Accurate Prediction of Protein Disordered Regions by Mining Protein Structure Data

By Jianlin Cheng, Michael J. Sweredoski, Pierre Baldi. 2005

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In Molecular Biology

Citations in the corpus

Prediction of intrinsic disorder and its use in functional proteomics. (2007, methodology): “DISpro uses large 1D recursive neural networks trained with a variety of compositional, evolutionary, and derived attributes (77).”
DeepCNF-D: Predicting Protein Order/Disorder Regions by Weighted Deep Convolutional Neural Fields (2015, methodology): “This dataset, built by Cheng et al. [23] in May 2004, consists of 723 non-redundant chains which span at least 30 amino acids and were solved by X-ray diffraction with a resolution of around 2.5 Å.”
PONDR-FIT: A Meta-Predictor of Intrinsically Disordered Amino Acids (2010, methodology): “These include the early PONDR series [19–21], DisEMBL [22], DISOPRED [23], and NORSnet [24], which used artificial neural networks; PONDR®VSL2 [21], DISOPRED2 [25], and POODLE [26], which employed support vector machines; and DISPro [27], which used a combination of neural networks and Bayesian meth”
Intrinsic disorder and functional proteomics. (2007, background): “(88) DISpro http://www.igb.uci.edu/servers/psss.html Recursive neural network based on sequence profiles, predicted secondary structure and relative solvent accessibility.”
A microscale protein NMR sample screening pipeline (2009, methodology): “…runs a wide range of disorder prediction software, including DISEMBL (Linding et al. 2003a), DISOPRED2 (Ward et al. 2004), DISPro (Cheng et al. 2005), DRIP-PRED (MacCallum 2006), FoldIndex (Prilusky et al. 2005), FoldUnfold (Galzitskaya et al. 2006), GlobPlot2 (Linding et al.…”
Improving protein disorder prediction by deep bidirectional long short‐term memory recurrent neural networks (2016, methodology): “ANNs have also enjoyed widespread success in many accurate predictors, such as DisEMBL (Linding et al. , 2003 a ), DISOPRED (Jones and Ward, 2003), DISpro (Cheng et al. , 2005), NORSnet (Schlessinger et al. , 2007 a ), the PONDR series (Romero et al. , 2001), PROFbval (Schlessinger et al. , 2006), R”
MFDp2 (2013, methodology): “…18-21 (2) methods that are based on classifiers generated with the help of machine learning algorithms, 22-37 (3) meta-methods that are based on a consensus of multiple base predictors 38-44 and iv) approaches that find disordered residues through an analysis of the predicted 3D structural models.”
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].”
Machine Learning Methods for Protein Structure Prediction (2008, methodology): “The prediction of protein domain boundaries [33], [40]–[42] and disordered regions [43]–[45] are still at an early stage of development, while already showing promising results.”