GlobPlot: exploring protein sequences for globularity and disorder

By Rune Linding, Robert B. Russell, Victor Neduva, Toby J. Gibson. 2003

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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.”
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.”
DisProt: a database of protein disorder (2005, background): “We have parsed the August 5, 2003 release of PDB and extracted all protein chains with gaps of three or more residues in the ATOM list of X-ray characterized proteins.”
High-throughput production of human proteins for crystallization: The SGC experience (2010, background): “This amino acid distribution is similar to that reported in Linding et al. (2003) for coiled regions, and is used in algorithms that predict disordered regions in proteins.”
Comprehensive comparative assessment of in-silico predictors of disordered regions. (2012, methodology): “Our assessment considers sixteen disorder predictors that cover the fours types of the methods, including four relative propensity-based methods including GlobPlot [[20]], IUPred [[21]], FoldIndex [[22]], and Ucon [[23]]; eight machine learning-based solutions such as DisEMBL [[25]], DISOPRED2 [[26]”
Bioinformatical approaches to characterize intrinsically disordered/unstructured proteins (2010, methodology): “As the aim of GlobPlot approach is to identify ordered domains, it also eliminates short segments predicted as ordered that are too short to fold on their own, and a similar filter is applied for disordered segments.”
Prediction of intrinsic disorder and its use in functional proteomics. (2007, background): “GlobPlot is based on derived amino acid propensities for disordered regions (66).”
Improved Disorder Prediction by Combination of Orthogonal Approaches (2009, background): “Some methods focus on the fact that unstructured regions tend to have low hydrophobicity/high net-charge [3,36], high loop content [37], and few stable intra-chain contacts [38,39].”
Natively Unstructured Loops Differ from Other Loops (2007, background): “Flexible loops were enriched in amino acids with net charges such as lysine and glutamate (as described before [16,39]).”