ESpritz: accurate and fast prediction of protein disorder

Walsh, Ian; Martin, Alberto J. M.; Domenico, Tomás Di; Tosatto, Silvio C. E.

doi:10.1093/bioinformatics/btr682

Cited by 488 publications

(442 citation statements)

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“…The proteomes were assigned to their taxonomic lineage based on the National Center for Biotechnology Information (NCBI) [36] We applied two fast and accurate disordered predictors, IUPred [37,38] and Espritz [39], to obtain putative disordered residues and segments. We used two versions of IUPred that were designed for predictions of long and short disordered segments, respectively, and three versions of Espritz that consider disorder annotations based on nuclear magnetic resonance (NMR) structures, X-ray crystal structures, and experimental annotations from DisProt database [40].…”

Section: Methodsmentioning

confidence: 99%

“…The resulting UniProt Complete Proteome Dataset (UCPD) includes 231,466 proteins (3.6 % of all considered proteins) from 59 species in archaea, 4,285,619 proteins (66.6 %) from 471 species in bacteria, 1,901,810 proteins (29.5 %) from 110 species in eukaryota, and 19,841 proteins (0.3 %) from 325 viral proteomes; see Supplementary Table 1. All 965 proteomes were used to characterize disorder at the taxonomic domain level, while 225 small proteomes (with less than 30 proteins) were excluded when performing analysis at the species level.We applied two fast and accurate disordered predictors, IUPred [37,38] and Espritz [39], to obtain putative disordered residues and segments. We used two versions of IUPred that were designed for predictions of long and short disordered segments, respectively, and three versions of Espritz that consider disorder annotations based on nuclear magnetic resonance (NMR) structures, X-ray crystal structures, and experimental annotations from DisProt database [40].…”

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confidence: 99%

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Exceptionally abundant exceptions: comprehensive characterization of intrinsic disorder in all domains of life

Peng

Yan

Fan

et al. 2014

Cell. Mol. Life Sci.

342

265

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consensus-based disorder predictions, and for the first time comprehensively characterized intrinsic disorder at proteomic and protein levels from all significant perspectives, including abundance, cellular localization, functional roles, evolution, and impact on structural coverage. We show that intrinsic disorder is more abundant and has a unique profile in eukaryotes. We map disorder into archaea, bacterial and eukaryotic cells, and demonstrate that it is preferentially located in some cellular compartments. Functional analysis that considers over 1,200 annotations shows that certain functions are exclusively implemented by intrinsically disordered proteins and regions, and that some of them are specific to certain domains of life. We reveal that disordered regions are often targets for various post-translational modifications, but primarily in the eukaryotes and viruses. Using a phylogenetic tree for 14 eukaryotic and 112 bacterial species, we analyzed relations between disorder, sequence conservation and evolutionary speed. We provide a complete analysis that clearly shows that intrinsic disorder is exceptionally and uniquely abundant in each domain of life. Keywords Intrinsic disorder · Intrinsically disordered proteins · Intrinsically disordered regions · Cellular localization · Post-translational modifications · Evolutionary speed IntroductionIt is now recognized that in addition to globular, transmembrane and fibrillar proteins that are known to be characterized by unique three dimensional (3D)-structure, there is another tribe of proteins, which, being biologically functional, do not have unique 3D-structures in their native Abstract Recent years witnessed increased interest in intrinsically disordered proteins and regions. These proteins and regions are abundant and possess unique structural features and a broad functional repertoire that complements ordered proteins. However, modern studies on the abundance and functions of intrinsically disordered proteins and regions are relatively limited in size and scope of their analysis. To fill this gap, we performed a broad and detailed computational analysis of over 6 million proteins from 59 archaea, 471 bacterial, 110 eukaryotic and 325 viral proteomes. We used arguably more accurate Electronic supplementary material The online version of this article (doi:10.1007/s00018-014-1661-9) contains supplementary material, which is available to authorized users. 3states under the physiologic conditions in vitro and in vivo [1][2][3][4][5]. The members of this novel tribe are known as intrinsically disordered proteins (IDPs). Their structures are defined as highly dynamic ensembles of flexible conformations, where sampling of a large portion of a polypeptide's available conformational space is allowed. Although IDPs and intrinsically disordered regions (IDRs) in proteins are devoid of stable 3D-structures, they possess crucial biological functions and play multiple important roles in living organisms. In fact, the conformational plasticity associated with intrins...

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Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

Exceptionally abundant exceptions: comprehensive characterization of intrinsic disorder in all domains of life

Peng

Yan

Fan

et al. 2014

Cell. Mol. Life Sci.

342

265

View full text Add to dashboard Cite

show abstract

“…It is recommended to save the resulting figure since it serves as a useful illustration (see Fig. 1 108 and PV2. 102 The visual console of D The green-and-white bar in the middle of the plot shows the predicted disorder agreement between these 9 predictors, with green parts corresponding to disordered regions by consensus.…”

Section: Searching Databases Dedicated To Idpsmentioning

confidence: 99%

“…ESpritz (http://protein.bio.unipd.it/espritz/) is based on a machine learning method which does not require sliding windows or any complex sources of information (Bi-directional Recursive Neural Networks (BRNN)). 108 It includes 3 version that predict disorder based on the annotations from X-ray crystal structures, NMR-derived structures and the DisProt database.…”

Section: Predictors Trained On Data Sets Of Disordered Proteinsmentioning

confidence: 99%

“…Each line ends with the corresponding numeric score. MobiDB also generates consensus disorder scores based on the outputs of 10 disorder predictors, including the 3 varieties of ESpritz(ESpritz-Xray, ESpritz-DisProt, and ESpritz-NMR), 108 2 versions of IUPred (IUPred-S and IUPred-L), 104 Table and the Prediction Table shown numerically locations of disordered regions are located. PED (Proteins Ensemble Database) (http://pedb.…”

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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

104

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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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Are casein micelles extracellular condensates formed by liquid‐liquid phase separation?

et al. 2022

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Casein micelles are extracellular polydisperse assemblies of unstructured casein proteins. Caseins are the major component of milk. Within casein micelles, casein molecules are stabilised by binding to calcium phosphate nanoclusters and, by acting as molecular chaperones, through multivalent interactions. In the light of such interactions, we discuss whether casein micelles can be considered as extracellular condensates formed by liquid-liquid phase separation. We analyse the sequence, structure and interactions of caseins in comparison with proteins forming intracellular condensates. Furthermore, we review the similarities between caseins and small heat-shock proteins whose chaperone activity is linked to phase separation of proteins. By bringing these observations together, we describe a regulatory mechanism for protein condensates, as exemplified by casein micelles.

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ESpritz: accurate and fast prediction of protein disorder

Abstract: Both a web server for high-throughput analysis and a Linux executable version of ESpritz are available from: http://protein.bio.unipd.it/espritz/.

Cited by 488 publications

References 44 publications

Exceptionally abundant exceptions: comprehensive characterization of intrinsic disorder in all domains of life

Exceptionally abundant exceptions: comprehensive characterization of intrinsic disorder in all domains of life

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

Are casein micelles extracellular condensates formed by liquid‐liquid phase separation?

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