In-silico prediction of disorder content using hybrid sequence representation

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 p...

• Type: Journal
• Year: 2011
• Where: BMCBI
• Authors: Marcin J. Mizianty, Tuo Zhang, Bin Xue, Yaoqi Zhou, A. Keith Dunker, Vladimir N. Uversky, Lukasz A. Kurgan
• Keywords: Biometrics, BMCBI 2011, Evolutionary Profiles, Level Aggregation, Test Dataset