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Practical use of chemical shift databases for protein solid-state NMR: 2D chemical shift maps and amino-acid assignment with secondary-structure information

机译：化学位移数据库在蛋白质固态NMR中的实际使用：二维化学位移图和具有二级结构信息的氨基酸分配

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We introduce a Python-based program that utilizes the large database of 13C and 15N chemical shifts in the Biological Magnetic Resonance Bank to rapidly predict the amino acid type and secondary structure from correlated chemical shifts. The program, called PACSYlite Unified Query (PLUQ), is designed to help assign peaks obtained from 2D 13C–13C, 15N–13C, or 3D 15N–13C–13C magic-angle-spinning correlation spectra. We show secondary-structure specific 2D 13C–13C correlation maps of all twenty amino acids, constructed from a chemical shift database of 262,209 residues. The maps reveal interesting conformation-dependent chemical shift distributions and facilitate searching of correlation peaks during amino-acid type assignment. Based on these correlations, PLUQ outputs the most likely amino acid types and the associated secondary structures from inputs of experimental chemical shifts. We test the assignment accuracy using four high-quality protein structures. Based on only the Cα and Cβ chemical shifts, the highest-ranked PLUQ assignments were 40–60 % correct in both the amino-acid type and the secondary structure. For three input chemical shifts (CO–Cα–Cβ or N–Cα–Cβ), the first-ranked assignments were correct for 60 % of the residues, while within the top three predictions, the correct assignments were found for 80 % of the residues. PLUQ and the chemical shift maps are expected to be useful at the first stage of sequential assignment, for combination with automated sequential assignment programs, and for highly disordered proteins for which secondary structure analysis is the main goal of structure determination.
机译：我们介绍了一个基于Python的程序，该程序利用了生物磁共振库中 13 C和 15 N化学位移的大型数据库来快速预测氨基酸类型和二级结构来自相关的化学位移。该程序称为PACSYlite统一查询（PLUQ），旨在帮助分配从2D 13 C– 13 C， 15 N– 13 C或3D 15 N– 13 C– 13 C幻角自旋相关光谱。我们显示了所有二十个氨基酸的二级结构特有的2D 13 C– 13 C相关图，由262,209个残基的化学位移数据库构建。这些图揭示了有趣的构象依赖性化学位移分布，并有助于在氨基酸类型分配过程中搜索相关峰。基于这些相关性，PLUQ从实验化学位移的输入中输出最可能的氨基酸类型和相关的二级结构。我们使用四个高质量的蛋白质结构测试分配准确性。仅基于Cα和Cβ化学位移，在氨基酸类型和二级结构上，排名最高的PLUQ分配正确率为40–60％。对于三个输入化学位移（CO–Cα–Cβ或N–Cα–Cβ），对于60％的残留物，排名第一的分配正确，而在前三个预测中，对于80％的残留物，发现正确的分配。残留物。预期PLUQ和化学位移图将在顺序分配的第一阶段，与自动顺序分配程序结合使用以及对于高度无序的蛋白质（二级结构分析是确定结构的主要目标）非常有用。

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