Abstract
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.
Highlights
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
Development of large-scale proteomics technologies for analysis of genes and proteins and their functions is a major focus of post-genomic biology. mRNA expression monitoring using gene chips and protein expression analysis using twodimensional (2D)1 (PAGE) are powerful and widely used technologies for characterizing biological systems and pathways
One of the most powerful aspects of the Fuzzy Functional Forms (FFFs) technology is its ability to identify functional sites accurately in both experimentally determined and computationally modeled protein structures [41]. Another advantage of the FFF technology is that it does not rely on function annotation transfer based on global sequence alignment
Summary
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. MRNA expression monitoring using gene chips and protein expression analysis using twodimensional (2D) (PAGE) are powerful and widely used technologies for characterizing biological systems and pathways The power of these techniques is demonstrated, for example, by the use of transcript profiling to classify cancer subtypes [1,2,3,4]. Approaches aimed at functional analysis of proteomes are being developed These include, for example, computational methods utilizing sequence comparison [13, 14], methods focused on functional site analysis [15,16,17], methods identifying protein-protein interactions [18], chemical proteomics approaches aimed at tagging functional sites on a large scale (19 –22), and metabolomic methods [23]. To overcome limitations of individual analyses and to provide a more accurate and precise functional analysis, we have combined synergistic computational and chemical proteomics approaches to fractionate the well-studied yeast proteome into functional subsets with high confidence
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