Crystal Recognition
Crystal Recognition - Related Videos
06:19
High-Throughput Screening to Obtain Crystal Hits for Protein Crystallography
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Cited by 14 •
2023
This protocol details high-throughput crystallization screening, ranging from the 1,536 microassay plate preparation to the end of a 6 week experimental time window. Details are included about the sample setup, the imaging obtained, and how users can perform analyses using an artificial intelligence-enabled graphical user interface to quickly and efficiently identify macromolecular crystallization conditions.
11:55
Physiological Correlates of Emotion Recognition
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2023
Source: Laboratories of Jonas T. Kaplan and Sarah I. Gimbel—University of Southern California The autonomic nervous system (ANS) controls the activity of the body's internal organs and regulates changes in their activity depending on the current environment. The vagus nerve, which innervates many of the internal organs, is an important part of the system. When our brain senses danger, vagal tone is inhibited, leading to a set of changes in the body designed to make us more prepared to fight or...
07:39
Protein Crystallization
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2023
Protein crystallization, obtaining a solid lattice of biomolecules, elucidates protein structure and enables the study of protein function. Crystallization involves drying purified protein under a combination of many factors, including pH, temperature, ionic strength, and protein concentration. Once crystals are obtained, the protein structure can be elucidated by x-ray diffraction and computation of an electron density model. This video introduces protein crystallization and shows a general...
13:38
Synthesis of Biocompatible Liquid Crystal Elastomer Foams as Cell Scaffolds for 3D Spatial Cell Cultures
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Cited by 9 •
2017
This study presents a methodology to prepare 3D, biodegradable, foam-like cell scaffolds based on biocompatible side-chain liquid crystal elastomers (LCEs). Confocal microscopy experiments show that foam-like LCEs allow for cell attachment, proliferation, and the spontaneous alignment of C2C12s myoblasts.
07:42
On-Chip Crystallization and Large-Scale Serial Diffraction at Room Temperature
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Cited by 4 •
2022
This contribution describes how to set up protein crystallization on crystal-on-crystal devices and how to perform automated serial data collection at room temperature using the on-chip crystallization platform.
FAQs
Crystal recognition relies on a combination of features rather than a single visual cue. Defined geometric faces suggest ordered growth, while consistent morphology helps distinguish one crystalline form from irregular amorphous precipitate. This comparison is useful because precipitate can otherwise be mistaken for a promising crystal during evaluation of biomolecular crystallization samples.
Birefringence provides an optical clue that complements visible shape. Under polarized light, it is associated with the regular molecular arrangement of a crystal lattice, whereas amorphous precipitate lacks that same ordered basis. Using this observation alongside morphology strengthens the decision about whether a sample merits further consideration for structural analysis.
Precipitant concentration, pH, temperature, and incubation time influence whether a sample produces material that can be assessed as crystalline or amorphous. Consequently, recognition is not only a classification step; it also supplies feedback for optimizing conditions. Comparing observations across these variables helps identify which settings generate promising ordered material for later analysis.
Examine morphology for consistency and defined geometric faces, then use polarized light to check for birefringence. Interpreting these observations together helps separate ordered crystalline material from amorphous precipitate. This combined assessment provides a practical basis for deciding whether the sample should be retained as a candidate for subsequent X-ray diffraction work.
It is most useful after a biomolecular crystallization attempt, when researchers must decide which samples or conditions deserve attention. Recognition guides selection of promising samples and helps focus optimization of precipitant concentration, pH, temperature, or incubation time. In this way, visual and optical evidence can inform the next experimental choice rather than treating every sample identically.
Successful recognition identifies samples that may be suitable for X-ray diffraction, linking the visual assessment of a crystallization experiment to structural analysis. The resulting work can support structural information about proteins, nucleic acids, and their complexes. Thus, recognition serves as an early selection point between crystallization observations and investigations of biomolecular structure.