No abstract

Kinase inhibitors are important cancer therapeutics. Polypharmacology is commonly observed, requiring thorough target deconvolution to understand drug mechanism of action. Using chemical proteomics, we analyzed the target spectrum of 243 clinically evaluated kinase drugs. The data revealed previously unknown targets for established drugs, offered a perspective on the "druggable" kinome, highlighted (non)kinase off-targets, and suggested potential therapeutic applications. Integration of phosphoproteomic data refined drug-affected pathways, identified response markers, and strengthened rationale for combination treatments. We exemplify translational value by discovering SIK2 (salt-inducible kinase 2) inhibitors that modulate cytokine production in primary cells, by identifying drugs against the lung cancer survival marker MELK (maternal embryonic leucine zipper kinase), and by repurposing cabozantinib to treat FLT3-ITD-positive acute myeloid leukemia. This resource, available via the ProteomicsDB database, should facilitate basic, clinical, and drug discovery research and aid clinical decision-making.

Genome‐, transcriptome‐ and proteome‐wide measurements provide insights into how biological systems are regulated. However, fundamental aspects relating to which human proteins exist, where they are expressed and in which quantities are not fully understood. Therefore, we generated a quantitative proteome and transcriptome abundance atlas of 29 paired healthy human tissues from the Human Protein Atlas project representing human genes by 18,072 transcripts and 13,640 proteins including 37 without prior protein‐level evidence. The analysis revealed that hundreds of proteins, particularly in testis, could not be detected even for highly expressed mRNA s, that few proteins show tissue‐specific expression, that strong differences between mRNA and protein quantities within and across tissues exist and that protein expression is often more stable across tissues than that of transcripts. Only 238 of 9,848 amino acid variants found by exome sequencing could be confidently detected at the protein level showing that proteogenomics remains challenging, needs better computational methods and requires rigorous validation. Many uses of this resource can be envisaged including the study of gene/protein expression regulation and biomarker specificity evaluation.

Plants are indispensable for life on earth and represent organisms of extreme biological diversity with unique molecular capabilities 1. Here, we present a quantitative atlas of the transcriptomes, proteomes and phosphoproteomes of 30 tissues of the model plant Arabidopsis thaliana. It provides initial answers to how many genes exist as proteins (>18,000), where they are expressed, in which approximate quantities (>6 orders of magnitude dynamic range) and to what extent they are phosphorylated (>43,000 sites). We present examples for how the data may be used, for instance, to discover proteins translated from short open reading frames, to uncover sequence motifs involved in protein expression regulation, to identify tissue-specific protein complexes or phosphorylation-mediated signaling events to name a few. Interactive access to this unique resource for the plant community is provided via ProteomicsDB and ATHENA which include powerful bioinformatics tools to explore and characterize Arabidopsis proteins, their modifications and interplay. Main The plant model organism Arabidopsis thaliana (AT) has revolutionized our understanding of plant biology and influenced many other areas of the life sciences 1. Knowledge derived from Arabidopsis has also provided mechanistic understanding of important agronomic traits in crop species 2. The Arabidopsis genome was sequenced 20 years ago and hundreds of natural variants have since been analyzed at the genome and epigenome level 3,4. In contrast, the Arabidopsis proteome as the main executer of most biological processes is far less comprehensively characterized. To address this gap, we used state-of-the-art mass spectrometry and RNA sequencing (RNA-seq) to provide the first integrated proteomic, phosphoproteomic and transcriptomic atlas of Arabidopsis. Illustrated by selected examples, we show how this rich molecular resource can be used to explore the function of single proteins or entire pathways across multiple omics levels. Multi-omics atlas of Arabidopsis We generated an expression atlas covering, on average, 17,603 ± 1,317 transcripts, 14,430 ± 911 proteins and 14,689 ± 2,509 phosphorylation sites (p-sites) per tissue, using a reproducible biochemical and analytical approach (Fig. 1a,b; Extended Data Fig. 1a-c; Supplementary Data 1,2). In total, the protein expression data covers 18,210 of the 27,655 protein-coding genes (66%) annotated in Araport11 5. This is a substantial increase compared to the percentage of genes with protein level evidence reported in UniProt (27%) 6 and more than double the number of proteins identified in an earlier tissue proteome analysis 7 (Fig. 1c, Extended Data Fig. 1d-f). In addition, we report tissue-resolved quantitative evidence for a total of 43,903 p-sites making this study the most comprehensive single Arabidopsis phosphoproteome published to date (Fig. 1c). 47% of the expressed proteome was found to be phosphorylated in at least one instance, confirming earlier analyses of individual

Nano-flow liquid chromatography tandem mass spectrometry (nano-flow LC-MS/MS) is the mainstay in proteome research because of its excellent sensitivity but often comes at the expense of robustness. Here we show that micro-flow LC-MS/MS using a 1 × 150 mm column shows excellent reproducibility of chromatographic retention time (<0.3% coefficient of variation, CV) and protein quantification (<7.5% CV) using data from >2000 samples of human cell lines, tissues and body fluids. Deep proteome analysis identifies >9000 proteins and >120,000 peptides in 16 h and sample multiplexing using tandem mass tags increases throughput to 11 proteomes in 16 h. The system identifies >30,000 phosphopeptides in 12 h and protein-protein or protein-drug interaction experiments can be analyzed in 20 min per sample. We show that the same column can be used to analyze >7500 samples without apparent loss of performance. This study demonstrates that micro-flow LC-MS/MS is suitable for a broad range of proteomic applications.