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Related Experiment Videos

Addressing the intrinsic disorder bottleneck in structural proteomics.

Christopher J Oldfield1, Eldon L Ulrich, Yugong Cheng

  • 1Center for Eukaryotic Structural Genomics, Biochemistry Department, University of Wisconsin, Madison, Wisconsin 53706, USA.

Proteins
|March 25, 2005
PubMed
Summary

Filtering disordered proteins using computational algorithms significantly improves the efficiency of eukaryotic structural genomics. This approach enhances the yield of viable protein structure determination candidates.

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Area of Science:

  • Structural biology
  • Proteomics
  • Biophysics

Background:

  • The Center for Eukaryotic Structural Genomics (CESG) employs high-throughput pipelines for eukaryotic protein structure determination.
  • Nuclear Magnetic Resonance (NMR) spectroscopy is crucial for structure determination and screening for stable protein structures.

Purpose of the Study:

  • To retrospectively analyze the impact of filtering disordered proteins on structure determination success.
  • To compare the Predictor of Naturally Disordered Regions (PONDR) algorithm with 13 other disorder prediction methods.

Main Methods:

  • Utilized NMR spectroscopy for screening 71 protein targets (70 from Arabidopsis thaliana, 1 from Caenorhabditis elegans) labeled with nitrogen-15.
  • Applied the PONDR algorithm to predict intrinsically disordered regions in proteins.

Related Experiment Videos

  • Compared PONDR's performance against 13 alternative disorder prediction algorithms using sequence data.

    • Main Results:

    • Implementing PONDR-based filtering of potentially disordered proteins enhanced the success rate of identifying viable structure determination candidates.
    • The study identified PONDR as a highly effective algorithm for predicting protein disorder.
    • Removing predicted disordered proteins significantly improved the overall efficiency of the structural proteomics pipeline.

    Conclusions:

    • Pre-filtering eukaryotic protein targets predicted as disordered by optimal algorithms like PONDR substantially boosts structural proteomics efficiency.
    • This strategy refines the selection of protein targets, leading to a higher yield of successful structure determinations.
    • The findings support the integration of computational disorder prediction into high-throughput structural biology workflows.