Improving protein order-disorder classification using charge-hydropathy plots

Fei Huang, Christopher J. Oldfield, Bin Xue, Wei Lun Hsu, Jingwei Meng, Xiaowen Liu, Li Shen, Pedro Romero, Vladimir N. Uversky, A. Keith Dunker

Research output: Contribution to journalArticle

29 Scopus citations

Abstract

Background: The earliest whole protein order/disorder predictor (Uversky et al., Proteins, 41: 415-427 (2000)), herein called the charge-hydropathy (C-H) plot, was originally developed using the Kyte-Doolittle (1982) hydropathy scale (Kyte & Doolittle., J. Mol. Biol, 157: 105-132(1982)). Here the goal is to determine whether the performance of the C-H plot in separating structured and disordered proteins can be improved by using an alternative hydropathy scale. Results: Using the performance of the CH-plot as the metric, we compared 19 alternative hydropathy scales, with the finding that the Guy (1985) hydropathy scale (Guy, Biophys. J, 47:61-70(1985)) was the best of the tested hydropathy scales for separating large collections structured proteins and intrinsically disordered proteins (IDPs) on the C-H plot. Next, we developed a new scale, named IDP-Hydropathy, which further improves the discrimination between structured proteins and IDPs. Applying the C-H plot to a dataset containing 109 IDPs and 563 non-homologous fully structured proteins, the Kyte-Doolittle (1982) hydropathy scale, the Guy (1985) hydropathy scale, and the IDP-Hydropathy scale gave balanced two-state classification accuracies of 79%, 84%, and 90%, respectively, indicating a very substantial overall improvement is obtained by using different hydropathy scales. A correlation study shows that IDP-Hydropathy is strongly correlated with other hydropathy scales, thus suggesting that IDP-Hydropathy probably has only minor contributions from amino acid properties other than hydropathy. Conclusion: We suggest that IDP-Hydropathy would likely be the best scale to use for any type of algorithm developed to predict protein disorder.

Original languageEnglish (US)
Article numberS4
JournalBMC bioinformatics
Volume15
Issue number17
DOIs
StatePublished - Dec 16 2014

Keywords

  • Intrinsically disordered proteins
  • Natively unstructured or unfolded proteins
  • Structure and disorder prediction
  • Support vector machines

ASJC Scopus subject areas

  • Structural Biology
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics

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    Huang, F., Oldfield, C. J., Xue, B., Hsu, W. L., Meng, J., Liu, X., Shen, L., Romero, P., Uversky, V. N., & Dunker, A. K. (2014). Improving protein order-disorder classification using charge-hydropathy plots. BMC bioinformatics, 15(17), [S4]. https://doi.org/10.1186/1471-2105-15-S17-S4