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Identification of Post-translational Modifications of Plant Protein Complexes
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Disentangling the complexity of low complexity proteins.

Pablo Mier1, Lisanna Paladin2, Stella Tamana3

  • 1Institute of Organismic and Molecular Evolution, Johannes Gutenberg University of Mainz, Mainz, Germany.

Briefings in Bioinformatics
|January 31, 2019
PubMed
Summary
This summary is machine-generated.

Low complexity regions (LCRs) in proteins have varied definitions, often linked to composition bias. Understanding LCRs requires integrating multiple predictive tools to fully grasp their structural roles.

Keywords:
composition biasdisorderlow complexity regionsstructure

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Area of Science:

  • Protein bioinformatics
  • Computational biology
  • Structural biology

Background:

  • Low complexity regions (LCRs) are prevalent in protein sequences, with diverse definitions centered on amino acid composition.
  • LCRs are often characterized by composition bias, which is frequently associated with intrinsic disorder.
  • However, some LCRs, particularly repetitive sequences, can adopt ordered structures, presenting a dichotomy in their functional roles.

Purpose of the Study:

  • To critically review the definitions of sequence complexity in LCRs.
  • To explore the relationship between LCR sequence properties and their structural implications.
  • To highlight the challenges and opportunities in predicting LCR structure and function.

Main Methods:

  • Statistical analysis of sequence properties related to low complexity.
  • Review of existing methodologies for measuring sequence complexity and composition bias.
  • Illustrative examples to demonstrate the link between LCRs, composition bias, repeats, and structure.

Main Results:

  • Multiple definitions exist for LCRs, often equating them to regions with biased amino acid composition.
  • Composition bias in LCRs can correlate with both disorder and ordered structures, especially in repetitive elements.
  • Current statistical measures alone are insufficient to fully capture the structural diversity of LCRs.

Conclusions:

  • A combined approach using various predictive tools and experimental measurements is recommended for accurate LCR characterization.
  • Improved and standardized annotation of LCRs in sequence databases is crucial for advancing prediction accuracy.
  • Enhanced understanding of LCRs will improve insights into protein evolution, structure-function relationships, and the role of disordered regions.