Physics-Based De Novo Prediction of RNA 3D Structures

Cao, Song; Chen, Shi‐Jie

doi:10.1021/jp112059y

Cited by 152 publications

(197 citation statements)

References 85 publications

(229 reference statements)

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“…The notable advantage of Vfold is the ability to generate RNA structures with cross-linked loops and helices such as pseudoknots and complexes. [86] Five-bead Model. For higher resolution, a five-bead model was developed by Ren and co-workers.…”

Section: Coarse-grained Modelmentioning

confidence: 99%

RNA structure prediction: Progress and perspective

Shi¹,

Wu²,

Wang³

et al. 2014

Chinese Phys. B

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Many recent exciting discoveries have revealed the versatility of RNAs and their impo rtance in a variety of cellular functions which are strongly coupled to RNA structures. To understand the functions of RNAs, some structure prediction models have been developed in recent years. In this review, the progress in computational models for RNA structure prediction is introduced and the distinguishing features of many outstanding algorithms are discussed, emphasizing three dimensional (3D) structure prediction. A promising coarse-grained model for predicting RNA 3D structure, stability and salt effect is also introduced briefly. Finally, we discuss the major challenges in the RNA 3D structure modeling.

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Section: Coarse-grained Modelmentioning

confidence: 99%

RNA structure prediction: Progress and perspective

Shi¹,

Wu²,

Wang³

et al. 2014

Chinese Phys. B

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show abstract

“…The Vfold model, proposed by Chen's group at the University of Missouri, builds tertiary structures of RNA molecules based on their method of secondary structure folding kinetics [43]. Vfold is a coarse-grained model that uses the phosphorus atom (P), carbon atoms (C4), and virtual base atoms to represent each part of a nucleotide structure.…”

Section: Rna Secondary Structure Predictionmentioning

confidence: 99%

Large-scale study of long non-coding RNA functions based on structure and expression features

Zhao

Wang

Chen

et al. 2013

Sci. China Life Sci.

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Mammals and other complex organisms can transcribe an abundance of long non-coding RNAs (lncRNAs) that fulfill a wide variety of regulatory roles in many biological processes. These roles, including as scaffolds and as guides for protein-coding genes, mainly depend on the structure and expression level of lncRNAs. In this review, we focus on the current methods for analyzing lncRNA structure and expression, which is basic but necessary information for in-depth, large-scale analysis of lncRNA functions. The ENCODE project, which has published 30 papers to date, including a few that extensively characterize long non-coding RNAs (lncRNAs), has revealed that 76% of the human genome is transcribed to produce a range of lncRNAs [1]. The landscape of lncRNAs in mammals was unveiled by the rapid progress of deep sequencing technology [2] and computational methods to identify lncRNA [3,4]. These lncRNAs participate in a wide variety of biological processes, such as imprinting control, cell differentiation, immune responses through regulating expression, and activity and localization of protein coding genes [5,6]. However, the function and mechanisms of most lncRNAs are still unknown. Here, we combine our work and other related work to systematically illustrate the current methods to study lncRNA function through their structures and expression profiles. RNA secondary structure predictionSecondary structures of RNAs are the basis of their tertiary structures, so we will first briefly review methods for their prediction. Such methods predict standard Watson-Crick base pairs (AU and CG) and non-standard base pairs in a RNA sequence. There are many methods for RNA secondary structure prediction, which are based on different principles. Here we focus on two types of commonly used methods: the minimum free energy method [7][8][9][10][11], and the multiple sequence alignment method [1214]. The minimum free energy methods are now the most widely used methods of RNA secondary structure predic-

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“…Therefore, there is a crucial need for novel methods of determining the 3D structures of RNAs. Computational modeling of RNA 3D structures offers the opportunity to incorporate the structural features of RNAs extracted from known RNA structures (Das and Baker, 2007;Jonikas et al, 2009;Jossinet and Westhof, 2005;Major et al, 1993;Major et al, 1991;Massire et al, 1998;Parisien and Major, 2008;Shapiro et al, 2007;Tsai et al, 2003), to integrate physical and chemical principles (Cao and Chen, 2011;Ding et al, 2008), and to include experimentally derived structural information in modeling (Jonikas et al, 2009). For instance, several recent RNA 3D structure modeling methods (Cao and Chen, 2011;Das and Baker, 2007;Ding et al, 2008;Parisien and Major, 2008) have yielded accurate structure predictions of small RNAs from sequence alone, highlighting the predictive power of RNA modeling approaches in general.…”

mentioning

confidence: 99%

“…As RNA size increases, the available conformational space increases exponentially and the effects of force field inaccuracy accumulate. As a result, tertiary structure prediction for large RNAs with complex topologies is beyond the reach of the current ab initio approaches (Cao and Chen, 2011;Das and Baker, 2007;Ding et al, 2008;Parisien and Major, 2008). On the other hand, many biophysical and biochemical methods have been developed to probe RNA secondary and tertiary structure.…”

mentioning

confidence: 99%