Energy and carbon accounting to compare bioenergy crops.

To compare the utility of current and future biofuels and biofuel feedstocks in an objective manner can be extremely challenging. This challenge exists because agricultural data are inherently variable, experimental techniques are crop-dependent, and the literatures usually report relative, rather than absolute, values. Here, we discuss the 'PETRO approach', a systematic approach to evaluate new crops. This approach accounts for not only the capture of solar energy but also the capture of atmospheric carbon (as CO2) to generate a final carbon-based liquid fuel product. The energy yield, per unit area, of biofuel crops grown in different climate zones can thus be benchmarked and quantitatively compared in terms of both carbon gain and solar energy conversion efficiency.

High-resolution functional proteomics by active-site peptide profiling.

Characterization and functional annotation of the large number of proteins predicted from genome sequencing projects poses a major scientific challenge. Whereas several proteomics techniques have been developed to quantify the abundance of proteins, these methods provide little information regarding protein function. Here, we present a gel-free platform that permits ultrasensitive, quantitative, and high-resolution analyses of protein activities in proteomes, including highly problematic samples such as undiluted plasma. We demonstrate the value of this platform for the discovery of both disease-related enzyme activities and specific inhibitors that target these proteins.

Synergistic computational and experimental proteomics approaches for more accurate detection of active serine hydrolases in yeast.

An analysis of the structurally and catalytically diverse serine hydrolase protein family in the Saccharomyces cerevisiae proteome was undertaken using two independent but complementary, large-scale approaches. The first approach is based on computational analysis of serine hydrolase active site structures; the second utilizes the chemical reactivity of the serine hydrolase active site in complex mixtures. These proteomics approaches share the ability to fractionate the complex proteome into functional subsets. Each method identified a significant number of sequences, but 15 proteins were identified by both methods. Eight of these were unannotated in the Saccharomyces Genome Database at the time of this study and are thus novel serine hydrolase identifications. Three of the previously uncharacterized proteins are members of a eukaryotic serine hydrolase family, designated as Fsh (family of serine hydrolase), identified here for the first time. OVCA2, a potential human tumor suppressor, and DYR-SCHPO, a dihydrofolate reductase from Schizosaccharomyces pombe, are members of this family. Comparing the combined results to results of other proteomic methods showed that only four of the 15 proteins were identified in a recent large-scale, "shotgun" proteomic analysis and eight were identified using a related, but similar, approach (neither identifies function). Only 10 of the 15 were annotated using alternate motif-based computational tools. The results demonstrate the precision derived from combining complementary, function-based approaches to extract biological information from complex proteomes. The chemical proteomics technology indicates that a functional protein is being expressed in the cell, while the computational proteomics technology adds details about the specific type of function and residue that is likely being labeled. The combination of synergistic methods facilitates analysis, enriches true positive results, and increases confidence in novel identifications. This work also highlights the risks inherent in annotation transfer and the use of scoring functions for determination of correct annotations.

Inhibitor focusing: direct selection of drug targets from proteomes using activity-based probes.

In the latter stages of drug discovery and development, assays that establish drug selectivity and toxicity are important when side effects, which are often due to lack of specificity, determine drug candidate viability. There has been no comprehensive or systematic methodology to measure these factors outside of whole-animal assays, and such phenomenological assays generally fail to establish the additional targets of a given small molecule, or the molecular origin of toxicity. Consequently, small-molecule development programs destined for failure often reach advanced stages of testing, and the money and time invested in such programs could be saved if information on selectivity were available early in the process. Here, we present a methodology that utilizes chemical ABPs in combination with small-molecule inhibitors to selectively label small-molecule binding sites in whole proteomic samples. In principle, the ABP and small molecule will compete for similar binding sites, such that the small molecule will protect against modification by the ABP. Thus, after removal of the small molecule, the binding site for the ABP will be revealed, and a second probe can then be used to label the small-molecule binding sites selectively. To demonstrate this experimentally, we mapped the binding sites of the DPP4 inhibitor, IT, in a number of different tissue types.