Comparison of continuous and pulsed labeling amide hydrogen exchange/mass spectrometry for studies of protein dynamics

AbstractIn contrast to the rigid structures portrayed by X-ray diffraction, proteins in solution display constant motion which leads to populations that are momentarily unfolded. To begin to understand protein dynamics, we must have experimental methods for determining rates of folding and unfolding, as well as for identifying structures of folding and unfolding intermediates. Amide hydrogen exchange has become an important tool for such measurements. When urea is used to stabilize unfolded forms of proteins, the refolding rates may become slower than the rates of isotope exchange. In such cases, the intermolecular distribution of deuterium among the entire population of molecules may become bimodal, giving rise to a bimodal distribution of isotope peaks in mass spectra of the protein or its peptic fragments. When the protein is exposed continuously to D2O, the relative intensities of the two envelopes of isotope peaks give an integrated account of populations participating in the folding/unfolding process. However, when the protein is exposed only briefly to D2O, the relative intensities of the two envelopes of isotope peaks give an instantaneous measure of the folded/unfolded populations. Application of these two labeling methods to a large protein, aldolase, is described along with a discussion of specific parameters required to optimize these experiments

Protein hydrogen exchange determined by mass spectrometry: A useful tool for studying the kinetics and thermodynamics of protein unfolding

Protein amide hydrogen exchange has long been used as a sensitive probe of protein high-order structure and structural changes. The principal method of determining rates of hydrogen exchange in proteins has been multi-dimensional NMR. However, NMR is limited to the study of small, highly soluble proteins. A new method, based on using different hydrogen/deuterium exchange techniques, protein fragmentation and mass spectrometry to determine dynamics of protein unfolding, was developed. In this method, the protein can be fragmented by an acid protease after the protein undergoes H/D exchange. Since the protein is digested into small peptides before the analysis, there is no limit in the size of the protein that can be studied. Different hydrogen/deuterium exchange labeling techniques in this new method were first evaluated using rabbit muscle aldolase (Mr 157 kDa) as a model. The method was then used to study the unfolding process of aldolase in a denaturant, urea. The hydrogen exchange results show that three domains of aldolase unfolded cooperatively with different rate constants in urea. Although the unfolding domains do not correlate well with units of secondary structure, the unfolding rates do con-elate with exposure of the amide hydrogens to the solvent. Kinetic fitting for the data of a time course study of aldolase unfolding suggested that the aldolase unfolded sequentially in urea. This new method can be applied to determine the stability of the partly unfolded states under physiological conditions by extrapolating results determined under denaturing conditions. When collision induced dissociation MS/MS was used with this method, more detailed structural information could be determined. Combining hydrogen/deuterium exchange, protein fragmentation, and mass spectrometry provides a powerful tool in the study of protein structural changes

Dynamicview: Distribution, evolution and visualization of research areas in computer science

Abstract. It is tedious and error-prone to query search engines manually in order to accumulate a large body of factual information. Search engines retrieve and rank potentially relevant documents for human perusal, but do not extract facts, or fuse information from multiple documents. This paper introduces DynamicView, a Semantic Web application for researchers to query, browse and visualize distribution and evolution of research areas in computer science. Present and historical web pages of top 20 universities in USA and China are analyzed, and research areas of faculties in computer science are extracted automatically by segmentation based algorithm. Different ontologies of ACM and MST classification systems are combined by SKOS vocabularies, and the classification of research areas is learned from the ACM Digital Library. Query results including numbers of researchers and their locations are visualized in SVG map and animation. Interestingly, great differences of hot topics do exist between the two countries, and the number of researchers in certain areas changed greatly from the year 2000 to 2005.