In recent years, great progress has been made in the research of RNA function, and more and more RNA functions have been discovered. The function of RNA is highly dependent on its 3D structure, the RNA tertiary structure includes the RNA 3D structure and RNA tertiary interaction, so the RNA tertiary structure prediction has also attracted extensive attention. There are many RNA tertiary structure prediction algorithms. According to the traditional classification methods, the existing RNA tertiary structure prediction algorithms can be divided into two categories: the RNA tertiary structure prediction algorithm based on knowledge mining and the RNA tertiary structure prediction algorithm based on physics. On this basis, this paper further refines the RNA tertiary structure prediction algorithm based on physics in traditional classification, and proposes a new refinement classification method based on conformational sampling method, namely RNA tertiary structure prediction algorithm based on physical fragment assembly conformational sampling method and RNA tertiary structure prediction algorithm based on Stepwise ansatz conformational sampling method. We make a comparative analysis of RNA tertiary structure prediction algorithms, and put forward some suggestions for improving the energy function in the next step, in order to find an RNA tertiary structure prediction algorithm that can achieve atomic accuracy.
Published in | Computational Biology and Bioinformatics (Volume 9, Issue 1) |
DOI | 10.11648/j.cbb.20210901.12 |
Page(s) | 15-20 |
Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
Copyright |
Copyright © The Author(s), 2021. Published by Science Publishing Group |
RNA Structure Prediction, Tertiary Structure, Conformational Sampling, Atomic Accuracy, Stepwise Ansatz
[1] | Xiang J. Non-coding RNA Tertiary Structure Prediction and Analysis [D]. Wuhan: Huazhong University of Science & Technology, 2018: 1-6. |
[2] | Wang Sh, Li X. Structure and Regulation Mechanism of Riboseswitch [J]. Biotechnology Bulletin, 2010 (5): 16–22. |
[3] | Shi Y, Wu Y, et al. RNA Structure Prediction: Progress and Perspective [J]. Chinese Physics B, 2014, 23 (07): 92-101. |
[4] | Fürtig Boris, Richter Christian, et al. NMR Spectroscopy of RNA [J]. ChemBioChem, 2003, 4 (10): 936-62. |
[5] | Kong Q. Research on RNA fold structure prediction algorithm based on basin hopping graph with pseudoknots [D]. Jinan: Shandong Jianzhu University, 2019: 6-14. |
[6] | Jin L. Coarse-grained modeling for double-stranded RNA 3D structure and thermal stability [D]. Wuhan: Wuhan University, 2019: 2-17. |
[7] | Wang Y, Assessment of tertiary structure and statistical analysis of torsion angles of non-coding RNA [D]. Wuhan: Huazhong University of Science & Technology, 2013: 2-4. |
[8] | Leontis Neocles B, Stombaugh Jesse, Westhof Eric. The non-Watson-Crick base pairs and their associated isostericity matrices [J]. Nucleic acids research, 2002, 30 (16): 3497-531. |
[9] | Liu Zh. Studies on algorithms for RNA structure prediction including pseudoknots [D]. Jinan: Shandong University, 2014: 1-7. |
[10] | Massire. C, E. Westhof. MANIP: An interactive tool for modelling RNA [J]. Journal of Molecular Graphics and Modelling, 1998, 16 (4): 197-205. |
[11] | Bruce A Shapiro, Yaroslava G Yingling, et al. Bridging the gap in RNA structure prediction [J]. 2007, 17 (2): 157-165. |
[12] | Samuel Coulbourn Flores, Russ B. Altman. Turning limited experimental information into 3D models of RNA [J]. Rna-a Publication of the Rna Society, 2010, 16 (9): 1769-1778. |
[13] | Rother M, Rother K, et al. ModeRNA: a tool for comparative modeling of RNA 3D structure [J]. Nucleic Acid Res, 2011, 39 (10): 4007-22. |
[14] | Wang J. Prediction of nucleic acid molecular structure [D]. Huazhong University of Science and Technology, 2017: 10-26. |
[15] | Li J. RNA Tertiary Structure Prediction [D]. Nanjing: Nanjing University, 2017: 7-14. |
[16] | Rhiju Das, David Baker. Automated de novo Prediction of Native-Like RNA Tertiary Structures [J]. Proceedings of the National Academy of Sciences of the United States of America, 2007, 104 (37): 14664-14669. |
[17] | Rhiju Das, John Karanicolas, David Baker. Atomic accuracy in predicting and designing noncanonical RNA structure [J]. Nature Methods, 2010, 7 (4): 291-294. |
[18] | Parin Sripakdeevong, Wipapat Kladwang, Rhiju Das. An enumerative stepwise ansatz enables atomic-accuracy RNA loop modeling [J]. Proceedings of the National Academy of Sciences of the United States of America, 2011, 108 (51): 20573-20578. |
[19] | Andrew M. Watkins, Caleb Geniesse, et al. Blind prediction of noncanonical RNA structure at atomic accuracy [J]. Science Advances, 2018, 4 (5): eaar 5316. |
[20] | Alford Rebecca F, Leaver-Fay Andrew, et al. The Rosetta All-Atom Energy Function for Macromolecular Modeling and Design [J]. Journal of chemical theory and computation, 2017, 13 (6): 3031-3048. |
APA Style
Yurong Yang, Zhendong Liu. (2021). A Comprehensive Review of Predicting Method of RNA Tertiary Structure. Computational Biology and Bioinformatics, 9(1), 15-20. https://doi.org/10.11648/j.cbb.20210901.12
ACS Style
Yurong Yang; Zhendong Liu. A Comprehensive Review of Predicting Method of RNA Tertiary Structure. Comput. Biol. Bioinform. 2021, 9(1), 15-20. doi: 10.11648/j.cbb.20210901.12
AMA Style
Yurong Yang, Zhendong Liu. A Comprehensive Review of Predicting Method of RNA Tertiary Structure. Comput Biol Bioinform. 2021;9(1):15-20. doi: 10.11648/j.cbb.20210901.12
@article{10.11648/j.cbb.20210901.12, author = {Yurong Yang and Zhendong Liu}, title = {A Comprehensive Review of Predicting Method of RNA Tertiary Structure}, journal = {Computational Biology and Bioinformatics}, volume = {9}, number = {1}, pages = {15-20}, doi = {10.11648/j.cbb.20210901.12}, url = {https://doi.org/10.11648/j.cbb.20210901.12}, eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.cbb.20210901.12}, abstract = {In recent years, great progress has been made in the research of RNA function, and more and more RNA functions have been discovered. The function of RNA is highly dependent on its 3D structure, the RNA tertiary structure includes the RNA 3D structure and RNA tertiary interaction, so the RNA tertiary structure prediction has also attracted extensive attention. There are many RNA tertiary structure prediction algorithms. According to the traditional classification methods, the existing RNA tertiary structure prediction algorithms can be divided into two categories: the RNA tertiary structure prediction algorithm based on knowledge mining and the RNA tertiary structure prediction algorithm based on physics. On this basis, this paper further refines the RNA tertiary structure prediction algorithm based on physics in traditional classification, and proposes a new refinement classification method based on conformational sampling method, namely RNA tertiary structure prediction algorithm based on physical fragment assembly conformational sampling method and RNA tertiary structure prediction algorithm based on Stepwise ansatz conformational sampling method. We make a comparative analysis of RNA tertiary structure prediction algorithms, and put forward some suggestions for improving the energy function in the next step, in order to find an RNA tertiary structure prediction algorithm that can achieve atomic accuracy.}, year = {2021} }
TY - JOUR T1 - A Comprehensive Review of Predicting Method of RNA Tertiary Structure AU - Yurong Yang AU - Zhendong Liu Y1 - 2021/03/10 PY - 2021 N1 - https://doi.org/10.11648/j.cbb.20210901.12 DO - 10.11648/j.cbb.20210901.12 T2 - Computational Biology and Bioinformatics JF - Computational Biology and Bioinformatics JO - Computational Biology and Bioinformatics SP - 15 EP - 20 PB - Science Publishing Group SN - 2330-8281 UR - https://doi.org/10.11648/j.cbb.20210901.12 AB - In recent years, great progress has been made in the research of RNA function, and more and more RNA functions have been discovered. The function of RNA is highly dependent on its 3D structure, the RNA tertiary structure includes the RNA 3D structure and RNA tertiary interaction, so the RNA tertiary structure prediction has also attracted extensive attention. There are many RNA tertiary structure prediction algorithms. According to the traditional classification methods, the existing RNA tertiary structure prediction algorithms can be divided into two categories: the RNA tertiary structure prediction algorithm based on knowledge mining and the RNA tertiary structure prediction algorithm based on physics. On this basis, this paper further refines the RNA tertiary structure prediction algorithm based on physics in traditional classification, and proposes a new refinement classification method based on conformational sampling method, namely RNA tertiary structure prediction algorithm based on physical fragment assembly conformational sampling method and RNA tertiary structure prediction algorithm based on Stepwise ansatz conformational sampling method. We make a comparative analysis of RNA tertiary structure prediction algorithms, and put forward some suggestions for improving the energy function in the next step, in order to find an RNA tertiary structure prediction algorithm that can achieve atomic accuracy. VL - 9 IS - 1 ER -