Statler College of Engineering and Mining Resources
Lane Department of Computer Science and Electrical Engineering
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.
Digital Commons Citation
Suresh, V; Liu, Liang; Adjeroh, Donald; and Zhou, Xiaobo, "RPI-Pred: predicting ncRNA-protein interaction using sequence and structural information" (2015). Faculty & Staff Scholarship. 2251.
Suresh, V., Liu, L., Adjeroh, D., & Zhou, X. (2015). RPI-Pred: predicting ncRNA-protein interaction using sequence and structural information. Nucleic Acids Research, 43(3), 1370–1379. https://doi.org/10.1093/nar/gkv020