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X-ray Crystallography

X-ray Crystallography

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...

$X-ray Diffraction of Biological Samples$ 01:10

X-ray Diffraction of Biological Samples

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.
According to Bragg's law, when X-rays strike the sample positioned on a stage, the rays are scattered by the electron clouds around the sample atoms. The X-ray diffraction or scattering is caused by constructive interference of the X-ray waves that reflect off the internal...

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Related Experiment Video

Updated: Jan 9, 2026

Biochemical and Structural Characterization of the Carbohydrate Transport Substrate-binding-protein SP0092

Biochemical and Structural Characterization of the Carbohydrate Transport Substrate-binding-protein SP0092

Published on: October 2, 2017

Sensitive detection of structural dynamics using a statistical framework for comparative crystallography.

Doeke R Hekstra^1,2, Harrison K Wang^1,3, Margaret A Klureza^1,4

¹Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, USA.

Science Advances

|December 3, 2025

Summary

This summary is machine-generated.

This study introduces a new machine learning method for comparative crystallography. It enhances the detection of subtle protein changes and drug interactions by improving how crystallographic data is analyzed.

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Last Updated: Jan 9, 2026

Biochemical and Structural Characterization of the Carbohydrate Transport Substrate-binding-protein SP0092

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Published on: October 2, 2017

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Area of Science:

Structural Biology
Biophysics
Computational Biology

Background:

Protein conformational changes are vital for function and drug targeting.
X-ray crystallography reveals atomic details but standard methods miss subtle or heterogeneous changes.
Systematic errors in crystallographic data often obscure real molecular differences.

Purpose of the Study:

To develop an advanced machine learning approach for comparative crystallography.
To improve the detection of subtle molecular differences and protein dynamics.
To enhance the identification of drug-target interactions using structural data.

Main Methods:

Utilized a variational deep learning framework (Careless).
Integrated multivariate, structured priors from crystallographic theory.
Jointly estimated systematic errors (scales) and structure factors for improved data analysis.

Main Results:

Significantly improved detection of protein dynamics.
Enhanced identification of element-specific anomalous signals.
Robust detection of drug candidate binding events.

Conclusions:

The new method offers a powerful approach to comparative crystallography.
This technique can reveal subtle structural changes previously missed.
Potential applications include studying protein dynamics and drug discovery.