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Cited by 240

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An approach to rapid protein crystallization using nanodroplets

Santarsiero, B. D., Yegian, D. T., Lee, C. C., Spraggon, G., Gu, J., Scheibe, D., Uber, D. C., Cornell, E. W., Nordmeyer, R. A., Kolbe, W. F., Jin, J., Jones, A. L., Jaklevic, J. M., Schultz, P. G., Stevens, R. C.·2002·Journal of Applied Crystallography

Abstract

An approach that enables up to a two order of magnitude reduction in the amount of protein required and a tenfold reduction in the amount of time required for vapor-diffusion protein crystallization is reported. A prototype high-throughput automated system was used for the production of diffraction-quality crystals for a variety of proteins from a screen of 480 conditions using drop volumes as small as 20 nL. This approach results in a significant reduction in the time and cost of protein structure determination, and allows for larger and more efficient screens of crystallization parameter space. The ability to produce diffraction-quality crystals rapidly with minimal quantities of protein enables high-throughput efforts in structural genomics and structure-based drug discovery.

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Metadata

Authors
Santarsiero, B. D., Yegian, D. T., Lee, C. C., Spraggon, G., Gu, J., Scheibe, D., Uber, D. C., Cornell, E. W., Nordmeyer, R. A., Kolbe, W. F., Jin, J., Jones, A. L., Jaklevic, J. M., Schultz, P. G., & Stevens, R. C.
Venue
Journal of Applied Crystallography
Published
2002-03-22
Volume
35
Issue
2
Pages
278-281
ISSN
0021-8898 (ISSN-L)
OpenAlex
W2094504398
Cited by
240
References
0
FWCI
18.25
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: An approach to rapid protein crystallization using nanodroplets. https://app.cochat.ai/papers/10.1107/s0021889802001474
The paper
Santarsiero, B. D., Yegian, D. T., Lee, C. C., Spraggon, G., Gu, J., Scheibe, D., Uber, D. C., Cornell, E. W., Nordmeyer, R. A., Kolbe, W. F., Jin, J., Jones, A. L., Jaklevic, J. M., Schultz, P. G., & Stevens, R. C. (2002). An approach to rapid protein crystallization using nanodroplets. Journal of Applied Crystallography. https://doi.org/10.1107/s0021889802001474

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.