PONDR-FIT: A meta-predictor of intrinsically disordered amino acids

Bin Xue, Roland L. Dunbrack, Robert W. Williams, A. Keith Dunker, Vladimir N. Uversky

Research output: Contribution to journalArticle

606 Scopus citations

Abstract

Protein intrinsic disorder is becoming increasingly recognized in proteomics research. While lacking structure, many regions of disorder have been associated with biological function. There are many different experimental methods for characterizing intrinsically disordered proteins and regions; nevertheless, the prediction of intrinsic disorder from amino acid sequence remains a useful strategy especially for many large-scale proteomic investigations. Here we introduced a consensus artificial neural network (ANN) prediction method, which was developed by combining the outputs of several individual disorder predictors. By eight-fold cross-validation, this meta-predictor, called PONDR-FIT, was found to improve the prediction accuracy over a range of 3 to 20% with an average of 11% compared to the single predictors, depending on the datasets being used. Analysis of the errors shows that the worst accuracy still occurs for short disordered regions with less than ten residues, as well as for the residues close to order/disorder boundaries. Increased understanding of the underlying mechanism by which such meta-predictors give improved predictions will likely promote the further development of protein disorder predictors. Access to PONDR-FIT is available at www.disprot.org.

Original languageEnglish (US)
Pages (from-to)996-1010
Number of pages15
JournalBiochimica et Biophysica Acta - Proteins and Proteomics
Volume1804
Issue number4
DOIs
StatePublished - Apr 1 2010

Keywords

  • Highly dynamic
  • Highly flexible
  • Intrinsically disordered
  • Intrinsically unstructured
  • Natively unfolded
  • PONDR
  • Predictor
  • Structurally disordered

ASJC Scopus subject areas

  • Biochemistry
  • Biophysics
  • Analytical Chemistry
  • Molecular Biology

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