Predicting intrinsic disorder in proteins: an overview

By B. He, Kejun Wang, Yunlong Liu, Bin Xue, Vladimir N Uversky, A. Keith Dunker. 2009

427 citations recorded by the source corpus.

Subfields: Materials Chemistry, Molecular Biology

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In Materials Chemistry

In Molecular Biology

Citations in the corpus

IUPred2A: context-dependent prediction of protein disorder as a function of redox state and protein binding (2018, methodology): “So far, over 50 prediction methods have been developed using a wide arse-nal of approaches, including simple amino acid propensity scales, simplified biophysical models, machine learning techniques and meta-servers (15–17).”
Continuous Distributed Representation of Biological Sequences for Deep Proteomics and Genomics (2015, neutral): “Several studies have focused on disordered peptides and their functional analysis in recent years [22 – 24].”
DeepCNF-D: Predicting Protein Order/Disorder Regions by Weighted Deep Convolutional Neural Fields (2015, methodology): “Due to the laborious and expensive nature of these experiments, numerous computational approaches have been proposed to predict the order/disorder regions [5].”
Intrinsically Semi-disordered State and Its Role in Induced Folding and Protein Aggregation (2013, methodology): “All other methods developed so far ([50) are dedicated to a two-state prediction of order and disorder [11].”
PONDR-FIT: A Meta-Predictor of Intrinsically Disordered Amino Acids (2010, methodology): “Starting with small collections of In addition to differences in the computational method being used, various predictors have also selected different combinations of input features[18].”
A decade and a half of protein intrinsic disorder: Biology still waits for physics (2013, background): “30 The fact that the sequences of ordered and disordered proteins and regions are noticeably different suggested that IDPs clearly constitute a separate entity inside the protein kingdom, that these proteins can be reliably predicted using various computational tools, [37] [38] [39] [40] [41] [42] a”
Prediction of protein disorder based on IUPred (2018, background): “20,21 One class of methods relies on simple amino acid propensity scales, such as various types of hydrophobicity scales, the propensity to participate in regular secondary structure elements, or the combination of these factors.”
Comprehensive comparative assessment of in-silico predictors of disordered regions. (2012, methodology): “A comprehensive listing and summary of these methods are included in three recent reviews [1,[13],[18]], as well as at http://www.”
Intrinsically Disordered Proteins Studied by NMR Spectroscopy (2015, background): “By now more than 50 disorder predictors have been published (He et al. 2009).”
The s2D method: simultaneous sequence-based prediction of the statistical populations of ordered and disordered regions in proteins. (2015, background): “Detailed experimental characterization of structure and disorder in proteins remains a difficult and costly task, which is associated with the risk of obtaining inconclusive results especially when not employing a broad range of different techniques [7–12,14].”
D2P2: database of disordered protein predictions (2012, neutral): “Because of the biases of structural resolution and the relative ease of task for disorder prediction methods compared with de novo fold prediction, many algorithms have been developed to discover novel regions of disorder from amino acid sequence alone (13,14).”
Comprehensive review of methods for prediction of intrinsic disorder and its molecular functions (2017, methodology): “[32], the development of predictors of IDPs and IDRs can be divided into three periods: the first generation (1979–2001), the second generation (2002–2006), and the third generation (2007 onwards).”