MAPU 2.0: high-accuracy proteomes mapped to genomes

The MAPU 2.0 database contains proteomes of organelles, tissues and cell types measured by mass spectrometry (MS)-based proteomics. In contrast to other databases it is meant to contain a limited number of experiments and only those with very high-resolution and -accuracy data. MAPU 2.0 displays the proteomes of organelles, tissues and body fluids or conversely displays the occurrence of proteins of interest in all these proteomes. The new release addresses MS-specific problems including ambiguous peptide-to-protein assignments and it provides insight into general functional features on the protein level ranging from gene ontology classification to comprehensive SwissProt annotation. Moreover, the derived proteomic data are used to annotate the genomes using Distributed Annotation Service (DAS) via EnsEMBL services. MAPU 2.0 is a model for a database specifically designed for high-accuracy proteomics and a member of the ProteomExchange Consortium. It is available on line at http://www.mapuproteome.com

The proteome landscape of the kingdoms of life.

Proteins carry out the vast majority of functions in all biological domains, but for technological reasons their large-scale investigation has lagged behind the study of genomes. Since the first essentially complete eukaryotic proteome was reported(1), advances in mass-spectrometry-based proteomics(2)have enabled increasingly comprehensive identification and quantification of the human proteome(3-6). However, there have been few comparisons across species(7,8), in stark contrast with genomics initiatives(9). Here we use an advanced proteomics workflow-in which the peptide separation step is performed by a microstructured and extremely reproducible chromatographic system-for the in-depth study of 100 taxonomically diverse organisms. With two million peptide and 340,000 stringent protein identifications obtained in a standardized manner, we double the number of proteins with solid experimental evidence known to the scientific community. The data also provide a large-scale case study for sequence-based machine learning, as we demonstrate by experimentally confirming the predicted properties of peptides fromBacteroides uniformis. Our results offer a comparative view of the functional organization of organisms across the entire evolutionary range. A remarkably high fraction of the total proteome mass in all kingdoms is dedicated to protein homeostasis and folding, highlighting the biological challenge of maintaining protein structure in all branches of life. Likewise, a universally high fraction is involved in supplying energy resources, although these pathways range from photosynthesis through iron sulfur metabolism to carbohydrate metabolism. Generally, however, proteins and proteomes are remarkably diverse between organisms, and they can readily be explored and functionally compared at www.proteomesoflife.org