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Related Concept Videos

Determination of Crystal Structures01:29

Determination of Crystal Structures

In the late 1800s, the revelation that light extended beyond visible wavelengths led to the discovery of X-rays by Wilhelm Roentgen. Recognized as high-energy electromagnetic radiation with short wavelengths, X-rays prompted exploration into their interaction with crystals. Max von Laue proposed in 1912 that the periodic arrangement of atoms, ions, or molecules in crystals would cause them to diffract X-rays, a hypothesis confirmed through experiments with copper sulfate and zinc sulfide...
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The size of the unit cell and the arrangement of atoms in a crystal may be determined from measurements of the diffraction of X-rays by the crystal, termed X-ray crystallography.
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X-ray diffraction or XRD is an analytical tool that utilizes X-rays to study ordered structures such as crystalline organic and inorganic samples, polycrystalline materials, proteins, carbohydrates, and drugs.
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Structural Studies of Macromolecules in Solution using Small Angle X-Ray Scattering
07:19

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Published on: November 5, 2018

Polarizable atomic multipole X-ray refinement: weighting schemes for macromolecular diffraction.

T D Fenn1, M J Schnieders

  • 1Department of Bioengineering, Stanford University, Stanford, California, USA. fenn@stanford.edu

Acta Crystallographica. Section D, Biological Crystallography
|November 22, 2011
PubMed
Summary
This summary is machine-generated.

A new static weight method for macromolecular crystallographic refinement improves model accuracy. This approach, using the AMOEBA force field, balances refinement targets and enhances data quality across resolutions.

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Area of Science:

  • Structural Biology
  • Crystallography
  • Computational Chemistry

Background:

  • Traditional macromolecular crystallographic refinement uses weighting schemes based on comparing gradients or Hessian diagonal terms of chemical and data-based targets.
  • Limitations exist in these schemes, particularly with maximum-likelihood targets influenced by model and data errors.

Purpose of the Study:

  • To develop and validate a transferable static weight for macromolecular crystallographic refinement.
  • To demonstrate the benefits of this static weight across various data resolutions, especially for low-resolution data.

Main Methods:

  • Derived an optimal static weight from first principles based on the congruence between maximum-likelihood and polarizable atomic multipole electrostatics (AMOEBA) chemical potentials.
  • Implemented and tested the static weight in X-ray crystallographic refinement using the AMOEBA force field.
  • Evaluated model quality using R(free) and MolProbity scores across a range of data resolutions.

Main Results:

  • The derived static weight is transferable across a broad range of data resolutions, from low to high.
  • Models refined with the static weight are balanced, optimizing both R(free) and MolProbity scores.
  • Classical automatic weighting schemes resulted in underfitting or overfitting and poor model geometry.

Conclusions:

  • A first-principles derived static weight offers a superior alternative to traditional weighting schemes in macromolecular crystallography.
  • This method is effective across all data resolutions, eliminating the need for resolution-dependent parameterization.
  • The approach enhances model quality and accuracy, particularly benefiting low-resolution crystallographic data.