title: Improving protein order-disorder classification using charge-hydropathy plots.
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.
  
  
  CONCLUSIONS
  We suggest that IDP-Hydropathy would likely be the best scale to use for any type of algorithm developed to predict protein disorder.
authors.forename: Fei
authors.initials: F
authors.lastname: Huang
authors.forename: Christopher J
authors.initials: CJ
authors.lastname: Oldfield
authors.forename: Bin
authors.initials: B
authors.lastname: Xue
authors.forename: Wei-Lun
authors.initials: WL
authors.lastname: Hsu
authors.forename: Jingwei
authors.initials: J
authors.lastname: Meng
authors.forename: Xiaowen
authors.initials: X
authors.lastname: Liu
authors.forename: Li
authors.initials: L
authors.lastname: Shen
authors.forename: Pedro
authors.initials: P
authors.lastname: Romero
authors.forename: Vladimir N
authors.initials: VN
authors.lastname: Uversky
authors.forename: A
authors.initials: A
authors.lastname: Dunker
mesh_terms: D000465:Algorithms; D000596:Amino Acids / Q000737:chemistry*; D000818:Animals; D030562:Databases, Protein; D006801:Humans; D057927:Hydrophobic and Hydrophilic Interactions; D064267:Intrinsically Disordered Proteins / Q000737:chemistry*; D011506:Proteins / Q000737:chemistry* / Q000145:classification*
chemical_list: D000596:Amino Acids; D064267:Intrinsically Disordered Proteins; D011506:Proteins