Improving protein order-disorder classification using charge-hydropathy plots

BMC Bioinformatics

Research

Fei Huang¹  A Keith Dunker¹  Li Shen¹  Jingwei Meng¹  Christopher J Oldfield¹  Xiaowen Liu¹  Wei-Lun Hsu¹  Pedro Romero²  Bin Xue³  Vladimir N Uversky⁴ 

[1] Center for Computational Biology and Bioinformatics, Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, Indiana, USA;Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, Wisconsin, USA;Department of Cell Biology, Microbiology, and Molecular Biology, University of South Florida, Tampa, Florida, USA;Department of Molecular Medicine, University of South Florida, Tampa, Florida, USA;USF Health Byrd Alzheimer's Research Institute, Morsani College of Medicine, University of South Florida, Tampa, Florida, USA;Institute for Biological Instrumentation, Russian Academy of Sciences, 142290, Pushchino, Moscow Region, Russia;

关键词: Intrinsically disordered proteins;    natively unstructured or unfolded proteins;    structure and disorder prediction;    support vector machines;   

DOI  :  10.1186/1471-2105-15-S17-S4

来源: Springer

【 摘 要 】

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 & 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.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, 47:61-70(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.ConclusionWe suggest that IDP-Hydropathy would likely be the best scale to use for any type of algorithm developed to predict protein disorder.

【 授权许可】

Unknown   
© Huang et al.; licensee BioMed Central Ltd. 2014. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.