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Related Experiment Videos

Rapid knot detection and application to protein structure prediction.

Firas Khatib1, Matthew T Weirauch, Carol A Rohl

  • 1Department of Biomolecular Engineering, University of California at Santa Cruz, Santa Cruz, CA 95064, USA. bort@soe.ucsc.edu

Bioinformatics (Oxford, England)
|July 29, 2006
PubMed
Summary

Protein structure prediction models can contain knots, which are rare in native proteins. A new algorithm, Knotfind, detects these knots, leading to fewer knotted models in subsequent predictions and the discovery of a novel knot in a known protein.

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Area of Science:

  • Computational biology
  • Structural bioinformatics
  • Protein structure prediction

Background:

  • Knots are infrequent in native proteins but can arise in computational models.
  • Current protein structure prediction methods often do not model the folding process, allowing knots to form in models.
  • The Knotfind algorithm was developed to address knot detection in protein models.

Purpose of the Study:

  • To develop and implement a fast knot detection algorithm for protein structure prediction.
  • To analyze the frequency and characteristics of knots in protein models generated by the Rosetta method.
  • To improve protein structure prediction by reducing the occurrence of knots in models.

Main Methods:

  • Development of the Knotfind algorithm for efficient knot detection in protein structures.

Related Experiment Videos

  • Application of Knotfind to analyze large datasets of protein models from CASP 5 and CASP 6.
  • Utilizing insights from CASP 5 analysis to refine structure prediction strategies for CASP 6.

    • Main Results:

    • Knotfind successfully detected knots in protein models, enabling analysis of their prevalence.
    • Refinements in the Rosetta prediction method, informed by Knotfind analysis, significantly reduced the proportion of knotted models in CASP 6.
    • A previously unknown deep trefoil knot was identified in acetylornithine transcarbamylase using Knotfind on Protein Data Bank structures.

    Conclusions:

    • Knotfind is an effective and computationally efficient tool for detecting knots in protein structure models.
    • The study demonstrates that analyzing and addressing knots in models can improve the accuracy of protein structure prediction.
    • The discovery of a new knot in a biological protein highlights the utility of Knotfind for structural biology research.