Optimizing Long Intrinsic Disorder Predictors with Protein Evolutionary Information

By Kang Peng, Slobodan Vucetic, Predrag Radivojac, C. Brown, A. Dunker, Zoran Obradovic. 2005

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In Molecular Biology

Citations in the corpus

Modeling aspects of the language of life through transfer-learning protein sequences (2019, methodology): “The idea was applied to other tasks, including the prediction of transmembrane regions [11 – 13], solvent accessibility [14], residue flexibility (B-values) [15, 16], inter-residue contacts [17] and protein disorder [15, 18 – 20].”
Prediction of intrinsic disorder and its use in functional proteomics. (2007, background): “PONDR VL3 is an ensemble of feed-forward neural networks that uses evolutionary information and is trained on long disordered regions (65).”
Comprehensive review of methods for prediction of intrinsic disorder and its molecular functions (2017, methodology): “Several second generation methods including PONDRVL3P [51], DISOPRED2 [6], PROFbval [56], DISpro [57], and NORSp [58], use this new type of the input.”
HHsenser: exhaustive transitive profile search using HMM–HMM comparison (2006, background): “Examples are the prediction of secondary structure (1), solvent accessebility (2), disorder (3), transmembrane helices (4), intraprotein contacts (5), protein–protein interactions (6), subcellular localization (7), internal repeats (8), deleterious mutations (9) and conserved or subtype-specific fun”
Showing your ID: intrinsic disorder as an ID for recognition, regulation and cell signaling (2005, background): “PONDR1 VLXT identifies the region from 270 to 287 in GSK3 as likely to be disordered, while PONDR1 VL2 (Vucetic et al., 2001) and VL3E (Peng et al., 2005) both identify the same shorter segment, from 291 to 296, as likely to be disordered.”
Flexible nets: disorder and induced fit in the associations of p53 and 14-3-3 with their partners (2008, methodology): “The recently developed Various Short-Long, version 1 (PONDR®-VSL1) algorithm is an ensemble of logistic regression models that predict per-residue order-disorder [85,150].”
PONDR-FIT: A Meta-Predictor of Intrinsically Disordered Amino Acids (2010, methodology): “The individual predictors used in the analysis are PONDR ® VLXT [19], PONDR ® VL3 [20], PONDR ® VSL2 [21], IUPred [31], FoldIndex [33], and TopIDP [45].”
Modeling the language of life – Deep Learning Protein Sequences (2019, methodology): “The idea was 21 applied to other tasks, including the prediction of transmembrane regions [11-13], solvent 22 accessibility [14], residue flexibility (B-values) [15, 16], inter-residue contacts [17] and protein 23 disorder [15, 18-20].”
Length-dependent prediction of protein intrinsic disorder (2006, methodology): “The small differences between linear and nonlinear models are consistent with the observations in our previous studies [19,24], which reflected the linear nature of the disorder prediction problem.”