CDF it all: Consensus prediction of intrinsically disordered proteins based on various cumulative distribution functions

Bin Xue, Christopher J. Oldfield, A. Keith Dunker, Vladimir N. Uversky

Research output: Contribution to journalArticle

94 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)1469-1474
Number of pages6
JournalFEBS Letters
Volume583
Issue number9
DOIs
StatePublished - May 6 2009

    Fingerprint

Keywords

  • Accuracy
  • CDF
  • Intrinsically disordered protein
  • Prediction

ASJC Scopus subject areas

  • Biochemistry
  • Biophysics
  • Cell Biology
  • Genetics
  • Molecular Biology
  • Structural Biology

Cite this