Identification of zinc-ligated cysteine residues based on 13Cα and 13Cβ chemical shift data

被引:77
|
作者
Kornhaber, GJ
Snyder, D
Moseley, HNB
Montelione, GT
机构
[1] Rutgers State Univ, Dept Mol Biol & Biochem, Ctr Adv Biotechnol & Med, Piscataway, NJ 08854 USA
[2] Univ Med & Dent New Jersey, Robert Wood Johnson Med Sch, Dept Biochem & Mol Biol, Piscataway, NJ 08854 USA
[3] NE Struct Genom Consortium, Piscataway, NJ 08854 USA
基金
美国国家卫生研究院;
关键词
chemical shift distribution analysis; logistic regression analysis; Zn-ligated cysteine;
D O I
10.1007/s10858-006-0027-5
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
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.
引用
收藏
页码:259 / 269
页数:11
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