scholarly journals Functional Equivalence of Retroviral MA Domains in Facilitating Psi RNA Binding Specificity by Gag

Viruses ◽  
2016 ◽  
Vol 8 (9) ◽  
pp. 256 ◽  
Author(s):  
Tiffiny Rye-McCurdy ◽  
Erik Olson ◽  
Shuohui Liu ◽  
Christiana Binkley ◽  
Joshua-Paolo Reyes ◽  
...  
Keyword(s):  
Rna Binding ◽  
Binding Specificity ◽  
Functional Equivalence ◽  
Psi Rna

scholarly journals Expanding RNA binding specificity and affinity of engineered PUF domains

2018 ◽  
Vol 46 (9) ◽  
pp. 4771-4782 ◽  
Author(s):  
Yang-Yang Zhao ◽  
Miao-Wei Mao ◽  
Wen-Jing Zhang ◽  
Jue Wang ◽  
Hai-Tao Li ◽  
...  
Keyword(s):  
Rna Binding ◽  
Binding Specificity ◽  
Binding Specificity And Affinity

scholarly journals Echovirus-9 protein 2C binds single-stranded RNA unspecifically

2000 ◽  
Vol 81 (10) ◽  
pp. 2481-2484 ◽  
Author(s):  
Marcus Klein ◽  
Hans J. Eggers ◽  
Birgit Nelsen-Salz
Keyword(s):  
Rna Binding ◽  
Binding Specificity ◽  
Secondary Structures ◽  
Multiple Functions ◽  
E Coli ◽  
Linear Dna ◽  
Gel Retardation ◽  
Rna Fragments ◽  
Poliovirus Protein

Polypeptide 2C is essential for picornavirus replication. Although many data on multiple functions of this highly conserved protein are available, the mechanism of RNA binding is still obscure. In this work, protein 2C of echovirus-9 strain Barty was expressed as a histidine-tagged protein in E. coli followed by nondenaturing purification to homogeneity. After incubation of 2C protein with different kinds of RNA fragments, binding was shown in gel retardation assays. Competition experiments revealed that 2C targets linear RNA unspecifically; however, single-stranded linear DNA does not react with this protein. In contrast to poliovirus, protein 2C of echovirus-9 only recognizes RNA with a low content of secondary structures. This may be a first hint of a different binding specificity of 2C in echo- and polioviruses.

scholarly journals Two RNA-binding Domains Determine the RNA-binding Specificity of Nucleolin

1997 ◽  
Vol 272 (20) ◽  
pp. 13109-13116 ◽  
Author(s):  
Guillaume Serin ◽  
Gérard Joseph ◽  
Laurence Ghisolfi ◽  
Marielle Bauzan ◽  
Monique Erard ◽  
...  
Keyword(s):  
Rna Binding ◽  
Binding Specificity ◽  
Binding Domains ◽  
Rna Binding Domains

scholarly journals Identification of RNA Binding Specificity for the TET-family Proteins

2008 ◽  
Vol 52 (1) ◽  
pp. 213-214 ◽  
Author(s):  
K. Takahama ◽  
K. Kino ◽  
S. Arai ◽  
R. Kurokawa ◽  
T. Oyoshi
Keyword(s):  
Rna Binding ◽  
Binding Specificity

scholarly journals A Caenorhabditis elegans PUF protein family with distinct RNA binding specificity

RNA ◽  
2008 ◽  
Vol 14 (8) ◽  
pp. 1550-1557 ◽  
Author(s):  
C. R. Stumpf ◽  
J. Kimble ◽  
M. Wickens
Keyword(s):  
Caenorhabditis Elegans ◽  
Rna Binding ◽  
Binding Specificity ◽  
Protein Family ◽  
Puf Protein

scholarly journals Augmented base pairing networks encode RNA-small molecule binding preferences

2020 ◽  
Vol 48 (14) ◽  
pp. 7690-7699
Author(s):  
Carlos Oliver ◽  
Vincent Mallet ◽  
Roman Sarrazin Gendron ◽  
Vladimir Reinharz ◽  
William L Hamilton ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Structure Function ◽  
Small Molecule ◽  
Rna Structure ◽  
Rna Binding ◽  
Binding Specificity ◽  
Representation Learning ◽  
Learning Task ◽  
Graph Representation ◽  
Base Pairing

Abstract RNA-small molecule binding is a key regulatory mechanism which can stabilize 3D structures and activate molecular functions. The discovery of RNA-targeting compounds is thus a current topic of interest for novel therapies. Our work is a first attempt at bringing the scalability and generalization abilities of machine learning methods to the problem of RNA drug discovery, as well as a step towards understanding the interactions which drive binding specificity. Our tool, RNAmigos, builds and encodes a network representation of RNA structures to predict likely ligands for novel binding sites. We subject ligand predictions to virtual screening and show that we are able to place the true ligand in the 71st–73rd percentile in two decoy libraries, showing a significant improvement over several baselines, and a state of the art method. Furthermore, we observe that augmenting structural networks with non-canonical base pairing data is the only representation able to uncover a significant signal, suggesting that such interactions are a necessary source of binding specificity. We also find that pre-training with an auxiliary graph representation learning task significantly boosts performance of ligand prediction. This finding can serve as a general principle for RNA structure-function prediction when data is scarce. RNAmigos shows that RNA binding data contains structural patterns with potential for drug discovery, and provides methodological insights for possible applications to other structure-function learning tasks. The source code, data and a Web server are freely available at http://rnamigos.cs.mcgill.ca.

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