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Related Experiment Videos

Natively unstructured loops differ from other loops.

Avner Schlessinger1, Jinfeng Liu, Burkhard Rost

  • 1Department of Biochemistry and Molecular Biophysics, Columbia University, New York, New York, USA. as2067@columbia.edu

Plos Computational Biology
|July 31, 2007
PubMed
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Natively unstructured protein regions are key to organismal complexity. A new neural network method, NORSnet, accurately predicts these regions by identifying nonregular secondary structure segments, improving protein annotation.

Area of Science:

  • * Molecular Biology
  • * Bioinformatics
  • * Structural Biology

Background:

  • * Natively unstructured or disordered protein regions are abundant in eukaryotes and crucial for functional complexity, but challenging to study computationally.
  • * Existing methods often predict these regions by training on outliers in ordered protein structures.
  • * These disordered regions often evade experimental structure determination.

Purpose of the Study:

  • * To introduce a novel computational approach, NORSnet, for predicting natively unstructured protein regions.
  • * To leverage a neural network trained on predicted secondary structure information, specifically focusing on nonregular secondary structure (NORS) regions.
  • * To address limitations of previous methods by avoiding overlap between training and testing data and enabling proteome-wide analysis.

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Main Methods:

  • * Development of NORSnet, a neural network method trained on predicted secondary structure information.
  • * Hypothesis that very long contiguous segments with nonregular secondary structure (NORS regions) differ from regular loops.
  • * Training focused on distinguishing well-structured and unstructured loops based on NORS features.

Main Results:

  • * NORSnet successfully distinguished between well-structured and unstructured loops, validating the core hypothesis.
  • * Benchmarks showed NORSnet performed well on experimental data, identifying previously unannotated unstructured regions.
  • * NORSnet identified unstructured regions at domain boundaries more frequently than expected by chance and found them in 50%-70% of worm proteins with many interaction partners.

Conclusions:

  • * NORSnet is a valuable tool for predicting natively unstructured protein regions, particularly long unstructured loops.
  • * The method enhances structural genomics efforts by flagging potential disordered regions in proteins.
  • * Disordered regions, especially long unstructured loops, play a significant role in molecular networks.