calculation of entropy of long loops in different secondary structures. Our study on the effect of asymmetric size of loops suggest that loop entropy of internal loops is largely determined by the total loop length, and is only marginally affected by the asymmetric size of the two loops. Our finding suggests that the significant asymmetric effects of loop length in internal loops measured by experiments are likely to be partially enthalpic. Our method can be applied to develop improved energy parameters important for studying RNA stability and folding, and for predicting RNA secondary and tertiary structures. The discrete model and the program used to calculate loop entropy can be downloaded at

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“…16,20 Here we consider two effective virtual bonds that connect atoms P-C 4 and atoms C 4 -P (Fig. 2), and their torsion angles along the backbone, θ and η.…”

Section: B Virtual Bond Representation and Discrete K-state Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Discrete state model and accurate estimation of loop entropy of RNA secondary structures

Jian

Lin

Chen

et al. 2008

The Journal of Chemical Physics

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“…The stability of RNA secondary structure and the fact that formation of secondary and tertiary structure is often temporally separated simplifies consideration of the folding process [51] (see also [28]). However, RNA secondary structure topology is more complex than the topology of proteins, and understanding this complexity and the folding constraints introduced by secondary structure topology will require advanced modeling and experimental approaches [52,53].…”

Section: Simple Forces Underlying the Complex Behavior Of Rnamentioning

confidence: 99%

Unwinding RNA's secrets: advances in the biology, physics, and modeling of complex RNAs

Chu

Herschlag

2008

Current Opinion in Structural Biology

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SummaryThe rapid development of our understanding of the diverse biological roles fulfilled by non-coding RNA has motivated interest in the basic macromolecular behavior, structure, and function of RNA. We focus on two areas in the behavior of complex RNAs. First, we present advances in the understanding of how RNA folding is accomplished in vivo by presenting a mechanism for the action of DEAD-box proteins. Members of this family are intimately associated with almost all cellular processes involving RNA, mediating RNA structural rearrangements and chaperoning their folding. Next, we focus on advances in understanding and characterizing the basic biophysical forces that govern the folding of complex RNAs. Ultimately we expect that a confluence and synergy between these approaches will lead to profound understanding of RNA and its biology.The explosion in our knowledge about noncoding RNA (ncRNA) -RNA that is not translated into protein -has expanded interest in RNA beyond the traditional RNA community. Diverse ncRNAs include the recently-discovered riboswitches, whose novel gene-regulation function is induced by the binding of small metabolites [1]; most of the so-called "Human Accelerated Regions" (HAR) of the human genome, whose recent evolution may be linked to the emergence of the human brain [2]; and many ncRNAs of unknown function [3]. These discoveries underscore the need to understand the fundamental macromolecular behavior of RNA, its structure and dynamics, and how these RNAs are handled and exploited in biology.Study of catalytic RNAs, which allow detection of correct folding via activity measurements, has established basic thermodynamic and kinetic properties of RNA folding. Large catalytic RNAs such as the group I intron from Tetrahymena thermophila and the RNase P RNA from Bacillus subtilis fold at vastly different rates under different conditions upon addition of Mg 2+ and have been shown to fold via multiple parallel pathways, in which different molecules follow different routes with different rates and intermediates, to a common final folded structure [4,5]. These observations support the common view that RNAs traverse rugged energy landscapes as they fold [6,7]. However, little is known about the true shape of these

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“…pseudoknots (19; 20) or base triplets (21). For a proper description of this broader class of structural motifs a statistical mechanics polymer model (e.g.…”

Section: Introductionmentioning

confidence: 99%

Folding Kinetics of Large RNAs

Geis

Flamm

Wolfinger

et al. 2008

Journal of Molecular Biology

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We introduce here a heuristic approach to kinetic RNA folding that constructs secondary structures by stepwise combination of building blocks. These blocks correspond to sub-sequences and their thermodynamically optimal structures. These are determined by the standard dynamic programming approach to RNA folding. Folding trajectories are modeled at base pair resolution using the Morgan-Higgs heuristic and a barrier tree based heuristic to connect combinations of the local building blocks. Implemented in the program Kinwalker, the algorithm allows cotranscriptional folding and can be used to fold sequences of up to about 1500 nucleotides in length. A detailed comparison with several well-studied examples from the literature, including the delayed folding of bacteriophage cloverleaf structures, the ASR riboswitch, and the Hok RNA, shows an excellent agreement of predicted trajectories and experimental evidence. The software is available as part of the Vienna RNA Package.

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Predicting RNA folding thermodynamics with a reduced chain representation model

Cited by 128 publications

References 44 publications

Discrete state model and accurate estimation of loop entropy of RNA secondary structures

Discrete state model and accurate estimation of loop entropy of RNA secondary structures

Unwinding RNA's secrets: advances in the biology, physics, and modeling of complex RNAs

Folding Kinetics of Large RNAs

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