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In-silico prediction of disorder content using hybrid sequence representation

Marcin J Mizianty1, Tuo Zhang23, Bin Xue24, Yaoqi Zhou23, A Keith Dunker2, Vladimir N Uversky245 and Lukasz Kurgan12*

Author Affiliations

1 Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta T6G 2V4, Canada

2 Institute for Intrinsically Disordered Protein Research, Center for Computational Biology and Bioinformatics, Indiana University Schools of Medicine and Informatics, Indianapolis, Indiana 46202, USA

3 Indiana University School of Informatics, Indiana University-Purdue University, Indianapolis, Indiana 46202, USA

4 Department of Molecular Medicine, University of South Florida, Tampa, Florida 33612, USA

5 Institute for Biological Instrumentation, Russian Academy of Sciences, 142290 Pushchino, Moscow Region, Russia

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BMC Bioinformatics 2011, 12:245  doi:10.1186/1471-2105-12-245

Published: 17 June 2011



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.


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.


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 webcite.