Quantification of trace elements in human serum fractions by liquid chromatography and inductively coupled plasma mass spectrometry

On‐line coupling of LC and ICP‐MS has been used for fractionation and detection of species of Cu, Fe, I, Se and Zn in human serum. It has been shown that anion exchange chromatography provided better separation capability (both intra‐ and inter‐element) than size‐exclusion chromatography. The mobile phases for ion exchange chromatography consisted of Tris–HNO₃ buffer and ammonium salt (nitrate, acetate or formate). Formate was found to be the best mobile phase counter ion, enabling good chromatographic separation, and is acceptable for mass spectrometry too. The quantitative evaluation of element concentrations adhering to individual fractions was performed by the peak area normalization method. The repeatability of results ranged from 3 to 15% (depending on the element concentration level) and represented the main part of the result uncertainty. The accuracy of Cu and Zn fraction determinations was confirmed by comparison with the isotope dilution technique. Copyright © 2006 John Wiley & Sons, Ltd.     

Strategies for measurements of pseudocontact shifts in protein NMR spectroscopy.

Paramagnetic metal ions bound to proteins generate a dipolar field that can be accurately probed by pseudocontact shifts (PCS) displayed by the protein's nuclear spins. PCS are highly useful for determining the coordinates of individual spins in the molecule and for rapid structure determinations of entire protein-protein and protein-ligand complexes. However, PCS measurements require reliable resonance assignments for the molecule in its paramagnetic state and in a diamagnetic reference state. This article discusses different approaches for pairwise resonance assignments, with emphasis on a strategy which exploits chemical exchange between the two states.     

金属蛋白研究中几个值得注意的动向

本文叙述了金属蛋白和金属酶研究中近年来几个值得注意的发展动向，即与金属离子相关的疾病（特别是神经退行性疾病）、金属离子在蛋白质的折叠、聚集和装配中的作用，金属伴侣分子、金属蛋白的设计和构建、金属蛋白与DNA相互作用。     

Evaluation of soybean seed protein extraction focusing on metalloprotein analysis

Two methods of protein extraction for soybean seeds were evaluated in terms of preservation of the metal ions bound to proteins after the extraction and separation procedures. The proteins were firstly separated according to their molar masses by polyacrylamide gel electrophoresis. Then, the protein bands were mapped by synchrotron radiation X-ray fluorescence in order to establish which metal ions were present in each one. Finally, some mapped protein bands were decomposed by microwave-assisted combustion and Ca, Cu, K, Mg, Mn, and Zn were quantified by inductively coupled plasma mass spectrometry or inductively coupled plasma optical emission spectrometry. The extraction methods studied were Method A (based on the treatment of ground soybean seeds with hexane and their extraction with Tris–HCl and β-mercaptoethanol) and Method B (based on the treatment of ground soybean seeds with petroleum ether and their extraction with Tris–HCl, dithiothreitol, phenylmethanesulfonyl fluoride, sodium dodecyl sulfate and potassium chloride). The best method was Method B, in which a 78% higher extraction efficiency was obtained when compared to Method A. Additionally, the metal-protein interactions were more appropriately preserved when Method B was applied, where the most affected ions were those that are bound weakly to proteins, such as Ca, K, and Mg.     

Towards a black‐box for biological EXAFS data analysis. II. Automatic BioXAS Refinement and Analysis (ABRA)

In biological systems, X‐ray absorption spectroscopy (XAS) can determine structural details of metal binding sites with high resolution. Here a method enabling an automated analysis of the corresponding EXAFS data is presented, utilizing in addition to least‐squares refinement the prior knowledge about structural details and important fit parameters. A metal binding motif is characterized by the type of donor atoms and their bond lengths. These fit results are compared by bond valance sum analysis and target distances with established structures of metal binding sites. Other parameters such as the Debye–Waller factor and shift of the Fermi energy provide further insights into the quality of a fit. The introduction of mathematical criteria, their combination and calibration allows an automated analysis of XAS data as demonstrated for a number of examples. This presents a starting point for future applications to all kinds of systems studied by XAS and allows the algorithm to be transferred to data analysis in other fields.     

Synergic approach to XAFS analysis for the identification of most probable binding motifs for mononuclear zinc sites in metalloproteins

In the present work a data analysis approach, based on XAFS data, is proposed for the identification of most probable binding motifs of unknown mononuclear zinc sites in metalloproteins. This approach combines multiple‐scattering EXAFS analysis performed within the rigid‐body refinement scheme, non‐muffin‐tin ab initio XANES simulations, average structural information on amino acids and metal binding clusters provided by the Protein Data Bank, and Debye–Waller factor calculations based on density functional theory. The efficiency of the method is tested by using three reference zinc proteins for which the local structure around the metal is already known from protein crystallography. To show the applicability of the present analysis to structures not deposited in the Protein Data Bank, the XAFS spectra of six mononuclear zinc binding sites present in diverse membrane proteins, for which we have previously proposed the coordinating amino acids by applying a similar approach, is also reported. By comparing the Zn K‐edge XAFS features exhibited by these proteins with those pertaining to the reference structures, key spectral characteristics, related to specific binding motifs, are observed. These case studies exemplify the combined data analysis proposed and further support its validity.     

Catalysis and Electron Transfer in De Novo Designed Helical Scaffolds

The relationship between protein structure and function is one of the greatest puzzles within biochemistry. De novo metalloprotein design is a way to wipe the board clean and determine what is required to build in function from the ground up in an unrelated structure. This Review focuses on protein design efforts to create de novo metalloproteins within alpha‐helical scaffolds. Examples of successful designs include those with carbonic anhydrase or nitrite reductase activity by incorporating a ZnHis₃ or CuHis₃ site, or that recapitulate the spectroscopic properties of unique electron‐transfer sites in cupredoxins (CuHis₂Cys) or rubredoxins (FeCys₄). This work showcases the versatility of alpha helices as scaffolds for metalloprotein design and the progress that is possible through careful rational design. Our studies cover the invariance of carbonic anhydrase activity with different site positions and scaffolds, refinement of our cupredoxin models, and enhancement of nitrite reductase activity up to 1000‐fold.     

Machine Learning Approaches for Metalloproteins

Metalloproteins are a family of proteins characterized by metal ion binding, whereby the presence of these ions confers key catalytic and ligand-binding properties. Due to their ubiquity among biological systems, researchers have made immense efforts to predict the structural and functional roles of metalloproteins. Ultimately, having a comprehensive understanding of metalloproteins will lead to tangible applications, such as designing potent inhibitors in drug discovery. Recently, there has been an acceleration in the number of studies applying machine learning to predict metalloprotein properties, primarily driven by the advent of more sophisticated machine learning algorithms. This review covers how machine learning tools have consolidated and expanded our comprehension of various aspects of metalloproteins (structure, function, stability, ligand-binding interactions, and inhibitors). Future avenues of exploration are also discussed.     

A Hybrid Ring‐Opening Metathesis Polymerization Catalyst Based on an Engineered Variant of the β‐Barrel Protein FhuA

A β‐barrel protein hybrid catalyst was prepared by covalently anchoring a Grubbs–Hoveyda type olefin metathesis catalyst at a single accessible cysteine amino acid in the barrel interior of a variant of β‐barrel transmembrane protein ferric hydroxamate uptake protein component A (FhuA). Activity of this hybrid catalyst type was demonstrated by ring‐opening metathesis polymerization of a 7‐oxanorbornene derivative in aqueous solution.