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Related Concept Videos

Polymer Classification: Crystallinity01:21

Polymer Classification: Crystallinity

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Unlike ionic or small covalent molecules, polymers do not form crystalline solids due to the diffusion limitations of their long-chain structures. However, polymers contain microscopic crystalline domains separated by amorphous domains.
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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.
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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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Recrystallization: Solid–Solution Equilibria01:10

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Recrystallization is a purification technique used to separate impurities from solid compounds. In this technique, no chemical reactions occur. Instead, it exploits physical properties only, specifically, the solubility differences between the desired compound and impurities, either at a single temperature or at different temperatures, and under other selected conditions. The solid-solution equilibrium (solubility equilibrium) of each component in the solution represents a binary phase...
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Factors Affecting Dissolution: Polymorphism, Amorphism and Pseudopolymorphism01:21

Factors Affecting Dissolution: Polymorphism, Amorphism and Pseudopolymorphism

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Polymorphism refers to the existence of a drug substance in multiple crystalline forms, known as polymorphs. Recently, this term has been expanded to include solvates (forms containing a solvent), amorphous forms (non-crystalline forms), and desolvated solvates (forms from which the solvent has been removed).
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Determination of Crystal Structures01:29

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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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Updated: Mar 22, 2026

Protein Crystallization for X-ray Crystallography
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Protein Crystallization for X-ray Crystallography

Published on: January 16, 2011

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Protein Crystallizability.

Pawel Smialowski1, Philip Wong2

  • 1Biomedical Center Munich, Ludwig-Maximilians-University, Großhaderner Strasse 9, 82152, Martinsried, Germany. pawelsm@gmail.com.

Methods in Molecular Biology (Clifton, N.J.)
|April 27, 2016
PubMed
Summary
This summary is machine-generated.

Selecting optimal protein targets for crystallization is crucial for structure research. While progress has been made, predicting protein crystallization behavior and conditions remains a significant challenge for structural biologists.

Keywords:
Construct optimizationCrystallization conditionsImproving crystallizationProtein crystallization

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Area of Science:

  • Structural Biology
  • Biophysics
  • Computational Biology

Background:

  • Obtaining high-quality crystals for protein structure determination is a persistent bottleneck in structural biology.
  • Current methods for protein crystallization often rely on empirical screening, which can be time-consuming and inefficient.

Purpose of the Study:

  • To summarize and compare existing methods for selecting protein targets for crystallization.
  • To evaluate algorithms for predicting crystallization success and designing optimal crystallization conditions.
  • To highlight the advantages and disadvantages of different target selection and condition design approaches.

Main Methods:

  • Review and comparison of computational algorithms for predicting protein crystallization success.
  • Analysis of target selection strategies, including those for overall structure determination and those specific to the crystallization step.
  • Examination of factors such as data size, redundancy, model overfitting, and evaluation metrics in predictive modeling.

Main Results:

  • Several sequence properties have been identified as relevant for improving protein crystallization.
  • Algorithms exist for predicting success across various stages of structure determination, as well as for the crystallization step itself.
  • The evaluation of these methods reveals challenges related to data characteristics and model construction.

Conclusions:

  • Despite advancements, accurately predicting protein crystallization behavior and identifying optimal conditions remains a complex challenge.
  • Further development of predictive models and robust evaluation strategies is needed to enhance success rates in protein crystallography.
  • Improved target selection and condition design are essential for accelerating protein structure research.