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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.
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Updated: Jun 20, 2026

Combining X-Ray Crystallography with Small Angle X-Ray Scattering to Model Unstructured Regions of Nsa1 from S. Cerevisiae

Combining X-Ray Crystallography with Small Angle X-Ray Scattering to Model Unstructured Regions of Nsa1 from S. Cerevisiae

Published on: January 10, 2018

Modeling discrete heterogeneity in X-ray diffraction data by fitting multi-conformers.

Henry van den Bedem¹, Ankur Dhanik, Jean Claude Latombe

¹Joint Center for Structural Genomics, Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA.

Acta Crystallographica. Section D, Biological Crystallography

|September 23, 2009

Summary

This summary is machine-generated.

This study introduces a new algorithm for modeling protein structural heterogeneity in crystals. The multi-conformer model accurately represents protein structures at high resolution, improving agreement with diffraction data.

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Combining X-Ray Crystallography with Small Angle X-Ray Scattering to Model Unstructured Regions of Nsa1 from S. Cerevisiae

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Microfluidic Chips for In Situ Crystal X-ray Diffraction and In Situ Dynamic Light Scattering for Serial Crystallography

Published on: April 24, 2018

Area of Science:

Structural Biology
Crystallography
Computational Biology

Background:

Proteins exist as an ensemble of conformers in their native state, enabling interactions with binding partners.
Crystallization can retain some protein structural heterogeneity, but extracting these features from diffraction data is challenging.

Purpose of the Study:

To develop and present a novel algorithm for the automatic modeling of discrete heterogeneity in protein crystals.
To assess the performance of a multi-conformer modeling approach in representing protein structural variations.

Main Methods:

Development of an algorithm for automatic modeling of discrete heterogeneity.
Application of a single multi-conformer model with correlated structural features.
Analysis of diffraction data agreement at high (above 2 Å) and lower resolutions.

Main Results:

The multi-conformer model showed improved agreement with diffraction data at high resolution compared to single-conformer models.
The developed model effectively represents the set of protein structures present in the crystal.
At resolutions below 2 Å, multi-conformer models did not improve agreement, suggesting variability reflects uncertainty rather than coordinated motion.

Conclusions:

The new algorithm enables reliable extraction of heterogeneous features from crystallographic data at high resolution.
Multi-conformer modeling is a powerful tool for representing protein structural ensembles in crystalline states.
The interpretation of multi-conformer model variability differs between high and low-resolution crystallographic data.