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Journal articles on the topic 'RNA flexibility'

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Published: 20 July 2024

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1

Hagerman, Paul J. "FLEXIBILITY OF RNA." Annual Review of Biophysics and Biomolecular Structure 26, no. 1 (June 1997): 139–56. http://dx.doi.org/10.1146/annurev.biophys.26.1.139.

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2

Darst, S. A., N. Opalka, P. Chacon, A. Polyakov, C. Richter, G. Zhang, and W. Wriggers. "Conformational flexibility of bacterial RNA polymerase." Proceedings of the National Academy of Sciences 99, no. 7 (March 19, 2002): 4296–301. http://dx.doi.org/10.1073/pnas.052054099.

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3

Sutton, Julie, and Lois Pollack. "RNA Flexibility Depends on Structural Context." Biophysical Journal 108, no. 2 (January 2015): 27a. http://dx.doi.org/10.1016/j.bpj.2014.11.174.

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4

Warden, Meghan S., Kai Cai, Gabriel Cornilescu, Jordan E. Burke, Komala Ponniah, Samuel E. Butcher, and Steven M. Pascal. "Conformational flexibility in the enterovirus RNA replication platform." RNA 25, no. 3 (December 21, 2018): 376–87. http://dx.doi.org/10.1261/rna.069476.118.

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5

Zhuo, Chen, Chengwei Zeng, Rui Yang, Haoquan Liu, and Yunjie Zhao. "RPflex: A Coarse-Grained Network Model for RNA Pocket Flexibility Study." International Journal of Molecular Sciences 24, no. 6 (March 13, 2023): 5497. http://dx.doi.org/10.3390/ijms24065497.

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RNA regulates various biological processes, such as gene regulation, RNA splicing, and intracellular signal transduction. RNA’s conformational dynamics play crucial roles in performing its diverse functions. Thus, it is essential to explore the flexibility characteristics of RNA, especially pocket flexibility. Here, we propose a computational approach, RPflex, to analyze pocket flexibility using the coarse-grained network model. We first clustered 3154 pockets into 297 groups by similarity calculation based on the coarse-grained lattice model. Then, we introduced the flexibility score to quantify the flexibility by global pocket features. The results show strong correlations between the flexibility scores and root-mean-square fluctuation (RMSF) values, with Pearson correlation coefficients of 0.60, 0.76, and 0.53 in Testing Sets I–III. Considering both flexibility score and network calculations, the Pearson correlation coefficient was increased to 0.71 in flexible pockets on Testing Set IV. The network calculations reveal that the long-range interaction changes contributed most to flexibility. In addition, the hydrogen bonds in the base–base interactions greatly stabilize the RNA structure, while backbone interactions determine RNA folding. The computational analysis of pocket flexibility could facilitate RNA engineering for biological or medical applications.
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6

Hyeon, Changbong, Ruxandra I. Dima, and D. Thirumalai. "Size, shape, and flexibility of RNA structures." Journal of Chemical Physics 125, no. 19 (November 21, 2006): 194905. http://dx.doi.org/10.1063/1.2364190.

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7

Kilburn, John D., Joon Ho Roh, Liang Guo, Robert M. Briber, and Sarah A. Woodson. "RNA Flexibility and Folding in Crowded Solutions." Biophysical Journal 102, no. 3 (January 2012): 644a. http://dx.doi.org/10.1016/j.bpj.2011.11.3506.

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8

Rau, M., W. T. Stump, and K. B. Hall. "Intrinsic flexibility of snRNA hairpin loops facilitates protein binding." RNA 18, no. 11 (September 25, 2012): 1984–95. http://dx.doi.org/10.1261/rna.035006.112.

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9

Fairman, Connor W., Andrew M. L. Lever, and Julia C. Kenyon. "Evaluating RNA Structural Flexibility: Viruses Lead the Way." Viruses 13, no. 11 (October 22, 2021): 2130. http://dx.doi.org/10.3390/v13112130.

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Our understanding of RNA structure has lagged behind that of proteins and most other biological polymers, largely because of its ability to adopt multiple, and often very different, functional conformations within a single molecule. Flexibility and multifunctionality appear to be its hallmarks. Conventional biochemical and biophysical techniques all have limitations in solving RNA structure and to address this in recent years we have seen the emergence of a wide diversity of techniques applied to RNA structural analysis and an accompanying appreciation of its ubiquity and versatility. Viral RNA is a particularly productive area to study in that this economy of function within a single molecule admirably suits the minimalist lifestyle of viruses. Here, we review the major techniques that are being used to elucidate RNA conformational flexibility and exemplify how the structure and function are, as in all biology, tightly linked.
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10

Hetzke, Thilo, Marc Vogel, Dnyaneshwar B. Gophane, Julia E. Weigand, Beatrix Suess, Snorri Th Sigurdsson, and Thomas F. Prisner. "Influence of Mg2+ on the conformational flexibility of a tetracycline aptamer." RNA 25, no. 1 (October 18, 2018): 158–67. http://dx.doi.org/10.1261/rna.068684.118.

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11

Bao, Lei, Xi Zhang, Lei Jin, and Zhi-Jie Tan. "Flexibility of nucleic acids: From DNA to RNA." Chinese Physics B 25, no. 1 (January 2016): 018703. http://dx.doi.org/10.1088/1674-1056/25/1/018703.

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12

Faustino, Ignacio, Alberto Pérez, and Modesto Orozco. "Toward a Consensus View of Duplex RNA Flexibility." Biophysical Journal 99, no. 6 (September 2010): 1876–85. http://dx.doi.org/10.1016/j.bpj.2010.06.061.

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13

Hohng, Sungchul, Timothy J. Wilson, Elliot Tan, Robert M. Clegg, David M. J. Lilley, and Taekjip Ha. "Conformational Flexibility of Four-way Junctions in RNA." Journal of Molecular Biology 336, no. 1 (February 2004): 69–79. http://dx.doi.org/10.1016/j.jmb.2003.12.014.

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14

Dalluge, J. "Conformational flexibility in RNA: the role of dihydrouridine." Nucleic Acids Research 24, no. 6 (March 15, 1996): 1073–79. http://dx.doi.org/10.1093/nar/24.6.1073.

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15

Bonin, M. "Analysis of RNA flexibility by scanning force spectroscopy." Nucleic Acids Research 30, no. 16 (August 15, 2002): 81e—81. http://dx.doi.org/10.1093/nar/gnf080.

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16

Haque, Farzin, Fengmei Pi, Zhengyi Zhao, Shanqing Gu, Haibo Hu, Hang Yu, and Peixuan Guo. "RNA versatility, flexibility, and thermostability for practice in RNA nanotechnology and biomedical applications." Wiley Interdisciplinary Reviews: RNA 9, no. 1 (November 3, 2017): e1452. http://dx.doi.org/10.1002/wrna.1452.

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17

Fulle, Simone, and Holger Gohlke. "Analyzing the Flexibility of RNA Structures by Constraint Counting." Biophysical Journal 94, no. 11 (June 2008): 4202–19. http://dx.doi.org/10.1529/biophysj.107.113415.

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18

Fernández-Tornero, Carlos, Bettina Böttcher, Umar Jan Rashid, Ulrich Steuerwald, Beate Flörchinger, Damien P. Devos, Doris Lindner, and Christoph W. Müller. "Conformational flexibility of RNA polymerase III during transcriptional elongation." EMBO Journal 29, no. 22 (October 22, 2010): 3762–72. http://dx.doi.org/10.1038/emboj.2010.266.

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19

Fulle, Simone, and Holger Gohlke. "Constraint counting on RNA structures: Linking flexibility and function." Methods 49, no. 2 (October 2009): 181–88. http://dx.doi.org/10.1016/j.ymeth.2009.04.004.

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20

Kasprzak, Wojciech, Eckart Bindewald, Tae-Jin Kim, Luc Jaeger, and Bruce A. Shapiro. "Use of RNA structure flexibility data in nanostructure modeling." Methods 54, no. 2 (June 2011): 239–50. http://dx.doi.org/10.1016/j.ymeth.2010.12.010.

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21

Boerneke, Mark A., and Thomas Hermann. "Conformational flexibility of viral RNA switches studied by FRET." Methods 91 (December 2015): 35–39. http://dx.doi.org/10.1016/j.ymeth.2015.09.013.

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22

Sutton, Julie L., and Lois Pollack. "Tuning RNA Flexibility with Helix Length and Junction Sequence." Biophysical Journal 109, no. 12 (December 2015): 2644–53. http://dx.doi.org/10.1016/j.bpj.2015.10.039.

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23

Badorrek, Christopher S., and Kevin M. Weeks. "RNA flexibility in the dimerization domain of a gamma retrovirus." Nature Chemical Biology 1, no. 2 (June 5, 2005): 104–11. http://dx.doi.org/10.1038/nchembio712.

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24

Pun, Chi Seng, Brandon Yung Sin Yong, and Kelin Xia. "Weighted-persistent-homology-based machine learning for RNA flexibility analysis." PLOS ONE 15, no. 8 (August 21, 2020): e0237747. http://dx.doi.org/10.1371/journal.pone.0237747.

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25

Kostek, Seth A., Patricia Grob, Sacha De Carlo, J. Slaton Lipscomb, Florian Garczarek, and Eva Nogales. "Molecular Architecture and Conformational Flexibility of Human RNA Polymerase II." Structure 14, no. 11 (November 2006): 1691–700. http://dx.doi.org/10.1016/j.str.2006.09.011.

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26

Wilkinson, Thomas A., Lingyang Zhu, Weidong Hu, and Yuan Chen. "Retention of Conformational Flexibility in HIV-1 Rev−RNA Complexes†." Biochemistry 43, no. 51 (December 2004): 16153–60. http://dx.doi.org/10.1021/bi048409e.

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27

Gabel, Frank, Die Wang, Dominique Madern, Anthony Sadler, Kwaku Dayie, Maryam Zamanian Daryoush, Dietmar Schwahn, Giuseppe Zaccai, Xavier Lee, and Bryan R. G. Williams. "Dynamic Flexibility of Double-stranded RNA Activated PKR in Solution." Journal of Molecular Biology 359, no. 3 (June 2006): 610–23. http://dx.doi.org/10.1016/j.jmb.2006.03.049.

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28

Noy, Agnes, Alberto Pérez, Filip Lankas, F. Javier Luque, and Modesto Orozco. "Relative Flexibility of DNA and RNA: a Molecular Dynamics Study." Journal of Molecular Biology 343, no. 3 (October 2004): 627–38. http://dx.doi.org/10.1016/j.jmb.2004.07.048.

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29

Shikanai, Toshiharu. "RNA editing in plants: Machinery and flexibility of site recognition." Biochimica et Biophysica Acta (BBA) - Bioenergetics 1847, no. 9 (September 2015): 779–85. http://dx.doi.org/10.1016/j.bbabio.2014.12.010.

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30

Feig, Michael, and Zachary F. Burton. "RNA polymerase II flexibility during translocation from normal mode analysis." Proteins: Structure, Function, and Bioinformatics 78, no. 2 (August 5, 2009): 434–46. http://dx.doi.org/10.1002/prot.22560.

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31

Melidis, Lazaros, Iain B. Styles, and Michael J. Hannon. "Targeting structural features of viral genomes with a nano-sized supramolecular drug." Chemical Science 12, no. 20 (2021): 7174–84. http://dx.doi.org/10.1039/d1sc00933h.

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MD simulations and Markov state modeling explore induced fit binding of metallo-helicates to bulges in dynamic TAR RNA, reproduce experimental data, show how RNA conformational flexibility is reduced, and give mechanistic insight into insertion.
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32

He, Jiahua, Huanyu Tao, and Sheng-You Huang. "Protein-ensemble–RNA docking by efficient consideration of protein flexibility through homology models." Bioinformatics 35, no. 23 (May 14, 2019): 4994–5002. http://dx.doi.org/10.1093/bioinformatics/btz388.

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AbstractMotivationGiven the importance of protein–ribonucleic acid (RNA) interactions in many biological processes, a variety of docking algorithms have been developed to predict the complex structure from individual protein and RNA partners in the past decade. However, due to the impact of molecular flexibility, the performance of current methods has hit a bottleneck in realistic unbound docking. Pushing the limit, we have proposed a protein-ensemble–RNA docking strategy to explicitly consider the protein flexibility in protein–RNA docking through an ensemble of multiple protein structures, which is referred to as MPRDock. Instead of taking conformations from MD simulations or experimental structures, we obtained the multiple structures of a protein by building models from its homologous templates in the Protein Data Bank (PDB).ResultsOur approach can not only avoid the reliability issue of structures from MD simulations but also circumvent the limited number of experimental structures for a target protein in the PDB. Tested on 68 unbound–bound and 18 unbound–unbound protein–RNA complexes, our MPRDock/DITScorePR considerably improved the docking performance and achieved a significantly higher success rate than single-protein rigid docking whether pseudo-unbound templates are included or not. Similar improvements were also observed when combining our ensemble docking strategy with other scoring functions. The present homology model-based ensemble docking approach will have a general application in molecular docking for other interactions.Availability and implementationhttp://huanglab.phys.hust.edu.cn/mprdock/Supplementary informationSupplementary data are available at Bioinformatics online.
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33

Chan, Clarence W., Deanna Badong, Rakhi Rajan, and Alfonso Mondragón. "Crystal structures of an unmodified bacterial tRNA reveal intrinsic structural flexibility and plasticity as general properties of unbound tRNAs." RNA 26, no. 3 (December 17, 2019): 278–89. http://dx.doi.org/10.1261/rna.073478.119.

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34

de Almeida Ribeiro, Euripedes, Mads Beich-Frandsen, Petr V. Konarev, Weifeng Shang, Branislav Večerek, Georg Kontaxis, Hermann Hämmerle, et al. "Structural flexibility of RNA as molecular basis for Hfq chaperone function." Nucleic Acids Research 40, no. 16 (June 18, 2012): 8072–84. http://dx.doi.org/10.1093/nar/gks510.

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35

Krüger, Dennis M., Johannes Bergs, Sina Kazemi, and Holger Gohlke. "Target Flexibility in RNA−Ligand Docking Modeled by Elastic Potential Grids." ACS Medicinal Chemistry Letters 2, no. 7 (April 12, 2011): 489–93. http://dx.doi.org/10.1021/ml100217h.

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36

Chao, Jeffrey A., G. S. Prasad, Susan A. White, C. David Stout, and James R. Williamson. "Inherent Protein Structural Flexibility at the RNA-binding Interface of L30e." Journal of Molecular Biology 326, no. 4 (February 2003): 999–1004. http://dx.doi.org/10.1016/s0022-2836(02)01476-6.

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37

De Carlo, Sacha, Christophe Carles, Michel Riva, and Patrick Schultz. "Cryo-negative Staining Reveals Conformational Flexibility Within Yeast RNA Polymerase I." Journal of Molecular Biology 329, no. 5 (June 2003): 891–902. http://dx.doi.org/10.1016/s0022-2836(03)00510-2.

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38

Kasprzak, Wojciech K., Kirill A. Afonin, Eckart Bindewald, Praneet S. Puppala, Tae-Jin Kim, Michael T. Zimmermann, Robert L. Jernigan, and Bruce A. Shapiro. "Coarse-Grained Computational Characterization of RNA Nanocube Flexibility Correlates with Experiments." Biophysical Journal 104, no. 2 (January 2013): 16a. http://dx.doi.org/10.1016/j.bpj.2012.11.119.

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39

Zacharias, Martin, and Paul J. Hagerman. "The Influence of Symmetric Internal Loops on the Flexibility of RNA." Journal of Molecular Biology 257, no. 2 (March 1996): 276–89. http://dx.doi.org/10.1006/jmbi.1996.0162.

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40

Guruge, Ivantha, Ghazaleh Taherzadeh, Jian Zhan, Yaoqi Zhou, and Yuedong Yang. "B -factor profile prediction for RNA flexibility using support vector machines." Journal of Computational Chemistry 39, no. 8 (November 21, 2017): 407–11. http://dx.doi.org/10.1002/jcc.25124.

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41

Lozano, Gloria, Alejandro Trapote, Jorge Ramajo, Xavier Elduque, Anna Grandas, Jordi Robles, Enrique Pedroso, and Encarnación Martínez-Salas. "Local RNA flexibility perturbation of the IRES element induced by a novel ligand inhibits viral RNA translation." RNA Biology 12, no. 5 (March 16, 2015): 555–68. http://dx.doi.org/10.1080/15476286.2015.1025190.

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42

Murchie, Alastair I. H., Ben Davis, Catherine Isel, Mohammad Afshar, Martin J. Drysdale, Justin Bower, Andrew J. Potter, et al. "Structure-based Drug Design Targeting an Inactive RNA Conformation: Exploiting the Flexibility of HIV-1 TAR RNA." Journal of Molecular Biology 336, no. 3 (February 2004): 625–38. http://dx.doi.org/10.1016/j.jmb.2003.12.028.

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43

Noble, C. G., S. P. Lim, Y. L. Chen, C. W. Liew, L. Yap, J. Lescar, and P. Y. Shi. "Conformational Flexibility of the Dengue Virus RNA-Dependent RNA Polymerase Revealed by a Complex with an Inhibitor." Journal of Virology 87, no. 9 (February 13, 2013): 5291–95. http://dx.doi.org/10.1128/jvi.00045-13.

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44

Chuwdhury, GS, Irene Oi-Lin Ng, and Daniel Wai-Hung Ho. "scAnalyzeR: A Comprehensive Software Package With Graphical User Interface for Single-Cell RNA Sequencing Analysis and its Application on Liver Cancer." Technology in Cancer Research & Treatment 21 (January 2022): 153303382211427. http://dx.doi.org/10.1177/15330338221142729.

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Introduction: The application of single-cell RNA sequencing to delineate tissue heterogeneity and complexity has become increasingly popular. Given its tremendous resolution and high-dimensional capacity for in-depth investigation, single-cell RNA sequencing offers an unprecedented research power. Although some popular software packages are available for single-cell RNA sequencing data analysis and visualization, it is still a big challenge for their usage, as they provide only a command-line interface and require significant level of bioinformatics skills. Methods: We have developed scAnalyzeR, which is a single-cell RNA sequencing analysis pipeline with an interactive and user-friendly graphical interface for analyzing and visualizing single-cell RNA sequencing data. It accepts single-cell RNA sequencing data from various technology platforms and different model organisms (human and mouse) and allows flexibility in input file format. It provides functionalities for data preprocessing, quality control, basic summary statistics, dimension reduction, unsupervised clustering, differential gene expression, gene set enrichment analysis, correlation analysis, pseudotime cell trajectory inference, and various visualization plots. It also provides default parameters for easy usage and allows a wide range of flexibility and optimization by accepting user-defined options. It has been developed as a docker image that can be run in any docker-supported environment including Linux, Mac, and Windows, without installing any dependencies. Results: We compared the performance of scAnalyzeR with 2 other graphical tools that are popular for analyzing single-cell RNA sequencing data. The comparison was based on the comprehensiveness of functionalities, ease of usage and flexibility, and execution time. In general, scAnalyzeR outperformed the other tested counterparts in various aspects, demonstrating its superior overall performance. To illustrate the usefulness of scAnalyzeR in cancer research, we have analyzed the in-house liver cancer single-cell RNA sequencing dataset. Liver cancer tumor cells were revealed to have multiple subpopulations with distinctive gene expression signatures. Conclusion: scAnalyzeR has comprehensive functionalities and demonstrated usability. We anticipate more functionalities to be adopted in the future development.
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45

Rohayem, Jacques, Katrin Jäger, Ivonne Robel, Ulrike Scheffler, Achim Temme, and Wolfram Rudolph. "Characterization of norovirus 3Dpol RNA-dependent RNA polymerase activity and initiation of RNA synthesis." Journal of General Virology 87, no. 9 (September 1, 2006): 2621–30. http://dx.doi.org/10.1099/vir.0.81802-0.

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Norovirus (NV) 3Dpol is a non-structural protein predicted to play an essential role in the replication of the NV genome. In this study, the characteristics of NV 3Dpol activity and initiation of RNA synthesis have been examined in vitro. Recombinant NV 3Dpol, as well as a 3Dpol active-site mutant were expressed in Escherichia coli and purified. NV 3Dpol was able to synthesize RNA in vitro and displayed flexibility with respect to the use of Mg2+ or Mn2+ as a cofactor. NV 3Dpol yielded two different products when incubated with synthetic RNA in vitro: (i) a double-stranded RNA consisting of two single strands of opposite polarity or (ii) the single-stranded RNA template labelled at its 3′ terminus by terminal transferase activity. Initiation of RNA synthesis occurred de novo rather than by back-priming, as evidenced by the fact that the two strands of the double-stranded RNA product could be separated, and by dissociation in time-course analysis of terminal transferase and RNA synthesis activities. In addition, RNA synthesis was not affected by blocking of the 3′ terminus of the RNA template by a chain terminator, sustaining de novo initiation of RNA synthesis. NV 3Dpol displays in vitro properties characteristic of RNA-dependent RNA polymerases, allowing the implementation of this in vitro enzymic assay for the development and validation of antiviral drugs against NV, a so far non-cultivated virus and an important human pathogen.
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46

Tavallaie, Roya, Nadim Darwish, D. Brynn Hibbert, and J. Justin Gooding. "Nucleic-acid recognition interfaces: how the greater ability of RNA duplexes to bend towards the surface influences electrochemical sensor performance." Chemical Communications 51, no. 92 (2015): 16526–29. http://dx.doi.org/10.1039/c5cc05450h.

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47

Vázquez, Ana López, José M. Martín Alonso, and Francisco Parra. "Mutation Analysis of the GDD Sequence Motif of a Calicivirus RNA-Dependent RNA Polymerase." Journal of Virology 74, no. 8 (April 15, 2000): 3888–91. http://dx.doi.org/10.1128/jvi.74.8.3888-3891.2000.

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ABSTRACT The RNA-dependent RNA polymerase from rabbit hemorrhagic disease virus, a calicivirus, is known to have a conserved GDD amino acid motif and several additional regions of sequence homology with all types of polymerases. To test whether both aspartic acid residues are in fact involved in the catalytic activity and metal ion coordination of the enzyme, several defined mutations have been made in order to replace them by glutamate, asparagine, or glycine. All six mutant enzymes were produced in Escherichia coli, and their in vitro poly(U) polymerase activity was characterized. The results demonstrated that the first aspartate residue was absolutely required for enzyme function and that some flexibility existed with respect to the second, which could be replaced by glutamate.
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48

Mishler, D. M., A. B. Christ, and J. A. Steitz. "Flexibility in the site of exon junction complex deposition revealed by functional group and RNA secondary structure alterations in the splicing substrate." RNA 14, no. 12 (October 24, 2008): 2657–70. http://dx.doi.org/10.1261/rna.1312808.

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49

Chamberlin, Stacy I., and Kevin M. Weeks. "Mapping Local Nucleotide Flexibility by Selective Acylation of 2‘-Amine Substituted RNA." Journal of the American Chemical Society 122, no. 2 (January 2000): 216–24. http://dx.doi.org/10.1021/ja9914137.

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50

Egli, M., G. Minasov, L. Su, and A. Rich. "Metal ions and flexibility in a viral RNA pseudoknot at atomic resolution." Proceedings of the National Academy of Sciences 99, no. 7 (March 19, 2002): 4302–7. http://dx.doi.org/10.1073/pnas.062055599.

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