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Human Proteomes – From Basic Science To Understanding Drug Action
Author(s) -
Kuster Bernhard
Publication year - 2016
Publication title -
the faseb journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.709
H-Index - 277
eISSN - 1530-6860
pISSN - 0892-6638
DOI - 10.1096/fasebj.30.1_supplement.101.2
Subject(s) - proteome , human proteome project , proteomics , computational biology , drug discovery , transcriptome , proteogenomics , translational efficiency , biology , genomics , genome , bioinformatics , messenger rna , translation (biology) , gene expression , genetics , gene
Proteomes are characterized by large protein‐abundance differences, cell‐type‐ and time‐dependent expression patterns and post‐translational modifications, all of which carry biological information that is not accessible by genomics or transcriptomics. Here we present a mass‐spectrometry‐ based draft of the human proteome and a public, high‐performance, in‐memory database for real‐time analysis of terabytes of big data, called ProteomicsDB. The information assembled from human tissues, cell lines and body fluids enabled estimation of the size of the protein‐coding genome, the analysis of messenger RNA and protein‐expression profiles of human tissues revealed conserved control of protein abundance and integration of drug‐sensitivity data enabled the identification of proteins predicting resistance or sensitivity. The same overall technology also allows tracking drug actions in cells and lysates on a target‐class or proteome‐wide scale with many implications for drug discovery. We present examples for the use of chemical probes and chemical proteomics to find new uses of existing drugs, to identify toxicity targets, to elucidate the mechanism of action of drugs in cancer cells, the identification of resistance mechanisms and how the activity status of cancer cells as assessed by post‐translational modification of proteomes influences the efficacy of drugs. Support or Funding Information Center for Integrated Protein Science Munich German Consortium for Translational Cancer Research SAP SE