Protein disorder prediction: implications for structural proteomics.

By Rune Linding, Lars Juhl Jensen, Francesca Diella, Peer Bork, Toby J. Gibson, Robert B. Russell. 2003

1,257 citations recorded by the source corpus.

Subfields: Materials Chemistry, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Materials Chemistry

In Molecular Biology

Citations in the corpus

SPOT-Disorder2: Improved Protein Intrinsic Disorder Prediction by Ensembled Deep Learning (2019, methodology): “See above-mentioned references [64–66 , 68– 70 ] for further information. of We further employed other independent test datasets to compare our methods with other top performing methods for DisProt228 that are available to us as either a local implementation or online server.”
NetSurfP-2.0: improved prediction of protein structural features by integrated deep learning (2018, methodology): “Our tool is focused on protein regions with missing atomic coordinates in their solved structures, corresponding to the REMARK-465 regions in the DisEMBL annotation.(34) We have compared our prediction to both RaptorX and SPOT-disorder, a method developed by the same group that developed Spider-3.”
RaptorX-Property: a web server for protein structure property prediction (2016, background): “For instance, atoms may be missing in PDB structures due to technical problems instead of a disordered state (33).”
InterPro in 2017—beyond protein family and domain annotations (2016, methodology): “Eight different algorithms (IUPredshort, IUPred-long (44), GlobPlot (45), DisEMBL-465, DisEMBL-HL (46), Espritz-DisProt, Espritz-NMR and Espritz-X-ray (47)) were chosen for their speed and orthogonality of approaches.”
Comprehensive large-scale assessment of intrinsic protein disorder (2015, methodology): “Machine learning, especially neural networks, has been widely used to predict protein disorder (Eickholt and Cheng, 2013; Hirose et al ., 2007; Ishida and Kinoshita, 2007; Linding et al. , 2003b; Walsh et al ., 2011, 2012; Ward et al ., 2004; Yang et al ., 2005).”
Hybrid feature selection by combining filters and wrappers (2011, methodology): “In the experiments, we applied the proposed hybrid feature selection mechanism to the problems of disordered protein prediction (Linding et al., 2003) and gene selection of microarray cancer data (Guyon, Weston, Barnhill, & Vapnik, 2002).”
MobiDB 3.0: more annotations for intrinsic disorder, conformational diversity and interactions in proteins (2017, positive): “0 includes the same set of disorder predictors used in the previous release: ESpritz (47), IUpred (48), DisEMBL (49) and VSL2b (50).”
Prediction of intrinsic disorder and its use in functional proteomics. (2007, methodology): “DisEMBL server uses a support vector machine (67), trained on three proposed”
InterPro: the protein sequence classification resource in 2025 (2024, positive): “MobiDB-lite generates a consensus by integrating several predictors, such as DisEMBL ( 52 ), ESpritz ( 53 ) and IUPred ( 54 ).”
PONDR-FIT: A Meta-Predictor of Intrinsically Disordered Amino Acids (2010, background): “Commonly used features include the following: amino acid sequence [22,25], amino acid composition [19–21,26], The prediction results from a collection of predictors can be used as the inputs for another predictor, called a meta-predictor.”
Structural insights into the mycobacteria transcription initiation complex from analysis of X-ray crystal structures (2017, background): “Secondary structure prediction algorithms predict a total lack of secondary structure for residues 1–143 (refs 48,49), and one a -helix is predicted ( B residues 145–160) as observed in our structure (Figs 5 and 6a).”
Comprehensive review of methods for prediction of intrinsic disorder and its molecular functions (2017, methodology): “MobiDB offers access the putative disorder generated by ten predictors: three versions of Espritz [80], two versions of IUPred [46], two versions of DisEMBL [54], GlobPlot [45], PONDR-VSL2b [52], and RONN [69].”
MetaDisorder: a meta-server for the prediction of intrinsic disorder in proteins (2012, methodology): “…flexible loops with high B-factors, missing coordinates in X-ray structures, regions of low-complexity and prone to aggregation. local installation [21] DISOPRED2 SVM trained to predict residues with missing coordinates. local installation [22] DISpro Recursive neural networks (RNNs) trained to…”