title: Predicting protein-binding regions in RNA using nucleotide profiles and compositions.
abstract: Motivated by the increased amount of data on protein-RNA interactions and the availability of complete genome sequences of several organisms, many computational methods have been proposed to predict binding sites in protein-RNA interactions. However, most computational methods are limited to finding RNA-binding sites in proteins instead of protein-binding sites in RNAs. Predicting protein-binding sites in RNA is more challenging than predicting RNA-binding sites in proteins. Recent computational methods for finding protein-binding sites in RNAs have several drawbacks for practical use.
  
  
  
  We developed a new support vector machine (SVM) model for predicting protein-binding regions in mRNA sequences. The model uses sequence profiles constructed from log-odds scores of mono- and di-nucleotides and nucleotide compositions. The model was evaluated by standard 10-fold cross validation, leave-one-protein-out (LOPO) cross validation and independent testing. Since actual mRNA sequences have more non-binding regions than protein-binding regions, we tested the model on several datasets with different ratios of protein-binding regions to non-binding regions. The best performance of the model was obtained in a balanced dataset of positive and negative instances. 10-fold cross validation with a balanced dataset achieved a sensitivity of 91.6%, a specificity of 92.4%, an accuracy of 92.0%, a positive predictive value (PPV) of 91.7%, a negative predictive value (NPV) of 92.3% and a Matthews correlation coefficient (MCC) of 0.840. LOPO cross validation showed a lower performance than the 10-fold cross validation, but the performance remains high (87.6% accuracy and 0.752 MCC). In testing the model on independent datasets, it achieved an accuracy of 82.2% and an MCC of 0.656. Testing of our model and other state-of-the-art methods on a same dataset showed that our model is better than the others.
  
  
  
  Sequence profiles of log-odds scores of mono- and di-nucleotides were much more powerful features than nucleotide compositions in finding protein-binding regions in RNA sequences. But, a slight performance gain was obtained when using the sequence profiles along with nucleotide compositions. These are preliminary results of ongoing research, but demonstrate the potential of our approach as a powerful predictor of protein-binding regions in RNA. The program and supporting data are available at http://bclab.inha.ac.kr/RBPbinding .
authors.affiliation: Department of Computer Science and Engineering, Inha University, Incheon, 22212, South Korea.
authors.forename: Daesik
authors.initials: D
authors.lastname: Choi
authors.affiliation: Department of Computer Science and Engineering, Inha University, Incheon, 22212, South Korea.
authors.forename: Byungkyu
authors.initials: B
authors.lastname: Park
authors.affiliation: Department of Computer Science and Engineering, Inha University, Incheon, 22212, South Korea.
authors.forename: Hanju
authors.initials: H
authors.lastname: Chae
authors.affiliation: Department of Computer Science and Engineering, Inha University, Incheon, 22212, South Korea.
authors.forename: Wook
authors.initials: W
authors.lastname: Lee
authors.affiliation: Department of Computer Science and Engineering, Inha University, Incheon, 22212, South Korea. khan@inha.ac.kr.
authors.forename: Kyungsook
authors.initials: K
authors.lastname: Han
mesh_terms: D019295:Computational Biology / Q000379:methods*; D006801:Humans; D009711:Nucleotides / Q000378:metabolism*; D012313:RNA / Q000737:chemistry* / Q000378:metabolism*; D016601:RNA-Binding Proteins / Q000378:metabolism*; D060388:Support Vector Machine
chemical_list: D009711:Nucleotides; D016601:RNA-Binding Proteins; D012313:RNA
keywords: Prediction method; Protein-binding region; RNA-protein interaction