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

Abstract

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.

Quantitative proteomics and network analysis of SSA1 and SSB1 deletion mutants reveals robustness of chaperone HSP70 network in Saccharomyces cerevisiae

“…Workload was calculated using the same approach reported previously 8, 32 that presumes that in steady state the net change in protein concentration is zero, and hence we can estimate the synthesis rate of individual substrates using known protein half‐lives 51. Using protein abundances reported in PaxDB 13, the workload equates to a folding flux (number of molecules synthesized per min), which constitutes the work to be met by the chaperone pool. This is then assigned to the chaperones responsible for the folding of each substrate on a pro rata basis.…”

“…Any missing k deg values where replaced by the geometric mean across the entire turnover dataset; this was to ensure the mean k deg of the dataset remained unchanged. Protein concentrations were taken from PaxDB 13, using those reported from the de Godoy SILAC dataset 44.…”

“…They are also highly abundant in unstressed yeast cells, estimated at 8178 ppm for SSA1 and 2320 ppm for SSB1 (in the top 5% of yeast proteins by abundance according to PaxDB) 13. The two proteins are highly homologous to each other (63% sequence identity) and also to their closely related paralogues SSA2 and SSB2 ; SSA1/SSA2 sequence identity stands at 98%, while SSB1/SSB2 is 99% 14.…”

Protein abundance is key to distinguish promiscuous from functional phosphorylation based on evolutionary information

“…We consider separately disordered (a,b) and ordered regions (c,d). In all cases, the probability for a protein or a residue to be phosphorylated is dependent on its cellular abundance, as obtained from the Pax-db database [54]. Abundance classes represent percentiles of the entire Pax-db data.…”

Exploring the potential of public proteomics data

“…There are, however, several protein expression databases, most notably MOPED 113 and PaxDb 114, which can be used to extract information about the expression levels of individual proteins. Both resources routinely make use of publicly available data in PRIDE and PeptideAtlas, among others.…”