title: SPOT-Seq-RNA: predicting protein-RNA complex structure and RNA-binding function by fold recognition and binding affinity prediction.
abstract: RNA-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/.
authors.affiliation: School of Informatics, Indiana University Purdue University, Indianapolis, IN, USA.
authors.forename: Yuedong
authors.initials: Y
authors.lastname: Yang
authors.forename: Huiying
authors.initials: H
authors.lastname: Zhao
authors.forename: Jihua
authors.initials: J
authors.lastname: Wang
authors.forename: Yaoqi
authors.initials: Y
authors.lastname: Zhou
mesh_terms: D001665:Binding Sites; D030541:Databases, Genetic; D008958:Models, Molecular; D015394:Molecular Structure; D011485:Protein Binding; D012313:RNA / Q000737:chemistry*; D016601:RNA-Binding Proteins / Q000737:chemistry*; D012984:Software; D064878:Web Browser
chemical_list: D016601:RNA-Binding Proteins; D012313:RNA