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Sequence-based analysis of protein degradation rates.

Miguel Correa Marrero1, Aalt D J van Dijk1,2,3, Dick de Ridder1

  • 1Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.

Proteins
|May 27, 2017
PubMed
Summary

Simple protein sequence features can predict protein degradation rates, revealing intrinsic disorder

Keywords:
data miningintrinsic disordermachine learningmultivariate regressionprotein metabolismprotein turnoverproteolysissequence analysissupport vector machine

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Area of Science:

  • Cellular biology
  • Proteomics
  • Biochemistry

Background:

  • Protein turnover is crucial for cellular homeostasis.
  • Determinants of protein lifetime are not well understood.
  • Predicting protein degradation from sequence is challenging.

Purpose of the Study:

  • To identify sequence-derived properties that determine protein degradation rates.
  • To develop predictive models for protein degradation.
  • To investigate the roles of intrinsic disorder and PEST regions.

Main Methods:

  • Utilized two experimental datasets of protein degradation rates.
  • Developed predictive models based on sequence features.
  • Analyzed the contribution of intrinsic disorder and PEST regions.

Main Results:

  • Simple sequence features accurately predict protein degradation rates.
  • Intrinsic disorder significantly influences protein lifetime.
  • PEST regions' effect is explained by their intrinsic disorder.
  • Degradation determinants vary across cell types and conditions.

Conclusions:

  • Sequence-based models can effectively predict protein degradation.
  • Intrinsic disorder is a key determinant of protein stability.
  • Computational models for protein degradation can be advanced.
  • Understanding protein life cycles requires considering cellular context.