The analogous and opposing roles of double-stranded RNA-binding proteins in bacterial resistance

Chemical shift assignments of DRB2 domains, a dsRNA binding protein in A. thaliana RNAi pathway

In Arabidopsis thaliana, micro-RNA regulation is primarily controlled by DCL1, an RNase III enzyme, and its associated proteins. DCL1, together with DRB2, governs a specific group of miRNAs that induce the inhibition of target mRNA translation. DRB2 is a multi-domain protein containing two N-terminal dsRNA binding domains (dsRBD) separated by a linker, followed by an unstructured C-terminal tail. The two dsRBDs in DRB2 are involved in recognizing the miRNA precursor and aiding DCL1 in generating 21-nucleotide-long miRNA. Our study presents a nearly complete backbone chemical shift assignment of both dsRBDs and the side-chain assignment of the first dsRBD in DRB2. The data presented here lays the groundwork for future investigations into the structural, dynamic, and functional aspects of DRB2.

Phased small RNA-mediated systemic signaling in plants

Systemic acquired resistance (SAR) involves the generation of systemically transported signal that arms distal plant parts against secondary infections. We show that two phased 21-nucleotide (nt) trans-acting small interfering RNA3a RNAs (tasi-RNA) derived from TAS3a and synthesized within 3 hours of pathogen infection are the early mobile signal in SAR. TAS3a undergoes alternate polyadenylation, resulting in the generation of 555- and 367-nt transcripts. The 555-nt transcripts likely serves as the sole precursor for tasi-RNAs D7 and D8, which cleave Auxin response factors (ARF) 2, 3, and 4 to induce SAR. Conversely, increased expression of ARF3 represses SAR. Knockout mutations in TAS3a or RNA silencing components required for tasi-RNA biogenesis compromise SAR without altering levels of known SAR-inducing chemicals. Both tasi-ARFs and the 367-nt transcripts are mobile and transported via plasmodesmata. Together, we show that tasi-ARFs are the early mobile signal in SAR.

EDLMFC: an ensemble deep learning framework with multi-scale features combination for ncRNA-protein interaction prediction

Background

Non-coding RNA (ncRNA) and protein interactions play essential roles in various physiological and pathological processes. The experimental methods used for predicting ncRNA–protein interactions are time-consuming and labor-intensive. Therefore, there is an increasing demand for computational methods to accurately and efficiently predict ncRNA–protein interactions.

Results

In this work, we presented an ensemble deep learning-based method, EDLMFC, to predict ncRNA–protein interactions using the combination of multi-scale features, including primary sequence features, secondary structure sequence features, and tertiary structure features. Conjoint k-mer was used to extract protein/ncRNA sequence features, integrating tertiary structure features, then fed into an ensemble deep learning model, which combined convolutional neural network (CNN) to learn dominating biological information with bi-directional long short-term memory network (BLSTM) to capture long-range dependencies among the features identified by the CNN. Compared with other state-of-the-art methods under five-fold cross-validation, EDLMFC shows the best performance with accuracy of 93.8%, 89.7%, and 86.1% on RPI1807, NPInter v2.0, and RPI488 datasets, respectively. The results of the independent test demonstrated that EDLMFC can effectively predict potential ncRNA–protein interactions from different organisms. Furtherly, EDLMFC is also shown to predict hub ncRNAs and proteins presented in ncRNA–protein networks of Mus musculus successfully.

Conclusions

In general, our proposed method EDLMFC improved the accuracy of ncRNA–protein interaction predictions and anticipated providing some helpful guidance on ncRNA functions research. The source code of EDLMFC and the datasets used in this work are available at https://github.com/JingjingWang-87/EDLMFC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04069-9.

RNA-binding protein recognition based on multi-view deep feature and multi-label learning

RNA-binding protein (RBP) is a class of proteins that bind to and accompany RNAs in regulating biological processes. An RBP may have multiple target RNAs, and its aberrant expression can cause multiple diseases. Methods have been designed to predict whether a specific RBP can bind to an RNA and the position of the binding site using binary classification model. However, most of the existing methods do not take into account the binding similarity and correlation between different RBPs. While methods employing multiple labels and Long Short Term Memory Network (LSTM) are proposed to consider binding similarity between different RBPs, the accuracy remains low due to insufficient feature learning and multi-label learning on RNA sequences. In response to this challenge, the concept of RNA-RBP Binding Network (RRBN) is proposed in this paper to provide theoretical support for multi-label learning to identify RBPs that can bind to RNAs. It is experimentally shown that the RRBN information can significantly improve the prediction of unknown RNA-RBP interactions. To further improve the prediction accuracy, we present the novel computational method iDeepMV which integrates multi-view deep learning technology under the multi-label learning framework. iDeepMV first extracts data from the views of amino acid sequence and dipeptide component based on the RNA sequences as the original view. Deep neural network models are then designed for the respective views to perform deep feature learning. The extracted deep features are fed into multi-label classifiers which are trained with the RNA-RBP interaction information for the three views. Finally, a voting mechanism is designed to make comprehensive decision on the results of the multi-label classifiers. Our experimental results show that the prediction performance of iDeepMV, which combines multi-view deep feature learning models with RNA-RBP interaction information, is significantly better than that of the state-of-the-art methods. iDeepMV is freely available at http://www.csbio.sjtu.edu.cn/bioinf/iDeepMV for academic use. The code is freely available at http://github.com/uchihayht/iDeepMV.