Linear motifs: Evolutionary interaction switches

@article{Neduva2005LinearME,
  title={Linear motifs: Evolutionary interaction switches},
  author={Victor Neduva and Rob Russell},
  journal={FEBS Letters},
  year={2005},
  volume={579}
}
Attributes of short linear motifs.
TLDR
Analysis of curated instances currently available in the Eukaryotic Linear Motif database suggest that functional SLiMs have higher levels of conservation than their surrounding residues, frequently evolve convergently, preferentially occur in disordered regions and often form a secondary structure when bound to their interaction partner.
Local structural disorder imparts plasticity on linear motifs
TLDR
It is demonstrated that LMs in average are embedded in locally unstructured regions, while their amino acid composition and charge/hydropathy properties exhibit a mixture characteristic of folded and disordered proteins.
Short linear motifs – ex nihilo evolution of protein regulation
TLDR
A mechanism of ex nihilo SLiM evolution is proposed – the evolution of a novelSLiM from “nothing” – adding a functional module to a previously non-functional region of protein sequence to create a large functional motif space.
A million peptide motifs for the molecular biologist.
Computational prediction of short linear motifs from protein sequences.
TLDR
This review explores the current knowledge of SLiMs and how it can be applied to the task of predicting them computationally from protein sequences and highlights the importance of correctly modelling evolutionary relationships and the probability of false positive predictions.
Evidence for the Concerted Evolution between Short Linear Protein Motifs and Their Flanking Regions
TLDR
The results suggest that flanking regions are relevant for linear motif–mediated interactions, both at the structural and sequence level, and indicate that the prediction of linear motif instances can be enriched with contextual information by performing a sequence analysis similar to the one presented here.
DILIMOT: discovery of linear motifs in proteins
TLDR
DILIMOT (DIscovery of LInear MOTifs), a server for the prediction of these short linear motifs within a set of proteins, finds statistically over-represented motifs likely to be responsible for it.
Classification of protein protein interactions
TLDR
PiType is reported, a method for classifying protein interactions into obligate/non-obligate as well as into SP/ME based exclusively on sequence and network information.
Systematic Discovery of New Recognition Peptides Mediating Protein Interaction Networks
TLDR
The approach based on motif over-representation in non-homologous sequences, rediscovers known motifs and predicts dozens of others that will give molecular insight into protein networks and greatly illuminate cellular processes.
A tree-based conservation scoring method for short linear motifs in multiple alignments of protein sequences
TLDR
The conservation score improves the prediction of linear motifs, by discarding those matches that are unlikely to be functional because they have not been conserved during the evolution of the protein sequences.
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