Coupled folding and binding with α-helix-forming molecular recognition elements

By Christopher J. Oldfield, Yugong Cheng, Marc S. Cortese, Pedro Romero, Vladimir N Uversky, A. Keith Dunker. 2005

574 citations recorded by the source corpus.

Subfields: Materials Chemistry, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/31eb6d7ce2d90152fc973857b62d332a8649ab1c

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

Improving protein disorder prediction by deep bidirectional long short‐term memory recurrent neural networks (2016, methodology): “Several machine-learning tools were developed specifically for prediction of binding sites in disorder (Garner et al. , 1999), such as alpha-MoRF (Oldfield et al. , 2005), MoRFpred (Disfani et al. , 2012), ANCHOR (Me´sz (cid:2) aros et al. , 2009) and DISOPRED3 (Jones and Cozzetto, 2015).”
Flexible nets: disorder and induced fit in the associations of p53 and 14-3-3 with their partners (2008, methodology): “The latter is one of the highest accuracy prediction algorithms available [86], whereas the former has been observed to be especially useful in identifying binding regions within longer regions of disorder [87-89] and to be much better at identifying such sites as compared to a number of different d”
MoRFpred, a computational tool for sequence-based prediction and characterization of short disorder-to-order transitioning binding regions in proteins (2012, background): “…observed in disorder predictions (Callaghan et al., 2004; Cheng et al., 2007; Dosztanyi et al., 2009; Garner et al., 1999; Mészáros et al., 2009; Oldfield et al., 2005) and as sequence patterns called short sequence motifs (Obenauer et al. 2003) or linear motifs (Davey et al., 2006; Puntervoll…”
A decade and a half of protein intrinsic disorder: Biology still waits for physics (2013, background): “[263] [264] [265] Targeting disorder-based interactions should enable the development of more effective drug discovery techniques.”
Intrinsically Semi-disordered State and Its Role in Induced Folding and Protein Aggregation (2013, background): “Previously, the observed connection between induced folding and a dip in disorder probability [48, 49] has motivated development of neural network-based alpha-MoRF predictors [50] and SVM-based MoRF-predictor [23] with predicted disorder”