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THESEUS: maximum likelihood superpositioning and analysis of macromolecular structures.

Douglas L Theobald1, Deborah S Wuttke

  • 1Department of Chemistry and Biochemistry, University of Colorado at Boulder, Boulder, CO 80309-0215, USA. douglas.theobald@colorado.edu

Bioinformatics (Oxford, England)
|June 17, 2006
PubMed
Summary

THESEUS offers accurate macromolecular structure superpositioning using maximum likelihood (ML) analysis. This method improves accuracy by down-weighting variable regions and correcting atomic correlations, enhancing structural comparisons.

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Area of Science:

  • Structural biology
  • Computational chemistry
  • Bioinformatics

Background:

  • Conventional methods like ordinary least-squares (LS) are limited in accuracy.
  • Macromolecular structure analysis requires robust superpositioning techniques.

Purpose of the Study:

  • To introduce THESEUS, a command-line program for maximum likelihood (ML) superpositioning and analysis of macromolecular structures.
  • To provide a more accurate and robust alternative to conventional superpositioning methods.

Main Methods:

  • Utilizes maximum likelihood (ML) as the optimization criterion for superpositioning.
  • Down-weights variable structural regions and corrects for correlations among atoms.
  • Performs principal components analysis for analyzing atomic correlations within structural ensembles.

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Main Results:

  • ML superpositions offer substantially improved accuracy compared to LS methods.
  • The method is robust and insensitive to the selection of atoms for analysis.
  • Provides both likelihood-based and frequentist statistics for evaluating superposition adequacy and structural differences.

Conclusions:

  • THESEUS provides a powerful tool for accurate and reliable analysis of macromolecular structures.
  • The ML approach enhances the understanding of structural similarities and differences.
  • The software is available under the GNU open source license.