Using mass spectrometry (MS) to obtain information about a higher order structure of protein requires that a protein's structural properties are encoded into the mass of that protein. Covalent labeling (CL) with reagents that can irreversibly modify solvent accessible amino acid side chains is an effective way to encode structural information into the mass of a protein, as this information can be read-out in a straightforward manner using standard MS-based proteomics techniques. The differential reactivity of proteins under two or more conditions can be used to distinguish protein topologies, conformations, and/or binding sites. CL-MS methods have been effectively used for the structural analysis of proteins and protein complexes, particularly for systems that are difficult to study by other more traditional biochemical techniques. This review provides an overview of the non-specific CL approaches that have been combined with MS with a particular emphasis on the reagents that are commonly used, including hydroxyl radicals, carbenes, and diethylpyrocarbonate. We describe the reagent and protein factors that affect the reactivity of amino acid side chains. We also include details about experimental design and workflow, data analysis, recent applications, and some future prospects of CL-MS methods.

No abstract

No abstract

Amyloid aggregates are associated with several debilitating diseases, and there are numerous efforts to develop small molecule treatments against these diseases. One challenge associated with these efforts is determining protein binding site information for potential therapeutics because amyloid‐forming proteins rapidly form oligomers and aggregates, making traditional protein structural analysis techniques challenging. Using β-2‐microglobulin (β2m) as a model amyloid‐forming protein along with two recently identified small molecule amyloid inhibitors (i.e. rifamycin SV and doxycycline), we demonstrate that covalent labeling and mass spectrometry (MS) can be used to map small‐molecule binding sites for a rapidly aggregating protein. Specifically, three different covalent labeling reagents, namely diethylpyrocarbonate, 2,3‐butanedione, and the reagent pair EDC/GEE, are used together to pinpoint the binding sites of rifamycin SV, doxycycline, and another molecule, suramin, which binds but does not inhibit Cu(II)‐induced β2m amyloid formation. The labeling results reveal binding sites that are consistent with the known effects of these molecules on β2m amyloid formation and are in general agreement with molecular docking results. We expect that this combined covalent labeling approach will be applicable to other protein/small molecule systems that are difficult to study by traditional means.

No abstract

Hydrogen–deuterium exchange (HDX) mass spectrometry (MS) and covalent labeling (CL) MS are typically considered to be complementary methods for protein structural analysis, because one probes the protein backbone, while the other probes side chains. For protein–ligand interactions, we demonstrate in this work that the two labeling techniques can provide synergistic structural information about protein–ligand binding when reagents like diethylpyrocarbonate (DEPC) are used for CL because of the differences in the reaction rates of DEPC and HDX. Using three model protein–ligand systems, we show that the slower time scale for DEPC labeling makes it only sensitive to changes in solvent accessibility and insensitive to changes in protein structural fluctuations, whereas HDX is sensitive to changes in both solvent accessibility and structural fluctuations. When used together, the two methods more clearly reveal binding sites and ligand-induced changes to structural fluctuations that are distant from the binding site, which is more comprehensive information than either technique alone can provide. We predict that these two methods will find widespread usage together for more deeply understanding protein–ligand interactions.

Molybdenum carbides have been expected to be one of the promising catalysts for the hydrogen evolution reaction (HER) due to their similar d-band electronic structures to the Pt-group metals. However, the weaker hydrogen-adsorption ability of MoC severely hinders its applications. Guided by density functional theory calculations, we put forward a strategy to design the novel MoC-based electrocatalyst with surface reconstruction through sulfur doping. The incorporation of minor sulfur not only greatly increases the number of active sites and intrinsic activity but also optimizes the electronic structure to improve the electron transfer efficiency. As a result, the as-prepared sulfur-substituted MoC tackles the limitation of the Volmer step and exhibits superior HER performance with a small Tafel slope of 48 mV dec −1 . Theoretical investigations demonstrate that the terminal sulfur plays a critical role in facilitating a close to zero hydrogen adsorption energy (ΔG H* ) and a lower hydrogen release barrier.

The combination of covalent labeling (CL) and mass spectrometry (MS) has emerged as a useful tool for studying protein structure due to its good structural coverage, the ability to study proteins in mixtures, and its high sensitivity. Diethylpyrocarbonate (DEPC) is an effective CL reagent that can label N-termini and the side chains of several nucleophilic residues, providing information for about 30% of the residues in the average protein. For DEPC to provide accurate structural information, the extent of labeling must be controlled to minimize label-induced structural perturbations. In this work, we establish a quantitative correlation between general protein structural factors and DEPC reaction rates by measuring the reaction rate coefficients for several model proteins. Using principal component and regression analyses, we find that the solvent accessible surface areas of histidine and lysine residues in proteins are the primary factors that determine a protein’s reactivity toward DEPC, despite the fact that other more abundant residues, such as tyrosine, threonine, and serine, are also labeled by DEPC. From the statistical analysis, a model emerges that can be used to predict the reactivity of a protein based on its structure and sequence, allowing the optimal DEPC concentration to be chosen for a given protein. The resulting model is supported by cross-validation studies and by accurately predicting of the reactivity of five test proteins. Overall, our model reveals interesting insight into the reactivity of proteins with DEPC, and it will facilitate identification of optimal DEPC labeling conditions for proteins.