Helix‐7 in Argonaute2 shapes the microRNA seed region for rapid target recognition

Klum, Shannon; Chandradoss, Stanley D.; Schirle, N.T.; Joo, Chirlmin; MacRae, Ian J.

doi:10.15252/embj.201796474

Cited by 71 publications

(102 citation statements)

References 44 publications

(100 reference statements)

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“…The performance 672 gap between miRAW and the descriptor-based approaches suggests that current 673 knowledge is still not sufficient to accurately capture all aspects of the miRNA targeting 674 process. This is consistent with recent studies, e.g., [3], [7] and [31], which 675 demonstrate that the whole miRNA can play a relevant role in many functional miRNA 676 targets. Based on these findings, we took advantage of deep learning methodology to 677 incorporate the whole miRNA sequence for target prediction.…”

supporting

confidence: 89%

“…This also aligns 697 with recent studies which investigated potential binding sites based on microarray 698 expression data that indicate a significant role for miRNA nucleotide 9 [8] and 699 structure studies [31] that demonstrate off-site targeting in the 3' region of the miRNA 700 is achieved by a pivoting structural element α helix-7 within the Ago2 protein that region. This allows Ago2 to rapidly screen potential targets to dynamically search for 703 non-canonical sites.…”

supporting

confidence: 81%

“…29 Almost all target prediction methods are rule-based or adopt machine learning (ML) 30 methodology with varying success. Rule-based systems incorporate various 31 human-crafted descriptors to represent miRNA:gene target binding (e.g. type of pairs in 32 the site, binding stability, or conservation of the target site among species).…”

mentioning

confidence: 99%

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miRAW: A deep learning approach to predict miRNA targets by analyzing whole miRNA transcripts

Plà

Zhong

Rayner

2017

Preprint

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MicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression by binding to partially complementary regions within the 3'UTR of their target genes. Computational methods play an important role in target prediction and assume that the miRNA "seed region" (nt 2 to 8) is required for functional targeting, but typically only identify 80% of known bindings. Recent studies have highlighted a role for the entire miRNA, suggesting that a more flexible methodology is needed.We present a novel approach for miRNA target prediction based on Deep Learning (DL) which, rather than incorporating any knowledge (such as seed regions), investigates the entire miRNA and 3'UTR mRNA nucleotides to learn a uninhibited set of feature descriptors related to the targeting process.We collected more than 150,000 experimentally validated homo sapiens miRNA:gene targets and cross referenced them with different CLIP-Seq, CLASH and iPAR-CLIP datasets to obtain 20,000 validated miRNA:gene exact target sites. Using this data, we implemented and trained a deep neural network -composed of autoencoders and a feed-forward network -able to automatically learn features describing miRNA-mRNA interactions and assess functionality. Predictions were then refined using information such as site location or site accessibility energy.In a comparison using independent datasets, our DL approach consistently outperformed existing prediction methods, recognizing the seed region as a common feature in the targeting process, but also identifying the role of pairings outside this region. Thermodynamic analysis also suggests that site accessibility plays a role in targeting but that it cannot be used as a sole indicator for functionality.Data and source code available at: https://bitbucket.org/account/user/bipous/projects/MIRAW Author summary microRNAs are small RNA molecules that regulate biological processes by binding to the 3'UTR of a gene and their dysregulation is associated with several diseases. Computationally predicting these targets remains a challenge as they only partially match their target and so there can be hundreds of targets for a single microRNA.Current tools assume that most of the knowledge defining a microRNA-gene interaction can be captured by analysing the binding produced in the seed region (≈ the first 8nt in the miRNA). However, recent studies show that the whole microRNA can be important and form non-canonical targets. Here, we use a target prediction methodology miRAW: deep learning for miRNA target prediction -Pla et al.

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supporting

confidence: 89%

supporting

confidence: 81%

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miRAW: A deep learning approach to predict miRNA targets by analyzing whole miRNA transcripts

Plà

Zhong

Rayner

2017

Preprint

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“…In principle, targeting efficacy should be a function of the affinity between target RNAs and AGO-miRNA complexes. However, binding affinities have been determined for only a few target sequences of only three miRNAs (5)(6)(7)(8)(9)(10)(11).…”

Section: Introductionmentioning

confidence: 99%

The biochemical basis of microRNA targeting efficacy

McGeary

Lin

Shi

et al. 2018

Preprint

112

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MicroRNAs (miRNAs) act within Argonaute proteins to guide repression of mRNA targets. Although various approaches have provided insight into target recognition, the sparsity of miRNA-target affinity measurements has limited understanding and prediction of targeting efficacy. Here, we adapted RNA bind-n-seq to enable measurement of relative binding affinities between Argonaute-miRNA complexes and all ≤12-nucleotide sequences. This approach revealed noncanonical target sites unique to each miRNA, miRNA-specific differences in canonical target-site affinities, and a 100-fold impact of dinucleotides flanking each site. These data enabled construction of a biochemical model of miRNA-mediated repression, which was extended to all miRNA sequences using a convolutional neural network. This model substantially improved prediction of cellular repression, thereby providing a biochemical basis for quantitatively integrating miRNAs into gene-regulatory networks.

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“…Base pairing beyond nucleotide 7 requires a conformational change of helix-7 to accommodate. At the same time, this conformational change stabilizes the base pairing of nucleotides 6 and 7 of the guide (Figure 4(d)) after which base pairing of additional nucleotides can also take place [66]. Fluorescence single-molecule in vitro studies have given further proof of this two-state binding mode.…”

Section: Experimental Evidence Of Two-state Searchmentioning

confidence: 86%

Facilitated diffusion of Argonaute-mediated target search

Cui

Joo

2019

RNA Biology

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Argonaute (Ago) proteins are of key importance in many cellular processes. In eukaryotes, Ago can induce translational repression followed by deadenylation and degradation of mRNA molecules through base pairing of microRNAs (miRNAs) with a complementary target on a mRNA sequence. In bacteria, Ago eliminates foreign DNA through base pairing of siDNA (small interfering DNA) with a target on a DNA sequence. Effective targeting activities of Ago require fast recognition of the cognate target sequence among numerous off-target sites. Other target search proteins such as transcription factors (TFs) are known to rely on facilitated diffusion for this goal, but it is undetermined to what extent these small nucleic acid-guided proteins utilize this mechanism. Here, we review recent single-molecule studies on Ago target search. We discuss the consequences of the recent findings on the search mechanism. Furthermore, we discuss the open standing research questions that need to be addressed for a complete picture of facilitated target search by small nucleic acids. ARTICLE HISTORY

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Helix‐7 in Argonaute2 shapes the microRNA seed region for rapid target recognition

Cited by 71 publications

References 44 publications

miRAW: A deep learning approach to predict miRNA targets by analyzing whole miRNA transcripts

miRAW: A deep learning approach to predict miRNA targets by analyzing whole miRNA transcripts

The biochemical basis of microRNA targeting efficacy

Facilitated diffusion of Argonaute-mediated target search

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