Mathias Wilhelm

Mass-spectrometry-based draft of the human proteome

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, and identified organ-specific proteins and a large number of translated lincRNAs (long intergenic non-coding RNAs). 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 proteome profiles also hold considerable promise for analysing the composition and stoichiometry of protein complexes. ProteomicsDB thus enables navigation of proteomes, provides biological insight and fosters the development of proteomic technology.

Global Proteome Analysis of the NCI-60 Cell Line Panel

The NCI-60 cell line collection is a very widely used panel for the study of cellular mechanisms of cancer in general and in vitro drug action in particular. It is a model system for the tissue types and genetic diversity of human cancers and has been extensively molecularly characterized. Here, we present a quantitative proteome and kinome profile of the NCI-60 panel covering, in total, 10,350 proteins (including 375 protein kinases) and including a core cancer proteome of 5,578 proteins that were consistently quantified across all tissue types. Bioinformatic analysis revealed strong cell line clusters according to tissue type and disclosed hundreds of differentially regulated proteins representing potential biomarkers for numerous tumor properties. Integration with public transcriptome data showed considerable similarity between mRNA and protein expression. Modeling of proteome and drug-response profiles for 108 FDA-approved drugs identified known and potential protein markers for drug sensitivity and resistance. To enable community access to this unique resource, we incorporated it into a public database for comparative and integrative analysis (http://wzw.tum.de/proteomics/nci60).

A scalable approach for protein false discovery rate estimation in large proteomic data sets

Calculating the number of confidently identified proteins and estimating false discovery rate (FDR) is a challenge when analyzing very large proteomic data sets such as entire human proteomes. Biological and technical heterogeneity in proteomic experiments further add to the challenge and there are strong differences in opinion regarding the conceptual validity of a protein FDR and no consensus regarding the methodology for protein FDR determination. There are also limitations inherent to the widely used classic target–decoy strategy that particularly show when analyzing very large data sets and that lead to a strong over-representation of decoy identifications. In this study, we investigated the merits of the classic, as well as a novel target–decoy-based protein FDR estimation approach, taking advantage of a heterogeneous data collection comprised of ∼19,000 LC-MS/MS runs deposited in ProteomicsDB (https://www.proteomicsdb.org). The “picked” protein FDR approach treats target and decoy sequences of the same protein as a pair rather than as individual entities and chooses either the target or the decoy sequence depending on which receives the highest score. We investigated the performance of this approach in combination with q-value based peptide scoring to normalize sample-, instrument-, and search engine-specific differences. The “picked” target–decoy strategy performed best when protein scoring was based on the best peptide q-value for each protein yielding a stable number of true positive protein identifications over a wide range of q-value thresholds. We show that this simple and unbiased strategy eliminates a conceptual issue in the commonly used “classic” protein FDR approach that causes overprediction of false-positive protein identification in large data sets. The approach scales from small to very large data sets without losing performance, consistently increases the number of true-positive protein identifications and is readily implemented in proteomics analysis software.

Optimized Chemical Proteomics Assay for Kinase Inhibitor Profiling

Solid supported probes have proven to be an efficient tool for chemical proteomics. The kinobeads technology features kinase inhibitors covalently attached to Sepharose for affinity enrichment of kinomes from cell or tissue lysates. This technology, combined with quantitative mass spectrometry, is of particular interest for the profiling of kinase inhibitors. It often leads to the identification of new targets for medicinal chemistry campaigns where it allows a two-in-one binding and selectivity assay. The assay can also uncover resistance mechanisms and molecular sources of toxicity. Here we report on the optimization of the kinobead assay resulting in the combination of five chemical probes and four cell lines to cover half the human kinome in a single assay (∼ 260 kinases). We show the utility and large-scale applicability of the new version of kinobeads by reprofiling the small molecule kinase inhibitors Alvocidib, Crizotinib, Dasatinib, Fasudil, Hydroxyfasudil, Nilotinib, Ibrutinib, Imatinib, and Sunitinib.

The target landscape of clinical kinase drugs

An atlas for drug interactions Kinase inhibitors are an important class of drugs that block certain enzymes involved in diseases such as cancer and inflammatory disorders. There are hundreds of kinases within the human body, so knowing the kinase “target” of each drug is essential for developing successful treatment strategies. Sometimes clinical trials can fail because drugs bind more than one target. Yet sometimes off-target effects can be beneficial, and drugs can be repurposed for treatment of additional diseases. Klaeger et al. performed a comprehensive analysis of 243 kinase inhibitors that are either approved for use or in clinical trials. They provide an open-access resource of target summaries that could help researchers develop better drugs, understand how existing drugs work, and design more effective clinical trials. Science, this issue p. eaan4368 The druggable kinome is unraveled. INTRODUCTION Molecularly targeted drugs such as imatinib and crizotinib have revolutionized the treatment of certain blood and lung cancers because of their remarkable clinical success. Over the past 20 years, protein kinases have become a major class of drug targets because these signaling biomolecules are often deregulated in disease, particularly in cancer. Today, 37 small kinase inhibitors (KIs) are approved medicines worldwide and more than 250 drug candidates are undergoing clinical evaluation. RATIONALE Although it is commonly accepted that most KIs target more than one protein, the extent to which this information is available to the public varies greatly between drugs. It would seem important to thoroughly characterize the target spectrum of any drug because additional off-targets may offer opportunities, not only for repurposing but also to explain undesired side effects. To this end, we used a chemical proteomic approach (kinobeads) and quantitative mass spectrometry to characterize the target space of 243 clinical KIs that are approved drugs or have been tested in humans. RESULTS The number of targets for a given drug differed substantially. Whereas some compounds showed exquisite selectivity, others targeted more than 100 kinases simultaneously, making it difficult to attribute their biological effects to any particular mode of action. Also of note is that recently developed irreversible KIs can address more kinases than their intended targets epidermal growth factor receptor (EGFR) and Bruton’s tyrosine kinase (BTK). Collectively, the evaluated KIs targeted 220 kinases with submicromolar affinity, offering a view of the druggable kinome and enabling the development of a universal new selectivity metric termed CATDS (concentration- and target-dependent selectivity). All drug profiles can be interactively explored in ProteomicsDB and a purpose-built shinyApp. Many uses of this unique data and analysis resource by the scientific community can be envisaged, of which we can only highlight a few. The profiles identified many new targets for established drugs, thus improving our understanding of how these drugs might exert their phenotypic effects. For example, we evaluated novel salt-inducible kinase 2 (SIK2) inhibitors for their ability to modulate tumor necrosis factor–α (TNFα) and interleukin-10 (IL-10) production, which may allow repurposing these drugs for inflammatory conditions. Integrating target space information with phosphoproteomic analysis of several EGFR inhibitors enabled the identification of drug response markers inside and outside the canonical EGFR signaling pathway. Off-target identification may also inform drug discovery projects using high-value clinical molecules as lead compounds. We illustrate such a case by a novel structure-affinity relationship analysis of MELK inhibitors based on target profiles and cocrystal structures. To assess the repurposing potential of approved or clinically advanced compounds, we used cell-based assays and mouse xenografts to show that golvatinib and cabozantinib may be used for the treatment of acute myeloid leukemia (AML) based on their FLT3 inhibitory activity. CONCLUSION This work provides a rich data resource describing the target landscape of 243 clinically tested KIs. It is the most comprehensive study to date and illustrates how the information may be used in basic research, drug discovery, or clinical decision-making. Schematic representation of identifying the druggable kinome. A chemical proteomic approach revealed quantitative interaction profiles of 243 clinically evaluated small-molecule KIs covering half of the human kinome. Results can be interactively explored in ProteomicsDB and inform basic biology, drug discovery, and clinical decision-making. 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.

Ion mobility tandem mass spectrometry enhances performance of bottom-up proteomics

One of the limiting factors in determining the sensitivity of tandem mass spectrometry using hybrid quadrupole orthogonal acceleration time-of-flight instruments is the duty cycle of the orthogonal ion injection system. As a consequence, only a fraction of the generated fragment ion beam is collected by the time-of-flight analyzer. Here we describe a method utilizing postfragmentation ion mobility spectrometry of peptide fragment ions in conjunction with mobility time synchronized orthogonal ion injection leading to a substantially improved duty cycle and a concomitant improvement in sensitivity of up to 10-fold for bottom-up proteomic experiments. This enabled the identification of 7500 human proteins within 1 day and 8600 phosphorylation sites within 5 h of LC-MS/MS time. The method also proved powerful for multiplexed quantification experiments using tandem mass tags exemplified by the chemoproteomic interaction analysis of histone deacetylases with Trichostatin A.

Building ProteomeTools based on a complete synthetic human proteome

We describe ProteomeTools, a project building molecular and digital tools from the human proteome to facilitate biomedical research. Here we report the generation and multimodal liquid chromatography–tandem mass spectrometry analysis of >330,000 synthetic tryptic peptides representing essentially all canonical human gene products, and we exemplify the utility of these data in several applications. The resource (available at http://www.proteometools.org) will be extended to >1 million peptides, and all data will be shared with the community via ProteomicsDB and ProteomeXchange.

Combining peak- and chromatogram-based retention time alignment algorithms for multiple chromatography-mass spectrometry datasets

BackgroundModern analytical methods in biology and chemistry use separation techniques coupled to sensitive detectors, such as gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS). These hyphenated methods provide high-dimensional data. Comparing such data manually to find corresponding signals is a laborious task, as each experiment usually consists of thousands of individual scans, each containing hundreds or even thousands of distinct signals. In order to allow for successful identification of metabolites or proteins within such data, especially in the context of metabolomics and proteomics, an accurate alignment and matching of corresponding features between two or more experiments is required. Such a matching algorithm should capture fluctuations in the chromatographic system which lead to non-linear distortions on the time axis, as well as systematic changes in recorded intensities. Many different algorithms for the retention time alignment of GC-MS and LC-MS data have been proposed and published, but all of them focus either on aligning previously extracted peak features or on aligning and comparing the complete raw data containing all available features.ResultsIn this paper we introduce two algorithms for retention time alignment of multiple GC-MS datasets: multiple alignment by bidirectional best hits peak assignment and cluster extension (BIPACE) and center-star multiple alignment by pairwise partitioned dynamic time warping (CeMAPP-DTW). We show how the similarity-based peak group matching method BIPACE may be used for multiple alignment calculation individually and how it can be used as a preprocessing step for the pairwise alignments performed by CeMAPP-DTW. We evaluate the algorithms individually and in combination on a previously published small GC-MS dataset studying the Leishmania parasite and on a larger GC-MS dataset studying grains of wheat (Triticum aestivum).ConclusionsWe have shown that BIPACE achieves very high precision and recall and a very low number of false positive peak assignments on both evaluation datasets. CeMAPP-DTW finds a high number of true positives when executed on its own, but achieves even better results when BIPACE is used to constrain its search space. The source code of both algorithms is included in the OpenSource software framework Maltcms, which is available fromhttp://maltcms.sf.net. The evaluation scripts of the present study are available from the same source.

mz5: Space- and Time-efficient Storage of Mass Spectrometry Data Sets

Across a host of MS-driven-omics fields, researchers witness the acquisition of ever increasing amounts of high throughput MS data and face the need for their compact yet efficiently accessible storage. Addressing the need for an open data exchange format, the Proteomics Standards Initiative and the Seattle Proteome Center at the Institute for Systems Biology independently developed the mzData and mzXML formats, respectively. In a subsequent joint effort, they defined an ontology and associated controlled vocabulary that specifies the contents of MS data files, implemented as the newer mzML format. All three formats are based on XML and are thus not particularly efficient in either storage space requirements or read/write speed. This contribution introduces mz5, a complete reimplementation of the mzML ontology that is based on the efficient, industrial strength storage backend HDF5. Compared with the current mzML standard, this strategy yields an average file size reduction to ∼54% and increases linear read and write speeds ∼3–4-fold. The format is implemented as part of the ProteoWizard project and is available under a permissive Apache license. Additional information and download links are available from http://software.steenlab.org/mz5.

A classifier based on accurate mass measurements to aid large-scale, unbiased glycoproteomics

Determining which glycan moieties occupy specific N-glycosylation sites is a highly challenging analytical task. Arguably, the most common approach involves LC-MS and LC-MS/MS analysis of glycopeptides generated by proteases with high cleavage site specificity; however, the depth achieved by this approach is modest. Nonglycosylated peptides are a major challenge to glycoproteomics, as they are preferentially selected for data-dependent MS/MS due to higher ionization efficiencies and higher stoichiometric levels in moderately complex samples. With the goal of improving glycopeptide coverage, a mass defect classifier was developed that discriminates between peptides and glycopeptides in complex mixtures based on accurate mass measurements of precursor peaks. By using the classifier, glycopeptides that were not fragmented in an initial data-dependent acquisition run may be targeted in a subsequent analysis without any prior knowledge of the glycan or protein species present in the mixture. Additionally, from probable glycopeptides that were poorly fragmented, tandem mass spectra may be reacquired using optimal glycopeptide settings. We demonstrate high sensitivity (0.892) and specificity (0.947) based on an in silico dataset spanning >100,000 tryptic entries. Comparable results were obtained using chymotryptic species. Further validation using published data and a fractionated tryptic digest of human urinary proteins was performed, yielding a sensitivity of 0.90 and a specificity of 0.93. Lists of glycopeptides may be generated from an initial proteomics experiment, and we show they may be efficiently targeted using the classifier. Considering the growing availability of high accuracy mass analyzers, this approach represents a simple and broadly applicable means of increasing the depth of MS/MS-based glycoproteomic analyses.