title: In-silico prediction of disorder content using hybrid sequence representation.
abstract: BACKGROUND
  Intrinsically disordered proteins play important roles in various cellular activities and their prevalence was implicated in a number of human diseases. The knowledge of the content of the intrinsic disorder in proteins is useful for a variety of studies including estimation of the abundance of disorder in protein families, classes, and complete proteomes, and for the analysis of disorder-related protein functions. The above investigations currently utilize the disorder content derived from the per-residue disorder predictions. We show that these predictions may over-or under-predict the overall amount of disorder, which motivates development of novel tools for direct and accurate sequence-based prediction of the disorder content.
  
  
  RESULTS
  We hypothesize that sequence-level aggregation of input information may provide more accurate content prediction when compared with the content extracted from the local window-based residue-level disorder predictors. We propose a novel predictor, DisCon, that takes advantage of a small set of 29 custom-designed descriptors that aggregate and hybridize information concerning sequence, evolutionary profiles, and predicted secondary structure, solvent accessibility, flexibility, and annotation of globular domains. Using these descriptors and a ridge regression model, DisCon predicts the content with low, 0.05, mean squared error and high, 0.68, Pearson correlation. This is a statistically significant improvement over the content computed from outputs of ten modern disorder predictors on a test dataset with proteins that share low sequence identity with the training sequences. The proposed predictive model is analyzed to discuss factors related to the prediction of the disorder content.
  
  
  CONCLUSIONS
  DisCon is a high-quality alternative for high-throughput annotation of the disorder content. We also empirically demonstrate that the DisCon's predictions can be used to improve binary annotations of the disordered residues from the real-value disorder propensities generated by current residue-level disorder predictors. The web server that implements the DisCon is available at http://biomine.ece.ualberta.ca/DisCon/.
authors.affiliation: Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta T6G 2V4, Canada.
authors.forename: Marcin J
authors.initials: MJ
authors.lastname: Mizianty
authors.forename: Tuo
authors.initials: T
authors.lastname: Zhang
authors.forename: Bin
authors.initials: B
authors.lastname: Xue
authors.forename: Yaoqi
authors.initials: Y
authors.lastname: Zhou
authors.forename: A Keith
authors.initials: AK
authors.lastname: Dunker
authors.forename: Vladimir N
authors.initials: VN
authors.lastname: Uversky
authors.forename: Lukasz
authors.initials: L
authors.lastname: Kurgan
mesh_terms: D000595:Amino Acid Sequence; D002468:Cell Physiological Phenomena; D006801:Humans; D017510:Protein Folding; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism
chemical_list: D011506:Proteins