Hierarchical Assembly of RNA Three-Dimensional Structures Based on Loop Templates

Xu, Xiaojun; Chen, Shijie

doi:10.1021/acs.jpcb.7b10102

Cited by 39 publications

(50 citation statements)

References 69 publications

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“…We made initial estimates of the pseudoknot structure using a variety of tools that have been developed for blind prediction of RNA structure (14,15,(17)(18)(19)(20)(21), where the only input was the sequence and the expected secondary structure, as shown in Fig. 1.…”

Section: Resultsmentioning

confidence: 99%

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Modeling the structure of the frameshift stimulatory pseudoknot in SARS-CoV-2 reveals multiple possible conformers

Omar

Zhao

Sekar

et al. 2020

Preprint

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The coronavirus causing the COVID-19 pandemic, SARS-CoV-2, uses −1 programmed ribosomal frameshifting (−1 PRF) to control the relative expression of viral proteins. As modulating −1 PRF can inhibit viral replication, the RNA pseudoknot stimulating −1 PRF may be a fruitful target for therapeutics treating COVID-19. We modeled the unusual 3stem structure of the stimulatory pseudoknot of SARS-CoV-2 computationally, using multiple blind structural prediction tools followed by μs-long molecular dynamics simulations. The results were compared for consistency with nuclease-protection assays and single-molecule force spectroscopy measurements of the SARS-CoV-1 pseudoknot, to determine the most likely conformations. We found several possible conformations for the SARS-CoV-2 pseudoknot, all having an extended stem 3 but with different packing of stems 1 and 2. Several conformations featured rarely-seen threading of a single strand through the junction formed between two helices. These structural models may help interpret future experiments and support efforts to discover ligands inhibiting −1 PRF in SARS-CoV-2.

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Section: Resultsmentioning

confidence: 99%

“…Blind structure prediction: Initial structures for input into MD simulations were obtained using multiple platforms for blind RNA structure prediction: SimRNA (17), Rosetta FARFAR2 (14,15), RNAComposer (18), RNAvista (19), RNA2D3D (20), and Vfold (21). For the blind predictions, we assumed the secondary structure shown in Fig.…”

Section: Methodsmentioning

confidence: 99%

Modeling the structure of the frameshift stimulatory pseudoknot in SARS-CoV-2 reveals multiple possible conformers

Omar

Zhao

Sekar

et al. 2020

Preprint

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“…To build all-atom structures for single-stranded loops using VfoldLA ( 39 ). VfoldLA builds RNA 3D structures through the assembly of 3D templates for single-stranded loops (hairpin, helix2 and tails, shown in Supplementary Figure S1 ) and A-form helices.…”

Section: Algorithm and Methodsmentioning

confidence: 99%

“…Therefore, the use of cross-linked contacts can significantly improve the conformational sampling quality for 3D structure prediction. Furthermore, we here also develop and illustrate a multi-stage method, which incorporates Vfold3D, VfoldLA ( 39 ) and tertiary topologies, in RNA 3D structure prediction. By taking the advantage of the strong topological constraints imposed by the tertiary contacts, as shown in the benchmark tests, our multi-stage model can provide improved predictions for RNA 3D structures with cross-linked base pairs.…”

Section: Introductionmentioning

confidence: 99%

Topological constraints of RNA pseudoknotted and loop-kissing motifs: applications to three-dimensional structure prediction

Chen

2020

Nucleic Acids Research

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Abstract An RNA global fold can be described at the level of helix orientations and relatively flexible loop conformations that connect the helices. The linkage between the helices plays an essential role in determining the structural topology, which restricts RNA local and global folds, especially for RNA tertiary structures involving cross-linked base pairs. We quantitatively analyze the topological constraints on RNA 3D conformational space, in particular, on the distribution of helix orientations, for pseudoknots and loop-loop kissing structures. The result shows that a viable conformational space is predominantly determined by the motif type, helix size, and loop size, indicating a strong topological coupling between helices and loops in RNA tertiary motifs. Moreover, the analysis indicates that (cross-linked) tertiary contacts can cause much stronger topological constraints on RNA global fold than non-cross-linked base pairs. Furthermore, based on the topological constraints encoded in the 2D structure and the 3D templates, we develop a 3D structure prediction approach. This approach can be further combined with structure probing methods to expand the capability of computational prediction for large RNA folds.

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“…In this context, experiments such as X-ray crystallography, nuclear magnetic resonance (NMR) spectroscopy, and cryoelectron microscopy are the most reliable methods of determining RNA 3D structures, but they are costly, time-consuming, or technically challenging due to the physical and chemical nature of RNAs. As a result, many computational methods have been developed to predict RNA tertiary structures [ 6 – 32 ]. These methods usually have a generator producing a large set of structural candidates and a discriminator evaluating these generated candidates.…”

Section: Introductionmentioning

confidence: 99%

RNA3DCNN: Local and global quality assessments of RNA 3D structures using 3D deep convolutional neural networks

Zhu

Wang

et al. 2018

PLoS Comput Biol

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Quality assessment is essential for the computational prediction and design of RNA tertiary structures. To date, several knowledge-based statistical potentials have been proposed and proved to be effective in identifying native and near-native RNA structures. All these potentials are based on the inverse Boltzmann formula, while differing in the choice of the geometrical descriptor, reference state, and training dataset. Via an approach that diverges completely from the conventional statistical potentials, our work explored the power of a 3D convolutional neural network (CNN)-based approach as a quality evaluator for RNA 3D structures, which used a 3D grid representation of the structure as input without extracting features manually. The RNA structures were evaluated by examining each nucleotide, so our method can also provide local quality assessment. Two sets of training samples were built. The first one included 1 million samples generated by high-temperature molecular dynamics (MD) simulations and the second one included 1 million samples generated by Monte Carlo (MC) structure prediction. Both MD and MC procedures were performed for a non-redundant set of 414 RNAs. For two training datasets (one including only MD training samples and the other including both MD and MC training samples), we trained two neural networks, named RNA3DCNN_MD and RNA3DCNN_MDMC, respectively. The former is suitable for assessing near-native structures, while the latter is suitable for assessing structures covering large structural space. We tested the performance of our method and made comparisons with four other traditional scoring functions. On two of three test datasets, our method performed similarly to the state-of-the-art traditional scoring function, and on the third test dataset, our method was far superior to other scoring functions. Our method can be downloaded from https://github.com/lijunRNA/RNA3DCNN.

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Hierarchical Assembly of RNA Three-Dimensional Structures Based on Loop Templates

Cited by 39 publications

References 69 publications

Modeling the structure of the frameshift stimulatory pseudoknot in SARS-CoV-2 reveals multiple possible conformers

Modeling the structure of the frameshift stimulatory pseudoknot in SARS-CoV-2 reveals multiple possible conformers

Topological constraints of RNA pseudoknotted and loop-kissing motifs: applications to three-dimensional structure prediction

RNA3DCNN: Local and global quality assessments of RNA 3D structures using 3D deep convolutional neural networks

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