Felix Reisen

Distinct Roles of Secreted HtrA Proteases from Gram-negative Pathogens in Cleaving the Junctional Protein and Tumor Suppressor E-cadherin

Background: The function of HtrA proteases in bacterial infections is widely unknown. Results: Secreted HtrA from various bacterial pathogens exhibits a conserved specificity for cleavage of E-cadherin. Conclusion: HtrA-mediated E-cadherin cleavage is a prevalent novel mechanism in bacterial pathogenesis. Significance: HtrA activity plays a direct role in the pathogenesis of different bacteria. The periplasmic chaperone and serine protease HtrA is important for bacterial stress responses and protein quality control. Recently, we discovered that HtrA from Helicobacter pylori is secreted and cleaves E-cadherin to disrupt the epithelial barrier, but it remained unknown whether this maybe a general virulence mechanism. Here, we show that important other pathogens including enteropathogenic Escherichia coli, Shigella flexneri, and Campylobacter jejuni, but not Neisseria gonorrhoeae, cleaved E-cadherin on host cells. HtrA deletion in C. jejuni led to severe defects in E-cadherin cleavage, loss of cell adherence, paracellular transmigration, and basolateral invasion. Computational modeling of HtrAs revealed a conserved pocket in the active center exhibiting pronounced proteolytic activity. Differential E-cadherin cleavage was determined by an alanine-to-glutamine exchange in the active center of neisserial HtrA. These data suggest that HtrA-mediated E-cadherin cleavage is a prevalent pathogenic mechanism of multiple Gram-negative bacteria representing an attractive novel target for therapeutic intervention to combat bacterial infections.

Phenotype-based high-content chemical library screening identifies statins as inhibitors of in vivo lymphangiogenesis

Lymphangiogenesis plays an important role in promoting cancer metastasis to sentinel lymph nodes and beyond and also promotes organ transplant rejection. We used human lymphatic endothelial cells to establish a reliable three-dimensional lymphangiogenic sprouting assay with automated image acquisition and analysis for inhibitor screening. This high-content phenotype-based assay quantifies sprouts by automated fluorescence microscopy and newly developed analysis software. We identified signaling pathways involved in lymphangiogenic sprouting by screening the Library of Pharmacologically Active Compounds (LOPAC)1280 collection of pharmacologically relevant compounds. Hit characterization revealed that mitogen-activated protein kinase kinase (MEK) 1/2 inhibitors substantially block lymphangiogenesis in vitro and in vivo. Importantly, the drug class of statins, for the first time, emerged as potent inhibitors of lymphangiogenic sprouting in vitro and of corneal and cutaneous lymphangiogenesis in vivo. This effect was mediated by inhibition of the 3-hydroxy-3-methylglutaryl-coenzyme A (HMG-CoA) reductase and subsequently the isoprenylation of Rac1. Supplementation with the enzymatic products of HMG-CoA reductase functionally rescued lymphangiogenic sprouting and the recruitment of Rac1 to the plasma membrane.

Reaction-driven de novo design, synthesis and testing of potential type II kinase inhibitors

BACKGROUND De novo design of drug-like compounds with a desired pharmacological activity profile has become feasible through innovative computer algorithms. Fragment-based design and simulated chemical reactions allow for the rapid generation of candidate compounds as blueprints for organic synthesis. METHODS We used a combination of complementary virtual-screening tools for the analysis of de novo designed compounds that were generated with the aim to inhibit inactive polo-like kinase 1 (Plk1), a target for the development of cancer therapeutics. A homology model of the inactive state of Plk1 was constructed and the nucleotide binding pocket conformations in the DFG-in and DFG-out state were compared. The de novo-designed compounds were analyzed using pharmacophore matching, structure-activity landscape analysis, and automated ligand docking. One compound was synthesized and tested in vitro. RESULTS The majority of the designed compounds possess a generic architecture present in known kinase inhibitors. Predictions favor kinases as targets of these compounds but also suggest potential off-target effects. Several bioisosteric replacements were suggested, and de novo designed compounds were assessed by automated docking for potential binding preference toward the inactive (type II inhibitors) over the active conformation (type I inhibitors) of the kinase ATP binding site. One selected compound was successfully synthesized as suggested by the software. The de novo-designed compound exhibited inhibitory activity against inactive Plk1 in vitro, but did not show significant inhibition of active Plk1 and 38 other kinases tested. CONCLUSIONS Computer-based de novo design of screening candidates in combination with ligand- and receptor-based virtual screening generates motivated suggestions for focused library design in hit and lead discovery. Attractive, synthetically accessible compounds can be obtained together with predicted on- and off-target profiles and desired activities.

Discovery and biological evaluation of a novel class of dual microsomal prostaglandin E2 synthase-1/5-lipoxygenase inhibitors based on 2-[(4,6-diphenethoxypyrimidin-2-yl)thio]hexanoic acid.

Various inflammatory diseases are associated with the excessive formation of leukotrienes (LTs) and prostaglandins (PGs). Herein, we present a novel class of dual inhibitors of 5-lipoxygenase (5-LO) and microsomal prostaglandin E(2) synthase-1 (mPGES-1), key enzymes in the formation of LTs and PGE(2), respectively. On the basis of the structure of 2-[(4,6-diphenethoxypyrimidin-2-yl)thio]hexanoic acid (1), we performed a detailed SAR analysis, and mechanistic studies were carried out to elucidate the mode of 5-LO inhibition. Interestingly, the pyrimidine ring including the thioether of 1 could be replaced by a simple benzyl or a benzylidene moiety yielding a novel series of bioactive 2-benzylidene- and 2-benzylhexanoic acids exemplified by 2-(2,3-diphenethoxybenzylidene)hexanoic acid, 29 (IC(50) 5-LO = 0.8 μM; mPGES-1 = 1.1 μM). Importantly, none of the novel bioactive derivatives strongly inhibited cyclooxygenase activities. Together, we provide novel promising lead compounds for the treatment of inflammatory diseases valuable for further investigations in vivo.

Identification of UV-protective activators of nuclear factor erythroid derived 2-related factor 2 (Nrf2) by combining a chemical library screen with computer-based virtual screening

Background: The Nrf2 transcription factor is a master regulator of cellular antioxidant defense systems. Results: We identified novel Nrf2 activators in keratinocytes with low toxicity and strong UV-protective potential. Conclusion: Chemical library screening combined with virtual screening is a potent strategy to identify optimized Nrf2 activators. Significance: Our new Nrf2 activators are potential lead compounds for the development of drugs for skin protection under stress conditions. Nuclear factor erythroid-derived 2-related factor 2 (Nrf2) is a master regulator of cellular antioxidant defense systems, and activation of this transcription factor is a promising strategy for protection of skin and other organs from environmental insults. To identify efficient Nrf2 activators in keratinocytes, we combined a chemical library screen with computer-based virtual screening. Among 14 novel Nrf2 activators, the most potent compound, a nitrophenyl derivative of 2-chloro-5-nitro-N-phenyl-benzamide, was characterized with regard to its molecular mechanism of action. This compound induced the expression of cytoprotective genes in keratinocytes isolated from wild-type but not from Nrf2-deficient mice. Most importantly, it showed low toxicity and protected primary human keratinocytes from UVB-induced cell death. Therefore, it represents a potential lead compound for the development of drugs for skin protection under stress conditions. Our study demonstrates that chemical library screening combined with advanced computational similarity searching is a powerful strategy for identification of bioactive compounds, and it points toward an innovative therapeutic approach against UVB-induced skin damage.

Virtual screening for compounds that mimic protein–protein interface epitopes

Modulation of protein–protein interactions (PPI) has emerged as a new concept in rational drug design. Here, we present a computational protocol for identifying potential PPI inhibitors. Relevant regions of interfaces (epitopes) are predicted for three‐dimensional protein models and serve as queries for virtual compound screening. We present a computational screening protocol that incorporates two different pharmacophore models. One model is based on the mathematical concept of autocorrelation vectors and the other utilizes fuzzy labeled graphs. In a proof‐of‐concept study, we were able to identify serine protease inhibitors using a predicted trypsin epitope as query. Our virtual screening framework may be suited for rapid identification of PPI inhibitors and suggesting bioactive tool compounds. Copyright for JCC Journal:

Reaction-MQL: line notation for functional transformation.

Representation of chemical reactions is pivotal for different purposes in cheminformatics. We present an extension of the molecular query language (MQL), which combines readable style with meaningful rules for string representation of reactions and unambiguous product formation. The concept of functional groups is used to describe the transformations. Functional groups are defined in terms of substructure queries and are processed by graph transformations. Molecular educt graphs are transformed by application of beginning-, end-, and reaction-matrices to obtain the product graph without consideration of stereochemistry. Both directions of a transformation are possible. We implemented the concept of Reaction-MQL in Java employing the Chemistry Development Kit.

From Virtual Screening to Bioactive Compounds by Visualizing and Clustering of Chemical Space

Identification and visualization of ‘activity islands’ in chemical space is presented as a straightforward method for rapid automated identification of bioactive compounds and drug target profiling. We successfully applied this computational technique to finding inhibitors of Helicobacter pylori protease HtrA with new molecular scaffolds, and to deorphanizing of a compound from a combinatorial oxadiazole library. Bioactive molecules were discovered with minimal experimental effort. The results demonstrate that visualization of ‘chemical space’ provides an intuitive approach to molecular design and virtual screening in drug discovery, even in the absence of a three-dimensional receptor structure. Visualization of chemical data can help understand the structure of compound distributions in chemical space and guide molecular design experiments. [1–5] Commonly applied visualization techniques in chemistry are principal component analysis (PCA) [6] and self-organizing maps (SOMs, Kohonen networks). [7–9] Both methods have proven their value for visualization of compound libraries and virtual screening. Still, they suffer from several drawbacks. For example, a SOM’s quality to separate data depends on the chosen map size, i.e. the number of ‘neurons’ (local clusters, Voronoi fields), and determining the actual quality of a computed SOM projection is nontrivial. A perceived disadvantage is that SOMs lack immediate interpretability due to nonlinear projection. While for low-dimensional data linear projection by PCA seems to be preferable, SOMs have shown to produce more robust projections of high-dimensional data. [10] Here, we present a method for visualizing and interpreting high-dimensional chemical data, which is complementary to SOM and PCA projection and overcomes some of their disadvantages and limitations. The projection is based on stochastic proximity embedding (SPE). [11] SPE embeds data in a low-dimensional space in such a way that pairwise distances between compounds are preserved. As a consequence, patterns in the original high-dimensional data distribution become accessible to visual inspection. Making such patterns visible supports our intuitive interpretation how a molecular representation might distinguish between sets of compounds (e.g., active vs inactive) and create some kind of order in data space. Once activity islands are identified in the visualization, the compounds that form such local clusters can be extracted and subjected to biochemical tests. [12–15]

Target Profile Prediction and Practical Evaluation of a Biginelli-Type Dihydropyrimidine Compound Library

We present a self-organizing map (SOM) approach to predicting macromolecular targets for combinatorial compound libraries. The aim was to study the usefulness of the SOM in combination with a topological pharmacophore representation (CATS) for selecting biologically active compounds from a virtual combinatorial compound collection, taking the multi-component Biginelli dihydropyrimidine reaction as an example. We synthesized a candidate compound from this library, for which the SOM model suggested inhibitory activity against cyclin-dependent kinase 2 (CDK2) and other kinases. The prediction was confirmed in an in vitro panel assay comprising 48 human kinases. We conclude that the computational technique may be used for ligand-based in silico pharmacology studies, off-target prediction, and drug re-purposing, thereby complementing receptor-based approaches.