Tim Geppert

Helicobacter pylori HtrA is a new secreted virulence factor that cleaves E-cadherin to disrupt intercellular adhesion

Mammalian and prokaryotic high‐temperature requirement A (HtrA) proteins are chaperones and serine proteases with important roles in protein quality control. Here, we describe an entirely new function of HtrA and identify it as a new secreted virulence factor from Helicobacter pylori, which cleaves the ectodomain of the cell‐adhesion protein E‐cadherin. E‐cadherin shedding disrupts epithelial barrier functions allowing H. pylori designed to access the intercellular space. We then designed a small‐molecule inhibitor that efficiently blocks HtrA activity, E‐cadherin cleavage and intercellular entry of H. pylori.

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.

Discovery of Small‐Molecule Interleukin‐2 Inhibitors from a DNA‐Encoded Chemical Library

Libraries of chemical compounds individually coupled to encoding DNA tags (DNA-encoded chemical libraries) hold promise to facilitate exceptionally efficient ligand discovery. We constructed a high-quality DNA-encoded chemical library comprising 30,000 drug-like compounds; this was screened in 170 different affinity capture experiments. High-throughput sequencing allowed the evaluation of 120 million DNA codes for a systematic analysis of selection strategies and statistically robust identification of binding molecules. Selections performed against the tumor-associated antigen carbonic anhydrase IX (CA IX) and the pro-inflammatory cytokine interleukin-2 (IL-2) yielded potent inhibitors with exquisite target specificity. The binding mode of the revealed pharmacophore against IL-2 was confirmed by molecular docking. Our findings suggest that DNA-encoded chemical libraries allow the facile identification of drug-like ligands principally to any protein of choice, including molecules capable of disrupting high-affinity protein-protein interactions.

Inhibitors of Helicobacter pylori Protease HtrA Found by ‘Virtual Ligand’ Screening Combat Bacterial Invasion of Epithelia

Background The human pathogen Helicobacter pylori (H. pylori) is a main cause for gastric inflammation and cancer. Increasing bacterial resistance against antibiotics demands for innovative strategies for therapeutic intervention. Methodology/Principal Findings We present a method for structure-based virtual screening that is based on the comprehensive prediction of ligand binding sites on a protein model and automated construction of a ligand-receptor interaction map. Pharmacophoric features of the map are clustered and transformed in a correlation vector (‘virtual ligand’) for rapid virtual screening of compound databases. This computer-based technique was validated for 18 different targets of pharmaceutical interest in a retrospective screening experiment. Prospective screening for inhibitory agents was performed for the protease HtrA from the human pathogen H. pylori using a homology model of the target protein. Among 22 tested compounds six block E-cadherin cleavage by HtrA in vitro and result in reduced scattering and wound healing of gastric epithelial cells, thereby preventing bacterial infiltration of the epithelium. Conclusions/Significance This study demonstrates that receptor-based virtual screening with a permissive (‘fuzzy’) pharmacophore model can help identify small bioactive agents for combating bacterial infection.

Homology Model Adjustment and Ligand Screening with a Pseudoreceptor of the Human Histamine H4 Receptor

A new pseudoreceptor modeling method (PRPS) was applied to the refinement of a homology model of the human histamine H4 receptor (H4R), the prediction of a ligand binding site, and virtual screening. Retrieval of two new H4R ligands demonstrates the biological relevance of the pseudoreceptor model and provides a means for finding new hits and leads in the early phases of drug discovery.

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.

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:

Protein-protein docking by shape-complementarity and property matching.

We present a computational approach to protein‐protein docking based on surface shape complementarity (“ProBinder”). Within this docking approach, we implemented a new surface decomposition method that considers local shape features on the protein surface. This new surface shape decomposition results in a deterministic representation of curvature features on the protein surface, such as “knobs,” “holes,” and “flats” together with their point normals. For the actual docking procedure, we used geometric hashing, which allows for the rapid, translation‐, and rotation‐free comparison of point coordinates. Candidate solutions were scored based on knowledge‐based potentials and steric criteria. The potentials included electrostatic complementarity, desolvation energy, amino acid contact preferences, and a van‐der‐Waals potential. We applied ProBinder to a diverse test set of 68 bound and 30 unbound test cases compiled from the Dockground database. Sixty‐four percent of the protein‐protein test complexes were ranked with an root mean square deviation (RMSD) < 5 Å to the target solution among the top 10 predictions for the bound data set. In 82% of the unbound samples, docking poses were ranked within the top ten solutions with an RMSD < 10 Å to the target solution.

Fuzzy matched pairs: a means to determine the pharmacophore impact on molecular interaction.

Within this work, a methodological extension of the matched molecular pair analysis is presented. The method is based on a pharmacophore retyping of the molecular graph and a consecutive matched molecular pair analysis. The features of the new methodology are exemplified using a large data set on CYP inhibition. We show that Fuzzy Matched Pairs can be used to extract activity and selectivity determining pharmacophoric features. Based on the fuzzy pharmacophore description, the method clusters molecular transfers and offers new opportunities for the combination of data from different sources, namely public and industry datasets.