PrDOS: prediction of disordered protein regions from amino acid sequence

Ishida, Takashi; Kinoshita, Kengo

doi:10.1093/nar/gkm363

Cited by 750 publications

(633 citation statements)

References 19 publications

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“…FoldIndex 32 does not provide values for the first and the last 25 residues of the input sequence; we used a modified version that provides these values, ensuring that the values for the rest of the sequence are identical with those provided by the original FoldIndex. PrDOS2 is the advanced version of the PrDOS disorder-prediction method 29 and takes the evolutionary conservation of the input sequence into account. It is not yet publicly available and we obtained the prediction results directly from the inventor of the method.…”

Section: Methodsmentioning

confidence: 99%

From protein sequence to dynamics and disorder with DynaMine

et al. 2013

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Protein function and dynamics are closely related; however, accurate dynamics information is difficult to obtain. Here based on a carefully assembled data set derived from experimental data for proteins in solution, we quantify backbone dynamics properties on the amino-acid level and develop DynaMine-a fast, high-quality predictor of protein backbone dynamics. DynaMine uses only protein sequence information as input and shows great potential in distinguishing regions of different structural organization, such as folded domains, disordered linkers, molten globules and pre-structured binding motifs of different sizes. It also identifies disordered regions within proteins with an accuracy comparable to the most sophisticated existing predictors, without depending on prior disorder knowledge or three-dimensional structural information. DynaMine provides molecular biologists with an important new method that grasps the dynamical characteristics of any protein of interest, as we show here for human p53 and E1A from human adenovirus 5.

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Section: Methodsmentioning

confidence: 99%

From protein sequence to dynamics and disorder with DynaMine

et al. 2013

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“…PrDOS (http://prdos.hgc.jp/cgi-bin/top.cgi) is composed of 2 predictors: a predictor based on the local amino acid sequence, and one based on template proteins (or homologous proteins for which structural information is available). 105 The first predictor is implemented using support vector machines for the position specific score matrix (or profile) of the input sequence. More precisely, a sliding window is used to map individual residues into a feature space, similar to secondary structure prediction used by methods like PSIPRED.…”

Section: Ond-crfmentioning

confidence: 99%

How disordered is my protein and what is its disorder for? A guide through the “dark side” of the protein universe

Lieutaud

Ferrón

Uversky

et al. 2016

Intrinsically Disordered Proteins

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In the last 2 decades it has become increasingly evident that a large number of proteins are either fully or partially disordered. Intrinsically disordered proteins lack a stable 3D structure, are ubiquitous and fulfill essential biological functions. Their conformational heterogeneity is encoded in their amino acid sequences, thereby allowing intrinsically disordered proteins or regions to be recognized based on properties of these sequences. The identification of disordered regions facilitates the functional annotation of proteins and is instrumental for delineating boundaries of protein domains amenable to structural determination with X-ray crystallization. This article discusses a comprehensive selection of databases and methods currently employed to disseminate experimental and putative annotations of disorder, predict disorder and identify regions involved in induced folding. It also provides a set of detailed instructions that should be followed to perform computational analysis of disorder.KEYWORDS disorder databases and metaservers; induced folding; intrinsic disorder; intrinsically disordered proteins; intrinsically disordered regions; prediction methods

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“…Structural disorder was predicted with MetaPrDOS [Ishida and Kinoshita, 2008], PrDOS [Ishida and Kinoshita, 2007], DISORPED2 [Ward et al, 2004], DisEMBL [Linding et al, 2003], DISPROT (VSL2P) [Peng et al, 2006], DISpro [Cheng et al, 2005], IUpred [Dosztanyi et al, 2005], and POODLE-S [Shimizu et al, 2007].…”

Section: Prediction Of Pathogenicitymentioning

confidence: 99%