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Automated MAD and MIR structure solution.

T C Terwilliger1, J Berendzen

  • 1Structural Biology Group, Mail Stop M888, Los Alamos National Laboratory, Los Alamos, NM 87545, USA. terwilliger@lanl.gov

Acta Crystallographica. Section D, Biological Crystallography
|March 25, 1999
PubMed
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Automating macromolecular crystallography, this study introduces criteria to evaluate heavy-atom substructures. This enables automated structure solution, accelerating genomic-scale structure determination.

Area of Science:

  • Macromolecular crystallography
  • Structural biology
  • Biophysics

Background:

  • X-ray diffraction data analysis for electron-density maps is often challenging and time-consuming.
  • Current methods like Multiple Isomorphous Replacement (MIR) and Multi-wavelength Anomalous Diffraction (MAD) rely heavily on subjective assessments of heavy-atom substructures.

Purpose of the Study:

  • To develop objective criteria for evaluating heavy-atom substructures in macromolecular crystallography.
  • To automate the process of solving crystal structures, thereby reducing manual intervention and improving efficiency.

Main Methods:

  • Developed a set of criteria to objectively assess the quality of heavy-atom partial solutions.
  • Transformed the crystal structure solution process into an optimization problem amenable to automation.

Related Experiment Videos

  • Utilized the SOLVE software to implement the automated structure solution pipeline.

    • Main Results:

    • Successfully automated the evaluation of heavy-atom substructures, converting a subjective process into an objective optimization problem.
    • The SOLVE software demonstrated capability in solving complex MAD datasets, including those with up to 52 selenium sites.
    • The developed automated process significantly streamlines the initial stages of structure determination.

    Conclusions:

    • The developed automated structure solution process represents a significant advancement for macromolecular crystallography.
    • This automation is a crucial step towards achieving fully automated structure determination, model building, and refinement.
    • The approach facilitates large-scale structural genomics initiatives by increasing throughput and reducing the time required for structure determination.