Stephan Aiche

OpenMS: a flexible open-source software platform for mass spectrometry data analysis

High-resolution mass spectrometry (MS) has become an important tool in the life sciences, contributing to the diagnosis and understanding of human diseases, elucidating biomolecular structural information and characterizing cellular signaling networks. However, the rapid growth in the volume and complexity of MS data makes transparent, accurate and reproducible analysis difficult. We present OpenMS 2.0 (http://www.openms.de), a robust, open-source, cross-platform software specifically designed for the flexible and reproducible analysis of high-throughput MS data. The extensible OpenMS software implements common mass spectrometric data processing tasks through a well-defined application programming interface in C++ and Python and through standardized open data formats. OpenMS additionally provides a set of 185 tools and ready-made workflows for common mass spectrometric data processing tasks, which enable users to perform complex quantitative mass spectrometric analyses with ease.

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.

Mechanism of cisplatin proximal tubule toxicity revealed by integrating transcriptomics, proteomics, metabolomics and biokinetics.

Cisplatin is one of the most widely used chemotherapeutic agents for the treatment of solid tumours. The major dose-limiting factor is nephrotoxicity, in particular in the proximal tubule. Here, we use an integrated omics approach, including transcriptomics, proteomics and metabolomics coupled to biokinetics to identify cell stress response pathways induced by cisplatin. The human renal proximal tubular cell line RPTEC/TERT1 was treated with sub-cytotoxic concentrations of cisplatin (0.5 and 2 μM) in a daily repeat dose treating regime for up to 14 days. Biokinetic analysis showed that cisplatin was taken up from the basolateral compartment, transported to the apical compartment, and accumulated in cells over time. This is in line with basolateral uptake of cisplatin via organic cation transporter 2 and bioactivation via gamma-glutamyl transpeptidase located on the apical side of proximal tubular cells. Cisplatin affected several pathways including, p53 signalling, Nrf2 mediated oxidative stress response, mitochondrial processes, mTOR and AMPK signalling. In addition, we identified novel pathways changed by cisplatin, including eIF2 signalling, actin nucleation via the ARP/WASP complex and regulation of cell polarization. In conclusion, using an integrated omic approach together with biokinetics we have identified both novel and established mechanisms of cisplatin toxicity.

qcML: An Exchange Format for Quality Control Metrics from Mass Spectrometry Experiments

Quality control is increasingly recognized as a crucial aspect of mass spectrometry based proteomics. Several recent papers discuss relevant parameters for quality control and present applications to extract these from the instrumental raw data. What has been missing, however, is a standard data exchange format for reporting these performance metrics. We therefore developed the qcML format, an XML-based standard that follows the design principles of the related mzML, mzIdentML, mzQuantML, and TraML standards from the HUPO-PSI (Proteomics Standards Initiative). In addition to the XML format, we also provide tools for the calculation of a wide range of quality metrics as well as a database format and interconversion tools, so that existing LIMS systems can easily add relational storage of the quality control data to their existing schema. We here describe the qcML specification, along with possible use cases and an illustrative example of the subsequent analysis possibilities. All information about qcML is available at http://code.google.com/p/qcml.

Evaluation of drug-induced neurotoxicity based on metabolomics, proteomics and electrical activity measurements in complementary CNS in vitro models

The present study was performed in an attempt to develop an in vitro integrated testing strategy (ITS) to evaluate drug-induced neurotoxicity. A number of endpoints were analyzed using two complementary brain cell culture models and an in vitro blood-brain barrier (BBB) model after single and repeated exposure treatments with selected drugs that covered the major biological, pharmacological and neuro-toxicological responses. Furthermore, four drugs (diazepam, cyclosporine A, chlorpromazine and amiodarone) were tested more in depth as representatives of different classes of neurotoxicants, inducing toxicity through different pathways of toxicity. The developed in vitro BBB model allowed detection of toxic effects at the level of BBB and evaluation of drug transport through the barrier for predicting free brain concentrations of the studied drugs. The measurement of neuronal electrical activity was found to be a sensitive tool to predict the neuroactivity and neurotoxicity of drugs after acute exposure. The histotypic 3D re-aggregating brain cell cultures, containing all brain cell types, were found to be well suited for OMICs analyses after both acute and long term treatment. The obtained data suggest that an in vitro ITS based on the information obtained from BBB studies and combined with metabolomics, proteomics and neuronal electrical activity measurements performed in stable in vitro neuronal cell culture systems, has high potential to improve current in vitro drug-induced neurotoxicity evaluation.

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.

MSSimulator: Simulation of mass spectrometry data.

Mass spectrometry coupled to liquid chromatography (LC-MS and LC-MS/MS) is commonly used to analyze the protein content of biological samples in large scale studies, enabling quantitation and identification of proteins and peptides using a wide range of experimental protocols, algorithms, and statistical models to analyze the data. Currently it is difficult to compare the plethora of algorithms for these tasks. So far, curated benchmark data exists for peptide identification algorithms but data that represents a ground truth for the evaluation of LC-MS data is limited. Hence there have been attempts to simulate such data in a controlled fashion to evaluate and compare algorithms. We present MSSimulator, a simulation software for LC-MS and LC-MS/MS experiments. Starting from a list of proteins from a FASTA file, the simulation will perform in-silico digestion, retention time prediction, ionization filtering, and raw signal simulation (including MS/MS), while providing many options to change the properties of the resulting data like elution profile shape, resolution and sampling rate. Several protocols for SILAC, iTRAQ or MS(E) are available, in addition to the usual label-free approach, making MSSimulator the most comprehensive simulator for LC-MS and LC-MS/MS data.

Workflows for automated downstream data analysis and visualization in large-scale computational mass spectrometry.

MS‐based proteomics and metabolomics are rapidly evolving research fields driven by the development of novel instruments, experimental approaches, and analysis methods. Monolithic analysis tools perform well on single tasks but lack the flexibility to cope with the constantly changing requirements and experimental setups. Workflow systems, which combine small processing tools into complex analysis pipelines, allow custom‐tailored and flexible data‐processing workflows that can be published or shared with collaborators. In this article, we present the integration of established tools for computational MS from the open‐source software framework OpenMS into the workflow engine Konstanz Information Miner (KNIME) for the analysis of large datasets and production of high‐quality visualizations. We provide example workflows to demonstrate combined data processing and visualization for three diverse tasks in computational MS: isobaric mass tag based quantitation in complex experimental setups, label‐free quantitation and identification of metabolites, and quality control for proteomics experiments.

From the desktop to the grid: scalable bioinformatics via workflow conversion

BackgroundReproducibility is one of the tenets of the scientific method. Scientific experiments often comprise complex data flows, selection of adequate parameters, and analysis and visualization of intermediate and end results. Breaking down the complexity of such experiments into the joint collaboration of small, repeatable, well defined tasks, each with well defined inputs, parameters, and outputs, offers the immediate benefit of identifying bottlenecks, pinpoint sections which could benefit from parallelization, among others. Workflows rest upon the notion of splitting complex work into the joint effort of several manageable tasks.There are several engines that give users the ability to design and execute workflows. Each engine was created to address certain problems of a specific community, therefore each one has its advantages and shortcomings. Furthermore, not all features of all workflow engines are royalty-free —an aspect that could potentially drive away members of the scientific community.ResultsWe have developed a set of tools that enables the scientific community to benefit from workflow interoperability. We developed a platform-free structured representation of parameters, inputs, outputs of command-line tools in so-called Common Tool Descriptor documents. We have also overcome the shortcomings and combined the features of two royalty-free workflow engines with a substantial user community: the Konstanz Information Miner, an engine which we see as a formidable workflow editor, and the Grid and User Support Environment, a web-based framework able to interact with several high-performance computing resources. We have thus created a free and highly accessible way to design workflows on a desktop computer and execute them on high-performance computing resources.ConclusionsOur work will not only reduce time spent on designing scientific workflows, but also make executing workflows on remote high-performance computing resources more accessible to technically inexperienced users. We strongly believe that our efforts not only decrease the turnaround time to obtain scientific results but also have a positive impact on reproducibility, thus elevating the quality of obtained scientific results.

Inferring Proteolytic Processes from Mass Spectrometry Time Series Data Using Degradation Graphs

Background Proteases play an essential part in a variety of biological processes. Besides their importance under healthy conditions they are also known to have a crucial role in complex diseases like cancer. In recent years, it has been shown that not only the fragments produced by proteases but also their dynamics, especially ex vivo, can serve as biomarkers. But so far, only a few approaches were taken to explicitly model the dynamics of proteolysis in the context of mass spectrometry. Results We introduce a new concept to model proteolytic processes, the degradation graph. The degradation graph is an extension of the cleavage graph, a data structure to reconstruct and visualize the proteolytic process. In contrast to previous approaches we extended the model to incorporate endoproteolytic processes and present a method to construct a degradation graph from mass spectrometry time series data. Based on a degradation graph and the intensities extracted from the mass spectra it is possible to estimate reaction rates of the underlying processes. We further suggest a score to rate different degradation graphs in their ability to explain the observed data. This score is used in an iterative heuristic to improve the structure of the initially constructed degradation graph. Conclusion We show that the proposed method is able to recover all degraded and generated peptides, the underlying reactions, and the reaction rates of proteolytic processes based on mass spectrometry time series data. We use simulated and real data to demonstrate that a given process can be reconstructed even in the presence of extensive noise, isobaric signals and false identifications. While the model is currently only validated on peptide data it is also applicable to proteins, as long as the necessary time series data can be produced.