Cube‐Based Synthesis of ESOPs for Large Functions

Zhang, Qiaowen; Wang, Pengjun; Hu, Jiang; Zhang, Huihong

doi:10.1049/cje.2018.01.006

Cited by 7 publications

(5 citation statements)

References 15 publications

(25 reference statements)

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“…Since these technics are costly and inefficient, computational methods have been developed to predict RNA structures. [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19] Computational algorithms usually include two steps: (i) generating structural candidates and (ii) selecting structures most likely to be the native. The second step requires a good scoring function that can assess the quality of the candidates.…”

Section: Introductionmentioning

confidence: 99%

RNAGCN: RNA tertiary structure assessment with a graph convolutional network

et al. 2022

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RNAs play crucial and versatile roles in cellular biochemical reactions. Since experimental approaches of determining their three-dimensional (3D) structures are costly and less efficient, it is greatly advantageous to develop computational methods to predict RNA 3D structures. For these methods, designing a model or scoring function for structure quality assessment is an essential step but this step poses challenges. In this study, we designed and trained a deep learning model to tackle this problem. The model was based on a Graph Convolutional Network (GCN) and named RNAGCN. The model provided a natural way of representing RNA structures, avoided complex algorithms to preserve atomic rotational equivalence, and was capable of extracting features automatically out of structural patterns. Testing results on two datasets convincingly demonstrated that RNAGCN performs similarly to or better than four leading scoring functions. Our approach provides an alternative way of RNA tertiary structure assessment and may facilitate RNA structure predictions. RNAGCN can be downloaded from https://gitee.com/dcw-RNAGCN/rnagcn.

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Section: Introductionmentioning

confidence: 99%

RNAGCN: RNA tertiary structure assessment with a graph convolutional network

et al. 2022

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“…[1][2][3][4] Similar to proteins, RNA can fold into subtle tertiary structures, and the biological functions of RNA are essentially determined by its specific threedimensional structure and the intrinsic dynamics encoded into the structure. [3][4][5][6][7][8][9] How to effectively extract the dynamical properties from the tertiary structure of RNA is critical for our understanding of RNA functions.…”

Section: Introductionmentioning

confidence: 99%

Enhancements of the Gaussian network model in describing nucleotide residue fluctuations for RNA

Wang¹,

Su²

2021

Chinese Phys. B

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Gaussian network model (GNM) is an efficient method to investigate the structural dynamics of biomolecules. However, the application of GNM on RNAs is not as good as that on proteins, and there is still room to improve the model. In this study, two novel approaches, named the weighted GNM (wGNM) and the force-constant-decayed GNM (fcdGNM), were proposed to enhance the performance of ENM in investigating the structural dynamics of RNAs. In wGNM, the force constant for each spring is weighted by the number of interacting heavy atom pairs between two nucleotides. In fcdGNM, all the pairwise nucleotides were connected by springs and the force constant decayed exponentially with the separate distance of the nucleotide pairs. The performance of these two proposed models was evaluated by using a non-redundant RNA structure database composed of 51 RNA molecules. The calculation results show that both the proposed models outperform the conventional GNM in reproducing the experimental B-factors of RNA structures. Compared with the conventional GNM, the Pearson correlation coefficient between the predicted and experimental B-factors was improved by 9.85% and 6.76% for wGNM and fcdGNM, respectively. Our studies provide two candidate methods for better revealing the dynamical properties encoded in RNA structures.

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“…[1][2][3][4] A comprehensive determination of RNA and RNA-related complex structures is critical to understanding the function and disease pathogenesis. [5][6][7][8][9][10][11][12][13][14] For example, the HIV trans-activation response (TAR) element is an RNA that interacts with Tat protein to ensure HIV transcription. [15][16][17] The TAR-Tat contacts are required for HIV trans-activation and replication.…”

Section: Introductionmentioning

confidence: 99%

Methods and applications of RNA contact prediction*

Wang

Zhao

2020

Chinese Phys. B

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The RNA tertiary structure is essential to understanding the function and biological processes. Unfortunately, it is still challenging to determine the large RNA structure from direct experimentation or computational modeling. One promising approach is first to predict the tertiary contacts and then use the contacts as constraints to model the structure. The RNA structure modeling depends on the contact prediction accuracy. Although many contact prediction methods have been developed in the protein field, there are only several contact prediction methods in the RNA field at present. Here, we first review the theoretical basis and test the performances of recent RNA contact prediction methods for tertiary structure and complex modeling problems. Then, we summarize the advantages and limitations of these RNA contact prediction methods. We suggest some future directions for this rapidly expanding field in the last.

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Cube‐Based Synthesis of ESOPs for Large Functions

Cited by 7 publications

References 15 publications

RNAGCN: RNA tertiary structure assessment with a graph convolutional network

RNAGCN: RNA tertiary structure assessment with a graph convolutional network

Enhancements of the Gaussian network model in describing nucleotide residue fluctuations for RNA

Methods and applications of RNA contact prediction*

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