title: Predicting protein-binding RNA nucleotides with consideration of binding partners.
abstract: In recent years several computational methods have been developed to predict RNA-binding sites in protein. Most of these methods do not consider interacting partners of a protein, so they predict the same RNA-binding sites for a given protein sequence even if the protein binds to different RNAs. Unlike the problem of predicting RNA-binding sites in protein, the problem of predicting protein-binding sites in RNA has received little attention mainly because it is much more difficult and shows a lower accuracy on average. In our previous study, we developed a method that predicts protein-binding nucleotides from an RNA sequence. In an effort to improve the prediction accuracy and usefulness of the previous method, we developed a new method that uses both RNA and protein sequence data. In this study, we identified effective features of RNA and protein molecules and developed a new support vector machine (SVM) model to predict protein-binding nucleotides from RNA and protein sequence data. The new model that used both protein and RNA sequence data achieved a sensitivity of 86.5%, a specificity of 86.2%, a positive predictive value (PPV) of 72.6%, a negative predictive value (NPV) of 93.8% and Matthews correlation coefficient (MCC) of 0.69 in a 10-fold cross validation; it achieved a sensitivity of 58.8%, a specificity of 87.4%, a PPV of 65.1%, a NPV of 84.2% and MCC of 0.48 in independent testing. For comparative purpose, we built another prediction model that used RNA sequence data alone and ran it on the same dataset. In a 10 fold-cross validation it achieved a sensitivity of 85.7%, a specificity of 80.5%, a PPV of 67.7%, a NPV of 92.2% and MCC of 0.63; in independent testing it achieved a sensitivity of 67.7%, a specificity of 78.8%, a PPV of 57.6%, a NPV of 85.2% and MCC of 0.45. In both cross-validations and independent testing, the new model that used both RNA and protein sequences showed a better performance than the model that used RNA sequence data alone in most performance measures. To the best of our knowledge, this is the first sequence-based prediction of protein-binding nucleotides in RNA which considers the binding partner of RNA. The new model will provide valuable information for designing biochemical experiments to find putative protein-binding sites in RNA with unknown structure.
authors.affiliation: Department of Computer Science and Engineering, Inha University, Incheon, South Korea.
authors.forename: Narankhuu
authors.initials: N
authors.lastname: Tuvshinjargal
authors.affiliation: Department of Computer Science and Engineering, Inha University, Incheon, South Korea.
authors.forename: Wook
authors.initials: W
authors.lastname: Lee
authors.affiliation: Department of Computer Science and Engineering, Inha University, Incheon, South Korea.
authors.forename: Byungkyu
authors.initials: B
authors.lastname: Park
authors.affiliation: Department of Computer Science and Engineering, Inha University, Incheon, South Korea. Electronic address: khan@inha.ac.kr.
authors.forename: Kyungsook
authors.initials: K
authors.lastname: Han
mesh_terms: D000465:Algorithms; D001483:Base Sequence; D001665:Binding Sites; D019295:Computational Biology; D018360:Crystallography, X-Ray; D030562:Databases, Protein; D008969:Molecular Sequence Data; D009711:Nucleotides / Q000737:chemistry*; D011237:Predictive Value of Tests; D011485:Protein Binding; D011487:Protein Conformation; D012313:RNA / Q000737:chemistry*; D012372:ROC Curve; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D017423:Sequence Analysis, RNA; D060388:Support Vector Machine
chemical_list: D009711:Nucleotides; D012313:RNA
keywords: Binding partners; Binding sites; RNA–protein interactions; Sequence-based prediction