Prediction and functional analysis of native disorder in proteins from the three kingdoms of life.

By Jonathan J. Ward, Jaspreet Singh Sodhi, Liam J. McGuffin, Bernard F. Buxton, David C. Jones. 2004

2,019 citations recorded by the source corpus.

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In Molecular Biology

Citations in the corpus

AlphaFold2 and its applications in the fields of biology and medicine (2023, neutral): “Although many approaches have been tried, 204 – 207 it is dif ﬁ cult for existing methods to predict the morphology, dynamics and interactions of disordered regions of proteins in solutions.”
SPOT-Disorder2: Improved Protein Intrinsic Disorder Prediction by Ensembled Deep Learning (2019, neutral): “These include the local versions of DISOPRED2 [48] and DISOPRED3 [49] (http://bioinfad-min.cs.ucl.ac.uk/downloads/DISOPRED/), MobiDB-lite [50], AUCpreD [18] (https://github.com/realbigws/RaptorX_Property_Fast), s2D [51] (http://www-mvsoftware.ch.cam.ac.uk/index.php/s2D), SPOT-Disorder [19],…”
IUPred2A: context-dependent prediction of protein disorder as a function of redox state and protein binding (2018, background): “However, these entries only provide a small sample of IDPs / IDRs that are widespread in all domains of life but are most prevalent in eukaryotic organisms (11–14).”
DFLpred: High-throughput prediction of disordered flexible linker regions in protein sequences (2016, positive): “IDPs are abundant in eukaryotes (Peng et al., 2015; Ward et al., 2004) and are typically involved in signaling, regulation, control, storage of small molecules, sites for posttranslational modifications (PTMs), transcription, translation and assembly of multi-protein complexes (Dunker et al., 2008;…”
Flexible nets (2005, background): “Furthermore, computational studies revealed that eukaryotes exhibit more disorder than either prokaryotes or archaea [34,60–62].”
Length-dependent prediction of protein intrinsic disorder (2006, methodology): “In some recent approaches, e.g. VL3-P [24] and DISOPRED2 [23], features are also derived from PSI-BLAST [35] generated profiles to incorporate evolutionary information.”
Prediction of intrinsic disorder and its use in functional proteomics. (2007, positive): “DISOPRED2 uses linear support vector machines (70,71).”
PrDOS: prediction of disordered protein regions from amino acid sequence (2007, methodology): “The second part assumes the conservation of intrinsic disorder in protein families (13,14), and is simply implemented using PSI-BLAST (15) and our own measure of disorder, as described later.”
Natively Unstructured Loops Differ from Other Loops (2007, methodology): “About 30%–60% of all eukaryotic proteins have been estimated to contain unstructured regions [24,42].”
Natively unfolded proteins. (2005, methodology): “Results from an analysis of the Saccharomyces genome also show that many of the functions associated with disordered regions are unique to eukaryotes, such as the organization and biogenesis of the cytoskeleton [ 15 ].”
Genome‐scale prediction of proteins with long intrinsically disordered regions (2014, background): “The earliest study considered only the S. cerevisiae proteome,3 which was followed by an analysis of human proteome.”
DeepCNF-D: Predicting Protein Order/Disorder Regions by Weighted Deep Convolutional Neural Fields (2015, methodology): “This pure sequence version of our method (called DeepCNF-D ami_only) can be applied to large-scale proteome analysis for disordered regions [31].”
Intrinsically Disordered Proteins and Their “Mysterious” (Meta)Physics (2019, background): “According to various computational analyses, the presumed portion of sequences with predicted long IDPRs (≥30 residues) is roughly the same in bacteria and archaea, but is drastically higher in eukaryota [2, 6, 8, 17, 25].”
The Role of Balanced Training and Testing Data Sets for Binary Classifiers in Bioinformatics (2013, neutral): “These methods were Prdos2 [14], Disopred3C [75], Zhou-Spine-D [16], CBRC_Poodle [17], and Multicom-refine [76].”
Improved Disorder Prediction by Combination of Orthogonal Approaches (2009, methodology): “These included NORSnet [11], DISOPRED2 [46], PROFbval [58,70] and Ucon (where several models were implemented) [59]; (4) for the reproduction of predictors similar to the amino acid propensity based methods FoldIndex [3,36] and IUPred [39], we calculated hydrophobicity/net-charge as described by Uve”