scholarly journals Prediction of RNA binding sites in proteins from amino acid sequence

RNA ◽  
2006 ◽  
Vol 12 (8) ◽  
pp. 1450-1462 ◽  
Author(s):  
M. Terribilini
Keyword(s):  
Amino Acid ◽  
Amino Acid Sequence ◽  
Binding Sites ◽  
Rna Binding ◽  
Rna Binding Sites

scholarly journals Isolation and Analysis of Mutants of Double-Stranded-RNA Bacteriophage φ6 with Altered Packaging Specificity

2003 ◽  
Vol 185 (15) ◽  
pp. 4572-4577 ◽  
Author(s):  
Jian Qiao ◽  
Xueying Qiao ◽  
Yang Sun ◽  
Leonard Mindich
Keyword(s):  
Amino Acid ◽  
Conformational Changes ◽  
Binding Sites ◽  
Rna Binding ◽  
Structural Protein ◽  
Double Stranded Rna ◽  
Major Structural Protein ◽  
Nucleoside Triphosphates ◽  
Hydrolysis Of ◽  
Rna Binding Sites

ABSTRACT The genomes of bacteriophage φ6 and its relatives are packaged through a mechanism that involves the recognition and translocation of the three different plus strand transcripts of the segmented double-stranded RNA genomes into preformed polyhedral structures called procapsids or inner cores. This packaging requires hydrolysis of nucleoside triphosphates and takes place in the order S-M-L. Packaging is dependent on unique sequences of about 200 nucleotides near the 5′ ends of plus strand transcripts of the three genomic segments. Changes in the pac sequences lead to loss of packaging ability but can be suppressed by second-site changes in RNA or amino acid changes in protein P1, the major structural protein of the procapsid. It appears that P1 is the determinant of the RNA binding sites, and it is suggested that the binding sites overlap or are conformational changes of the same domains.

CSBPI_Site：Multi-Information Sources of Features to RNA Binding Sites Prediction

2021 ◽  
Vol 15 ◽  
Author(s):  
Lichao Zhang ◽  
Zihong Huang ◽  
Liang Kong
Keyword(s):  
Computational Model ◽  
Binding Sites ◽  
Noncoding Rna ◽  
Computational Models ◽  
Information Sources ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Gradient Boosting ◽  
Position Information ◽  
Rna Binding Sites

Background: RNA-binding proteins establish posttranscriptional gene regulation by coordinating the maturation, editing, transport, stability, and translation of cellular RNAs. The immunoprecipitation experiments could identify interaction between RNA and proteins, but they are limited due to the experimental environment and material. Therefore, it is essential to construct computational models to identify the function sites. Objective: Although some computational methods have been proposed to predict RNA binding sites, the accuracy could be further improved. Moreover, it is necessary to construct a dataset with more samples to design a reliable model. Here we present a computational model based on multi-information sources to identify RNA binding sites. Method: We construct an accurate computational model named CSBPI_Site, based on xtreme gradient boosting. The specifically designed 15-dimensional feature vector captures four types of information (chemical shift, chemical bond, chemical properties and position information). Results: The satisfied accuracy of 0.86 and AUC of 0.89 were obtained by leave-one-out cross validation. Meanwhile, the accuracies were slightly different (range from 0.83 to 0.85) among three classifiers algorithm, which showed the novel features are stable and fit to multiple classifiers. These results showed that the proposed method is effective and robust for noncoding RNA binding sites identification. Conclusion: Our method based on multi-information sources is effective to represent the binding sites information among ncRNAs. The satisfied prediction results of Diels-Alder riboz-yme based on CSBPI_Site indicates that our model is valuable to identify the function site.

scholarly journals Mapping of In Vivo RNA-Binding Sites by Ultraviolet (UV)-Cross-Linking Immunoprecipitation (CLIP)

2018 ◽  
Vol 2018 (12) ◽  
pp. pdb.top097931 ◽  
Author(s):  
Jennifer C. Darnell ◽  
Aldo Mele ◽  
Ka Ying Sharon Hung ◽  
Robert B. Darnell
Keyword(s):  
Binding Sites ◽  
Rna Binding ◽  
Cross Linking ◽  
Rna Binding Sites

scholarly journals Predicting RNA-binding sites of proteins using support vector machines and evolutionary information

2008 ◽  
Vol 9 (Suppl 12) ◽  
pp. S6 ◽  
Author(s):  
Cheng-Wei Cheng ◽  
Emily Su ◽  
Jenn-Kang Hwang ◽  
Ting-Yi Sung ◽  
Wen-Lian Hsu
Keyword(s):  
Support Vector Machines ◽  
Binding Sites ◽  
Rna Binding ◽  
Evolutionary Information ◽  
Support Vector ◽  
Vector Machines ◽  
Rna Binding Sites

scholarly journals Crimean-Congo hemorrhagic fever virus nucleocapsid protein harbors distinct RNA-binding sites in the stalk and head domains

2019 ◽  
Vol 294 (13) ◽  
pp. 5023-5037 ◽  
Author(s):  
Subbiah Jeeva ◽  
Sheema Mir ◽  
Adrain Velasquez ◽  
Jacquelyn Ragan ◽  
Aljona Leka ◽  
...  
Keyword(s):  
Binding Sites ◽  
Nucleocapsid Protein ◽  
Rna Binding ◽  
Hemorrhagic Fever ◽  
Fever Virus ◽  
Crimean Congo Hemorrhagic Fever ◽  
Hemorrhagic Fever Virus ◽  
Rna Binding Sites
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