Identification of zinc-ligated cysteine residues based on 13Cα and 13Cβ chemical shift data
Identification of zinc-ligated cysteine residues based on 13Cα and 13Cβ chemical shift data
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DOI：
10.1007/s10858-006-0027-5
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发表时间：
2006-04-01
影响因子：
2.7
通讯作者：
Montelione, GT
中科院分区：
文献类型：
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作者：
Kornhaber, GJ;Snyder, D;Montelione, GT
尽管大量蛋白质包括结合金属作为其结构的一部分，但通过NMR鉴定与非顺磁性金属配位的氨基酸残基仍然是一个挑战。金属配体可以稳定天然结构和/或在潜在的生物化学中发挥关键的催化作用。原子的化学位移对它所处的电子环境极其敏感。化学位移数据可以提供有价值的洞察结构特征，包括金属连接。在这项研究中，我们表明，重叠的C-13 β化学位移分布的锌连接和非金属连接的半胱氨酸残基在很大程度上解决了包括相应的C-13 α化学位移信息，连同二级结构信息。我们证明了这一点与双变量分布图，统计与多变量方差分析（MANOVA）和分层逻辑回归分析。使用来自具有已知三维结构的79种蛋白质的287个C-13 α/C-13 β位移对，包括43个Zn连接的半胱氨酸残基的86个Ca-13和Cb-13位移，沿着相应的氧化态和二级结构信息，我们建立了区分氧化半胱氨酸、还原（非金属连接）半胱氨酸和Zn连接的半胱氨酸的逻辑回归模型。半胱氨酸/胱氨酸分类与一个化学模型，结合所有三种现象，导致锌连接的预测与召回，精度和F-测量的83.7%，和95.1%的准确度。该模型被应用于分析枯草芽孢杆菌IscU，参与铁硫簇组装的蛋白质。该模型预测IscU的所有三个半胱氨酸都是金属配体。我们通过（i）检查金属螯合对IscU的NMR谱的影响和（ii）电感耦合等离子体质谱分析证实了这些结果。为了进一步了解非半胱氨酸锌配体的出现频率，我们分析了蛋白质数据库，发现78%的锌配体是组氨酸和半胱氨酸（频率几乎相同），18%是酸性残基天冬氨酸和谷氨酸。
Although a significant number of proteins include bound metals as part of their structure, the identification of amino acid residues coordinated to non-paramagnetic metals by NMR remains a challenge. Metal ligands can stabilize the native structure and/or play critical catalytic roles in the underlying biochemistry. An atom's chemical shift is exquisitely sensitive to its electronic environment. Chemical shift data can provide valuable insights into structural features, including metal ligation. In this study, we demonstrate that overlapped C-13 beta chemical shift distributions of Zn-ligated and non-metal-ligated cysteine residues are largely resolved by the inclusion of the corresponding C-13 alpha chemical shift information, together with secondary structural information. We demonstrate this with a bivariate distribution plot, and statistically with a multivariate analysis of variance (MANOVA) and hierarchical logistic regression analysis. Using 287 C-13 alpha/C-13 beta shift pairs from 79 proteins with known three-dimensional structures, including 86 Ca-13 and Cb-13 shifts for 43 Zn-ligated cysteine residues, along with corresponding oxidation state and secondary structure information, we have built a logistic regression model that distinguishes between oxidized cystines, reduced (non-metal ligated) cysteines, and Zn-ligated cysteines. Classifying cysteines/cystines with a statisical model incorporating all three phenomena resulted in a predictor of Zn ligation with a recall, precision and F-measure of 83.7%, and an accuracy of 95.1%. This model was applied in the analysis of Bacillus subtilis IscU, a protein involved in iron-sulfur cluster assembly. The model predicts that all three cysteines of IscU are metal ligands. We confirmed these results by (i) examining the effect of metal chelation on the NMR spectrum of IscU, and (ii) inductively coupled plasma mass spectrometry analysis. To gain further insight into the frequency of occurrence of non-cysteine Zn ligands, we analyzed the Protein Data Bank and found that 78% of the Zn ligands are histidine and cysteine ( with nearly identical frequencies), and 18% are acidic residues aspartate and glutamate.