Faculty Opinions recommendation of RNA structure: advances and assessment of 3D structure prediction.

Abstract Background The understanding of the importance of RNA has dramatically changed over recent years. As in the case of proteins, the function of an RNA molecule is encoded in its tertiary structure, which in turn is determined by the molecule’s sequence. The prediction of tertiary structures of complex RNAs is still a challenging task. Results Using the observation that RNA sequences from the same RNA family fold into conserved structure, we test herein whether parallel modeling of RNA homologs can improve ab initio RNA structure prediction. EvoClustRNA is a multi-step modeling process, in which homologous sequences for the target sequence are selected using the Rfam database. Subsequently, independent folding simulations using Rosetta FARFAR and SimRNA are carried out. The model of the target sequence is selected based on the most common structural arrangement of the common helical fragments. As a test, on two blind RNA-Puzzles challenges, EvoClustRNA predictions ranked as the first of all submissions for the L-glutamine riboswitch and as the second for the ZMP riboswitch. Moreover, through a benchmark of known structures, we discovered several cases in which particular homologs were unusually amenable to structure recovery in folding simulations compared to the single original target sequence. Conclusion This work, for the first time to our knowledge, demonstrates the importance of the selection of the target sequence from an alignment of an RNA family for the success of RNA 3D structure prediction. These observations prompt investigations into a new direction of research for checking 3D structure “foldability” or “predictability” of related RNA sequences to obtain accurate predictions. To support new research in this area, we provide all relevant scripts in a documented and ready-to-use form. By exploring new ideas and identifying limitations of the current RNA 3D structure prediction methods, this work is bringing us closer to the near-native computational RNA 3D models.

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RNA structure prediction: from 2D to 3D

Emerging Topics in Life Sciences ◽

10.1042/etls20160027 ◽

2017 ◽

Vol 1 (3) ◽

pp. 275-285 ◽

Cited By ~ 8

Author(s):

Bernhard C. Thiel ◽

Christoph Flamm ◽

Ivo L. Hofacker

Keyword(s):

Secondary Structure ◽

Rna Structure ◽

Structure Prediction ◽

3D Structure ◽

Rna Structure Prediction ◽

2D Structure ◽

3D Structure Prediction ◽

Different Levels ◽

2D To 3D

We summarize different levels of RNA structure prediction, from classical 2D structure to extended secondary structure and motif-based research toward 3D structure prediction of RNA. We outline the importance of classical secondary structure during all those levels of structure prediction.

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RNA Structure: Advances and Assessment of 3D Structure Prediction

Annual Review of Biophysics ◽

10.1146/annurev-biophys-070816-034125 ◽

2017 ◽

Vol 46 (1) ◽

pp. 483-503 ◽

Cited By ~ 84

Author(s):

Zhichao Miao ◽

Eric Westhof

Keyword(s):

Rna Structure ◽

Structure Prediction ◽

3D Structure ◽

3D Structure Prediction

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Faculty Opinions recommendation of RNA structure: advances and assessment of 3D structure prediction.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.727484448.793548475 ◽

2018 ◽

Author(s):

Patrice Koehl

Keyword(s):

Rna Structure ◽

Structure Prediction ◽

3D Structure ◽

3D Structure Prediction

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RNA-Puzzles toolkit: a computational resource of RNA 3D structure benchmark datasets, structure manipulation, and evaluation tools

Nucleic Acids Research ◽

10.1093/nar/gkz1108 ◽

2019 ◽

Cited By ~ 2

Author(s):

Marcin Magnus ◽

Maciej Antczak ◽

Tomasz Zok ◽

Jakub Wiedemann ◽

Piotr Lukasiak ◽

...

Keyword(s):

Rna Structure ◽

Structure Prediction ◽

3D Structure ◽

Prediction Methods ◽

Computational Resource ◽

Structure Comparison ◽

Assessment Protocol ◽

Benchmark Datasets ◽

3D Structure Prediction ◽

Comparison Algorithms

Abstract Significant improvements have been made in the efficiency and accuracy of RNA 3D structure prediction methods during the succeeding challenges of RNA-Puzzles, a community-wide effort on the assessment of blind prediction of RNA tertiary structures. The RNA-Puzzles contest has shown, among others, that the development and validation of computational methods for RNA fold prediction strongly depend on the benchmark datasets and the structure comparison algorithms. Yet, there has been no systematic benchmark set or decoy structures available for the 3D structure prediction of RNA, hindering the standardization of comparative tests in the modeling of RNA structure. Furthermore, there has not been a unified set of tools that allows deep and complete RNA structure analysis, and at the same time, that is easy to use. Here, we present RNA-Puzzles toolkit, a computational resource including (i) decoy sets generated by different RNA 3D structure prediction methods (raw, for-evaluation and standardized datasets), (ii) 3D structure normalization, analysis, manipulation, visualization tools (RNA_format, RNA_normalizer, rna-tools) and (iii) 3D structure comparison metric tools (RNAQUA, MCQ4Structures). This resource provides a full list of computational tools as well as a standard RNA 3D structure prediction assessment protocol for the community.

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In silico 3D structure prediction and hydrogen peroxide binding study of wheat catalase

Interdisciplinary Sciences Computational Life Sciences ◽

10.1007/s12539-013-0154-0 ◽

2013 ◽

Vol 5 (1) ◽

pp. 77-83 ◽

Cited By ~ 4

Author(s):

Gopal Krishna Sahu ◽

Bibhuti Bhusan Sahoo ◽

Sneha Bhandari ◽

Shruti Pandey

Keyword(s):

Hydrogen Peroxide ◽

In Silico ◽

Structure Prediction ◽

3D Structure ◽

Binding Study ◽

3D Structure Prediction

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Bioinformatics Tools and Benchmarks for Computational Docking and 3D Structure Prediction of RNA-Protein Complexes

Genes ◽

10.3390/genes9090432 ◽

2018 ◽

Vol 9 (9) ◽

pp. 432 ◽

Cited By ~ 15

Author(s):

Chandran Nithin ◽

Pritha Ghosh ◽

Janusz Bujnicki

Keyword(s):

Rna Splicing ◽

Structure Prediction ◽

Protein Complexes ◽

3D Structure ◽

Structural Models ◽

Computational Prediction ◽

Computational Docking ◽

3D Structure Prediction ◽

Rna Protein Complexes

RNA-protein (RNP) interactions play essential roles in many biological processes, such as regulation of co-transcriptional and post-transcriptional gene expression, RNA splicing, transport, storage and stabilization, as well as protein synthesis. An increasing number of RNP structures would aid in a better understanding of these processes. However, due to the technical difficulties associated with experimental determination of macromolecular structures by high-resolution methods, studies on RNP recognition and complex formation present significant challenges. As an alternative, computational prediction of RNP interactions can be carried out. Structural models obtained by theoretical predictive methods are, in general, less reliable compared to models based on experimental measurements but they can be sufficiently accurate to be used as a basis for to formulating functional hypotheses. In this article, we present an overview of computational methods for 3D structure prediction of RNP complexes. We discuss currently available methods for macromolecular docking and for scoring 3D structural models of RNP complexes in particular. Additionally, we also review benchmarks that have been developed to assess the accuracy of these methods.

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