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High-throughput production of human proteins for crystallization: The SGC experience

Open access
Savitsky, Pavel, Bray, James, Cooper, Christopher D.O., Marsden, Brian D., Mahajan, Pravin, Burgess-Brown, Nicola A., Gileadi, Opher·2010·Journal of Structural Biology

Abstract

Producing purified human proteins with high yield and purity remains a considerable challenge. We describe the methods utilized in the Structural Genomics Consortium (SGC) in Oxford, resulting in successful purification of 48% of human proteins attempted; of those, the structures of approximately 40% were solved by X-ray crystallography. The main driver has been the parallel processing of multiple (typically 9-20) truncated constructs of each target; modest diversity in vectors and host systems; and standardized purification procedures. We provide method details as well as data on the properties of the constructs leading to crystallized proteins and the impact of methodological variants. These can be used to formulate guidelines for initial approaches to expression of new eukaryotic proteins.

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Metadata

Authors
Savitsky, P., Bray, J., Cooper, C. D. O., Marsden, B. D., Mahajan, P., Burgess-Brown, N. A., & Gileadi, O.
Venue
Journal of Structural Biology
Publisher
Elsevier BV
Published
2010-10
Volume
172
Issue
1
Pages
3-13
ISSN
1047-8477 (ISSN-L)
OpenAlex
W2025410059
Cited by
331
References
0
FWCI
6.95
Type
journal article
Language
en
Open access
hybrid

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CoChat. (2026). Citation check: High-throughput production of human proteins for crystallization: The SGC experience. https://app.cochat.ai/papers/10.1016/j.jsb.2010.06.008
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Savitsky, P., Bray, J., Cooper, C. D. O., Marsden, B. D., Mahajan, P., Burgess-Brown, N. A., & Gileadi, O. (2010). High-throughput production of human proteins for crystallization: The SGC experience. Journal of Structural Biology. https://doi.org/10.1016/j.jsb.2010.06.008

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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.