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Journal articles on the topic 'RNA-protein interactions'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Hall, Kathleen B. "RNA–protein interactions." Current Opinion in Structural Biology 12, no. 3 (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 (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 (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 (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 (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 (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 (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 (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 (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 (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–protei

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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 (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 (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 (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 (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 (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 (2012): 161–75. http://dx.doi.org/10.1002/wcms.1119.

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17

Kolář, Michal H., and Klára Hlouchová. "Evolution of protein-RNA interactions." Current Opinion in Structural Biology 94 (October 2025): 103109. https://doi.org/10.1016/j.sbi.2025.103109.

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18

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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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 (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 compl

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20

Imig, Jochen, Alexander Kanitz, and André P. Gerber. "RNA regulons and the RNA-protein interaction network." BioMolecular Concepts 3, no. 5 (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 a

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21

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 (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. Consider

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22

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 (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

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23

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 (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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24

Das, Arundhati, Tanvi Sinha, Sharmishtha Shyamal, and Amaresh Chandra Panda. "Emerging Role of Circular RNA–Protein Interactions." Non-Coding RNA 7, no. 3 (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, localiz

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25

Lang, Benjamin, Jae-Seong Yang, Mireia Garriga-Canut, et al. "Matrix-screening reveals a vast potential for direct protein-protein interactions among RNA binding proteins." Nucleic Acids Research 49, no. 12 (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 RB

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26

Stolarski, Ryszard. "Thermodynamics of specific protein-RNA interactions." Acta Biochimica Polonica 50, no. 2 (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

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27

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

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28

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

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29

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

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30

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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31

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

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32

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

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33

Sagar, Amit, and Bin Xue. "Recent Advances in Machine Learning Based Prediction of RNA-protein Interactions." Protein & Peptide Letters 26, no. 8 (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

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34

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 (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 transcrib

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35

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 (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 int

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36

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 (2019): 1357–69. http://dx.doi.org/10.1016/j.cell.2019.10.035.

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37

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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38

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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39

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 (2020): 334a. http://dx.doi.org/10.1016/j.bpj.2019.11.1863.

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40

Lin, Yunqing, Tianyuan Liu, Tianyu Cui, et al. "RNAInter in 2020: RNA interactome repository with increased coverage and annotation." Nucleic Acids Research 48, no. D1 (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

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41

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 (2011): 237–48. http://dx.doi.org/10.4161/rna.8.2.14991.

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42

Zhang, Ning, Haoyu Lu, Yuting Chen, et al. "PremPRI: Predicting the Effects of Missense Mutations on Protein–RNA Interactions." International Journal of Molecular Sciences 21, no. 15 (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 param

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43

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 (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 la

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44

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 (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

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45

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 int

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46

Ripin, Nina, and Roy Parker. "Are stress granules the RNA analogs of misfolded protein aggregates?" RNA 28, no. 1 (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, cell

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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

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48

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

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49

Höfler, Simone, and Olivier Duss. "Interconnections between m⁶A RNA modification, RNA structure, and protein–RNA complex assembly." Life Science Alliance 7, no. 1 (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 modi

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50

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 (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), take

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