Anna K. Schrey

Capture Compound Mass Spectrometry Sheds Light on the Molecular Mechanisms of Liver Toxicity of Two Parkinson Drugs

Capture compound mass spectrometry (CCMS) is a novel technology that helps understand the molecular mechanism of the mode of action of small molecules. The Capture Compounds are trifunctional probes: A selectivity function (the drug) interacts with the proteins in a biological sample, a reactivity function (phenylazide) irreversibly forms a covalent bond, and a sorting function (biotin) allows the captured protein(s) to be isolated for mass spectrometric analysis. Tolcapone and entacapone are potent inhibitors of catechol-O-methyltransferase (COMT) for the treatment of Parkinsons disease. We aimed to understand the molecular basis of the difference of both drugs with respect to side effects. Using Capture Compounds with these drugs as selectivity functions, we were able to unambiguously and reproducibly isolate and identify their known target COMT. Tolcapone Capture Compounds captured five times more proteins than entacapone Capture Compounds. Moreover, tolcapone Capture Compounds isolated mitochondrial and peroxisomal proteins. The major tolcapone-protein interactions occurred with components of the respiratory chain and of the fatty acid beta-oxidation. Previously reported symptoms in tolcapone-treated rats suggested that tolcapone might act as decoupling reagent of the respiratory chain (Haasio et al., 2002b). Our results demonstrate that CCMS is an effective tool for the identification of a drugs potential off targets. It fills a gap in currently used in vitro screens for drug profiling that do not contain all the toxicologically relevant proteins. Thereby, CCMS has the potential to fill a technological need in drug safety assessment and helps reengineer or to reject drugs at an early preclinical stage.

Dasatinib, imatinib and staurosporine capture compounds - Complementary tools for the profiling of kinases by Capture Compound Mass Spectrometry (CCMS).

Capture Compound Mass Spectrometry (CCMS) is a platform technology for the functional isolation of subproteomes. Here we report the synthesis of two new kinase Capture Compounds (CCs) based on the tyrosine-kinase specific inhibitors dasatinib and imatinib and compare their interaction profiles to that of our previously reported staurosporine-CCs. CCs are tri-functional molecules: they comprise a sorting function (e.g. the small molecule or drug of interest) which interacts with target proteins, a photo-activatable reactivity function to covalently trap the interacting proteins, and a sorting function to isolate the CC-protein conjugates from complex biological samples for protein identification by liquid chromatography/mass spectrometry (LC-MS/MS). We present data of CCMS experiments from human HepG2 cells and compare the profiles of the kinases isolated with dasatinib, imatinib and staurosporine CC, respectively. Dasatinib and imatinib have a more selective kinase binding profile than staurosporine. Moreover, the new CCs allow isolation and identification of additional kinases, complementing the staurosporine CC. The family of kinase CCs will be a valuable tool for the proteomic profiling of this important protein class. Besides sets of expected kinases we identified additional specific interactors; these off-targets may be of relevance in the view of the pharmacological profile of dasatinib and imatinib.

SAHA Capture Compound - A novel tool for the profiling of histone deacetylases and the identification of additional vorinostat binders

Suberoylanilide hydroxamic acid (SAHA) is a potent histone deacetylase (HDAC) inhibitor. Inhibitors of HDACs are used in cancer therapy based on the role HDACs play in transcription by regulating chromatin compaction and non‐histone proteins such as transcription factors. Profiling of HDAC expression is of interest in the functional proteomics analysis of cancer. Also, non‐HDAC proteins may interact with HDAC inhibitor drugs and contribute to the drug mode of action. We here present a tool for the unbiased chemical proteomic profiling of proteins that specifically interact with SAHA. We designed and synthesized a trifunctional Capture Compound containing SAHA as selectivity and identified HDACs1, 2, 3 and 6, known and predicted HDAC interactors from human‐derived HepG2 cell lysate, as well as a set of new potential non‐HDAC targets of SAHA. One of these non‐HDAC targets, isochorismatase domain‐containing protein 2 (ISOC2) is putative hydrolase associated with the negative regulation of the tumor‐suppressor p16(INK4a). We demonstrated the direct and dose‐dependent interaction of SAHA to the purified recombinant ISOC2 protein. Using SAHA Capture Compound mass spectrometry, we thus identified potential new SAHA target proteins in an entirely unbiased chemical proteomics approach.

GDP-capture compound--a novel tool for the profiling of GTPases in pro- and eukaryotes by capture compound mass spectrometry (CCMS).

The functional isolation of proteome subsets based on small molecule-protein interactions is an increasingly popular and promising field in functional proteomics. Entire protein families may be profiled on the basis of their common interaction with a metabolite or small molecule inhibitor. This is enabled by novel multifunctional small molecule probes. One platform approach in this field are Capture Compounds that contain a small molecule of interest to bind target proteins, a photo-activatable reactivity function to covalently trap bound proteins, and a sorting function to isolate Capture Compound-protein conjugates from complex biological samples for direct trypsinisation and protein identification by liquid chromatography/mass spectrometry (CCMS). We here present the synthesis and application of a novel GDP-Capture Compound for the functional enrichment of GTPases, a pivotal protein family that exerts key functions in signal transduction. We present data from CCMS experiments on two biological lysates from Escherichia coli and from human-derived Hek293 cells. The GDP-Capture Compound robustly captures a wide range of different GTPases from both systems and will be a valuable tool for the proteomic profiling of this important protein family.

Dabigatran and dabigatran ethyl ester: potent inhibitors of ribosyldihydronicotinamide dehydrogenase (NQO2).

Recent studies have revealed that compounds believed to be highly selective frequently address multiple target proteins. We investigated the protein interaction profile of the widely prescribed thrombin inhibitor dabigatran (1), resulting in the identification and subsequent characterization of an additional target enzyme. Our findings are based on an unbiased functional proteomics approach called capture compound mass spectrometry (CCMS) and were confirmed by independent biological assays. 1 was shown to specifically bind ribosyldihydronicotinamide dehydrogenase (NQO2), a detoxification oxidoreductase. Molecular dockings predicted and biological experiments confirmed that dabigatran ethyl ester (2) inhibits NQO2 even more effectively than the parent 1 itself. Our data show that 1 and 2 are inhibitors of NQO2, thereby revealing a possible new aspect in the mode of action of 1. We present a workflow employing chemical proteomics, molecular modeling, and functional assays by which a compounds protein-interaction profile can be determined and used to tune the binding affinity.

Using S-adenosyl-L-homocysteine capture compounds to characterize S-adenosyl-L-methionine and S-adenosyl-L-homocysteine binding proteins.

S-Adenosyl-l-methionine (SAM) is recognized as an important cofactor in a variety of biochemical reactions. As more proteins and pathways that require SAM are discovered, it is important to establish a method to quickly identify and characterize SAM binding proteins. The affinity of S-adenosyl-l-homocysteine (SAH) for SAM binding proteins was used to design two SAH-derived capture compounds (CCs). We demonstrate interactions of the proteins COMT and SAHH with SAH-CC with biotin used in conjunction with streptavidin-horseradish peroxidase. After demonstrating SAH-dependent photo-crosslinking of the CC to these proteins, we used a CC labeled with a fluorescein tag to measure binding affinity via fluorescence anisotropy. We then used this approach to show and characterize binding of SAM to the PR domain of PRDM2, a lysine methyltransferase with putative tumor suppressor activity. We calculated the Kd values for COMT, SAHH, and PRDM2 (24.1 ± 2.2 μM, 6.0 ± 2.9 μM, and 10.06 ± 2.87 μM, respectively) and found them to be close to previously established Kd values of other SAM binding proteins. Here, we present new methods to discover and characterize SAM and SAH binding proteins using fluorescent CCs.

Computational prediction of immune cell cytotoxicity

Immunotoxicity, defined as adverse effects of xenobiotics on the immune system, is gaining increasing attention in the approval process of industrial chemicals and drugs. In-vivo and ex-vivo experiments have been the gold standard in immunotoxicity assessment so far, so the development of in-vitro and in-silico alternatives is an important issue. In this paper we describe a widely applicable, easy-to use computational approach which can serve as an initial immunotoxicity screen of new chemical entities. Molecular fingerprints describing chemical structure were used as parameters in a machine-learning approach based on the Naïve-Bayes learning algorithm. The model was trained using blood-cell growth inhibition data from the NCI database and validated externally with several in-house and literature-derived data sets tested in cytotoxicity assays on different types on immune cells. Both cross-validations and external validations resulted in areas under the receiver operator curves (ROC/AUC) of 75% or higher. The classification of the validation data sets occurred with excellent specificities and fair to excellent selectivities, depending on the data set. This means that the probability of actual immunotoxicity is very high for compounds classified as immunotoxic, while the fraction of false negative predictions might vary. Thus, in a multistep immunotoxicity screening scheme, the classification as immunotoxic can be accepted without additional confirmation, while compounds classified as not immunotoxic will have to be subjected to further investigation.

Identification of Potential Off-target Toxicity Liabilities of Catechol-O-methyltransferase Inhibitors by Differential Competition Capture Compound Mass Spectrometry

Structurally related inhibitors of a shared therapeutic target may differ regarding potential toxicity issues that are caused by different off-target bindings. We devised a differential competition capture compound mass spectrometry (dCCMS) strategy to effectively differentiate off-target profiles. Tolcapone and entacapone are potent inhibitors of catechol-O-methyl transferase (COMT) for the treatment of Parkinsons disease. Tolcapone is also known for its hepatotoxic side effects even though it is therapeutically more potent than entacapone. Here, we identified 3-hydroxyisobutyryl-CoA hydrolase (HIBCH) as a possible toxicity-causing off-target of tolcapone, and this protein is not bound by the less toxic COMT inhibitor entacapone. Moreover, two novel compounds from a focused library synthesized in-house, N(2),N(2),N(3),N(3)-tetraethyl-6,7-dihydroxy-5-nitronaphthalene-2,3-dicarboxamide and 5-(3,4-dihydroxy-5-nitrobenzylidene)-3-ethylthiazolidine-2,4-dione, were utilized to gain insight into the structure-activity relationships in binding to COMT and the novel off-target HIBCH. These compounds, especially N(2),N(2),N(3),N(3)-tetraethyl-6,7-dihydroxy-5-nitronaphthalene-2,3-dicarboxamide, could serve as starting point for the development of improved and more specific COMT inhibitors.

ProTox-II: a webserver for the prediction of toxicity of chemicals

Abstract Advancement in the field of computational research has made it possible for the in silico methods to offer significant benefits to both regulatory needs and requirements for risk assessments, and pharmaceutical industry to assess the safety profile of a chemical. Here, we present ProTox-II that incorporates molecular similarity, pharmacophores, fragment propensities and machine-learning models for the prediction of various toxicity endpoints; such as acute toxicity, hepatotoxicity, cytotoxicity, carcinogenicity, mutagenicity, immunotoxicity, adverse outcomes pathways (Tox21) and toxicity targets. The predictive models are built on data from both in vitro assays (e.g. Tox21 assays, Ames bacterial mutation assays, hepG2 cytotoxicity assays, Immunotoxicity assays) and in vivo cases (e.g. carcinogenicity, hepatotoxicity). The models have been validated on independent external sets and have shown strong performance. ProTox-II provides a freely available webserver for in silico toxicity prediction for toxicologists, regulatory agencies, computational and medicinal chemists, and all users without login at http://tox.charite.de/protox_II. The webserver takes a two-dimensional chemical structure as an input and reports the possible toxicity profile of the chemical for 33 models with confidence scores, and an overall toxicity radar chart along with three most similar compounds with known acute toxicity.

Targeting G Protein‐Coupled Receptors by Capture Compound Mass Spectrometry: A Case Study with Sertindole

Unbiased chemoproteomic profiling of small‐molecule interactions with endogenous proteins is important for drug discovery. For meaningful results, all protein classes have to be tractable, including G protein‐coupled receptors (GPCRs). These receptors are hardly tractable by affinity pulldown from lysates. We report a capture compound (CC)‐based strategy to target and identify GPCRs directly from living cells. We synthesized CCs with sertindole attached to the CC scaffold in different orientations to target the dopamine D2 receptor (DRD2) heterologously expressed in HEK 293 cells. The structure–activity relationship of sertindole for DRD2 binding was reflected in the activities of the sertindole CCs in radioligand displacement, cell‐based assays, and capture compound mass spectrometry (CCMS). The activity pattern was rationalized by molecular modelling. The most‐active CC showed activities very similar to that of unmodified sertindole. A concentration of DRD2 in living cells well below 100 fmol used as an experimental input was sufficient for unambiguous identification of captured DRD2 by mass spectrometry. Our new CCMS workflow broadens the arsenal of chemoproteomic technologies to close a critical gap for the comprehensive characterization of drug–protein interactions.