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Effective methods to detect and quantify functionally linked regulatory proteins in complex biological samples are essential for investigating mammalian signaling pathways. Traditional immunoassays depend on proprietary reagents that are difficult to generate and multiplex, whereas global proteomic profiling can be tedious and can miss low abundance proteins. Here, we report a target-driven liquid chromatography-tandem mass spectrometry (LC-MS/MS) strategy for selectively examining the levels of multiple low abundance components of signaling pathways which are refractory to standard shotgun screening procedures and hence appear limited in current MS/MS repositories. Our stepwise approach consists of: (i) synthesizing microscale peptide arrays, including heavy isotope-labeled internal standards, for use as high quality references to (ii) build empirically validated high density LC-MS/MS detection assays with a retention time scheduling system that can be used to (iii) identify and quantify endogenous low abundance protein targets in complex biological mixtures with high accuracy by correlation to a spectral database using new software tools. The method offers a flexible, rapid, and cost-effective means for routine proteomic exploration of biological systems including “label-free” quantification, while minimizing spurious interferences. As proof-of-concept, we have examined the abundance of transcription factors and protein kinases mediating pluripotency and self-renewal in embryonic stem cell populations.

One-third of the 4,225 protein-coding genes of Escherichia coli K-12 remain functionally unannotated (orphans). Many map to distant clades such as Archaea, suggesting involvement in basic prokaryotic traits, whereas others appear restricted to E. coli, including pathogenic strains. To elucidate the orphans' biological roles, we performed an extensive proteomic survey using affinity-tagged E. coli strains and generated comprehensive genomic context inferences to derive a high-confidence compendium for virtually the entire proteome consisting of 5,993 putative physical interactions and 74,776 putative functional associations, most of which are novel. Clustering of the respective probabilistic networks revealed putative orphan membership in discrete multiprotein complexes and functional modules together with annotated gene products, whereas a machine-learning strategy based on network integration implicated the orphans in specific biological processes. We provide additional experimental evidence supporting orphan participation in protein synthesis, amino acid metabolism, biofilm formation, motility, and assembly of the bacterial cell envelope. This resource provides a “systems-wide” functional blueprint of a model microbe, with insights into the biological and evolutionary significance of previously uncharacterized proteins., Author Summary One goal of modern biology is to chart groups of proteins that act together to perform biological processes via direct and indirect interactions. Such groupings are sometimes called functional modules. The types of protein interactions within modules include physical interactions that generate protein complexes and biochemical associations that make up metabolic pathways. We have combined proteomic and bioinformatic tools, and used them to decipher a large number of protein interactions, complexes, and functional modules with high confidence. In addition, exploring the topology of the resulting interaction networks, we successfully predicted specific biological roles for a number of proteins with previously unknown functions, and identified some potential drug targets. Although our work is focused on E. coli, our phylogenetic projections suggest that a considerable fraction of our observations and predictions can be extrapolated to many other bacterial taxa. As all the data derived from this study are publicly available, others may build on our work for further hypothesis-driven studies of gene function discovery., A novel resource integrating proteomic and genome context-based tools provides a "systems-wide" functional blueprint ofE. coli, with insights into the biological and evolutionary significance of previously uncharacterized proteins.