ACCBN: ant-Colony-clustering-based bipartite network method for predicting long non-coding RNA–protein interactions

: In recent years, more and more evidence indicates that long non-coding RNA (lncRNA) plays a significant role in the development of complex biological processes, especially in RNA progressing, chromatin modification, and cell differentiation, as well as many other processes. Surprisingly, lncRNA has an inseparable relationship with human diseases such as cancer. Therefore, only by knowing more about the function of lncRNA can we better solve the problems of human diseases. However, lncRNAs need to bind to proteins to perform their biomedical functions. So we can reveal the lncRNA function by studying the relationship between lncRNA and protein. But due to the limitations of traditional experiments, researchers often use computational prediction models to predict lncRNA protein interactions. In this review, we summarize several computational models of the lncRNA protein interactions prediction base on semi-supervised learning during the past two years, and introduce their advantages and shortcomings briefly. Finally, the future research directions of lncRNA protein interaction prediction are pointed out.

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LPI-IBNRA: Long Non-coding RNA-Protein Interaction Prediction Based on Improved Bipartite Network Recommender Algorithm

Frontiers in Genetics ◽

10.3389/fgene.2019.00343 ◽

2019 ◽

Vol 10 ◽

Cited By ~ 7

Author(s):

Guobo Xie ◽

Cuiming Wu ◽

Yuping Sun ◽

Zhiliang Fan ◽

Jianghui Liu

Keyword(s):

Protein Interaction ◽

Bipartite Network ◽

Interaction Prediction ◽

Non Coding Rna ◽

Protein Interaction Prediction ◽

Recommender Algorithm ◽

Long Non Coding Rna

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CROSSalive: a web server for predicting the in vivo structure of RNA molecules

Bioinformatics ◽

10.1093/bioinformatics/btz666 ◽

2019 ◽

Author(s):

Riccardo Delli Ponti ◽

Alexandros Armaos ◽

Andrea Vandelli ◽

Gian Gaetano Tartaglia

Keyword(s):

Protein Interactions ◽

Rna Structure ◽

Cross Validation ◽

Supplementary Information ◽

Supplementary Data ◽

High Confidence ◽

Rna Molecules ◽

Non Coding Rna ◽

Long Non Coding Rna

Abstract Motivation RNA structure is difficult to predict in vivo due to interactions with enzymes and other molecules. Here we introduce CROSSalive, an algorithm to predict the single- and double-stranded regions of RNAs in vivo using predictions of protein interactions. Results Trained on icSHAPE data in presence (m6a+) and absence of N6 methyladenosine modification (m6a-), CROSSalive achieves cross-validation accuracies between 0.70 and 0.88 in identifying high-confidence single- and double-stranded regions. The algorithm was applied to the long non-coding RNA Xist (17 900 nt, not present in the training) and shows an Area under the ROC curve of 0.83 in predicting structured regions. Availability and implementation CROSSalive webserver is freely accessible at http://service.tartaglialab.com/new_submission/crossalive Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

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Probing Long Non-coding RNA-Protein Interactions

Frontiers in Molecular Biosciences ◽

10.3389/fmolb.2017.00045 ◽

2017 ◽

Vol 4 ◽

Cited By ~ 15

Author(s):

Jasmine Barra ◽

Eleonora Leucci

Keyword(s):

Protein Interactions ◽

Non Coding Rna ◽

Long Non Coding Rna

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The SINEB1 element in the long non-coding RNA Malat1 is necessary for TDP-43 proteostasis

Nucleic Acids Research ◽

10.1093/nar/gkz1176 ◽

2019 ◽

Vol 48 (5) ◽

pp. 2621-2642 ◽

Cited By ~ 11

Author(s):

Tuan M Nguyen ◽

Elena B Kabotyanski ◽

Lucas C Reineke ◽

Jiaofang Shao ◽

Feng Xiong ◽

...

Keyword(s):

Protein Interactions ◽

Binding Sites ◽

Repetitive Element ◽

Nuclear Speckles ◽

Nuclear Retention ◽

Unfolded Protein ◽

Non Coding Rna ◽

Non Coding Rnas ◽

Protein Response ◽

Long Non Coding Rna

Abstract Transposable elements (TEs) comprise a large proportion of long non-coding RNAs (lncRNAs). Here, we employed CRISPR to delete a short interspersed nuclear element (SINE) in Malat1, a cancer-associated lncRNA, to investigate its significance in cellular physiology. We show that Malat1 with a SINE deletion forms diffuse nuclear speckles and is frequently translocated to the cytoplasm. SINE-deleted cells exhibit an activated unfolded protein response and PKR and markedly increased DNA damage and apoptosis caused by dysregulation of TDP-43 localization and formation of cytotoxic inclusions. TDP-43 binds stronger to Malat1 without the SINE and is likely ‘hijacked’ by cytoplasmic Malat1 to the cytoplasm, resulting in the depletion of nuclear TDP-43 and redistribution of TDP-43 binding to repetitive element transcripts and mRNAs encoding mitotic and nuclear-cytoplasmic regulators. The SINE promotes Malat1 nuclear retention by facilitating Malat1 binding to HNRNPK, a protein that drives RNA nuclear retention, potentially through direct interactions of the SINE with KHDRBS1 and TRA2A, which bind to HNRNPK. Losing these RNA–protein interactions due to the SINE deletion likely creates more available TDP-43 binding sites on Malat1 and subsequent TDP-43 aggregation. These results highlight the significance of lncRNA TEs in TDP-43 proteostasis with potential implications in both cancer and neurodegenerative diseases.

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The linear neighborhood propagation method for predicting long non-coding RNA–protein interactions

Neurocomputing ◽

10.1016/j.neucom.2017.07.065 ◽

2018 ◽

Vol 273 ◽

pp. 526-534 ◽

Cited By ~ 71

Author(s):

Wen Zhang ◽

Qianlong Qu ◽

Yunqiu Zhang ◽

Wei Wang

Keyword(s):

Protein Interactions ◽

Non Coding Rna ◽

Long Non Coding Rna

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CROSSalive: a web server for predicting the in vivo structure of RNA molecules

10.1101/626085 ◽

2019 ◽

Author(s):

Riccardo Delli Ponti ◽

Alexandros Armaos ◽

Gian Gaetano Tartaglia

Keyword(s):

Protein Interactions ◽

Rna Structure ◽

Rna Secondary Structure ◽

Sequence Length ◽

Test Set ◽

Rna Molecules ◽

Non Coding Rna ◽

Map Data ◽

Long Non Coding Rna

ABSTRACTHere we introduce CROSSalive, an algorithm to predict the RNA secondary structure profile (double and single stranded regions) in vivo and without sequence length limitations. Using predictions of protein interactions CROSSalive predicts the effect of N6 adenosine methylation (m6a) on RNA structure. Trained on icSHAPE data in presence (m6a+) and absence (m6a-) of methylation CROSSalive achieves an accuracy of 0.88 on the test set. The algorithm was also applied to the murine long non-coding RNA Xist (17900 nt, not present in the training) and shows a Pearson’s correlation of 0.45 with SHAPE-map data. CROSSalive webserver is freely accessible at the following page: http://service.tartaglialab.com/new_submission/crossalive

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