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Direct-space methods in phase extension and phase determination. III. Phase refinement using Sayre's equation.

L S Refaat1, M M Woolfson

  • 1Physics Department, University of York, England.

Acta Crystallographica. Section D, Biological Crystallography
|November 1, 1995
PubMed
Summary

This study introduces an algorithm for refining crystallographic phases using the Sayre equation, showing effectiveness even with lower-resolution data. The method offers valuable phase refinement and extension, particularly when combined with other techniques.

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Area of Science:

  • Crystallography
  • Computational Biology
  • Structural Biology

Background:

  • Crystallographic phase determination is crucial for solving protein structures.
  • The Sayre equation offers a theoretical basis for phase refinement.
  • Existing methods may have limitations with varying data resolutions.

Purpose of the Study:

  • To develop and evaluate an algorithm for refining crystallographic phases based on the Sayre equation.
  • To assess the algorithm's performance across different data resolutions.
  • To explore the utility of the Sayre equation in conjunction with other refinement methods.

Main Methods:

  • Implementation of an algorithm utilizing Fourier transforms for phase refinement.
  • Testing the algorithm on moderate-sized protein structures with varying data resolutions (1.17 Å and 1.5 Å).

Related Experiment Videos

  • Integration with weighted multiple isomorphous replacement phases and real-space refinement procedures.

    • Main Results:

    • The algorithm achieved good phase refinement at atomic resolution (1.17 Å).
    • Useful, though less optimal, refinement was observed with lower-resolution data (1.5 Å).
    • The Sayre equation demonstrated value for phase refinement and extension, especially when constrained.

    Conclusions:

    • The Sayre equation remains a valuable tool for phase refinement and extension at atomic or near-atomic resolution.
    • The algorithm shows utility even with lower-resolution data, particularly when combined with other methods.
    • The approach is computationally efficient, suitable for large systems.