Artículos de revistas: "RNA-protein interactions"

1

Hall, Kathleen B. "RNA–protein interactions." Current Opinion in Structural Biology 12, no. 3 (June 2002): 283–88. http://dx.doi.org/10.1016/s0959-440x(02)00323-8.

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2

Wickens, Marvin P., and James E. Dahlberg. "RNA-protein interactions." Cell 51, no. 3 (November 1987): 339–42. http://dx.doi.org/10.1016/0092-8674(87)90629-5.

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3

Frankel, Alan D., Iain W. Mattaj, and Donald C. Rio. "RNA-protein interactions." Cell 67, no. 6 (December 1991): 1041–46. http://dx.doi.org/10.1016/0092-8674(91)90282-4.

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4

Nagai, Kiyoshi. "RNA-protein interactions." Current Opinion in Structural Biology 2, no. 1 (February 1992): 131–37. http://dx.doi.org/10.1016/0959-440x(92)90188-d.

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5

Puglisi, Joseph D. "RNA-protein interactions." Chemistry & Biology 2, no. 9 (September 1995): 581. http://dx.doi.org/10.1016/1074-5521(95)90121-3.

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6

Struhl, Kevin. "RNA-Protein Interactions." Current Protocols in Molecular Biology 73, no. 1 (January 2006): 27.0.1. http://dx.doi.org/10.1002/0471142727.mb2700s73.

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7

Garrett, Roger A. "RNA-protein interactions." FEBS Letters 375, no. 3 (November 20, 1995): 313. http://dx.doi.org/10.1016/0014-5793(95)90104-3.

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8

Doetsch, Martina, Renée Schroeder, and Boris Fürtig. "Transient RNA-protein interactions in RNA folding." FEBS Journal 278, no. 10 (April 13, 2011): 1634–42. http://dx.doi.org/10.1111/j.1742-4658.2011.08094.x.

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9

Osório, Joana. "Exploring protein–RNA interactions with RNA Tagging." Nature Reviews Genetics 17, no. 1 (November 16, 2015): 7. http://dx.doi.org/10.1038/nrg.2015.6.

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10

Wilson, Katie A., Ryan W. Kung, Simmone D’souza та Stacey D. Wetmore. "Anatomy of noncovalent interactions between the nucleobases or ribose and π-containing amino acids in RNA–protein complexes". Nucleic Acids Research 49, № 4 (5 лютого 2021): 2213–25. http://dx.doi.org/10.1093/nar/gkab008.

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Abstract A set of &gt;300 nonredundant high-resolution RNA–protein complexes were rigorously searched for π-contacts between an amino acid side chain (W, H, F, Y, R, E and D) and an RNA nucleobase (denoted π–π interaction) or ribose moiety (denoted sugar–π). The resulting dataset of &gt;1500 RNA–protein π-contacts were visually inspected and classified based on the interaction type, and amino acids and RNA components involved. More than 80% of structures searched contained at least one RNA–protein π-interaction, with π–π contacts making up 59% of the identified interactions. RNA–protein π–π and sugar–π contacts exhibit a range in the RNA and protein components involved, relative monomer orientations and quantum mechanically predicted binding energies. Interestingly, π–π and sugar–π interactions occur more frequently with RNA (4.8 contacts/structure) than DNA (2.6). Moreover, the maximum stability is greater for RNA–protein contacts than DNA–protein interactions. In addition to highlighting distinct differences between RNA and DNA–protein binding, this work has generated the largest dataset of RNA–protein π-interactions to date, thereby underscoring that RNA–protein π-contacts are ubiquitous in nature, and key to the stability and function of RNA–protein complexes.

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11

Predki, Paul F., L. Mike Nayak, Morris B. C. Gottlieb, and Lynne Regan. "Dissecting RNA-protein interactions: RNA-RNA recognition by Rop." Cell 80, no. 1 (January 1995): 41–50. http://dx.doi.org/10.1016/0092-8674(95)90449-2.

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12

Blanco, Francisco J., and Guillermo Montoya. "Transient DNA / RNA-protein interactions." FEBS Journal 278, no. 10 (March 30, 2011): 1643–50. http://dx.doi.org/10.1111/j.1742-4658.2011.08095.x.

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13

Bachand, F. "Human telomerase RNA-protein interactions." Nucleic Acids Research 29, no. 16 (August 15, 2001): 3385–93. http://dx.doi.org/10.1093/nar/29.16.3385.

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14

Landweber, L. F. "Testing ancient RNA-protein interactions." Proceedings of the National Academy of Sciences 96, no. 20 (September 28, 1999): 11067–68. http://dx.doi.org/10.1073/pnas.96.20.11067.

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15

Gopinath, Subash Chandra Bose. "Mapping of RNA–protein interactions." Analytica Chimica Acta 636, no. 2 (March 2009): 117–28. http://dx.doi.org/10.1016/j.aca.2009.01.052.

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16

Cirillo, Davide, Federico Agostini, and Gian Gaetano Tartaglia. "Predictions of protein-RNA interactions." Wiley Interdisciplinary Reviews: Computational Molecular Science 3, no. 2 (September 25, 2012): 161–75. http://dx.doi.org/10.1002/wcms.1119.

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17

Duncan, Caia DS, and Juan Mata. "Cotranslational protein-RNA associations predict protein-protein interactions." BMC Genomics 15, no. 1 (2014): 298. http://dx.doi.org/10.1186/1471-2164-15-298.

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18

Imig, Jochen, Alexander Kanitz, and André P. Gerber. "RNA regulons and the RNA-protein interaction network." BioMolecular Concepts 3, no. 5 (October 1, 2012): 403–14. http://dx.doi.org/10.1515/bmc-2012-0016.

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AbstractThe development of genome-wide analysis tools has prompted global investigation of the gene expression program, revealing highly coordinated control mechanisms that ensure proper spatiotemporal activity of a cell’s macromolecular components. With respect to the regulation of RNA transcripts, the concept of RNA regulons, which – by analogy with DNA regulons in bacteria – refers to the coordinated control of functionally related RNA molecules, has emerged as a unifying theory that describes the logic of regulatory RNA-protein interactions in eukaryotes. Hundreds of RNA-binding proteins and small non-coding RNAs, such as microRNAs, bind to distinct elements in target RNAs, thereby exerting specific and concerted control over posttranscriptional events. In this review, we discuss recent reports committed to systematically explore the RNA-protein interaction network and outline some of the principles and recurring features of RNA regulons: the coordination of functionally related mRNAs through RNA-binding proteins or non-coding RNAs, the modular structure of its components, and the dynamic rewiring of RNA-protein interactions upon exposure to internal or external stimuli. We also summarize evidence for robust combinatorial control of mRNAs, which could determine the ultimate fate of each mRNA molecule in a cell. Finally, the compilation and integration of global protein-RNA interaction data has yielded first insights into network structures and provided the hypothesis that RNA regulons may, in part, constitute noise ‘buffers’ to handle stochasticity in cellular transcription.

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19

Yang, Rui, Haoquan Liu, Liu Yang, Ting Zhou, Xinyao Li, and Yunjie Zhao. "RPpocket: An RNA–Protein Intuitive Database with RNA Pocket Topology Resources." International Journal of Molecular Sciences 23, no. 13 (June 21, 2022): 6903. http://dx.doi.org/10.3390/ijms23136903.

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RNA–protein complexes regulate a variety of biological functions. Thus, it is essential to explore and visualize RNA–protein structural interaction features, especially pocket interactions. In this work, we develop an easy-to-use bioinformatics resource: RPpocket. This database provides RNA–protein complex interactions based on sequence, secondary structure, and pocket topology analysis. We extracted 793 pockets from 74 non-redundant RNA–protein structures. Then, we calculated the binding- and non-binding pocket topological properties and analyzed the binding mechanism of the RNA–protein complex. The results showed that the binding pockets were more extended than the non-binding pockets. We also found that long-range forces were the main interaction for RNA–protein recognition, while short-range forces strengthened and optimized the binding. RPpocket could facilitate RNA–protein engineering for biological or medical applications.

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20

Armaos, Alexandros, Alessio Colantoni, Gabriele Proietti, Jakob Rupert, and Gian Gaetano Tartaglia. "catRAPID omics v2.0: going deeper and wider in the prediction of protein–RNA interactions." Nucleic Acids Research 49, W1 (June 4, 2021): W72—W79. http://dx.doi.org/10.1093/nar/gkab393.

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Abstract Prediction of protein–RNA interactions is important to understand post-transcriptional events taking place in the cell. Here we introduce catRAPID omics v2.0, an update of our web server dedicated to the computation of protein–RNA interaction propensities at the transcriptome- and RNA-binding proteome-level in 8 model organisms. The server accepts multiple input protein or RNA sequences and computes their catRAPID interaction scores on updated precompiled libraries. Additionally, it is now possible to predict the interactions between a custom protein set and a custom RNA set. Considerable effort has been put into the generation of a new database of RNA-binding motifs that are searched within the predicted RNA targets of proteins. In this update, the sequence fragmentation scheme of the catRAPID fragment module has been included, which allows the server to handle long linear RNAs and to analyse circular RNAs. For the top-scoring protein–RNA pairs, the web server shows the predicted binding sites in both protein and RNA sequences and reports whether the predicted interactions are conserved in orthologous protein–RNA pairs. The catRAPID omics v2.0 web server is a powerful tool for the characterization and classification of RNA-protein interactions and is freely available at http://service.tartaglialab.com/page/catrapid_omics2_group along with documentation and tutorial.

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21

CAI, JUN, YING HUANG, LIANG JI, and YANDA LI. "INFERRING PROTEIN-PROTEIN INTERACTIONS FROM MESSENGER RNA EXPRESSION PROFILES WITH SVM." Journal of Biological Systems 13, no. 03 (September 2005): 287–98. http://dx.doi.org/10.1142/s0218339005001525.

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In post-genomic biology, researchers in the field of proteome focus their attention on the networks of protein interactions that control the lives of cells and organisms. Protein-protein interactions play a useful role in dynamic cellular machinery. In this paper, we developed a method to infer protein-protein interactions based on the theory of support vector machine (SVM). For a given pair of proteins, a new strategy of calculating cross-correlation function of mRNA expression profiles was used to encode SVM vectors. We compared the performance with other methods of inferring protein-protein interaction. Results suggested that, through five-fold cross validation, our SVM model achieved a good prediction. It enables us to show that expression profiles in transcription level can be used to distinguish physical or functional interactions of proteins as well as sequence contents. Lastly, we applied our SVM classifier to evaluate data quality of interaction data sets from four high-throughput experiments. The results show that high-throughput experiments sacrifice some accuracy in determination of interactions because of limitation of experiment technologies.

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22

E, Beeram. "Mini review on Protein – Protein and DNA/RNA – protein interactions in biology." Asploro Journal of Biomedical and Clinical Case Reports 2, no. 2 (October 29, 2019): 82–83. http://dx.doi.org/10.36502/2019/asjbccr.6165.

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RNase H1 generally processes the RNA- DNA hybrids through non specific interaction between HBD and the ds RNA/DNA hybrid. There are no direct protein- protein interactions between the hybrid and HBD of RNase H1. The DNA binding region is highly conserved compared to RNA binding region and the Kd for RNA/DNA hybrid is less compared to ds RNA than to that of ds DNA [1]. HBD increases the processivity of RNase H1 and mutations in RNA binding region is tolerated compared to DBR. The RNA interacts between ɑ2 and β3 region with in the loop and with the protein in shallower minor groove.

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23

Das, Arundhati, Tanvi Sinha, Sharmishtha Shyamal, and Amaresh Chandra Panda. "Emerging Role of Circular RNA–Protein Interactions." Non-Coding RNA 7, no. 3 (August 4, 2021): 48. http://dx.doi.org/10.3390/ncrna7030048.

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Circular RNAs (circRNAs) are emerging as novel regulators of gene expression in various biological processes. CircRNAs regulate gene expression by interacting with cellular regulators such as microRNAs and RNA binding proteins (RBPs) to regulate downstream gene expression. The accumulation of high-throughput RNA–protein interaction data revealed the interaction of RBPs with the coding and noncoding RNAs, including recently discovered circRNAs. RBPs are a large family of proteins known to play a critical role in gene expression by modulating RNA splicing, nuclear export, mRNA stability, localization, and translation. However, the interaction of RBPs with circRNAs and their implications on circRNA biogenesis and function has been emerging in the last few years. Recent studies suggest that circRNA interaction with target proteins modulates the interaction of the protein with downstream target mRNAs or proteins. This review outlines the emerging mechanisms of circRNA–protein interactions and their functional role in cell physiology.

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24

Lang, Benjamin, Jae-Seong Yang, Mireia Garriga-Canut, Silvia Speroni, Moritz Aschern, Maria Gili, Tobias Hoffmann, Gian Gaetano Tartaglia, and Sebastian P. Maurer. "Matrix-screening reveals a vast potential for direct protein-protein interactions among RNA binding proteins." Nucleic Acids Research 49, no. 12 (June 16, 2021): 6702–21. http://dx.doi.org/10.1093/nar/gkab490.

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Abstract RNA-binding proteins (RBPs) are crucial factors of post-transcriptional gene regulation and their modes of action are intensely investigated. At the center of attention are RNA motifs that guide where RBPs bind. However, sequence motifs are often poor predictors of RBP-RNA interactions in vivo. It is hence believed that many RBPs recognize RNAs as complexes, to increase specificity and regulatory possibilities. To probe the potential for complex formation among RBPs, we assembled a library of 978 mammalian RBPs and used rec-Y2H matrix screening to detect direct interactions between RBPs, sampling &gt; 600 K interactions. We discovered 1994 new interactions and demonstrate that interacting RBPs bind RNAs adjacently in vivo. We further find that the mRNA binding region and motif preferences of RBPs deviate, depending on their adjacently binding interaction partners. Finally, we reveal novel RBP interaction networks among major RNA processing steps and show that splicing impairing RBP mutations observed in cancer rewire spliceosomal interaction networks. The dataset we provide will be a valuable resource for understanding the combinatorial interactions of RBPs with RNAs and the resulting regulatory outcomes.

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25

Stolarski, Ryszard. "Thermodynamics of specific protein-RNA interactions." Acta Biochimica Polonica 50, no. 2 (June 30, 2003): 297–318. http://dx.doi.org/10.18388/abp.2003_3688.

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Description of the recognition specificity between proteins and nucleic acids at the level of molecular interactions is one of the most challenging tasks in biophysics. It is key to understanding the course and control of gene expression and to the application of the thus acquired knowledge in chemotherapy. This review presents experimental results of thermodynamic studies and a discussion of the role of thermodynamics in formation and stability of functional protein-RNA complexes, with a special attention to the interactions involving mRNA 5' cap and cap-binding proteins in the initiation of protein biosynthesis in the eukaryotic cell. A theoretical framework for analysis of the thermodynamic parameters of protein-nucleic acid association is also briefly surveyed. Overshadowed by more spectacular achievements in structural studies, the thermodynamic investigations are of equal importance for full comprehension of biopolymers' activity in a quantitative way. In this regard, thermodynamics gives a direct insight into the energetic and entropic characteristics of complex macromolecular systems in their natural environment, aqueous solution, and thus complements the structural view derived from X-ray crystallography and multidimensional NMR. Further development of the thermodynamic approach toward interpretation of recognition and binding specificity in terms of molecular biophysics requires more profound contribution from statistical mechanics.

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26

Ramanathan, Muthukumar, Douglas F. Porter, and Paul A. Khavari. "Methods to study RNA–protein interactions." Nature Methods 16, no. 3 (February 25, 2019): 225–34. http://dx.doi.org/10.1038/s41592-019-0330-1.

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27

Autexier, C., and I. Triki. "Tetrahymena telomerase ribonucleoprotein RNA-protein interactions." Nucleic Acids Research 27, no. 10 (January 1, 1999): 2227–34. http://dx.doi.org/10.1093/nar/27.10.2227.

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28

Jones, S. "Protein-RNA interactions: a structural analysis." Nucleic Acids Research 29, no. 4 (February 15, 2001): 943–54. http://dx.doi.org/10.1093/nar/29.4.943.

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29

Rinn, John L., and Jernej Ule. "'Oming in on RNA–protein interactions." Genome Biology 15, no. 1 (2014): 401. http://dx.doi.org/10.1186/gb4158.

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30

Ule, Jernej. "Gene regulation via protein–RNA interactions." Methods 65, no. 3 (February 2014): 261–62. http://dx.doi.org/10.1016/j.ymeth.2014.02.015.

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31

Bink, H. H. J., and C. W. A. Pleij. "RNA-protein interactions in spherical viruses." Archives of Virology 147, no. 12 (November 1, 2002): 2261–79. http://dx.doi.org/10.1007/s00705-002-0891-6.

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32

Weissinger, Ronja, Lisa Heinold, Saira Akram, Ralf-Peter Jansen, and Orit Hermesh. "RNA Proximity Labeling: A New Detection Tool for RNA–Protein Interactions." Molecules 26, no. 8 (April 14, 2021): 2270. http://dx.doi.org/10.3390/molecules26082270.

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Multiple cellular functions are controlled by the interaction of RNAs and proteins. Together with the RNAs they control, RNA interacting proteins form RNA protein complexes, which are considered to serve as the true regulatory units for post-transcriptional gene expression. To understand how RNAs are modified, transported, and regulated therefore requires specific knowledge of their interaction partners. To this end, multiple techniques have been developed to characterize the interaction between RNAs and proteins. In this review, we briefly summarize the common methods to study RNA–protein interaction including crosslinking and immunoprecipitation (CLIP), and aptamer- or antisense oligonucleotide-based RNA affinity purification. Following this, we focus on in vivo proximity labeling to study RNA–protein interactions. In proximity labeling, a labeling enzyme like ascorbate peroxidase or biotin ligase is targeted to specific RNAs, RNA-binding proteins, or even cellular compartments and uses biotin to label the proteins and RNAs in its vicinity. The tagged molecules are then enriched and analyzed by mass spectrometry or RNA-Seq. We highlight the latest studies that exemplify the strength of this approach for the characterization of RNA protein complexes and distribution of RNAs in vivo.

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33

Masuda, Akio, Toshihiko Kawachi, and Kinji Ohno. "Rapidly Growing Protein-Centric Technologies to Extensively Identify Protein–RNA Interactions: Application to the Analysis of Co-Transcriptional RNA Processing." International Journal of Molecular Sciences 22, no. 10 (May 18, 2021): 5312. http://dx.doi.org/10.3390/ijms22105312.

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During mRNA transcription, diverse RNA-binding proteins (RBPs) are recruited to RNA polymerase II (RNAP II) transcription machinery. These RBPs bind to distinct sites of nascent RNA to co-transcriptionally operate mRNA processing. Recent studies have revealed a close relationship between transcription and co-transcriptional RNA processing, where one affects the other’s activity, indicating an essential role of protein–RNA interactions for the fine-tuning of mRNA production. Owing to their limited amount in cells, the detection of protein–RNA interactions specifically assembled on the transcribing RNAP II machinery still remains challenging. Currently, cross-linking and immunoprecipitation (CLIP) has become a standard method to detect in vivo protein–RNA interactions, although it requires a large amount of input materials. Several improved methods, such as infrared-CLIP (irCLIP), enhanced CLIP (eCLIP), and target RNA immunoprecipitation (tRIP), have shown remarkable enhancements in the detection efficiency. Furthermore, the utilization of an RNA editing mechanism or proximity labeling strategy has achieved the detection of faint protein–RNA interactions in cells without depending on crosslinking. This review aims to explore various methods being developed to detect endogenous protein–RNA interaction sites and discusses how they may be applied to the analysis of co-transcriptional RNA processing.

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34

Duss, Olivier, Galina A. Stepanyuk, Joseph D. Puglisi, and James R. Williamson. "Transient Protein-RNA Interactions Guide Nascent Ribosomal RNA Folding." Cell 179, no. 6 (November 2019): 1357–69. http://dx.doi.org/10.1016/j.cell.2019.10.035.

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35

Belsham, G. J., and N. Sonenberg. "RNA-protein interactions in regulation of picornavirus RNA translation." Microbiological reviews 60, no. 3 (1996): 499–511. http://dx.doi.org/10.1128/mmbr.60.3.499-511.1996.

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36

Belsham, G. J., and N. Sonenberg. "RNA-protein interactions in regulation of picornavirus RNA translation." Microbiological reviews 60, no. 3 (1996): 499–511. http://dx.doi.org/10.1128/mr.60.3.499-511.1996.

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37

Duss, Olivier, Galina A. Stepanyuk, Joseph D. Puglisi, and James R. Williamson. "Transient Protein-RNA Interactions Guide Nascent Ribosomal RNA Folding." Biophysical Journal 118, no. 3 (February 2020): 334a. http://dx.doi.org/10.1016/j.bpj.2019.11.1863.

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38

Sagar, Amit, and Bin Xue. "Recent Advances in Machine Learning Based Prediction of RNA-protein Interactions." Protein & Peptide Letters 26, no. 8 (September 11, 2019): 601–19. http://dx.doi.org/10.2174/0929866526666190619103853.

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The interactions between RNAs and proteins play critical roles in many biological processes. Therefore, characterizing these interactions becomes critical for mechanistic, biomedical, and clinical studies. Many experimental methods can be used to determine RNA-protein interactions in multiple aspects. However, due to the facts that RNA-protein interactions are tissuespecific and condition-specific, as well as these interactions are weak and frequently compete with each other, those experimental techniques can not be made full use of to discover the complete spectrum of RNA-protein interactions. To moderate these issues, continuous efforts have been devoted to developing high quality computational techniques to study the interactions between RNAs and proteins. Many important progresses have been achieved with the application of novel techniques and strategies, such as machine learning techniques. Especially, with the development and application of CLIP techniques, more and more experimental data on RNA-protein interaction under specific biological conditions are available. These CLIP data altogether provide a rich source for developing advanced machine learning predictors. In this review, recent progresses on computational predictors for RNA-protein interaction were summarized in the following aspects: dataset, prediction strategies, and input features. Possible future developments were also discussed at the end of the review.

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39

Lin, Yunqing, Tianyuan Liu, Tianyu Cui, Zhao Wang, Yuncong Zhang, Puwen Tan, Yan Huang, Jia Yu, and Dong Wang. "RNAInter in 2020: RNA interactome repository with increased coverage and annotation." Nucleic Acids Research 48, no. D1 (September 17, 2019): D189—D197. http://dx.doi.org/10.1093/nar/gkz804.

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Abstract Research on RNA-associated interactions has exploded in recent years, and increasing numbers of studies are not limited to RNA–RNA and RNA–protein interactions but also include RNA–DNA/compound interactions. To facilitate the development of the interactome and promote understanding of the biological functions and molecular mechanisms of RNA, we updated RAID v2.0 to RNAInter (RNA Interactome Database), a repository for RNA-associated interactions that is freely accessible at http://www.rna-society.org/rnainter/ or http://www.rna-society.org/raid/. Compared to RAID v2.0, new features in RNAInter include (i) 8-fold more interaction data and 94 additional species; (ii) more definite annotations organized, including RNA editing/localization/modification/structure and homology interaction; (iii) advanced functions including fuzzy/batch search, interaction network and RNA dynamic expression and (iv) four embedded RNA interactome tools: RIscoper, IntaRNA, PRIdictor and DeepBind. Consequently, RNAInter contains &gt;41 million RNA-associated interaction entries, involving more than 450 thousand unique molecules, including RNA, protein, DNA and compound. Overall, RNAInter provides a comprehensive RNA interactome resource for researchers and paves the way to investigate the regulatory landscape of cellular RNAs.

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40

Sola, Isabel, Pedro A. Mateos-Gomez, Fernando Almazan, Sonia Zuñiga, and Luis Enjuanes. "RNA-RNA and RNA-protein interactions in coronavirus replication and transcription." RNA Biology 8, no. 2 (March 2011): 237–48. http://dx.doi.org/10.4161/rna.8.2.14991.

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41

Zhang, Ning, Haoyu Lu, Yuting Chen, Zefeng Zhu, Qing Yang, Shuqin Wang, and Minghui Li. "PremPRI: Predicting the Effects of Missense Mutations on Protein–RNA Interactions." International Journal of Molecular Sciences 21, no. 15 (August 3, 2020): 5560. http://dx.doi.org/10.3390/ijms21155560.

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Protein–RNA interactions are crucial for many cellular processes, such as protein synthesis and regulation of gene expression. Missense mutations that alter protein–RNA interaction may contribute to the pathogenesis of many diseases. Here, we introduce a new computational method PremPRI, which predicts the effects of single mutations occurring in RNA binding proteins on the protein–RNA interactions by calculating the binding affinity changes quantitatively. The multiple linear regression scoring function of PremPRI is composed of three sequence- and eight structure-based features, and is parameterized on 248 mutations from 50 protein–RNA complexes. Our model shows a good agreement between calculated and experimental values of binding affinity changes with a Pearson correlation coefficient of 0.72 and the corresponding root-mean-square error of 0.76 kcal·mol−1, outperforming three other available methods. PremPRI can be used for finding functionally important variants, understanding the molecular mechanisms, and designing new protein–RNA interaction inhibitors.

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42

Burjoski, Vesper, and Anireddy S. N. Reddy. "The Landscape of RNA-Protein Interactions in Plants: Approaches and Current Status." International Journal of Molecular Sciences 22, no. 6 (March 11, 2021): 2845. http://dx.doi.org/10.3390/ijms22062845.

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RNAs transmit information from DNA to encode proteins that perform all cellular processes and regulate gene expression in multiple ways. From the time of synthesis to degradation, RNA molecules are associated with proteins called RNA-binding proteins (RBPs). The RBPs play diverse roles in many aspects of gene expression including pre-mRNA processing and post-transcriptional and translational regulation. In the last decade, the application of modern techniques to identify RNA–protein interactions with individual proteins, RNAs, and the whole transcriptome has led to the discovery of a hidden landscape of these interactions in plants. Global approaches such as RNA interactome capture (RIC) to identify proteins that bind protein-coding transcripts have led to the identification of close to 2000 putative RBPs in plants. Interestingly, many of these were found to be metabolic enzymes with no known canonical RNA-binding domains. Here, we review the methods used to analyze RNA–protein interactions in plants thus far and highlight the understanding of plant RNA–protein interactions these techniques have provided us. We also review some recent protein-centric, RNA-centric, and global approaches developed with non-plant systems and discuss their potential application to plants. We also provide an overview of results from classical studies of RNA–protein interaction in plants and discuss the significance of the increasingly evident ubiquity of RNA–protein interactions for the study of gene regulation and RNA biology in plants.

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43

Han, Shuo, Boxuan Simen Zhao, Samuel A. Myers, Steven A. Carr, Chuan He, and Alice Y. Ting. "RNA–protein interaction mapping via MS2- or Cas13-based APEX targeting." Proceedings of the National Academy of Sciences 117, no. 36 (August 24, 2020): 22068–79. http://dx.doi.org/10.1073/pnas.2006617117.

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RNA–protein interactions underlie a wide range of cellular processes. Improved methods are needed to systematically map RNA–protein interactions in living cells in an unbiased manner. We used two approaches to target the engineered peroxidase APEX2 to specific cellular RNAs for RNA-centered proximity biotinylation of protein interaction partners. Both an MS2-MCP system and an engineered CRISPR-Cas13 system were used to deliver APEX2 to the human telomerase RNA hTR with high specificity. One-minute proximity biotinylation captured candidate binding partners for hTR, including more than a dozen proteins not previously linked to hTR. We validated the interaction between hTR and theN6-methyladenosine (m6A) demethylase ALKBH5 and showed that ALKBH5 is able to erase the m6A modification on endogenous hTR. ALKBH5 also modulates telomerase complex assembly and activity. MS2- and Cas13-targeted APEX2 may facilitate the discovery of novel RNA–protein interactions in living cells.

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44

Tajmir-Riahi, H. A., C. N. N'soukpoé-Kossi, and D. Joly. "Structural analysis of protein–DNA and protein–RNA interactions by FTIR, UV-visible and CD spectroscopic methods." Spectroscopy 23, no. 2 (2009): 81–101. http://dx.doi.org/10.1155/2009/587956.

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In this chapter the fundamental question of how does protein–DNA or protein–RNA interaction affect the structures and dynamics of DNA, RNA and protein is addressed. Models for calf-thymus DNA and transfer RNA interactions with human serum albumin (HSA), ribonuclease A (RNase A) and deoxyribonuclease I (DNase I) are presented here, using Fourier Transform Infrared (FTIR) spectroscopy in conjunction with UV-visible and CD spectroscopic methods. In the models considered, the binding sites, stability and structural aspects of protein–DNA and protein–RNA are discussed and the effects of protein interaction on the secondary structures of DNA, RNA and protein were determined.

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45

Ripin, Nina, and Roy Parker. "Are stress granules the RNA analogs of misfolded protein aggregates?" RNA 28, no. 1 (October 20, 2021): 67–75. http://dx.doi.org/10.1261/rna.079000.121.

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Ribonucleoprotein granules are ubiquitous features of eukaryotic cells. Several observations argue that the formation of at least some RNP granules can be considered analogous to the formation of unfolded protein aggregates. First, unfolded protein aggregates form from the exposure of promiscuous protein interaction surfaces, while some mRNP granules form, at least in part, by promiscuous intermolecular RNA–RNA interactions due to exposed RNA surfaces when mRNAs are not engaged with ribosomes. Second, analogous to the role of protein chaperones in preventing misfolded protein aggregation, cells contain abundant “RNA chaperones” to limit inappropriate RNA–RNA interactions and prevent mRNP granule formation. Third, analogous to the role of protein aggregates in diseases, situations where RNA aggregation exceeds the capacity of RNA chaperones to disaggregate RNAs may contribute to human disease. Understanding that RNP granules can be considered as promiscuous, reversible RNA aggregation events allow insight into their composition and how cells have evolved functions for RNP granules.

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46

Tajmir-Riahi, H. A. "An overview of protein-DNA and protein-RNA interactions." Journal of the Iranian Chemical Society 3, no. 4 (December 2006): 297–304. http://dx.doi.org/10.1007/bf03245950.

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47

Li, Ao, Mengqu Ge, Yao Zhang, Chen Peng, and Minghui Wang. "Predicting Long Noncoding RNA and Protein Interactions Using Heterogeneous Network Model." BioMed Research International 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/671950.

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Recent study shows that long noncoding RNAs (lncRNAs) are participating in diverse biological processes and complex diseases. However, at present the functions of lncRNAs are still rarely known. In this study, we propose a network-based computational method, which is called lncRNA-protein interaction prediction based on Heterogeneous Network Model (LPIHN), to predict the potential lncRNA-protein interactions. First, we construct a heterogeneous network by integrating the lncRNA-lncRNA similarity network, lncRNA-protein interaction network, and protein-protein interaction (PPI) network. Then, a random walk with restart is implemented on the heterogeneous network to infer novel lncRNA-protein interactions. The leave-one-out cross validation test shows that our approach can achieve an AUC value of 96.0%. Some lncRNA-protein interactions predicted by our method have been confirmed in recent research or database, indicating the efficiency of LPIHN to predict novel lncRNA-protein interactions.

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48

Delgado Blanco, Javier, Leandro G. Radusky, Damiano Cianferoni, and Luis Serrano. "Protein-assisted RNA fragment docking (RnaX) for modeling RNA–protein interactions using ModelX." Proceedings of the National Academy of Sciences 116, no. 49 (November 15, 2019): 24568–73. http://dx.doi.org/10.1073/pnas.1910999116.

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RNA–protein interactions are crucial for such key biological processes as regulation of transcription, splicing, translation, and gene silencing, among many others. Knowing where an RNA molecule interacts with a target protein and/or engineering an RNA molecule to specifically bind to a protein could allow for rational interference with these cellular processes and the design of novel therapies. Here we present a robust RNA–protein fragment pair-based method, termed RnaX, to predict RNA-binding sites. This methodology, which is integrated into the ModelX tool suite (http://modelx.crg.es), takes advantage of the structural information present in all released RNA–protein complexes. This information is used to create an exhaustive database for docking and a statistical forcefield for fast discrimination of true backbone-compatible interactions. RnaX, together with the protein design forcefield FoldX, enables us to predict RNA–protein interfaces and, when sufficient crystallographic information is available, to reengineer the interface at the sequence-specificity level by mimicking those conformational changes that occur on protein and RNA mutagenesis. These results, obtained at just a fraction of the computational cost of methods that simulate conformational dynamics, open up perspectives for the engineering of RNA–protein interfaces.

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Tamburino, Alex M., Ebru Kaymak, Shaleen Shrestha, Amy D. Holdorf, Sean P. Ryder, and Albertha J. M. Walhout. "PRIMA: a gene-centered, RNA-to-protein method for mapping RNA-protein interactions." Translation 5, no. 1 (January 2, 2017): e1295130. http://dx.doi.org/10.1080/21690731.2017.1295130.

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50

Höfler, Simone, and Olivier Duss. "Interconnections between m⁶A RNA modification, RNA structure, and protein–RNA complex assembly." Life Science Alliance 7, no. 1 (November 7, 2023): e202302240. http://dx.doi.org/10.26508/lsa.202302240.

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Protein–RNA complexes exist in many forms within the cell, from stable machines such as the ribosome to transient assemblies like the spliceosome. All protein–RNA assemblies rely on spatially and temporally coordinated interactions between specific proteins and RNAs to achieve a functional form. RNA folding and structure are often critical for successful protein binding and protein–RNA complex formation. RNA modifications change the chemical nature of a given RNA and often alter its folding kinetics. Both these alterations can affect how and if proteins or other RNAs can interact with the modified RNA and assemble into complexes. N6-methyladenosine (m6A) is the most common base modification on mRNAs and regulatory noncoding RNAs and has been shown to impact RNA structure and directly modulate protein–RNA interactions. In this review, focusing on the mechanisms and available quantitative information, we discuss first how the METTL3/14 m6A writer complex is specifically targeted to RNA assisted by protein–RNA and other interactions to enable site-specific and co-transcriptional RNA modification and, once introduced, how the m6A modification affects RNA folding and protein–RNA interactions.

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