Database for the identification of the human proteome and its use across the scientific community. Users can browse proteins and chromosomes and contribute to the data repository.

ProteomicsDB is an effort dedicated to expedite the identification of the human proteome and its use across the scientific community. This human proteome data is assembled primarily using information from liquid chromatography tandem-mass-spectrometry (LC-MS/MS) experiments involving human tissues, cell lines and body fluids. Information is accessible for individual proteins, or on the basis of protein coverage on the encoding chromosome, and for peptide components of a protein. This collection provides access to the peptides identified for a given protein.

ProteomicsDB is a protein-centric in-memory database for the exploration of large collections of quantitative mass spectrometry-based proteomics data. ProteomicsDB was first released in 2014 to enable the interactive exploration of the first draft of the human proteome. To date, it contains quantitative data from 78 projects totalling over 19k LC-MS/MS experiments. A standardized analysis pipeline enables comparisons between multiple datasets to facilitate the exploration of protein expression across hundreds of tissues, body fluids and cell lines. We recently extended the data model to enable the storage and integrated visualization of other quantitative omics data. This includes transcriptomics data from e. g. NCBI GEO, protein-protein interaction information from STRING, functional annotations from KEGG, drug-sensitivity/selectivity data from several public sources and reference mass spectra from the ProteomeTools project. The extended functionality transforms ProteomicsDB into a multi-purpose resource connecting quantification and meta-data for each protein. The rich user interface helps researchers to navigate all data sources in either a protein-centric or multi-protein-centric manner. Several options are available to download data manually, while our application programming interface enables accessing quantitative data systematically.

This PXD project contains two projects published on ProteomicsDB (https://www.proteomicsDB.org) as integral part of the publication. The first project entitled 'human body map' (https://www.proteomicsdb.org/#projects/42) involves the analysis of 36 different human tissues and body fluids. The second project entitled 'Cellzome adopted' includes a collection of raw files which comprises identifications of 'missing proteins'.

Quantitative draft map of the proteomes of 41 healthy tissues of the animal model Mus musculus and a panel of 66 murine pancreatic ductal adenocarcinoma cell lines and. Our in-depth analysis provides evidence for proteins encoded by >16,000 genes (~76% of the total annotated protein-coding genes), where they are expressed and in which quantities. Moreover, a widespread yet tissue- and cell type-specific phosphorylation pattern is clearly represented in our data (>50,000 sites). We further leverage our analysis by integrating the phenotypic response of a the murine PDAC cell lines panel to cancer drugs and ionizing radiation, to shed light on the molecular mechanisms underlying drug action and radioresistance. This large and unique atlas is made available to the scientific community also via ProteomicsDB (https://www.proteomicsDB.org) and the interactive web application PACiFIC (http://pacific.proteomics.wzw.tum.de).

"The ProteomeTools project aims to derive molecular and digital tools from the human proteome to facilitate biomedical and life science research. Here, we describe the second iteration of the generation and multimodal LC-MS/MS analysis of >220,000 synthetic tryptic peptides representing nearly all canonical human gene products. This resource will be extended to 1.4 million peptides and all data will be made available to the public in ProteomicsDB."

Accompanying MaxQuant, Percolator and Picked Protein Group FDR files to reproduce results in the publication "Re-analysis of ProteomicsDB using an accurate, sensitive and scalable false discovery rate estimation approach for protein groups". The code for reproducing Protein Group FDRs is available on GitHub at https://github.com/kusterlab/picked_group_fdr

Use the unpack.sh bash script included here to unpack all archives, or adapt it to only unpack subsets of the data.

The ProteomeTools project aims to derive molecular and digital tools from the human proteome to facilitate biomedical and life science research. Here, we describe the generation and multimodal LC-MS/MS analysis of >350,000 synthetic tryptic peptides representing nearly all canonical human gene products. This resource will be extended to 1.4 million peptides within two years and all data will be made available to the public in ProteomicsDB.

The ProteomeTools project aims to derive molecular and digital tools from the human proteome to facilitate biomedical and life science research. Here, we describe the third iteration of the generation and multimodal LC-MS/MS analysis of >305,000 synthetic non-tryptic peptides representing HLA Class I & II ligands as well as peptides derived from the N-terminal proteases LysN and ApsN. This resource will be extended to 1.4 million peptides and all data will be made available to the public in ProteomicsDB.

List of off-target effects of OTSSP167 occurring at concentrations lower than 50nM, derived from the publicly available phosphoproteomics database at http://proteomicsdb.org.

Mutated KRAS is among the most frequent activating genetic alterations in cancer. Drug discovery efforts have led to inhibitors that block mutant KRAS activity. To better understand the molecular basis of their cytostatic rather than cytotoxic effects, we performed comprehensive dose-dependent proteome-wide target deconvolution, pathway engagement and protein expression characterization in response to KRAS, MEK, ERK, SHP2, and SOS1 inhibitors in pancreatic (KRAS G12C, G12D) and lung cancer (KRAS G12C) cell lines. Analysis of 688,716 dose-response curves available in ProteomicsDB revealed common and cell line-specific signaling networks dominated by KRAS activity. Time-dose experiments separated early ERK-driven from late CDK-mediated signaling leading to exit from the cell cycle. The transition occurred without substantial proteome re-modelling but extensive changes in phosphorylation and ubiquitinylation. Our resource highlights the complexity of KRAS signaling in cancer and places a large number of new proteins and their modifications into this functional context for further exploration.

Here, we provide all curve data processed with internal pipelines or CurveCurator v0.5.0. (https://github.com/kusterlab/curve_curator). All phospho proteome, whole proteome, ubiquitinome, and cysteine profiling data are in seperated zip folders. Within these folders, each subexperiment can be interactively reviewed. Next to each data set is the toml parameter file used to generate the curves.txt and dashboard.html files. The data structure and additional information separated by experiment is provided in the Meta file.