The pairwise energy content estimated from amino acid composition discriminates between folded and intrinsically unstructured proteins.

By Zsuzsanna Dosztányi, Veronika Csizmok, Peter Tompa, István Simon. 2005

1,016 citations recorded by the source corpus.

Subfields: Materials Chemistry, Molecular Biology, Spectroscopy

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Citations in the corpus

IUPred2A: context-dependent prediction of protein disorder as a function of redox state and protein binding (2018, background): “The basic assumption of this method is that intrinsically disordered proteins have a specific amino acid composition that does not allow the formation of enough favorable inter-residue interactions to stabilize a well-defined structural state (20,21).”
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.”
Prediction of protein disorder based on IUPred (2018, methodology): “Using this limit, 76% of positions of the IDP dataset were predicted to be disordered.(26) This energy estimation approach is at the core of the IUPred disorder prediction method.”
Length-dependent prediction of protein intrinsic disorder (2006, methodology): “Alternatively, various computational algorithms have been developed for predicting intrinsically disordered regions from amino acid sequence [ 15-29 ].”
Comprehensive review of methods for prediction of intrinsic disorder and its molecular functions (2017, methodology): “IUPred [46, 47] was authored by Dosztányi et al.”
ProViz—a web-based visualization tool to investigate the functional and evolutionary features of protein sequences (2016, background): “Sequence analysis tools can accurately define regions of proteins that are unlikely to have any structure in their native state (20,21) and predict protein membrane topology relative to membrane crossing regions (22).”
Prediction of intrinsic disorder and its use in functional proteomics. (2007, neutral): “IUPred is based on energy-derived coefficients (72,73).”
Showing your ID: intrinsic disorder as an ID for recognition, regulation and cell signaling (2005, background): “…Romero et al., 1997a,b; Li et al., 1999b; Uversky et al., 2000; Jones and Ward, 2003; Linding et al., 2003a,b; Liu and Rost, 2003; Pe’er et al., 2004; Thompson and Esnouf, 2004; Ward et al., 2004a; Weathers et al., 2004; Coeytaux and Poupon, 2005; Dosztanyi et al., 2005; Prilunski et al., 2005).”
IUPred3: prediction of protein disorder enhanced with unambiguous experimental annotation and visualization of evolutionary conservation (2021, methodology): “The basic assumption of this approach is that residues with favorable energies are ordered while residues with unfavorable energies are disordered (34).”
Computational identification of MoRFs in protein sequences (2015, methodology): “A propensity value is generated by computationally predicting each of these three properties using the energy estimation approach of IUPred (Dosztanyi et al., 2005a, b), and finally, a weighted sum is used to join these three propensities into a single score.”
A highly conserved program of neuronal microexons is misregulated in autistic brains (2014, methodology): “Disorder was predicted using IUPred (Dosztányi et al., 2005) with a disorder cutoff of 0.”
Computational Identification of MoRFs in Protein Sequences Using Hierarchical Application of Bayes Rule (2015, methodology): “Several computational tools are available to predict the locations of disordered segments in proteins, including DISOPRED [20], ESpritz [21], IUPred [22, 23], and SPINE-D [24].”