Absolute proteome and phosphoproteome dynamics during the cell cycle of Schizosaccharomyces pombe (Fission Yeast).

To quantify cell cycle-dependent fluctuations on a proteome-wide scale, we performed integrative analysis of the proteome and phosphoproteome during the four major phases of the cell cycle in Schizosaccharomyces pombe. In highly synchronized cells, we identified 3753 proteins and 3682 phosphorylation events and relatively quantified 65% of the data across all phases. Quantitative changes during the cell cycle were infrequent and weak in the proteome but prominent in the phosphoproteome. Protein phosphorylation peaked in mitosis, where the median phosphorylation site occupancy was 44%, about 2-fold higher than in other phases. We measured copy numbers of 3178 proteins, which together with phosphorylation site stoichiometry enabled us to estimate the absolute amount of protein-bound phosphate, as well as its change across the cell cycle. Our results indicate that 23% of the average intracellular ATP is utilized by protein kinases to phosphorylate their substrates to drive regulatory processes during cell division. Accordingly, we observe that phosphate transporters and phosphate-metabolizing enzymes are phosphorylated and therefore likely to be regulated in mitosis.

Construction and assessment of individualized proteogenomic databases for large-scale analysis of nonsynonymous single nucleotide variants.

Next-generation sequencing projects focusing on genomes and transcriptomes identify millions of single nucleotide variants (SNVs), many of which result in single amino acid substitutions. These nonsynonymous (ns) SNVs are typically not incorporated into protein sequence databases used to identify MS/MS data. Here, we perform a comparative analysis of the assembly of nsSNV-containing proteogenomic databases. We use a comprehensive transcriptome and proteome dataset of HeLa cells from the literature to derive and to incorporate SNVs into databases applicable to proteomics search engines, and to assess their performance in the identification of nsSNVs. We assemble the databases by (1) translation of SNV-containing transcripts into all possible reading frames, (2) translation of predicted reading frame, (3) prediction of nsSNVs and subsequent incorporation into canonical protein sequences. We show substantial differences between generated databases in terms of represented nsSNVs and theoretical search space, affecting sensitivity and specificity of database search. We query the databases with >2.2M high-resolution MS/MS spectra using MaxQuant software and identify 451 variant peptides, containing 401 nsSNVs. We conclude that prediction of reading frame and, if applicable, SNV effect result in comprehensive yet compact databases necessary to retain sensitivity in large-scale analysis of nsSNVs called from transcriptomics data.