Yang, YuedongZhao, HuiyingWang, JihuaZhou, Yaoqi2015-12-022015-12-022014Yang, Y., Zhao, H., Wang, J., & Zhou, Y. (2014). SPOT-Seq-RNA: Predicting protein-RNA complex structure and RNA-binding function by fold recognition and binding affinity prediction. Methods in Molecular Biology (Clifton, N.J.), 1137, 119–130. http://doi.org/10.1007/978-1-4939-0366-5_9https://hdl.handle.net/1805/7589RNA-binding proteins (RBPs) play key roles in RNA metabolism and post-transcriptional regulation. Computational methods have been developed separately for prediction of RBPs and RNA-binding residues by machine-learning techniques and prediction of protein-RNA complex structures by rigid or semiflexible structure-to-structure docking. Here, we describe a template-based technique called SPOT-Seq-RNA that integrates prediction of RBPs, RNA-binding residues, and protein-RNA complex structures into a single package. This integration is achieved by combining template-based structure-prediction software, SPARKS X, with binding affinity prediction software, DRNA. This tool yields reasonable sensitivity (46 %) and high precision (84 %) for an independent test set of 215 RBPs and 5,766 non-RBPs. SPOT-Seq-RNA is computationally efficient for genome-scale prediction of RBPs and protein-RNA complex structures. Its application to human genome study has revealed a similar sensitivity and ability to uncover hundreds of novel RBPs beyond simple homology. The online server and downloadable version of SPOT-Seq-RNA are available at http://sparks-lab.org/server/SPOT-Seq-RNA/.en-USBinding SitesDatabases, GeneticModels, MolecularMolecular StructureProtein BindingRNA -- ChemistryRNA-Binding Proteins -- Chemistry