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Correlated phasing of multiple isomorphous replacement data.

T C Terwilliger1, J Berendzen

  • 1Structural Biology Group, Los Alamos National Laboratory, NM 87545, USA.

Acta Crystallographica. Section D, Biological Crystallography
|July 1, 1996
PubMed
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This study introduces correlated phasing, a Bayesian method to improve macromolecule structure determination. It effectively handles correlated errors in multiple isomorphous replacement data, enhancing phase estimates for complex biological structures.

Area of Science:

  • Biophysics
  • Structural Biology
  • Crystallography

Background:

  • Multiple isomorphous replacement (MIR) is crucial for macromolecule structure determination.
  • Correlated errors in heavy-atom derivatives can hinder MIR accuracy.
  • Existing MIR analysis often assumes independent errors, limiting its effectiveness.

Purpose of the Study:

  • To develop a method to address correlated errors in MIR data.
  • To improve phase determination for macromolecule structures.

Main Methods:

  • A Bayesian approach was employed to derive phase probability distributions.
  • The new 'correlated-phasing' method was developed to account for correlated errors.
  • Simulated and real MIR data sets with correlated errors were analyzed.

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Main Results:

  • The correlated-phasing method significantly improves MIR phase estimates.
  • The method is effective even with substantial correlated errors in derivatives.
  • It addresses issues arising from protein conformational changes or crystal packing variations.

Conclusions:

  • Correlated phasing offers a robust solution for handling correlated errors in MIR.
  • This method enhances the accuracy of macromolecule structure determination.
  • The technique is computationally feasible for routine crystallographic applications.