Sanford Burnham Medical Research Institute, 10901 North Torrey Pines Road, La Jolla, CA 92037, USA.
J Biomol NMR. 2012 Nov;54(3):237-43. doi: 10.1007/s10858-012-9677-7. Epub 2012 Sep 25.
Chemical shift frequencies represent a time-average of all the conformational states populated by a protein. Thus, chemical shift prediction programs based on sequence and database analysis yield higher accuracy for rigid rather than flexible protein segments. Here we show that the prediction accuracy can be significantly improved by averaging over an ensemble of structures, predicted solely from amino acid sequence with the Rosetta program. This approach to chemical shift and structure prediction has the potential to be useful for guiding resonance assignments, especially in solid-state NMR structural studies of membrane proteins in proteoliposomes.
化学位移频率代表了蛋白质所有构象状态的时间平均值。因此，基于序列和数据库分析的化学位移预测程序对于刚性而非柔性蛋白质片段具有更高的准确性。在这里，我们表明，通过对仅使用 Rosetta 程序从氨基酸序列预测的结构进行结构的集合平均，可以显著提高预测精度。这种化学位移和结构预测方法有可能有助于指导共振分配，特别是在脂质体中的膜蛋白的固态 NMR 结构研究中。
