Document Type
Article
Publication Date
2011
Keywords
Protein Data Bank, Support Vector Regression, Globular Domain, Support Vector Regression Model, Position Specific Scoring Matrix
Digital Object Identifier (DOI)
https://doi.org/10.1186/1471-2105-12-245
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/.
Was this content written or created while at USF?
Yes
Citation / Publisher Attribution
BMC Bioinformatics, v. 12, art. 245
This article is published under license to BioMed Central Ltd. This is an Open Access article is distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Scholar Commons Citation
Mizianty, Marcin J.; Zhang, Tuo; Xue, Bin; Zhou, Yaoqi; Dunker, A. Keith; Uversky, Vladimir N.; and Kurgan, Lukasz, "In-silico Prediction of Disorder Content Using Hybrid Sequence Representation" (2011). Molecular Medicine Faculty Publications. 457.
https://digitalcommons.usf.edu/mme_facpub/457
Electronic Supplementary Material
