Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
2828862 | Journal of Structural Biology | 2010 | 10 Pages |
Abstract
The great power of protein crystallography to reveal biological structure is often limited by the tremendous effort required to produce suitable crystals. A hybrid crystal growth predictive model is presented that combines both experimental and sequence-derived data from target proteins, including novel variables derived from physico-chemical characterization such as R30, the ratio between a protein’s DSF intensity at 30 °C and at Tm. This hybrid model is shown to be more powerful than sequence-based prediction alone – and more likely to be useful for prioritizing and directing the efforts of structural genomics and individual structural biology laboratories.
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Authors
Frank H. Zucker, Christine Stewart, Jaclyn dela Rosa, Jessica Kim, Li Zhang, Liren Xiao, Jenni Ross, Alberto J. Napuli, Natascha Mueller, Lisa J. Castaneda, Stephen R. Nakazawa Hewitt, Tracy L. Arakaki, Eric T. Larson, Easwara Subramanian,