Protein flexibility and intrinsic disorder

By Predrag Radivojac, Zoran Obradovic, David Keith Smith, Guang Zhu, Slobodan Vucetic, C. Brown, J. David Lawson, A. Keith Dunker. 2004

342 citations recorded by the source corpus.

Subfields: Materials Chemistry, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Materials Chemistry

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].”
ProtTrans: Towards Cracking the Language of Life’s Code Through Self-Supervised Deep Learning and High Performance Computing (2020, background): “extremely biased by today’s experiments [75], [81], [82].”
The importance of intrinsic disorder for protein phosphorylation. (2004, background): “2 or 3 SD above protein means, respectively ( 52 ).”
Performance of mutation pathogenicity prediction methods on missense variants (2011, methodology): “, 2005], B-factor prediction [Radivojac et al., 2004], and disorder prediction by DisProt [Peng et al.”
Protein flexibility and rigidity predicted from sequence (2005, background): “Experimental data about B-values and predictions of flexibility were shown to be useful for predicting residues that cannot be crystallized.(28,41) More importantly, packing density is inversely proportional to thermal motion(42); therefore, the prediction of flexibility may help to unravel protein ”
To be folded or to be unfolded? (2004, background): “…was shown that a large portion of the sequences of natively unfolded proteins contains segments of low complexity and high predicted flexibility (Wootton 1994; Dunker et al. 1998; Romero et al. 1998, 1999; Galzitskaya et al. 2000; Obradovic et al. 2003; Vucetic et al. 2003; Radivojac et al. 2004).”
ProtTrans: Towards Cracking the Language of Life’s Code Through Self-Supervised Learning (2021, background): “Secondly, EI is not available for all proteins (intrinsically disordered proteins [26] or dark proteome [27]).”
Modeling the language of life – Deep Learning Protein Sequences (2019, background): “Other objectives that used the same idea included 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].”
Iterative saturation mutagenesis (ISM) for rapid directed evolution of functional enzymes (2007, methodology): “To identify such sites with some certainty, we turned to atomic displacement parameters obtained from X-ray data, namely the B-factors (or B -values”
PROFbval: predict flexible and rigid residues in proteins (2006, methodology): “Interestingly, surface residues in enzymes that are rigid often have a functional role (Schlessinger and Rost, 2005; Yuan, 2003).”
Showing your ID: intrinsic disorder as an ID for recognition, regulation and cell signaling (2005, background): “As already mentioned, the regions of crystallographic structures with high-B-factor values are relatively close in their properties to short disordered regions (Radivojac et al., 2004).”
Disorder Prediction Methods, Their Applicability to Different Protein Targets and Their Usefulness for Guiding Experimental Studies (2015, background): “interpret due to random or missing values obtained for the disordered regions [4,5].”
Intrinsic disorder and functional proteomics. (2007, background): “Disordered regions of different length show statistical differences (53), as suggested in an earlier study (54).”
Prediction of intrinsic disorder and its use in functional proteomics. (2007, background): “PONDR VSL incorporates the ideas of training separate models for short- (53) and long disordered regions (65), with subsequent combination of these models through a separately trained model (78,79).”
Improved sequence-based prediction of disordered regions with multilayer fusion of multiple information sources (2010, methodology): “GS-metaDisorder X X X X X Unpublished data MD X X X X X Schlessinger et al. (2009) metaPrDOS X Ishida and Kinoshita (2008) DISpro X X X Hecker et al.”
Improved Disorder Prediction by Combination of Orthogonal Approaches (2009, background): “unstructured regions are either positively or negatively charged, but on average, nearly neutral [27].”