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Related Experiment Videos

Exploring structural variability in X-ray crystallographic models using protein local optimization by torsion-angle

Jennifer L Knight1, Zhiyong Zhou, Emilio Gallicchio

  • 1The Scripps Research Institute, La Jolla, CA 92037, USA.

Acta Crystallographica. Section D, Biological Crystallography
|April 9, 2008
PubMed
Summary
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Automated methods using torsion-angle sampling can generate multiple protein models that fit X-ray data, aiding in understanding protein function and drug design.

Area of Science:

  • Structural biology
  • Computational chemistry
  • Biophysics

Background:

  • Understanding protein structural variability is crucial for predicting function and for in silico drug discovery.
  • Manual X-ray crystallographic refinement is time-consuming, necessitating automated methods for exploring conformational space.

Purpose of the Study:

  • To develop and demonstrate an automated method for generating multiple, structurally variable protein models from X-ray data.
  • To assess the ability of this method to capture biologically relevant conformational heterogeneity.

Main Methods:

  • Torsion-angle sampling of backbone and side-chain libraries using the Protein Local Optimization Program (PLOP).
  • Filtering models against chemical energy (using an effective potential) and electron density.

Related Experiment Videos

  • Minimization of a reciprocal-space X-ray target function.

    • Main Results:

    • The automated method successfully generated multiple models fitting X-ray data well across five proteins (1.0-2.8 A resolution).
    • Including electrostatic and implicit solvation terms in the effective potential yielded the lowest R(free) values.
    • The generated ensembles captured known structural variability linked to protein flexibility and function, as seen in HIV-1 protease, calmodulin, and SUMO-conjugating enzyme.

    Conclusions:

    • Automated torsion-angle sampling coupled with energy and electron density filtering provides a systematic approach to constructing ensembles of structurally variable protein models.
    • This method can differentiate physical conformational heterogeneity from coordinate uncertainty, improving the reliability of in silico targets.