title: Identification of zinc-ligated cysteine residues based on 13Calpha and 13Cbeta chemical shift data.
abstract: 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 13Cbeta chemical shift distributions of Zn-ligated and non-metal-ligated cysteine residues are largely resolved by the inclusion of the corresponding 13Calpha 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 13Calpha/13Cbeta shift pairs from 79 proteins with known three-dimensional structures, including 86 13Calpha and 13Cbeta 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 statistical 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.
authors.affiliation: Center for Advanced Biotechnology and Medicine, Department of Molecular Biology and Biochemistry, Rutgers University, Piscataway, NJ 08854, USA.
authors.forename: Gregory J
authors.initials: GJ
authors.lastname: Kornhaber
authors.forename: David
authors.initials: D
authors.lastname: Snyder
authors.forename: Hunter N B
authors.initials: HN
authors.lastname: Moseley
authors.forename: Gaetano T
authors.initials: GT
authors.lastname: Montelione
mesh_terms: D001412:Bacillus subtilis / Q000737:chemistry; D002247:Carbon Isotopes / Q000737:chemistry*; D002352:Carrier Proteins / Q000737:chemistry*; D003545:Cysteine / Q000737:chemistry*; D019992:Databases as Topic; D005544:Forecasting / Q000379:methods; D007506:Iron-Sulfur Proteins / Q000737:chemistry; D016014:Linear Models; D016015:Logistic Models; D008279:Magnetic Resonance Imaging; D013058:Mass Spectrometry / Q000379:methods; D019906:Nuclear Magnetic Resonance, Biomolecular / Q000379:methods; D010084:Oxidation-Reduction; D011487:Protein Conformation
chemical_list: D002247:Carbon Isotopes; D002352:Carrier Proteins; D007506:Iron-Sulfur Proteins; C017800:zinc-binding protein; D003545:Cysteine