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CDF it all: Consensus prediction of intrinsically disordered proteins based on various cumulative distribution functions
Author(s) -
Xue Bin,
Oldfield Christopher J.,
Dunker A. Keith,
Uversky Vladimir N.
Publication year - 2009
Publication title -
febs letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.593
H-Index - 257
eISSN - 1873-3468
pISSN - 0014-5793
DOI - 10.1016/j.febslet.2009.03.070
Subject(s) - intrinsically disordered proteins , cumulative distribution function , binary number , function (biology) , computer science , artificial neural network , distribution (mathematics) , artificial intelligence , statistical physics , mathematics , statistics , biology , physics , probability density function , biophysics , mathematical analysis , arithmetic , evolutionary biology
Many biologically active proteins are intrinsically disordered. A reasonable understanding of the disorder status of these proteins may be beneficial for better understanding of their structures and functions. The disorder contents of disordered proteins vary dramatically, with two extremes being fully ordered and fully disordered proteins. Often, it is necessary to perform a binary classification and classify a whole protein as ordered or disordered. Here, an improved error estimation technique was applied to develop the cumulative distribution function (CDF) algorithms for several established disorder predictors. A consensus binary predictor, based on the artificial neural networks, NN‐CDF, was developed by using output of the individual CDFs. The consensus method outperforms the individual predictors by 4–5% in the averaged accuracy.