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Metal ion cofactors afford proteins virtually unlimited catalytic potential, enable electron transfer reactions and have a great impact on protein stability (1,2). Consequently, metalloproteins have key roles in most biological processes, including respiration (iron and copper), photosynthesis (manganese) and drug metabolism (iron). Yet, predicting from genome sequence the numbers and types of metal an organism assimilates from its environment or uses in its metalloproteome is currently impossible because metal coordination sites are diverse and poorly recognized (2-4). We present here a robust, metal-based approach to determine all metals an organism assimilates and identify its metalloproteins on a genome-wide scale. This shifts the focus from classical protein-based purification to metal-based identification and purification by liquid chromatography, high-throughput tandem mass spectrometry (HT-MS/MS) and inductively coupled plasma mass spectrometry (ICP-MS) to characterize cytoplasmic metalloproteins from an exemplary microorganism (Pyrococcus furiosus). Of 343 metal peaks in chromatography fractions, 158 did not match any predicted metalloprotein. Unassigned peaks included metals known to be used (cobalt, iron, nickel, tungsten and zinc; 83 peaks) plus metals the organism was not thought to assimilate (lead, manganese, molybdenum, uranium and vanadium; 75 peaks). Purification of eight of 158 unexpected metal peaks yielded four novel nickeland molybdenum-containing proteins, whereas four purified proteins contained sub-stoichiometric amounts of misincorporated lead and uranium. Analyses of two additional microorganisms (Escherichia coli and Sulfolobus solfataricus) revealed species-specific assimilation of yet more unexpected metals. Metalloproteomes are therefore much more extensive and diverse than previously recognized, and promise to provide key insights for cell biology, microbial growth and toxicity mechanisms., Once revealed, a metal cofactor adds new dimensions to understanding protein structure and function; yet, the presence of metal is often unsuspected until the protein is analysed (1,2,5). For example, [...]

Abstract Background Metal-containing proteins comprise a diverse and sizable category within the proteomes of organisms, ranging from proteins that use metals to catalyze reactions to proteins in which metals play key structural roles. Unfortunately, reliably predicting that a protein will contain a specific metal from its amino acid sequence is not currently possible. We recently developed a generally-applicable experimental technique for finding metalloproteins on a genome-wide scale. Applying this metal-directed protein purification approach (ICP-MS and MS/MS based) to the prototypical microbe Pyrococcus furiosus conclusively demonstrated the extent and diversity of the uncharacterized portion of microbial metalloproteomes since a majority of the observed metal peaks could not be assigned to known or predicted metalloproteins. However, even using this technique, it is not technically feasible to purify to homogeneity all metalloproteins in an organism. In order to address these limitations and complement the metal-directed protein purification, we developed a computational infrastructure and statistical methodology to aid in the pursuit and identification of novel metalloproteins. Results We demonstrate that our methodology enables predictions of metal-protein interactions using an experimental data set derived from a chromatography fractionation experiment in which 870 proteins and 10 metals were measured over 2,589 fractions. For each of the 10 metals, cobalt, iron, manganese, molybdenum, nickel, lead, tungsten, uranium, vanadium, and zinc, clusters of proteins frequently occurring in metal peaks (of a specific metal) within the fractionation space were defined. This resulted in predictions that there are from 5 undiscovered vanadium- to 13 undiscovered cobalt-containing proteins in Pyrococcus furiosus. Molybdenum and nickel were chosen for additional assessment producing lists of genes predicted to encode metalloproteins or metalloprotein subunits, 22 for nickel including seven from known nickel-proteins, and 20 for molybdenum including two from known molybdo-proteins. The uncharacterized proteins are prime candidates for metal-based purification or recombinant approaches to validate these predictions. Conclusions We conclude that the largely uncharacterized extent of native metalloproteomes can be revealed through analysis of the co-occurrence of metals and proteins across a fractionation space. This can significantly impact our understanding of metallobiochemistry, disease mechanisms, and metal toxicity, with implications for bioremediation, medicine and other fields.

The determination of protein structures on a genomic scale requires both computing capacity and efficiency increases at many stages along the complex process. By combining bioinformatics workflow-management techniques, cluster-based computing and popular crystallographic structure-determination software packages, an efficient and powerful new tool for structural biology/genomics has been developed. Using the workflow manager and a simple web interface, the researcher can, in a few easy steps, set up hundreds of structure-determination jobs, each using a slightly different set of program input parameters, thus efficiently screening parameter space for the optimal input-parameter combination, i.e. a set of parameters that leads to a successful structure determination. Upon completion, results from the programs are harvested, analyzed, sorted based on success and presented to the user via the web interface. This approach has been applied with success in more than 30 cases. Examples of successful structure determinations based on single-wavelength scattering (SAS) are described and include cases where the 'rational' crystallographer-based selection of input parameters values had failed. [ABSTRACT FROM AUTHOR]

Presents a correction to the article "Parameter-Space Screening: A Powerful Tool for High-Throughput Crystal Structure Determination. Corrigendum," published in the previous issue of "Acta Crystallographica Section D."