Computational prediction of RNA tertiary structures using machine learning methods*

Huang, Bin; Du, Yuanyang; Zhang, Shuai; Li, Weihua; Wang, Jun; Zhang, Jian

doi:10.1088/1674-1056/abb303

Cited by 6 publications

(6 citation statements)

References 71 publications

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“…Due to space limitations, we cannot delve into all of them in detail. Furthermore, the field of biology has seen significant advancements in recent years due to the application of machine learning techniques [ 152 , 153 , 154 , 155 , 156 ]. For example, 3D structure prediction methods such as AlphaFold2 [ 157 ] and RoseTTAFold [ 158 ] have gained popularity due to their ability to accurately predict protein structures.…”

Section: Discussionmentioning

confidence: 99%

Computational Modeling of DNA 3D Structures: From Dynamics and Mechanics to Folding

Tan

Liu

et al. 2023

Molecules

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DNA carries the genetic information required for the synthesis of RNA and proteins and plays an important role in many processes of biological development. Understanding the three-dimensional (3D) structures and dynamics of DNA is crucial for understanding their biological functions and guiding the development of novel materials. In this review, we discuss the recent advancements in computer methods for studying DNA 3D structures. This includes molecular dynamics simulations to analyze DNA dynamics, flexibility, and ion binding. We also explore various coarse-grained models used for DNA structure prediction or folding, along with fragment assembly methods for constructing DNA 3D structures. Furthermore, we also discuss the advantages and disadvantages of these methods and highlight their differences.

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Section: Discussionmentioning

confidence: 99%

Computational Modeling of DNA 3D Structures: From Dynamics and Mechanics to Folding

Tan

Liu

et al. 2023

Molecules

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“…Thus, a user-friendly webserver could be further built after the deepened improvement for the tool. Very recent studies have shown that RNA scoring functions derived from deep learning of RNA 3D structures performed well in identification of accurate structural models ( Kurgan and Zhou, 2011 ; Li et al, 2018 ; Wang et al, 2018 ; Huang et al, 2020 ; Townshend et al, 2021 ), which suggests that more potential structural features of RNAs should be further mined with the aid of deep neural networks.…”

Section: Discussionmentioning

confidence: 99%

RNAStat: An Integrated Tool for Statistical Analysis of RNA 3D Structures

Guo

Tan

et al. 2022

Front. Bioinform.

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The 3D architectures of RNAs are essential for understanding their cellular functions. While an accurate scoring function based on the statistics of known RNA structures is a key component for successful RNA structure prediction or evaluation, there are few tools or web servers that can be directly used to make comprehensive statistical analysis for RNA 3D structures. In this work, we developed RNAStat, an integrated tool for making statistics on RNA 3D structures. For given RNA structures, RNAStat automatically calculates RNA structural properties such as size and shape, and shows their distributions. Based on the RNA structure annotation from DSSR, RNAStat provides statistical information of RNA secondary structure motifs including canonical/non-canonical base pairs, stems, and various loops. In particular, the geometry of base-pairing/stacking can be calculated in RNAStat by constructing a local coordinate system for each base. In addition, RNAStat also supplies the distribution of distance between any atoms to the users to help build distance-based RNA statistical potentials. To test the usability of the tool, we established a non-redundant RNA 3D structure dataset, and based on the dataset, we made a comprehensive statistical analysis on RNA structures, which could have the guiding significance for RNA structure modeling. The python code of RNAStat, the dataset used in this work, and corresponding statistical data files are freely available at GitHub (https://github.com/RNA-folding-lab/RNAStat).

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“…In recent years, machine learning approaches achieved great success in many fields, including computer vision, natural language modelling, [28,29] medical diagnosis, [30] physics, chemistry, computational biology, and so on. [31][32][33][34] Inspired by these successes, our group developed a scoring function for the assessment of RNA tertiary structure based on a three-dimensional convolutional network and named it RNA3DCNN. [35] Applications of graph convolutional network (GCN) [36][37][38][39][40][41] to represent molecular structures have been quite successful.…”

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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Computational prediction of RNA tertiary structures using machine learning methods*

Cited by 6 publications

References 71 publications

Computational Modeling of DNA 3D Structures: From Dynamics and Mechanics to Folding

Computational Modeling of DNA 3D Structures: From Dynamics and Mechanics to Folding

RNAStat: An Integrated Tool for Statistical Analysis of RNA 3D Structures

RNAGCN: RNA tertiary structure assessment with a graph convolutional network

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