Browsing by Person "Herzog, Anna-Maria"

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Development of new inhibitors directed against key enzymes of human pathogens
(2025) Herzog, Anna-Maria; Fritz, Günter
Infectious diseases are one of the biggest threats to global health. The unpredictable emergence and rapid spread of viral infections, as well as the increasing frequency of antimicrobial resistance in bacterial infections, are particularly challenging due to the lack of adequate treatment options. To address these limitations, the rapid development of novel pharmaceuticals directed against essential, highly conserved enzymes of human pathogens is of urgent need. Fragment-based drug discovery (FBDD) is an approach to de novo drug design. After identifying simple, low-molecular-weight molecules (fragments) that bind the target protein, selective, high-affinity lead compounds must be developed through optimization. The objectives of this study include investigating potential inhibitors of two proteins, the papain-like protease (PLpro) of the Severe Acute Respiratory Syndrome-Coronavirus 2 (SARS-CoV-2) and a bacterial respiratory enzyme, the Na+-translocating NADH:ubiquinone oxidoreductase (NQR), as well as methods to support the fragment-to-lead optimization process. The PLpro is a key enzyme of SARS-CoV-2 that ensures viral replication and suppresses the antiviral immune response. It is responsible for the proteolytic processing of the non-structural proteins 1-3 (nsp1-3) of the viral polyproteins, which are important for the replication and transcription complex. Correct formation of this complex is essential for replication of the viral genome and transcription of structural genes. Furthermore, the PLpro antagonizes the interferon stimulated antiviral response through specific cleavage of the interferon stimulated gene 15 (ISG15). ISG15 is conjugated to host and viral proteins during viral infection to promote the immune response and disrupt the proper function of viral proteins. Preventing polyprotein processing and ISG15 cleavage by inhibiting the PLpro represents a promising therapeutic option for the coronavirus disease 2019 (COVID-19). The PLpro cleavage activity in presence of the hop-derived compounds xanthohumol (XN), isoxanthohumol (IX), 6-prenylnaringenin (6PN), and 8-prenylnaringenin (8PN) was determined in fluorescence-based assays using the peptide Z-RLRGG-AMC, or ISG15-rhodamine and reveals inhibition by all four compounds. Western blot analysis of interferon β treated Human Embryonic Kidney (HEK) cell lysates validates the inhibition of ISG15 cleavage through IXN. Antiviral activity of XN and 6PN is shown in infection models using Caco-2 cells. Therefore, these hop-derived compounds are promising starting points for developing new antiviral drugs. The NQR is a central enzyme in the energy metabolism of bacteria, including pathogens such as Vibrio cholerae, and multidrug-resistant Klebsiella pneumoniae and Pseudomonas aeruginosa. By coupling the oxidation of NADH with the reduction of ubiquinone, it translocates sodium ions or protons from the cytoplasm in the periplasm. Through this translocation, electrochemical gradients, the sodium or proton motive force are generated across the cytoplasmic membrane. These motive forces are required for several metabolic processes, like substrate uptake, ATP synthesis, motility, or drug efflux by multidrug-resistant (MDR) efflux pumps. Interrupting the binding of NADH and, consequently, the generation of the electrochemical gradients could be a promising approach for developing novel antibiotics and new treatment options for multidrug-resistant pathogens. The compound [1-(4-chlorophenyl)-1H-1,2,3-triazol-4-yl]methanol (CPTM) was selected from the fragments identified during a crystallographic screening against the NqrF subunit of V. cholerae. The fragment binds in the part of the NADH-binding pocket where the adenosine residue of the NADH usually binds. It inhibits the NADH-oxidizing activity of the NqrF subunits of V. cholerae, K. pneumoniae, and P. aeruginosa, as well as the NQR complex of V. cholerae in a mixed mode of inhibition, with a significantly stronger inhibitory effect on the NQR complex. In growth assays, CPTM exhibited antibacterial activity against V. cholerae, and multidrug-resistant strains of K. pneumoniae, and P. aeruginosa. In a combinatorial treatment, the efficacy of erythromycin increases when combined with CPTM in V. cholerae and K. pneumoniae but decreases in P. aeruginosa. The inhibition by occupying the NADH-binding pocket and the identification of the promising inhibitor CPTM are basis for structure-based optimization to develop antibacterial agents. Specific NQR inhibition opens the door to combinatorial therapy options for multidrug-resistant pathogens, which could restore the efficacy of current antibiotics. In FBDD, the identification of initial hits is followed by an optimization to improve their affinity, specificity, and physicochemical properties through fragment growing, merging, or linking strategies. To support single steps of the subsequent analysis to identify the candidates with improved properties, many computational tools are available. EvaMol is a software that integrates several of these tools to cover the entire evaluation. It prepares a receptor and the database of input molecules, obtained by the optimization. In the following docking step, the best-fitting pose of each molecule is calculated. Then, two independent scoring algorithms assess the binding of the docked molecules. Finally, physicochemical properties and ligand efficiency metrics are calculated. This helps to prioritize the candidate molecules with the most promising features and accelerates fragment-to-lead development in early drug discovery. Overall, the findings of this study lay the foundation for the development of novel antiviral and antibacterial pharmaceuticals and provide a tool to facilitate the optimization process in FBDD pipelines. This contributes to the urgent need for novel treatments caused by infectious diseases.
EvaMol : A python tool for evaluating molecules in hit-to-lead optimization
(2025) Herzog, Anna-Maria; Steuber, Julia; Fritz, Günter
This Python script was developed as a tool in structure-based drug discovery processes, such as fragment-to-lead-optimization, where a large number of variants of an initially identified hit molecule have to be evaluated and ranked in silico. The tool facilitates the identification and selection of follow-up drug candidates with improved predicted pharmacokinetic and binding properties. These candidates can derive from different procedures like similarity search or systematic chemical modifications. The initial hit data are provided either as coordinates of the protein-molecule complex obtained experimentally or by in silico methods such as docking making the script a versatile tool adaptable to variable workflows.