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Sequence-structure specificity of a knowledge based energy function at the secondary structure level.

D Xu1, M A Unseren, Y Xu

  • 1Computational Biosciences Section, Life Sciences Division Center for Engineering Science Advanced Research, Computer Science and Mathematics Division, Oak Ridge National Laboratory, PO Box 2008, Oak Ridge, TN 37831-6480, USA. xud@ornl.gov

Bioinformatics (Oxford, England)
|June 27, 2000
PubMed
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This study evaluates a knowledge-based energy function for protein threading at the secondary structure level. It reveals insights into sequence-structure specificity, aiding in tertiary structure prediction and protein design.

Area of Science:

  • Computational Biology
  • Structural Bioinformatics
  • Protein Science

Background:

  • Investigates the sequence-structure specificity of knowledge-based energy functions.
  • Focuses on protein threading at the secondary structure level for detailed analysis.

Purpose of the Study:

  • To assess the strengths and weaknesses of an energy function at the fundamental secondary structure level.
  • To provide insights applicable to tertiary structure threading and protein design.

Main Methods:

  • Threaded 293 non-redundant proteins onto their native secondary structures.
  • Utilized 68 protein pairs with similar folds and low sequence identity for threading.
  • Analyzed the energy function components (one-body, pairwise, mutation) and applied it to designed sequences.

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Main Results:

  • Demonstrated the discerning power of the energy function and its components.
  • Identified patterns for favorable mutations in a designed 11-amino acid sequence.
  • Showcased methods to accelerate tertiary threading by filtering unfavorable secondary structure alignments.

Conclusions:

  • Secondary structure threading provides valuable insights for energy function evaluation.
  • The energy function aids in understanding sequence-structure relationships and protein design.
  • Filtering based on secondary structure threading can enhance tertiary structure prediction efficiency.