D2P2: database of disordered protein predictions

By Matt E. Oates, Pedro Romero, Takashi Ishida, Mohamed F. Ghalwash, Marcin J. Mizianty, Bin Xue, Zsuzsanna Dosztányi, Vladimir N Uversky, Zoran Obradovic, Lukasz Kurgan and 2 more. Nucleic Acids Res., 2012

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We present the Database of Disordered Protein Prediction (D2P2), available at http://d2p2.pro (including website source code). A battery of disorder predictors and their variants, VL-XT, VSL2b, PrDOS, PV2, Espritz and IUPred, were run on all protein sequences from 1765 complete proteomes (to be updated as more genomes are completed). Integrated with these results are all of the predicted (mostly structured) SCOP domains using the SUPERFAMILY predictor. These disorder/structure annotations together enable comparison of the disorder predictors with each other and examination of the overlap between disordered predictions and SCOP domains on a large scale. D2P2 will increase our understanding of the interplay between disorder and structure, the genomic distribution of disorder, and its evolutionary history. The parsed data are made available in a unified format for download as flat files or SQL tables either by genome, by predictor, or for the complete set. An interactive website provides a graphical view of each protein annotated with the SCOP domains and disordered regions from all predictors overlaid (or shown as a consensus). There are statistics and tools for browsing and comparing genomes and their disorder within the context of their position on the tree of life.

Subfields: Materials Chemistry, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/269a6271fa98bbdc2d456bae7fb419a77c88dc70

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Citations in the corpus

Structured states of disordered proteins from genomic sequences (2016, positive): “Many, if not most, proteins can adopt alternative functional conformations and around half of all human proteins contain regions of at least 40 residues classified as disordered by a number of different bioinformatic methods (Oates et al., 2013; van der Lee et al., 2014) (Figure 1; Table S1) includi”
Comprehensive review of methods for prediction of intrinsic disorder and its molecular functions (2017, methodology): “Predictions generated by each of the three versions of ESpritz are included the D(2)P(2) database [91] and the MobiDB database [90].”
A Unified Model for the Function of YTHDF Proteins in Regulating m6A-Modified mRNA (2020, methodology): “—The disordered region score was calculated using D2P2 (Oates et al., 2013).”
FuzDB: database of fuzzy complexes, a tool to develop stochastic structure-function relationships for protein complexes and higher-order assemblies (2016, background): “Similarly to the predictionbased data sets like D2P2 (20), it is suitable for larger-scale bioinformatics analyses of IDRs, but not for developing structure–function relationships.”
p53 Proteoforms and Intrinsic Disorder: An Illustration of the Protein Structure–Function Continuum Concept (2016, positive): “pro/) [113], which represents the outputs of IUPred [114], PONDR® VLXT [115], PrDOS [116], PONDR® VSL2B [117,118], PV2 [113], and ESpritz [119].”
Intrinsically Disordered Proteins Studied by NMR Spectroscopy (2015, background): “agreement between the two types of predictions (Oates et al. 2013).”
Prediction of protein disorder based on IUPred (2018, background): “Among such databases, D2P2 collects predictions generated by multiple programs for a large number of available genome sequences.(52) MobiDB provides disorder predictions for sequences available through the UniProt database and it is regularly updated to keep up with novel sequences.”
Transcription factors activate genes through the phase separation capacity of their activation domains (2018, positive): “An amino acid in a protein is considered disordered if at least 75 % of the algorithms employed by D2P2 (Oates et al., 2013) predict the residue to be disordered.”
MobiDB 2.0: an improved database of intrinsically disordered and mobile proteins (2014, background): “A similar largescale database, D2P2 (21), was published somewhat later to provide consensus predictions for ca.”