Medical Structural Genomics of Pathogenic Protozoa, School of Medicine, University of Washington, Seattle, WA 98195-7742, United States.
J Struct Biol. 2010 Jul;171(1):64-73. doi: 10.1016/j.jsb.2010.03.016. Epub 2010 Mar 27.
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.
蛋白质晶体学揭示生物结构的强大功能通常受到产生合适晶体所需的巨大努力的限制。本文提出了一种混合晶体生长预测模型，该模型结合了目标蛋白质的实验和序列衍生数据，包括源自物理化学特性的新变量，例如 R(30)，即蛋白质在 30°C 时的 DSF 强度与 T(m)时的强度之比。该混合模型被证明比仅基于序列的预测更强大——并且更有可能用于优先考虑和指导结构基因组学和个别结构生物学实验室的工作。
