Dataset Information

A Dynamic Search Process Underlies MicroRNA Targeting.

ABSTRACT: Argonaute proteins play a central role in mediating post-transcriptional gene regulation by microRNAs (miRNAs). Argonautes use the nucleotide sequences in miRNAs as guides for identifying target messenger RNAs for repression. Here, we used single-molecule FRET to directly visualize how human Argonaute-2 (Ago2) searches for and identifies target sites in RNAs complementary to its miRNA guide. Our results suggest that Ago2 initially scans for target sites with complementarity to nucleotides 2-4 of the miRNA. This initial transient interaction propagates into a stable association when target complementarity extends to nucleotides 2-8. This stepwise recognition process is coupled to lateral diffusion of Ago2 along the target RNA, which promotes the target search by enhancing the retention of Ago2 on the RNA. The combined results reveal the mechanisms that Argonaute likely uses to efficiently identify miRNA target sites within the vast and dynamic agglomeration of RNA molecules in the living cell.

SUBMITTER: Chandradoss SD

PROVIDER: S-EPMC4768356 | biostudies-literature | 2015 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A Dynamic Search Process Underlies MicroRNA Targeting.

Chandradoss Stanley D SD Schirle Nicole T NT Szczepaniak Malwina M MacRae Ian J IJ Joo Chirlmin C

Cell 20150701 1

Argonaute proteins play a central role in mediating post-transcriptional gene regulation by microRNAs (miRNAs). Argonautes use the nucleotide sequences in miRNAs as guides for identifying target messenger RNAs for repression. Here, we used single-molecule FRET to directly visualize how human Argonaute-2 (Ago2) searches for and identifies target sites in RNAs complementary to its miRNA guide. Our results suggest that Ago2 initially scans for target sites with complementarity to nucleotides 2-4 of ...[more]

PMID: 26140593

Similar Datasets

Project description:BACKGROUND:There are many different types of microRNAs (miRNAs) and elucidating their functions is still under intensive research. A fundamental step in functional annotation of a new miRNA is to classify it into characterized miRNA families, such as those in Rfam and miRBase. With the accumulation of annotated miRNAs, it becomes possible to use deep learning-based models to classify different types of miRNAs. In this work, we investigate several key issues associated with successful application of deep learning models for miRNA classification. First, as secondary structure conservation is a prominent feature for noncoding RNAs including miRNAs, we examine whether secondary structure-based encoding improves classification accuracy. Second, as there are many more non-miRNA sequences than miRNAs, instead of assigning a negative class for all non-miRNA sequences, we test whether using softmax output can distinguish in-distribution and out-of-distribution samples. Finally, we investigate whether deep learning models can correctly classify sequences from small miRNA families. RESULTS:We present our trained convolutional neural network (CNN) models for classifying miRNAs using different types of feature learning and encoding methods. In the first method, we explicitly encode the predicted secondary structure in a matrix. In the second method, we use only the primary sequence information and one-hot encoding matrix. In addition, in order to reject sequences that should not be classified into targeted miRNA families, we use a threshold derived from softmax layer to exclude out-of-distribution sequences, which is an important feature to make this model useful for real transcriptomic data. The comparison with the state-of-the-art ncRNA classification tools such as Infernal shows that our method can achieve comparable sensitivity and accuracy while being significantly faster. CONCLUSION:Automatic feature learning in CNN can lead to better classification accuracy and sensitivity for miRNA classification and annotation. The trained models and also associated codes are freely available at https://github.com/HubertTang/DeepMir.

Dataset Information

A Dynamic Search Process Underlies MicroRNA Targeting.

Publications

A Dynamic Search Process Underlies MicroRNA Targeting.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets