PONDR-FIT: A Meta-predictor of Intrinsically Disordered Amino Acids

Document Type

Article

Publication Date

2010

Keywords

Natively Unfolded, Intrinsically Unstructured, Intrinsically Disordered, Highly Flexible, Highly Dynamic, Structurally Disordered, Predictor, PONDR

Digital Object Identifier (DOI)

https://doi.org/10.1016/j.bbapap.2010.01.011

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.

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Citation / Publisher Attribution

Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics, v. 1804, issue 4, p. 996-1010

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