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Biophys Chem. 2016 Jun;213:6-16. doi: 10.1016/j.bpc.2016.03.005. Epub 2016 Apr 5.

Quantitative proteome-based guidelines for intrinsic disorder characterization.

Author information

1
Department of Molecular & Integrative Physiology, University of Michigan Medical School, Ann Arbor, MI, USA.
2
Department of Molecular & Integrative Physiology, University of Michigan Medical School, Ann Arbor, MI, USA; Department of Computational Medicine & Bioinformatics, University of Michigan Medical School, MI, USA; Brehm Center for Diabetes Research, University of Michigan Medical School, Ann Arbor, MI, USA. Electronic address: schnells@umich.edu.

Abstract

Intrinsically disordered proteins fail to adopt a stable three-dimensional structure under physiological conditions. It is now understood that many disordered proteins are not dysfunctional, but instead engage in numerous cellular processes, including signaling and regulation. Disorder characterization from amino acid sequence relies on computational disorder prediction algorithms. While numerous large-scale investigations of disorder have been performed using these algorithms, and have offered valuable insight regarding the prevalence of protein disorder in many organisms, critical proteome-based descriptive statistical guidelines that would enable the objective assessment of intrinsic disorder in a protein of interest remain to be established. Here we present a quantitative characterization of numerous disorder features using a rigorous non-parametric statistical approach, providing expected values and percentile cutoffs for each feature in ten eukaryotic proteomes. Our estimates utilize multiple ab initio disorder prediction algorithms grounded on physicochemical principles. Furthermore, we present novel threshold values, specific to both the prediction algorithms and the proteomes, defining the longest primary sequence length in which the significance of a continuous disordered region can be evaluated on the basis of length alone. The guidelines presented here are intended to improve the interpretation of disorder content and continuous disorder predictions from the proteomic point of view.

KEYWORDS:

Bioinformatics; Computational biology; Intrinsically disordered protein; Protein sequence; Protein structure; Proteomics

PMID:
27085142
DOI:
10.1016/j.bpc.2016.03.005
[Indexed for MEDLINE]

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