CDF it all: consensus prediction of intrinsically disordered proteins based on various cumulative distribution functions.
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.
Center for Computational Biology and Bioinformatics, Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, 410 W. 10th Street, HS 5009, Indianapolis, IN 46202, USA.
Bin
B
Xue
Christopher J
CJ
Oldfield
A Keith
AK
Dunker
Vladimir N
VN
Uversky
D000465:Algorithms; D016571:Neural Networks, Computer; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry*
D011506:Proteins