RPI-Pred: predicting ncRNA-protein interaction using sequence and structural information

Abstract

RNA-protein complexes are essential in mediating important fundamental cellular processes, such as transport and localization. In particular, ncRNA-protein interactions play an important role in post-transcriptional gene regulation like mRNA localization, mRNA stabilization, poly-adenylation, splicing and translation. The experimental methods to solve RNA-protein interaction prediction problem remain expensive and time-consuming. Here, we present the RPI-Pred (RNA-protein interaction predictor), a new support-vector machine-based method, to predict protein-RNA interaction pairs, based on both the sequences and structures. The results show that RPI-Pred can correctly predict RNA-protein interaction pairs with ∼94% prediction accuracy when using sequence and experimentally determined protein and RNA structures, and with ∼83% when using sequences and predicted protein and RNA structures. Further, our proposed method RPI-Pred was superior to other existing ones by predicting more experimentally validated ncRNA-protein interaction pairs from different organisms. Motivated by the improved performance of RPI-Pred, we further applied our method for reliable construction of ncRNA-protein interaction networks. The RPI-Pred is publicly available at: http://ctsb.is.wfubmc.edu/projects/rpi-pred.

LncRna CPS1-IT1 Suppresses Cell Proliferation, Invasion and Metastasis in Colorectal Cancer

“…In addition to miRNAs, much evidence has demonstrated that lncRNAs, defined as a type of RNA with a length of > 200 nucleotides, can regulate physiological and pathological processes by interacting with DNA, RNA and protein molecules [8, 28, 29]. The lncRNAs are generally deregulated in a wide variety of diseases, including Alzheimer’s disease, heart disease and cancer [30-34].…”

Bioinformatics tools for lncRNA research

“…Several computational methods have been developed for predicting RNAprotein interactions [47,48,49,50,51]. In these methods, machine learning approaches, such as Fisher's linear discriminant analysis (LDA), support vector machine (SVM), and random forest (RF), were used to discriminate the interacting RNA-protein pairs from non-interacting pairs.…”

“…These two methods focus on the prediction of lncRNA-protein interactions, and were benchmarked by using a few experimentally validated lncRNA-protein interactions, including HO-TAIR and XIST lncRNA. The third method, RPI-Pred [50], is a tool for predicting RNA-protein interactions using not only primary sequences but also the three dimensional structures of an RNA and a protein for the input data. In this method, structural motifs of proteins (called Protein Blocks)…”

Structure Prediction and Binding Site Analysis of Hepatotoxic Microcystin-LR Degrading MlrC-Like Protein from <i>Burkholderia</i> sp. using Computational Approaches

“…However, the three-dimensional configuration of MlrC-like protein or its interaction with MCYST-LR has not been worked out, although it could lead to valuable information related to the molecular structure, role and efficient binding with the protein. Theoretical approaches have been applied for structure prediction (Suresh et al, 2015). We report 3D structure of MlrC-like protein implementing a combined in silico approach like threading and abinitio for identification via composite modeling.…”