Martinsried, Germany
Martinsried, Germany

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Breitkopf S.B.,Max Planck Institute of Biochemistry | Breitkopf S.B.,Beth Israel Deaconess Medical Center | Oppermann F.S.,Max Planck Institute of Biochemistry | Oppermann F.S.,KINAXO Biotechnologies | And 6 more authors.
Journal of Proteome Research | Year: 2010

Inhibition of deregulated protein kinases by small molecule drugs has evolved into a major therapeutic strategy for the treatment of human malignancies. Knowledge about direct cellular targets of kinase-selective drugs and the identification of druggable downstream mediators of oncogenic signaling are relevant for both initial therapy selection and the nomination of alternative targets in case molecular resistance emerges. To address these issues, we performed a proof-of-concept proteomics study designed to monitor drug effects on the pharmacologically tractable subproteome isolated by affinity purification with immobilized, nonselective kinase inhibitors. We applied this strategy to chronic myeloid leukemia cells that express the transforming Bcr-Abl fusion kinase. We used SILAC to measure how cellular treatment with the Bcr-Abl inhibitor imatinib affects protein binding to a generic kinase inhibitor resin and further quantified site-specific phosphorylations on resin-retained proteins. Our integrated approach indicated additional imatinib target candidates, such as flavine adenine dinucleotide synthetase, as well as repressed phosphorylation events on downstream effectors not yet implicated in imatinib-regulated signaling. These included activity-regulating phosphorylations on the kinases Btk, Fer, and focal adhesion kinase, which may qualify them as alternative target candidates in Bcr-Abl-driven oncogenesis. Our approach is rather generic and may have various applications in kinase drug discovery. © 2010 American Chemical Society.


Mausbacher N.,Max Planck Institute of Biochemistry | Mausbacher N.,TU Munich | Schreiber T.B.,Max Planck Institute of Biochemistry | Daub H.,Max Planck Institute of Biochemistry | Daub H.,KINAXO Biotechnologies
Molecular and Cellular Proteomics | Year: 2010

The lipid mediator lysophosphatidic acid (LPA) is a serum component that regulates cellular functions such as proliferation, migration, and survival via specific G protein-coupled receptors. The underlying signaling mechanisms are still incompletely understood, including those that operate at the plasma membrane to modulate cell-cell and cell-matrix interactions in LPA-promoted cell migration. To explore LPA-evoked phosphoregulation with a focus on cell surface proteins, we combined glycoproteome enrichment by immobilized lectins with SILAC-based quantitative phosphoproteomics. We performed biological replicate analyses in SCC-9 squamous cell carcinoma cells and repeatedly quantified the effect of 1.5- and 5-min LPA treatment on more than 700 distinct phosphorylations in lectin-purified proteins. We detected many regulated phosphorylation events on various types of plasma membrane proteins such as cell adhesion molecules constituting adherens junctions, desmosomes, and hemidesmosomes. Several of these LPA-regulated phosphorylation sites have been characterized in a biological context other than G protein-coupled receptor signaling, and the transfer of this functional information suggests coordinated and multifactorial cell adhesion control in LPA-induced cell migration. Additionally, we identified LPA-mediated activation loop phosphorylation of the serine/threonine kinase Wnk1 and verified a role of Wnk1 for LPA-induced cell migration in knock-down experiments. In conclusion, the glycoproteome phosphoproteomics strategy described here sheds light on incompletely understood mechanisms in LPA-induced cell migratory behavior. © 2010 by The American Society for Biochemistry and Molecular Biology, Inc.


Schaab C.,KINAXO Biotechnologies
Methods in molecular biology (Clifton, N.J.) | Year: 2011

Regulation of protein phosphorylation plays an important role in many cellular processes, particularly in signal transduction. Diseases such as cancer and inflammation are often linked to aberrant signaling pathways. Mass spectrometry-based methods allow monitoring the phosphorylation status in an unbiased and quantitative manner. The analysis of this data requires the application of advanced statistical methods, some of which can be borrowed from the gene expression analysis field. Nevertheless, these methods have to be enhanced or complemented by new methods. After reviewing the key concepts of phosphoproteomics and some major data analysis methods, these tools are applied to a real-world data set.


Klammer M.,KINAXO Biotechnologies | Godl K.,KINAXO Biotechnologies | Tebbe A.,KINAXO Biotechnologies | Schaab C.,KINAXO Biotechnologies | Schaab C.,Max Planck Institute of Biochemistry
BMC Bioinformatics | Year: 2010

Background: Various high throughput methods are available for detecting regulations at the level of transcription, translation or posttranslation (e.g. phosphorylation). Integrating these data with protein networks should make it possible to identify subnetworks that are significantly regulated. Furthermore, such integration can support identification of regulated entities from often noisy high throughput data. In particular, processing mass spectrometry-based phosphoproteomic data in this manner may expose signal transduction pathways and, in the case of experiments with drug-treated cells, reveal the drug's mode of action.Results: Here, we introduce SubExtractor, an algorithm that combines phosphoproteomic data with protein network information from STRING to identify differentially regulated subnetworks and individual proteins. The method is based on a Bayesian probabilistic model combined with a genetic algorithm and rigorous significance testing. The Bayesian model accounts for information about both differential regulation and network topology. The method was tested with artificial data and subsequently applied to a comprehensive phosphoproteomics study investigating the mode of action of sorafenib, a small molecule kinase inhibitor.Conclusions: SubExtractor reliably identifies differentially regulated subnetworks from phosphoproteomic data by integrating protein networks. The method can also be applied to gene or protein expression data. © 2010 Klammer et al; licensee BioMed Central Ltd.


Conradt L.,TU Munich | Godl K.,KINAXO Biotechnologies | Schaab C.,KINAXO Biotechnologies | Tebbe A.,KINAXO Biotechnologies | And 8 more authors.
Neoplasia | Year: 2011

A placebo-controlled phase 3 trial demonstrated that the epidermal growth factor receptor (EGFR) inhibitor erlotinib in combination with gemcitabine was especially efficient in a pancreatic ductal adenocarcinoma (PDAC) subgroup of patients developing skin toxicity. However, EGFR expression was not predictive for response, and markers to characterize an erlotinib-responding PDAC group are currently missing. In this work, we observed high erlotinib IC50 values in a panel of human and murine PDAC cell lines. Using EGFR small interfering RNA, we detected that the erlotinib response was marginally influenced by EGFR. To find novel EGFR targets, we used an unbiased chemical proteomics approach for target identification and quality-controlled target affinity determination combined with quantitative mass spectrometry based on stable isotope labeling by amino acids in cell culture. In contrast to gefitinib, we observed a broad target profile of erlotinib in PDAC cells by quantitative proteomics. Six protein kinases bind to erlotinib with similar or higher affinity (K d = 0.09-0.358 μM) than the EGFR (K d 0.434 μM). We provide evidence that one of the novel erlotinib targets, ARG, contributes in part to the erlotinib response in a PDAC cell line. Our data show that erlotinib is a multikinase inhibitor, which can act independent of EGFR in PDAC. These findings may help to monitor future erlotinib trials in the clinic. © 2011 Neoplasia Press, Inc. All rights reserved.


Schaab C.,Max Planck Institute of Biochemistry | Schaab C.,KINAXO Biotechnologies | Geiger T.,Max Planck Institute of Biochemistry | Stoehr G.,Max Planck Institute of Biochemistry | And 2 more authors.
Molecular and Cellular Proteomics | Year: 2012

MS-based proteomics generates rapidly increasing amounts of precise and quantitative information. Analysis of individual proteomic experiments has made great strides, but the crucial ability to compare and store information across different proteome measurements still presents many challenges. For example, it has been difficult to avoid contamination of databases with low quality peptide identifications, to control for the inflation in false positive identifications when combining data sets, and to integrate quantitative data. Although, for example, the contamination with low quality identifications has been addressed by joint analysis of deposited raw data in some public repositories, we reasoned that there should be a role for a database specifically designed for high resolution and quantitative data. Here we describe a novel database termed MaxQB that stores and displays collections of large proteomics projects and allows joint analysis and comparison. We demonstrate the analysis tools of MaxQB using proteome data of 11 different human cell lines and 28 mouse tissues. The database-wide false discovery rate is controlled by adjusting the project specific cutoff scores for the combined data sets. The 11 cell line proteomes together identify proteins expressed from more than half of all human genes. For each protein of interest, expression levels estimated by label-free quantification can be visualized across the cell lines. Similarly, the expression rank order and estimated amount of each protein within each proteome are plotted. We used MaxQB to calculate the signal reproducibility of the detected peptides for the same proteins across different proteomes. Spearman rank correlation between peptide intensity and detection probability of identified proteins was greater than 0.8 for 64% of the proteome, whereas a minority of proteins have negative correlation. This information can be used to pinpoint false protein identifications, independently of peptide database scores. The information contained in MaxQB, including high resolution fragment spectra, is accessible to the community via a userfriendly web interface at http://www.biochem.mpg.de/maxqb. © 2012 by The American Society for Biochemistry and Molecular Biology, Inc.


Schmid A.B.,TU Munich | Lagleder S.,TU Munich | Lagleder S.,King Abdulaziz University | Grawert M.A.,TU Munich | And 13 more authors.
EMBO Journal | Year: 2012

Sti1/Hop is a modular protein required for the transfer of client proteins from the Hsp70 to the Hsp90 chaperone system in eukaryotes. It binds Hsp70 and Hsp90 simultaneously via TPR (tetratricopeptide repeat) domains. Sti1/Hop contains three TPR domains (TPR1, TPR2A and TPR2B) and two domains of unknown structure (DP1 and DP2). We show that TPR2A is the high affinity Hsp90-binding site and TPR1 and TPR2B bind Hsp70 with moderate affinity. The DP domains exhibit highly homologous α-helical folds as determined by NMR. These, and especially DP2, are important for client activation in vivo. The core module of Sti1 for Hsp90 inhibition is the TPR2A-TPR2B segment. In the crystal structure, the two TPR domains are connected via a rigid linker orienting their peptide-binding sites in opposite directions and allowing the simultaneous binding of TPR2A to the Hsp90 C-terminal domain and of TPR2B to Hsp70. Both domains also interact with the Hsp90 middle domain. The accessory TPR1-DP1 module may serve as an Hsp70-client delivery system for the TPR2A-TPR2B-DP2 segment, which is required for client activation in vivo. © 2012 European Molecular Biology Organization.


PubMed | KINAXO Biotechnologies
Type: | Journal: Methods in molecular biology (Clifton, N.J.) | Year: 2010

Regulation of protein phosphorylation plays an important role in many cellular processes, particularly in signal transduction. Diseases such as cancer and inflammation are often linked to aberrant signaling pathways. Mass spectrometry-based methods allow monitoring the phosphorylation status in an unbiased and quantitative manner. The analysis of this data requires the application of advanced statistical methods, some of which can be borrowed from the gene expression analysis field. Nevertheless, these methods have to be enhanced or complemented by new methods. After reviewing the key concepts of phosphoproteomics and some major data analysis methods, these tools are applied to a real-world data set.

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