PaxDb, a Database of Protein Abundance Averages Across All Three Domains of Life

By Miaoyan Wang, M. Weiss, Milan Simonovic, G. Haertinger, Sabine Schrimpf, Michael O. Hengartner, C. von Mering. Molecular & Cellular Proteomics, 2012

494 citations recorded by the source corpus.

Although protein expression is regulated both temporally and spatially, most proteins have an intrinsic, “typical” range of functionally effective abundance levels. These extend from a few molecules per cell for signaling proteins, to millions of molecules for structural proteins. When addressing fundamental questions related to protein evolution, translation and folding, but also in routine laboratory work, a simple rough estimate of the average wild type abundance of each detectable protein in an organism is often desirable. Here, we introduce a meta-resource dedicated to integrating information on absolute protein abundance levels; we place particular emphasis on deep coverage, consistent post-processing and comparability across different organisms. Publicly available experimental data are mapped onto a common namespace and, in the case of tandem mass spectrometry data, re-processed using a standardized spectral counting pipeline. By aggregating and averaging over the various samples, conditions and cell-types, the resulting integrated data set achieves increased coverage and a high dynamic range. We score and rank each contributing, individual data set by assessing its consistency against externally provided protein-network information, and demonstrate that our weighted integration exhibits more consistency than the data sets individually. The current PaxDb-release 2.1 (at http://pax-db.org/) presents whole-organism data as well as tissue-resolved data, and covers 85,000 proteins in 12 model organisms. All values can be seamlessly compared across organisms via pre-computed orthology relationships.

Subfields: Molecular Biology, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/bb7e0ab7d550826dccf91d2e6753ba7f65a471f5

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Citations in the corpus

The Proteomics Identifications (PRIDE) database and associated tools: status in 2013 (2012, background): “Some recently developed databases of this kind are the Model Organism Protein Expression Database (MOPED) (15), PaxDB (16) (both of them focused on protein expression information) and neXtProt (17).”
Improving the phenotype predictions of a yeast genome‐scale metabolic model by incorporating enzymatic constraints (2017, unknown): “For the other 311 enzymes in the model, the overall mass constraint previously mentioned was used, limiting the sum by 0.036 g gDW 1, which represents the remaining mass in the model according to PaxDB (see the Materials and Methods section).”
Version 4.0 of PaxDb: Protein abundance data, integrated across model organisms, tissues, and cell‐lines (2015, methodology): “(f) For each organism or tissue that has already been addressed by multiple available experiments/datasets, PaxDb conducts a weighted averaging to produce an integrated “best-estimate” dataset guided by the above quality estimates [19].”