2003
DOI: 10.1002/prot.10437
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Flavors of protein disorder

Abstract: Intrinsically disordered proteins are characterized by long regions lacking 3-D structure in their native states, yet they have been so far associated with 28 distinguishable functions. Previous studies showed that protein predictors trained on disorder from one type of protein often achieve poor accuracy on disorder of proteins of a different type, thus indicating significant differences in sequence properties among disordered proteins.

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Cited by 368 publications
(379 citation statements)
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“…4) and from modeling of their sequences (Supplemental Fig. S1) with disordered sequence predictors disEMBL (63), GlobPlot (64), PONDR (65,66), and DISOPRED2 (67). As noted in the RESULTS, the predicted presence of some of the intrinsically unstructured sequences of 272 and Zu5 in their parent spectrin binding domain attests to the context independence of this predicted disorder.…”
Section: Both Zu5 and 272 And Their Parent Spectrin Binding Domain Ofmentioning
confidence: 75%
“…4) and from modeling of their sequences (Supplemental Fig. S1) with disordered sequence predictors disEMBL (63), GlobPlot (64), PONDR (65,66), and DISOPRED2 (67). As noted in the RESULTS, the predicted presence of some of the intrinsically unstructured sequences of 272 and Zu5 in their parent spectrin binding domain attests to the context independence of this predicted disorder.…”
Section: Both Zu5 and 272 And Their Parent Spectrin Binding Domain Ofmentioning
confidence: 75%
“…The set of disordered proteins (DIS152 ) was developed based on the 145 disordered proteins used in our previous work 20 . This set was cleaned up by removing several incorrectly labeled chains as well as by adjusting order/disorder boundaries in a few others.…”
Section: Datasetsmentioning
confidence: 99%
“…In a previous study 20 , disorder predictors were built using ordinaryleast-squares (OLS) regression, logistic regression (LR), and neural networks (NN). A surprising result, but consistent with previous experience in protein secondary structur e prediction, was the relatively small difference between accuracies of linear predictors and neural networks.…”
Section: Pondr Vl3mentioning
confidence: 99%
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