Analysis of molecular recognition features (MoRFs).

By Amrita Mohan, Christopher J. Oldfield, Predrag Radivojac, Vladimir Vacic, Marc S. Cortese, A. Keith Dunker, Vladimir N Uversky. Journal of Molecular Biology, 2006

725 citations recorded by the source corpus.

Subfields: Materials Chemistry, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/5272aa5134e09ec2fc153def2435344952f73ddf

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Citations in the corpus

Natively unstructured regions in proteins identified from contact predictions (2007, background): “It has also been identified as a Molecular Recognition Feature (MoRF), i.e., it becomes structured upon binding to its target ( Mohan, et al., 2006 ).”
Intrinsically disordered proteins in human diseases: introducing the D2 concept. (2008, methodology): “In agreement with this model, recent computational studies of such binding showed that the disordered partner might contain a conformational preference for the structure it will take upon binding, and that these so-called preformed elements are helices (53, 94, 98, 145).”
Linking Folding and Binding (2009, background): “Indeed, it is frequently possible to identify these folding motifs, which have been termed molecular recognition elements or MoRFs, by bioinformatic analysis of the protein sequence [22 ].”
Improved Disorder Prediction by Combination of Orthogonal Approaches (2009, background): “For instance, the polyproline-II (PPII) helix is a ubiquitous helical structure motif that is found in extended conformation and is abundant in molecular recognition features (MoRF) of unstructured regions [32].”
MFSPSSMpred: identifying short disorder-to-order binding regions in disordered proteins based on contextual local evolutionary conservation (2013, background): “A number of studies have analyzed the attributes of MoRFs [1-9].”
Improving protein disorder prediction by deep bidirectional long short‐term memory recurrent neural networks (2016, background): “Early studies have shown that a dip in disorder probability is associated with induced folding (Mohan et al., 2006; Oldfield et al. , 2005).”
MoRFchibi SYSTEM: software tools for the identification of MoRFs in protein sequences (2016, background): “(3), who searched the Protein Data Bank (23) for short peptides (potential MoRFs) that are in complex with longer protein partners (presumably globular domains).”
Computational identification of MoRFs in protein sequences (2015, methodology): “However, in this study we focus only on computationally identifying MoRFs as defined initially by Mohan et al. (2006) and Disfani et al. (2012).”
Natural protein sequences are more intrinsically disordered than random sequences (2016, background): “The existence of helical regions in long disordered regions indicates that nature may also employ disorder to enhance plasticity for function because helices in disordered regions are one of the widely utilized motifs in protein–protein interactions [32].”
Orderly order in protein intrinsic disorder distribution: disorder in 3500 proteomes from viruses and the three domains of life (2012, background): “…undergo disorder-to-order transitions upon binding, which is crucial for recognition, regulation and signalling (Dunker et al., 2001; Dyson & Wright, 2002, 2005; Mohan et al., 2006; Oldfield, Cheng, Cortese, Romero, et al., 2005; Uversky et al., 2000; Vacic et al., 2007; Wright & Dyson, 1999).”
MoRFpred, a computational tool for sequence-based prediction and characterization of short disorder-to-order transitioning binding regions in proteins (2012, background): “We show that our predicted (new) MoRF regions have non-random sequence similarity with native MoRFs.”