DEFENDED PHDS
Phosphoproteomics for mode of action analysis of targeted cancer drugs
Svenja Wiechmann - December 2020
Targeted cancer drugs promise enhanced efficacy and less toxic side effects than conventional approaches. Still, the high prevalence of low responses, relapses as well as undesired adverse effects represents a major clinical obstacle. Towards a better knowledge of the mode of actions and applications of targeted cancer drugs, selected clinical small molecule kinase inhibitors and monoclonal antibodies were subjected to a global analysis by phosphoproteomics. It enabled a deep insight into the targets and effects on cancer signaling and shed light onto resistance mechanisms.
Multi-omics data integration and data model optimization in ProteomicsDB
Patroklos Samaras - October 2020
Integrating data from several omics technologies provides a more thorough view of what is happening inside a cell in the state of a disease. This thesis extends ProteomicsDB with generic data models, data integration procedures and real-time analysis tools to provide an online solution on omics data exploration, integration and exploitation. The new data and functionalities enhance the role of ProteomicsDB in experiment planning and hypothesis generation. The expansion of the platform to multiple organisms opens the way to inter-species comparisons.
A deep learning model for the proteome-wide prediction of peptide tandem mass spectra
Siegfried Gessulat - January 2020
Mass spectrometry-based proteomics is the leading technology to identify peptides. The identification strongly relies on software with database searching, and spectral library matching being two successful approaches. Both benefit from accurate predictions. This work presents a deep learning model whose predictions exceed experimental spectra from synthetic peptides in quality. It can be calibrated to different conditions and generalizes to various proteases. The utility of the model is shown in the context of both: database searching and spectral library matching.
Studying peptidoform-resolved proteome turnover
Jana Zecha - December 2019
Cellular proteins exist in a dynamic state in which they are continuously synthesized and degraded. Bottom-up proteomics technologies allow for the parallel assessment of turnover for thousands of proteins. Here, a new workflow for robust, time- and cost-efficient measurements of peptide turnover was established enabling the global study of hitherto widely neglected isoform- and modification-specific protein turnover. Further, the enormous potential of such measurements for prioritization of functional relevant modification sites is illustrated.
Building ProteomeTools based on a complete synthetic human proteome
Daniel P. Zolg - February 2019
Mass spectrometry (MS)-based bottom-up proteomics has evolved into an indispensable tool for the simultaneous analysis of large number of proteins. The core concept is matching mass spectra to peptide sequences to infer protein information. During this process, computational and statistical tools make assumptions to the presumed content of a sample and utilize probabilistic rankings to assign the most likely match. In analytical chemistry or metabolomics, the identity of analytes is validated using synthetic reference standards. Yet, comprehensive peptide reference libraries and high-quality reference spectra are lacking in order to implement such a stringent approach in proteomic workflows. This cumulative thesis comprises three original publications addressing this unmet need, describing the realization of comprehensive synthetic peptide libraries and systematically acquired high-quality mass spectra to advance human proteome research.
Comprehensive characterization of the human proteome by multi-omic analyses
Dongxue Wang - November 2018
Quantitating the differential expression of human genes in different cell types, tissues and organs will significantly increase our knowledge and understanding of human biology and disease. At the protein level, even fundamental aspects such as which proteins actually exist, where these are expressed and in what quantities, are still not fully resolved. To answer such questions, a quantitative proteomic analysis of 29 histologically-healthy human tissues was performed using high-resolution mass spectrometry. The 29 tissues cover many of the major tissues and organs of the human body and include a broad range of cell types. Via integration with sample-matched transcriptomic data, a systematic, quantitative and deep proteome and transcriptome abundance atlas was generated.
When chemical proteomics meets medicinal chemistry: Guided drug discovery towards EPHA2 inhibitors
Stephanie Heinzlmeir - December 2017
The receptor tyrosine kinase EPHA2 is involved in many diseases and gained interest as drug target. Chemical proteomics was used to identify clinical EPHA2 inhibitors, to select a lead structure for medicinal chemistry, and to guide the design and synthesis of EPHA2 inhibitors. Rationalized drug discovery was further supported by the development of a selectivity metric (CATDS) and a residue classification scheme for amino acids involved in drug binding. This combined approach was successful in generating more selective and effective EPHA2 inhibitors
Chemical Proteomics for Characterization of Small Molecule Kinase Inhibitors in Cancer and Inflammation
Huichao Qiao - June 2017
Protein kinases have been established as promising drug targets for treatment of various types of cancers. Small molecule kinase inhibitors have become increasingly important in cancer therapeutics. Currently, more than 33 FDA-approved kinase inhibitors have been used as promising targeted therapeutics, and numerous (>250) inhibitors are in various stages of clinical evaluation. Kinobeads technology in combination with high-resolution MS has emerged a powerful tool to understand the mode of action, to identify new targets, and to characterize potential off-target effects of small molecule kinase inhibitors. In this thesis, a dose-resolved quantitative chemical proteomics approach has been used to understand the mechanism of small molecule kinase inhibitors targeting CDK protein kinases and salt inducible kinases and to highlight their off-targets.
An in-memory platform for the exploration and analysis of big data in biology
Mathias Wilhelm - May 2017
Providing open and unrestricted access to mass spectrometry-based proteomics data offers researchers the ability to cross-compare their findings with previously conducted experiments. This thesis describes the implementation of ProteomicsDB, a versatile and performant database to store and analyze proteomics as well as transcriptomics data. ProteomicsDB was used to assemble and analysis a first draft of the human proteome. Based on this dataset, a novel protein false discovery estimation strategy designed for big data was developed.
Chemical Proteomics Reveals the Target Landscape of Clinical Kinase Inhibitors
Susan Kläger - March 2017
Protein kinases are key signaling molecules in the cell and catalyze the phosphate group transfer of ATP to their respective substrates. They have emerged as major drug target class, as they are often aberrant in diseases like cancer or inflammation. Small molecule kinase inhibitors provide one treatment option. Over 250 of these molecules are currently evaluated in clinical trials; over 30 have already been approved for human therapy. Most of them mimic ATP, thus targeting the ATP-binding pocket of kinases and preventing signal transduction via phosphate-transfer. As the ATP-binding pocket is quite conserved across the 518 protein kinases, many inhibitors can bind to more than one target protein. This polypharmacology can be advantageous and lead to the use of one drug in more than one indication. It might also lead to side effects and has influence on the mode of action of a drug. Therefore, thorough evaluation of the target space of a kinase inhibitor and its selectivity is necessary.
Application of mass spectrometry-based proteomics to study cancer drug resistance mechanisms
Heiner Koch - October 2016
Resistance to small molecule kinase inhibitors appears within weeks to months and represents a major drawback in clinical cancer therapies. However, quantitative mass spectrometry became a powerful tool to characterize molecular changes on the proteome and protein modification level on a system-wide scale. In this thesis high resolution mass spectrometry was applied to characterize adaptive and growth factor mediated mechanisms that result in cancer drug resistance.
Development of phospho- and chemoproteomic methods to study cellular signaling
Benjamin Ruprecht - 20 May 2016
Kinase mediated protein phosphorylation on serine, threonine and tyrosine side chains is an important post-translational modification which affects and governs a large body of cellular signaling in health and disease. In recent years, mass spectrometry-based proteomics has emerged as the prime technology for the large scale, explorative and proteome-wide analysis of the kinome and associated phosphorylation events.
Application of multivariate methods to the integrative analysis of high-throughput omics data
Chen Meng - 26 January 2016
More computational efforts are required to interpret biological knowledge from high-throughput omics data, such as genomics, transcriptomics and proteomics. Multivariate methods has shown great potential for this purpose, but the application of these methods to omics data analysis is still in its infancy. This thesis explored several applications of multivariate methods for the integrative analysis of multiple omics datasets, including exploratory analysis, clustering analysis and gene set analysis in an integrative manner. The results underscore the importance of analyzing multiple omics data sets in an integrative scheme.
Mass spectrometry based chemical proteomics for drug selectivity profiling
Dominic Helm - 13 July 2015
Mass spectrometry is the standard technology for chemical proteomics. Kinobead technology is an established tool in the field of chemical proteomics. The development of new mass spectrometry technologies is essential to advance the current performance of the study of kinase inhibitors. This thesis aimed to characterise a DIA approach for its use in a large scale chemical proteomics study. Further, the project focused on the development of a new ion mobility assisted DDA approach on a Q-TOF instrument and evaluated it for its application in bottom-up proteomics.
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Towards Comprehensive Identification of Proteins from MALDI imaging
Stefan Maier - 16 July 2014
MALDI imaging mass spectrometry is a powerful tool for the visualization of protein distributions in tissues. But as the molecular identity of masses detected by MALDI IMS often stays elusive the full potential of the technology cannot be unlocked. Based on the development of a method for the targeted isolation of the detectable proteins this thesis addresses the key issue of the field by combining bottom-up and top-down strategies for protein identification.
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Development and application of small molecule probes for kinase affinity purification and quantitative chemical proteomics
Xin Ku - 25 June 2014
Kinobeads technology using immobilized inhibitors has proven an efficient tool for kinase inhibitor profiling. This project aimed at developing new kinobead probes and hence extending the advantages of this technology to profile current and future kinase inhibitors. Known promiscuous kinase inhibitors were chemically modified to generate new kinobeads probes, which were then used to profile the clinical angiogenesis inhibitors.
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Computational Proteomics
Harald Marx - 11 June 2014
The key high-throughput technology to interrogate the proteome on a large scale is mass spectrometry based proteomics. To interpret the resulting experimental data, computational proteomics plays a critical role. The objective of this thesis was to develop novel approaches for database searching, in particular improve aspects of the theoretical search space and means to validate the results. The thesis concentrates on the construction process and composition of sequence databases and the subsequent statistical validation of peptide identifications and phosphorylation site localization.
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Quantitative chemical and phosphoproteomics for studying signaling in cancer
Fiona Pachl - 11 December 2013
Protein kinases are key regulators of major biological signaling processes in cell and a variety of diseases like cancer have been associated with deregulation of kinase activity. As a consequence, protein kinases are among the most intensively studied signaling molecules in pathophysiological biology and received considerable attention as therapeutic targets. This thesis addresses key issues of quantitative mass spectrometry to advance current chemoproteomic approaches to study kinase activity in cancer.
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Studies towards the proteome-wide detection, identification and quantification of protein glycosylation
Hannes Hahne - 5 November 2012
Glycosylation is one of the most abundant post-translational modifications of proteins and involved in virtually any cellular process. Mass spectrometry has evolved as key technology for the proteome-wide analysis of glycosylation. This thesis addressed key issues of tandem mass spectrometry to advance current proteomic and glycomic technologies for the system-wide analysis of O-GlcNAc proteins and N-linked glycosylation and demonstrate their utility using relevant biological models.
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Quantitative chemical proteomics for cancer characterization
Zhixiang Wu - 11 May 2012
Protein kinases are among the most intensively studied cellular signaling molecules in normal biology as well as many pathophysiological events, such as cancer. So far, numerous aberrant kinases have been revealed to be involved in most if not all the stages of tumorigenesis. Therefore, characterization of the cancer in a kinome-centric way may offer new insight of therapeutic invention. In this study, a kinase centric chemical proteomics assay (Kinobead) in conjunction with quantitative mass spectrometry is established and optimized, which enable quantitatively profiling around 150 kinases in parallel within 4% and 8% overall variance between technical and biological replicates respectively. First, it is employed to investigate 34 head and neck squamous cell carcinoma (HNSCC) cell lines resulting in 146 quantified kinases, of which 42 kinases showed statistically significant (p<0.05) expression inter-cell lines. Additional loss of function experiment using siRNA in high- and low- expressing cell lines further identified kinases including EGFR, EPHA2, LYN, JAK1, WEE1 and NEK9 involved in cell survival and proliferation. Among these, EGFR is confirmed as a drug target and EPHA2 is revealed to be novel drug target. Both contribute to around 20% and 15% of the HNSCC cell lines respectively. This notion is underscored by immunohistochemical analyses showing that high EGFR and EPHA2 expression is detected in a subset of HNSCC tissues. Downstream signaling pathway analysis suggests that EPHA2 promotes the cell proliferation via activating AKT and ERK signaling pathway. In addition the several significant candidates are the potential targets of the approved potent pan-SRC family kinase inhibitor dasatinib, which significantly reduces some but not all of HNSCC cell lines. These findings may lead to new therapeutic options for HNSCC patients. This may ultimately lead to a more rational approach to individualized cancer diagnosis and therapy. Second, together with a global approach, kinobead based profiling is applied to study the HSP90 dependent proteome, which enriches in signal transducer including many kinases, by investigating the protein response upon the HSP90 inhibition. Employing stable isotope labeling with amino acids in cell culture (SILAC) and quantitative mass spectrometry, >6,200 proteins are identified from four different human cell lines and ~1,600 proteins showed significant regulation upon drug treatment. Gene ontology and pathway/network analysis revealed common and cell type specific regulatory effects with strong connections to unfolded protein binding and protein kinase activity. Of the 288 identified protein kinases, 98 were downregulated (e.g. EGFR, BTK) and 17 up-regulated (e.g. AURA, AXL), in response to GA treatment, almost half of which are formerly unknown HSP90 client kinases. Furthermore pulsed-SILAC results suggested that protein down-regulation by HSP90 inhibition correlates with protein half life in many cases. Protein kinases show significantly shorter half lives than other proteins highlighting both challenges and opportunities for HSP90 inhibition in cancer therapy. The highly similar proteomic responses to the HSP90 drugs GA and PU-H71 suggest that both drugs work by similar molecular mechanisms. Several kinases (AXL, DDR1, TRIO) and other signaling proteins (BIRC6, ISG15, FLII) are validated as novel clients of HSP90 using HSP90 immunoprecipitation and affinity-based purification. Taken together, the strategy employed in this study is generic and therefore also of more general utility for the identification of novel drug targets and molecular pathway markers in tumors and broadly definition of the cellular proteome response to HSP90 inhibition provides a rich resource for further investigation relevant for the treatment of cancer.
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