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Related Concept Videos

X-ray Crystallography02:18

X-ray Crystallography

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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.
Diffraction
Diffraction is the change in the direction of travel experienced by an electromagnetic wave when it encounters a physical barrier whose dimensions are comparable to those of the wavelength of the light. X-rays are electromagnetic radiation with wavelengths about as long as the distance between neighboring...
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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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X-ray Diffraction of Biological Samples01:10

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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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Neutron Crystallography Data Collection and Processing for Modelling Hydrogen Atoms in Protein Structures
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Low Resolution Refinement of Atomic Models Against Crystallographic Data.

Robert A Nicholls1, Oleg Kovalevskiy1, Garib N Murshudov2

  • 1MRC Laboratory of Molecular Biology, Francis Crick Avenue, CB2 0QH, Cambridge, UK.

Methods in Molecular Biology (Clifton, N.J.)
|June 3, 2017
PubMed
Summary
This summary is machine-generated.

This review addresses challenges in low-resolution crystallographic refinement and map calculation. It highlights using prior knowledge and accurate likelihood functions to improve macromolecular models and maps.

Keywords:
Bayes’ theoremLow-resolutionMacromoleculesRefinement

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Area of Science:

  • Structural biology
  • Crystallography
  • Computational biology

Background:

  • Low-resolution refinement and map calculation in crystallography present significant challenges.
  • Accurate atomic models are crucial for interpreting experimental data.

Purpose of the Study:

  • To review problems in low-resolution crystallographic refinement and map calculation.
  • To discuss strategies for improving map quality and atomic model building.

Main Methods:

  • Application of Bayes' theorem for integrating experimental data with prior chemical and structural knowledge.
  • Utilizing prior information on bonds, angles, homologous structures, secondary structures, and hydrogen bonding.
  • Exploiting local conformational conservation and similarity of non-crystallographically related molecules.
  • Designing accurate likelihood functions linking model parameters to observed data.
  • Investigating the role of phases and the use of raw observed amplitudes.
  • Replacing noisy or absent observations with weighted calculated structure factors.

Main Results:

  • Prior knowledge significantly aids refinement by providing constraints and information.
  • Accurate likelihood functions are essential for reliable model-to-data correlation.
  • Using raw observed amplitudes can improve map correlation.
  • Strategic replacement of missing data with calculated structure factors can smoothen maps.
  • Map quality assessment should involve regions without atomic models.
  • Employing multiple blurred and sharpened maps aids atomic model building.

Conclusions:

  • Integrating diverse prior knowledge with experimental data improves crystallographic refinement.
  • Careful design of likelihood functions and phase information is critical for accurate map calculation.
  • The presented methods, implemented in CCP4 software (REFMAC5, ProSMART, LORESTR), enhance atomic model building from low-resolution data.