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Determination of Crystal Structures

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...

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...

Crystal Growth: Principles of Crystallization

Crystal Growth: Principles of Crystallization

Crystallization is a phase transformation process in which crystals are precipitated from a supersaturated solution or formed from other sources. During crystallization, atoms or molecules arrange themselves into a well-defined, rigid crystal lattice to minimize energy.
Initiating crystallization involves manipulating the concentration of the solute and the temperature of the solution. Since crystal growth occurs when the ratio of concentration and solubility of the solute in the solvent...

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Updated: Mar 5, 2026

An All-in-one Sample Holder for Macromolecular X-ray Crystallography with Minimal Background Scattering

An All-in-one Sample Holder for Macromolecular X-ray Crystallography with Minimal Background Scattering

Published on: July 6, 2019

Ab initio solution of macromolecular crystal structures without direct methods.

Airlie J McCoy¹, Robert D Oeffner¹, Antoni G Wrobel²

¹Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Cambridge CB2 0XY, United Kingdom.

Proceedings of the National Academy of Sciences of the United States of America

|March 23, 2017

Summary

This summary is machine-generated.

Molecular replacement for determining macromolecular crystal structures can now use smaller models. New understanding of signal-to-noise ratio allows ab initio solutions with high-resolution data, even from single atoms.

Keywords:

Shisa ab initio phasing likelihood macromolecular crystallography molecular replacement

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$Microcrystallography of Protein Crystals and In Cellulo Diffraction$

Microcrystallography of Protein Crystals and In Cellulo Diffraction

Published on: July 21, 2017

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Last Updated: Mar 5, 2026

An All-in-one Sample Holder for Macromolecular X-ray Crystallography with Minimal Background Scattering

An All-in-one Sample Holder for Macromolecular X-ray Crystallography with Minimal Background Scattering

Published on: July 6, 2019

Structural Studies of Macromolecules in Solution using Small Angle X-Ray Scattering

Structural Studies of Macromolecules in Solution using Small Angle X-Ray Scattering

Published on: November 5, 2018

$Microcrystallography of Protein Crystals and In Cellulo Diffraction$

Microcrystallography of Protein Crystals and In Cellulo Diffraction

Published on: July 21, 2017

Area of Science:

Structural biology
Crystallography
Biophysics

Background:

Molecular replacement (MR) is a primary method for solving macromolecular crystal structures.
MR typically requires a high-quality, near-complete model of the target structure.
Existing methods lack a detailed theoretical understanding of signal-to-noise limitations in MR.

Purpose of the Study:

To develop a theoretical framework for signal-to-noise ratio in likelihood-based MR searches.
To investigate the applicability of MR with incomplete or low-quality models.
To explore novel applications of MR, including ab initio structure determination.

Main Methods:

Developed a theoretical understanding of signal-to-noise ratio in likelihood-based MR.
Analyzed the influence of model completeness, quality, and data resolution on MR success.
Investigated the use of small structural fragments in MR searches.

Main Results:

Contrary to prior belief, MR is applicable across a continuum of model qualities and data resolutions.
Signal-to-noise ratio analysis predicts MR success based on model and data parameters.
High-resolution data enables ab initio solutions using fragments as small as single atoms, even for structures difficult for direct methods.

Conclusions:

Likelihood-based MR is more versatile than previously assumed.
The quality of the input model does not strictly limit MR applicability.
High-resolution diffraction data significantly expands the scope of MR, enabling de novo structure determination from minimal input information.