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Articles de revues sur le sujet « RNA flexibility »

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Auteur : Grafiati
Publié le 20 juillet 2024

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1

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

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

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

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

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Zhuo, Chen, Chengwei Zeng, Rui Yang, Haoquan Liu et Yunjie Zhao. « RPflex : A Coarse-Grained Network Model for RNA Pocket Flexibility Study ». International Journal of Molecular Sciences 24, no 6 (13 mars 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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Hyeon, Changbong, Ruxandra I. Dima et D. Thirumalai. « Size, shape, and flexibility of RNA structures ». Journal of Chemical Physics 125, no 19 (21 novembre 2006) : 194905. http://dx.doi.org/10.1063/1.2364190.

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

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

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Fairman, Connor W., Andrew M. L. Lever et Julia C. Kenyon. « Evaluating RNA Structural Flexibility : Viruses Lead the Way ». Viruses 13, no 11 (22 octobre 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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Hetzke, Thilo, Marc Vogel, Dnyaneshwar B. Gophane, Julia E. Weigand, Beatrix Suess, Snorri Th Sigurdsson et Thomas F. Prisner. « Influence of Mg2+ on the conformational flexibility of a tetracycline aptamer ». RNA 25, no 1 (18 octobre 2018) : 158–67. http://dx.doi.org/10.1261/rna.068684.118.

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

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

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Hohng, Sungchul, Timothy J. Wilson, Elliot Tan, Robert M. Clegg, David M. J. Lilley et Taekjip Ha. « Conformational Flexibility of Four-way Junctions in RNA ». Journal of Molecular Biology 336, no 1 (février 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 (15 mars 1996) : 1073–79. http://dx.doi.org/10.1093/nar/24.6.1073.

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

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

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Fulle, Simone, et Holger Gohlke. « Analyzing the Flexibility of RNA Structures by Constraint Counting ». Biophysical Journal 94, no 11 (juin 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 et Christoph W. Müller. « Conformational flexibility of RNA polymerase III during transcriptional elongation ». EMBO Journal 29, no 22 (22 octobre 2010) : 3762–72. http://dx.doi.org/10.1038/emboj.2010.266.

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Fulle, Simone, et Holger Gohlke. « Constraint counting on RNA structures : Linking flexibility and function ». Methods 49, no 2 (octobre 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 et Bruce A. Shapiro. « Use of RNA structure flexibility data in nanostructure modeling ». Methods 54, no 2 (juin 2011) : 239–50. http://dx.doi.org/10.1016/j.ymeth.2010.12.010.

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21

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

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22

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

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23

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

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24

Pun, Chi Seng, Brandon Yung Sin Yong et Kelin Xia. « Weighted-persistent-homology-based machine learning for RNA flexibility analysis ». PLOS ONE 15, no 8 (21 août 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 et Eva Nogales. « Molecular Architecture and Conformational Flexibility of Human RNA Polymerase II ». Structure 14, no 11 (novembre 2006) : 1691–700. http://dx.doi.org/10.1016/j.str.2006.09.011.

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Wilkinson, Thomas A., Lingyang Zhu, Weidong Hu et Yuan Chen. « Retention of Conformational Flexibility in HIV-1 Rev−RNA Complexes† ». Biochemistry 43, no 51 (décembre 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 et Bryan R. G. Williams. « Dynamic Flexibility of Double-stranded RNA Activated PKR in Solution ». Journal of Molecular Biology 359, no 3 (juin 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 et Modesto Orozco. « Relative Flexibility of DNA and RNA : a Molecular Dynamics Study ». Journal of Molecular Biology 343, no 3 (octobre 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 (septembre 2015) : 779–85. http://dx.doi.org/10.1016/j.bbabio.2014.12.010.

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30

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

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31

Melidis, Lazaros, Iain B. Styles et 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 et Sheng-You Huang. « Protein-ensemble–RNA docking by efficient consideration of protein flexibility through homology models ». Bioinformatics 35, no 23 (14 mai 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 et 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 (17 décembre 2019) : 278–89. http://dx.doi.org/10.1261/rna.073478.119.

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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 (18 juin 2012) : 8072–84. http://dx.doi.org/10.1093/nar/gks510.

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35

Krüger, Dennis M., Johannes Bergs, Sina Kazemi et Holger Gohlke. « Target Flexibility in RNA−Ligand Docking Modeled by Elastic Potential Grids ». ACS Medicinal Chemistry Letters 2, no 7 (12 avril 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 et James R. Williamson. « Inherent Protein Structural Flexibility at the RNA-binding Interface of L30e ». Journal of Molecular Biology 326, no 4 (février 2003) : 999–1004. http://dx.doi.org/10.1016/s0022-2836(02)01476-6.

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

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

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39

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

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

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Lozano, Gloria, Alejandro Trapote, Jorge Ramajo, Xavier Elduque, Anna Grandas, Jordi Robles, Enrique Pedroso et 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 (16 mars 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 (février 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 et 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 (13 février 2013) : 5291–95. http://dx.doi.org/10.1128/jvi.00045-13.

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Chuwdhury, GS, Irene Oi-Lin Ng et 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 (janvier 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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Rohayem, Jacques, Katrin Jäger, Ivonne Robel, Ulrike Scheffler, Achim Temme et Wolfram Rudolph. « Characterization of norovirus 3Dpol RNA-dependent RNA polymerase activity and initiation of RNA synthesis ». Journal of General Virology 87, no 9 (1 septembre 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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Tavallaie, Roya, Nadim Darwish, D. Brynn Hibbert et 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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Vázquez, Ana López, José M. Martín Alonso et Francisco Parra. « Mutation Analysis of the GDD Sequence Motif of a Calicivirus RNA-Dependent RNA Polymerase ». Journal of Virology 74, no 8 (15 avril 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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Mishler, D. M., A. B. Christ et 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 (24 octobre 2008) : 2657–70. http://dx.doi.org/10.1261/rna.1312808.

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

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50

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

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