Intrinsically unstructured proteins and their functions

By J. Dyson, Peter E. Wright. 2005

3,757 citations recorded by the source corpus.

Subfields: Materials Chemistry, Molecular Biology

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Citations in the corpus

SPOT-Disorder2: Improved Protein Intrinsic Disorder Prediction by Ensembled Deep Learning (2019, background): “Advantaged by their disordered ﬂexibility [4], IDPs play essential roles in signaling, assembling, and regulatory functions [5], and are implicated in numerous human diseases, such as cancer, amyloidoses, cardiovascular disease, neurodegenerative diseases, and various genetic diseases [6].”
Amino acid composition of proteins reduces deleterious impact of mutations (2013, methodology): “These models estimate properties of the optimal sequence by incorporating average interactions and the total number of possible sequences (design space), computed from sequence size and the number of amino acid types (20).”
Continuous Distributed Representation of Biological Sequences for Deep Proteomics and Genomics (2015, neutral): “Several studies have focused on disordered peptides and their functional analysis in recent years [22 – 24].”
A biophysical protein folding model accounts for most mutational fitness effects in viruses (2011, background): “This paradigm assumes that function requires well-defined structure, which is not true for intrinsically disordered proteins (13).”
mTOR inhibits autophagy by controlling ULK1 ubiquitylation, self-association and function through AMBRA1 and TRAF6 (2013, methodology): “(d) HEK293 cells were co-transfected with vectors encoding AMBRA1 together with ULK1–Myc FL (full length) or its deletion mutants encoding the kinase and the proline-rich domains (1829–1,051), and the ULK1 carboxy-terminal domain (11-828).”
Sequence signatures and mRNA concentration can explain two-thirds of protein abundance variation in a human cell line (2010, background): “dimensional structure, can also destabilize a protein (Dyson and Wright, 2005; Gsponer et al, 2008; Tompa et al, 2008).”
The s2D method: simultaneous sequence-based prediction of the statistical populations of ordered and disordered regions in proteins. (2015, background): “Detailed experimental characterization of structure and disorder in proteins remains a difficult and costly task, which is associated with the risk of obtaining inconclusive results especially when not employing a broad range of different techniques [7–12,14].”
The interplay between structure and function in intrinsically unstructured proteins (2005, background): “enable much freedom in orientational search [11] that permits the recognition of distant and/or discontinuous determinants on the target.”
PrDOS: prediction of disordered protein regions from amino acid sequence (2007, background): “Upon binding with ligands, disorder-to-order transitions are frequently observed, where the ﬂexibility of the disordered regions may be necessary to facilitate interactions with multiple partners with high-speciﬁcity and low-aﬃnity (6).”
Keap1 Recruits Neh2 through Binding to ETGE and DLG Motifs: Characterization of the Two-Site Molecular Recognition Model (2006, background): “5 ppm) (58), a feature typical of intrinsically disordered polypeptides (10, 55).”
A structural model of latent evolutionary potentials underlying neutral networks in proteins (2007, background): “Some proteins can be intrinsically disordered, lacking a folded structure altogether (Eisenmesser et al., 2005; Tompa, 2002; Gunasekaran et al., 2003; Dyson and Wright, 2005; Haynes et al., 2006).”
High-throughput prediction of RNA, DNA and protein binding regions mediated by intrinsic disorder (2015, methodology): “We considered the AA composition, sequence complexity and secondary structure based on the observations that IDRs are enriched in certain AAs (41), have low sequence complexity (42), and are biased in their secondary structure (43,44).”