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BMC Bioinformatics

, 15:S4

First Online: 16 December 2014

Abstract

BackgroundThe 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 and Doolittle., J. Mol. Biol, 157: 105-1321982. 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.

ResultsUsing 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, 47:61-701985 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.

ConclusionWe suggest that IDP-Hydropathy would likely be the best scale to use for any type of algorithm developed to predict protein disorder.

KeywordsIntrinsically disordered proteins natively unstructured or unfolded proteins structure and disorder prediction support vector machines An erratum to this article is available at http:-dx.doi.org-10.1186-s12859-015-0646-5.

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Autor: Fei Huang - Christopher J Oldfield - Bin Xue - Wei-Lun Hsu - Jingwei Meng - Xiaowen Liu - Li Shen - Pedro Romero - Vladim

Fuente: https://link.springer.com/







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