Non-coding RNA-protein Interaction Research Articles

NPI-DCGNN: An Accurate Tool for Identifying ncRNA-Protein Interactions Using a Dual-Channel Graph Neural Network.

Noncoding RNA (NcRNA)-protein interactions (NPIs) play fundamentally important roles in carrying out cellular activities. Although various predictors based on molecular features and graphs have been published to boost the identification of NPIs, most of them often ignore the information between known NPIs or exhibit insufficient learning ability from graphs, posing a significant challenge in effectively identifying NPIs. To develop a more reliable and accurate predictor for NPIs, in this article, we propose NPI-DCGNN, an end-to-end NPI predictor based on a dual-channel graph neural network (DCGNN). NPI-DCGNN initially treats the known NPIs as an ncRNA-protein bipartite graph. Subsequently, for each ncRNA-protein pair, NPI-DCGNN extracts two local subgraphs centered around the ncRNA and protein, respectively, from the bipartite graph. After that, it utilizes a dual-channel graph representation learning layer based on GNN to generate high-level feature representations for the ncRNA-protein pair. Finally, it employs a fully connected network and output layer to predict whether an interaction exists between the pair of ncRNA and protein. Experimental results on four experimentally validated datasets demonstrate that NPI-DCGNN outperforms several state-of-the-art NPI predictors. Our case studies on the NPInter database further demonstrate the prediction power of NPI-DCGNN in predicting NPIs. With the availability of the source codes (https://github.com/zhangxin11111/NPI-DCGNN), we anticipate that NPI-DCGNN could facilitate the studies of ncRNA interactome by providing highly reliable NPI candidates for further experimental validation.

NPI-GNN: Predicting ncRNA-protein interactions with deep graph neural networks.

Noncoding RNAs (ncRNAs) play crucial roles in many biological processes. Experimental methods for identifying ncRNA-protein interactions (NPIs) are always costly and time-consuming. Many computational approaches have been developed as alternative ways. In this work, we collected five benchmarking datasets for predicting NPIs. Based on these datasets, we evaluated and compared the prediction performances of existing machine-learning based methods. Graph neural network (GNN) is a recently developed deep learning algorithm for link predictions on complex networks, which has never been applied in predicting NPIs. We constructed a GNN-based method, which is called Noncoding RNA-Protein Interaction prediction using Graph Neural Networks (NPI-GNN), to predict NPIs. The NPI-GNN method achieved comparable performance with state-of-the-art methods in a 5-fold cross-validation. In addition, it is capable of predicting novel interactions based on network information and sequence information. We also found that insufficient sequence information does not affect the NPI-GNN prediction performance much, which makes NPI-GNN more robust than other methods. As far as we can tell, NPI-GNN is the first end-to-end GNN predictor for predicting NPIs. All benchmarking datasets in this work and all source codes of the NPI-GNN method have been deposited with documents in a GitHub repo (https://github.com/AshuiRUA/NPI-GNN).

Recent Advances on the Semi-Supervised Learning for Long Non-Coding RNA-Protein Interactions Prediction: A Review.

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.

A path-based computational model for long non-coding RNA-protein interaction prediction

Recently, lncRNAs have attracted accumulating attentions because more and more experimental researches have shown lncRNA can play critical roles in many biological processes. Predicting potential interactions between lncRNAs and proteins are key to understand the lncRNAs biological functions. But traditional biological experiments are expensive and time-consuming, network similarity methods provide a powerful solution to computationally predict lncRNA-protein interactions. In this work, a novel path-based lncRNA-protein interaction (PBLPI) prediction model is proposed by integrating protein semantic similarity, lncRNA functional similarity, known human lncRNA-protein interactions, and Gaussian interaction profile kernel similarity. PBLPI model utilizes three interlinked sub-graphs to construct a heterogeneous graph, and then infers potential lncRNA-protein interactions through depth-first search algorithm. Consequently, PBLPI achieves reliable performance in the frameworks of 5-fold cross validation (average AUC is 0.9244 and AUPR is 0.6478). In the case study, we use “Mus musculus” data to further validate the reliability of PBLPI method. It is anticipated that PBLPI would become a useful tool to identify potential lncRNA-protein interactions.

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

According to the latest research, lncRNAs (long non-coding RNAs) play a broad and important role in various biological processes by interacting with proteins. However, identifying whether proteins interact with a specific lncRNA through biological experimental methods is difficult, costly, and time-consuming. Thus, many bioinformatics computational methods have been proposed to predict lncRNA-protein interactions. In this paper, we proposed a novel approach called Long non-coding RNA-Protein Interaction Prediction based on Improved Bipartite Network Recommender Algorithm (LPI-IBNRA). In the proposed method, we implemented a two-round resource allocation and eliminated the second-order correlations appropriately on the bipartite network. Experimental results illustrate that LPI-IBNRA outperforms five previous methods, with the AUC values of 0.8932 in leave-one-out cross validation (LOOCV) and 0.8819 ± 0.0052 in 10-fold cross validation, respectively. In addition, case studies on four lncRNAs were carried out to show the predictive power of LPI-IBNRA.

Long non-coding RNA-protein interaction: the preliminary step to track their biological functions

RNA is not only an intermediate for the flow of genetic information between DNA and protein. The transcription of the entire genomes generates a myriad non-protein coding RNA in addition to protein coding genes. Among these transcripts, lncRNA which is the least well understood and they cannot be considered as merely transcriptional noises. Here, we review the emerging role of lncRNA interactions with binding proteins (RBP) to perform multidirectional functions in transcription and epigenetic gene regulations, control of mRNA splicing, translation and post translational modifications. In addition, lncRNAs may acts as cellular thermostat that helps in preserving of cellular homeostasis. Keywords: lncRNAs; RBPs; cellular hemostasis

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

Long non-coding RNAs (lncRNAs) have gained wide attentions because of their essential functions in a variety of biological processes. Though precise functions and mechanisms of most lncRNAs remain unknown, studies show that lncRNAs generally exert functions through interactions with the corresponding RNA-binding proteins. The experimental detection of lncRNA–protein interactions is costly and time-consuming. In this paper, we propose a linear neighborhood propagation method (LPLNP), to predict lncRNA–protein interactions. LPLNP calculates the linear neighborhood similarity in the feature space, and transfers it into the interaction space, and predict unobserved interactions between the lncRNAs and proteins by a label propagation process. Our study shows that the LPLNP model based on the known lncRNA–protein interactions can produce high-accuracy performances, achieving an AUPR score of 0.42. Furthermore, we incorporate biological information of lncRNAs and proteins into the LPLNP model, and can further increase the performances, achieving an AUPR score of 0.4584. The case study demonstrates that many lncRNA–protein interactions predicted by our method can be validated, indicating that our method is a useful tool for lncRNA–protein interaction prediction. The source code and the dataset used in the paper are available at: https://github.com/BioMedicalBigDataMiningLabWhu/lncRNA-protein-interaction-prediction.

Computational Methods for Predicting ncRNA-protein Interactions.

RNA-protein interactions (RPIs) play an important role in many cellular processes. In particular, noncoding RNA-protein interactions (ncRPIs) are involved in various gene regulations and human complex diseases. High-throughput experiments have provided a large number of valuable information about ncRPIs, but these experiments are expensive and timeconsuming. Therefore, some computational approaches have been developed to predict ncRPIs efficiently and effectively. In this work, we will describe the recent advance of predicting ncRPIs from the following aspects: i) the dataset construction; ii) the sequence and structural feature representation, and iii) the machine learning algorithm. The current methods have successfully predicted ncRPIs, but most of them trained and tested on the small benchmark datasets derived from ncRNA-protein complexes in PDB database. The generalization performance and robust of these existing methods need to be further improved. Concomitant with the large numbers of ncRPIs generated by high-throughput technologies, three future directions for predicting ncRPIs with machine learning should be paid attention. One direction is that how to effectively construct the negative sample set. Another is the selection of novel and effective features from the sequences and structures of ncRNAs and proteins. The third is the design of powerful predictor.

Abstract PR06: The enhancer landscape involves a core noncoding RNA protein interaction network for C-MYC expression

Abstract Many long non-coding RNAs (lncRNAs) that regulate the capacity of cells to undergo oncogenic transformation still remain unknown. However, their involvement in chromatin organization has been an emerging principle in cancer epigenomics. This recent concept has been documented through recent studies of the gene -poor chromosomal region 8q24 surrounding the C-MYC locus to understand the mechanisms enforcing or suppressing tumor cell growth from C-MYC transcription levels. In light of such examples informing us of the DNA elements and the epigenomic composition that become integrated into molecular programs that regulate C-MYC, some lncRNAs in non-genic regions are considered landmarks that enforce chromatin state changes to accommodate C-MYC expression. Here, we use an integrative genomic and proteomic approach to gain insight of how an endogenously tagged lncRNA PCAT-1 utilizes the chromatin and biochemical context to shape C-MYC locus topology to promote tumor cell growth, survival and fitness. Our results now find that WDR74 forms an integrative hub involving recognition of the m7G cap of lncRNA transcripts along with, RNMT, RAM, BRD4, β catenin, EZH2 that assist in a program that enforce C-MYC transcription and stabilize transcripts through utilizing a comprehensive mixture of cis-acting DNA elements, lncRNAs and trans functioning transcriptional and RNA capping complexes for prostate tumor cell growth. Our results now place the lncRNA PCAT-1 and the novel interacting WD40 repeat protein WDR74 at the axis along with β catenin, BRD4 and EZH2, and additional RBPs as a non -canonical chromatin complex to shape the chromosomal topology that benefit prostate cancer cell growth. Citation Format: Fan Zhang, Won-min Song, SiDe Li, Vashisht Ajay, Francesca Aguilo, Anindya Bagchi, James A. Wohlschlegel, Bin Zhang, Martin Walsh. The enhancer landscape involves a core noncoding RNA protein interaction network for C-MYC expression. [abstract]. In: Proceedings of the AACR Special Conference on Noncoding RNAs and Cancer: Mechanisms to Medicines ; 2015 Dec 4-7; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2016;76(6 Suppl):Abstract nr PR06.

A Bipartite Network-based Method for Prediction of Long Non-coding RNA–protein Interactions

As one large class of non-coding RNAs (ncRNAs), long ncRNAs (lncRNAs) have gained considerable attention in recent years. Mutations and dysfunction of lncRNAs have been implicated in human disorders. Many lncRNAs exert their effects through interactions with the corresponding RNA-binding proteins. Several computational approaches have been developed, but only few are able to perform the prediction of these interactions from a network-based point of view. Here, we introduce a computational method named lncRNA–protein bipartite network inference (LPBNI). LPBNI aims to identify potential lncRNA–interacting proteins, by making full use of the known lncRNA–protein interactions. Leave-one-out cross validation (LOOCV) test shows that LPBNI significantly outperforms other network-based methods, including random walk (RWR) and protein-based collaborative filtering (ProCF). Furthermore, a case study was performed to demonstrate the performance of LPBNI using real data in predicting potential lncRNA–interacting proteins.

De novo prediction of RNA–protein interactions from sequence information

Protein-RNA interactions are fundamentally important in understanding cellular processes. In particular, non-coding RNA-protein interactions play an important role to facilitate biological functions in signalling, transcriptional regulation, and even the progression of complex diseases. However, experimental determination of protein-RNA interactions remains time-consuming and labour-intensive. Here, we develop a novel extended naïve-Bayes-classifier for de novo prediction of protein-RNA interactions, only using protein and RNA sequence information. Specifically, we first collect a set of known protein-RNA interactions as gold-standard positives and extract sequence-based features to represent each protein-RNA pair. To fill the gap between high dimensional features and scarcity of gold-standard positives, we select effective features by cutting a likelihood ratio score, which not only reduces the computational complexity but also allows transparent feature integration during prediction. An extended naïve Bayes classifier is then constructed using these effective features to train a protein-RNA interaction prediction model. Numerical experiments show that our method can achieve the prediction accuracy of 0.77 even though only a small number of protein-RNA interaction data are available. In particular, we demonstrate that the extended naïve-Bayes-classifier is superior to the naïve-Bayes-classifier by fully considering the dependences among features. Importantly, we conduct ncRNA pull-down experiments to validate the predicted novel protein-RNA interactions and identify the interacting proteins of sbRNA CeN72 in C. elegans, which further demonstrates the effectiveness of our method.

Predicting RNA-protein interactions using only sequence information.

BackgroundRNA-protein interactions (RPIs) play important roles in a wide variety of cellular processes, ranging from transcriptional and post-transcriptional regulation of gene expression to host defense against pathogens. High throughput experiments to identify RNA-protein interactions are beginning to provide valuable information about the complexity of RNA-protein interaction networks, but are expensive and time consuming. Hence, there is a need for reliable computational methods for predicting RNA-protein interactions.ResultsWe propose RPISeq, a family of classifiers for predicting RNA-protein interactions using only sequence information. Given the sequences of an RNA and a protein as input, RPIseq predicts whether or not the RNA-protein pair interact. The RNA sequence is encoded as a normalized vector of its ribonucleotide 4-mer composition, and the protein sequence is encoded as a normalized vector of its 3-mer composition, based on a 7-letter reduced alphabet representation. Two variants of RPISeq are presented: RPISeq-SVM, which uses a Support Vector Machine (SVM) classifier and RPISeq-RF, which uses a Random Forest classifier. On two non-redundant benchmark datasets extracted from the Protein-RNA Interface Database (PRIDB), RPISeq achieved an AUC (Area Under the Receiver Operating Characteristic (ROC) curve) of 0.96 and 0.92. On a third dataset containing only mRNA-protein interactions, the performance of RPISeq was competitive with that of a published method that requires information regarding many different features (e.g., mRNA half-life, GO annotations) of the putative RNA and protein partners. In addition, RPISeq classifiers trained using the PRIDB data correctly predicted the majority (57-99%) of non-coding RNA-protein interactions in NPInter-derived networks from E. coli, S. cerevisiae, D. melanogaster, M. musculus, and H. sapiens.ConclusionsOur experiments with RPISeq demonstrate that RNA-protein interactions can be reliably predicted using only sequence-derived information. RPISeq offers an inexpensive method for computational construction of RNA-protein interaction networks, and should provide useful insights into the function of non-coding RNAs. RPISeq is freely available as a web-based server at http://pridb.gdcb.iastate.edu/RPISeq/.