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In-silico Prediction of Disorder Content Using Hybrid Sequence Representation by Marcin J Mizianty; Tuo Zhang; Bin Xue; Yaoqi Zhou; A Keith Dunker; Vladimir N Uversky; Lukasz Kurgan is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.

What is In-silico Prediction of Disorder Content Using Hybrid Sequence Representation about?

## 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

Who reads In-silico Prediction of Disorder Content Using Hybrid Sequence Representation?

Author

Marcin J Mizianty; Tuo Zhang; Bin Xue; Yaoqi Zhou; A Keith Dunker; Vladimir N Uversky; Lukasz Kurgan

Publisher

BioMed Central; Springer (Biomed Central Ltd.); [London]: BioMed Central, [2000]-; Springer Science and Business Media LLC; Society for Mining, Metallurgy and Exploration Inc. (ISSN 1471-2105)

Published

2011

Language

EN

Field

Biochemistry, Genetics and Molecular Biology (Life Sciences)