The eukaryotic initiation factor 3 (eIF3) is an essential, highly conserved multiprotein complex that is a key component in the recruitment and assembly of the translation initiation machinery. To better understand the molecular function of eIF3, we examined its composition and phosphorylation status in Saccharomyces cerevisiae. The yeast eIF3 complex contains five core components: Rpg1, Nip1, Prt1, Tif34, and Tif35. 2-D LC−MS/MS analysis of affinity purified eIF3 complexes showed that several other initiation factors (Fun12, Tif5, Sui3, Pab1, Hcr1, and Sui1) and the casein kinase 2 complex (CK2) copurify. In Vivo metabolic labeling of proteins with 32P revealed that Nip1 is phosphorylated. Using 2-D LC−MS/MS analysis of eIF3 complexes, we identified Prt1 phosphopeptides indicating phosphorylation at S22 and T707 and a Tif5 phosphopeptide with phosphorylation at T191. Additionally, we used immobilized metal affinity chromatography (IMAC) to enrich for eIF3 phosphopeptides and tandem mass spectrometry to identify phosphorylated residues. We found that three CK2 consensus sequences in Nip1 are phosphorylated: S98, S99, and S103. Using in vitro kinase assays, we showed that CK2 phophorylates Nip1 and that a synthetic Nip1 peptide containing S98, S99, and S103 competitively inhibits the reaction. Replacement of these three Nip1 serines with alanines causes a slow growth phenotype.Keywords: eIF3; IMAC; mass spectrometry; Nip1; phosphorylation; protein complex; protein kinase; translation; yeast;

Liquid chromatography coupled with tandem mass spectrometry (LC–MS/MS) has revolutionized the proteomics analysis of complexes, cells, and tissues. In a typical proteomic analysis, the tandem mass spectra from a LC–MS/MS experiment are assigned to a peptide by a search engine that compares the experimental MS/MS peptide data to theoretical peptide sequences in a protein database. The peptide spectra matches are then used to infer a list of identified proteins in the original sample. However, the search engines often fail to distinguish between correct and incorrect peptides assignments. In this study, we designed and implemented a novel algorithm called De-Noise to reduce the number of incorrect peptide matches and maximize the number of correct peptides at a fixed false discovery rate using a minimal number of scoring outputs from the SEQUEST search engine. The novel algorithm uses a three-step process: data cleaning, data refining through a SVM-based decision function, and a final data refining step based on proteolytic peptide patterns. Using proteomics data generated on different types of mass spectrometers, we optimized the De-Noise algorithm on the basis of the resolution and mass accuracy of the mass spectrometer employed in the LC–MS/MS experiment. Our results demonstrate De-Noise improves peptide identification compared to other methods used to process the peptide sequence matches assigned by SEQUEST. Because De-Noise uses a limited number of scoring attributes, it can be easily implemented with other search engines.

The mammalian cryptome consists of bioactive peptides generated by the proteolysis of precursor proteins. It is speculated that the cryptide repertoire increases the complexity of the proteome by an order of magnitude. Cryptides have been found to function in a wide range of processes including neuronal signaling, antigen presentation, and the inflammatory response. Due to their potential as therapeutic agents, there has been an increasing interest in studying cryptides. In this review, we discuss different approaches for discovering these hidden peptides and how proteomic tools can be utilized to aid in their identification and characterization.