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 R 30 , the ratio between a protein's DSF intensity at 30 °C and at T m . 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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