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Challenges in drug discovery including difficulties obtaining structural information on the ligand-protein complex are approached using multiple methods. Crystal structures of IMP-13, an antibiotic resistance protein, along with its natural antibiotic substrates are presented. Paramagnetic methods in drug discovery using NMR are investigated, and a deep learning approach for computational modelling positions of water molecules in protein structures for use in ligand optimisation is introduced. Herausforderungen in der Arzneimittelforschung einschließlich der Schwierigkeiten, strukturelle Informationen über den Ligand-Protein-Komplex zu erhalten, werden mit verschiedenen Methoden adressiert Kristallstrukturen von IMP-13, einem Antibiotikaresistenzprotein, zusammen mit seinen natürlichen Substraten werden vorgestellt. Paramagnetische Methoden in der Wirkstoffentwicklung mittels NMR werden untersucht, und die Anwendung von Deep Learning für die Modellierung der Positionen von Wassermolekülen in Proteinstrukturen für die Optimierung von Liganden wird vorgestellt

Methylated bases in tRNA, rRNA and mRNA control a variety of cellular processes, including protein synthesis, antimicrobial resistance and gene expression. Currently, bulk methods that report the average methylation state of ~104–107 cells are used to detect these modifications, obscuring potentially important biological information. Here, we use in situ hybridization of Molecular Beacons for single-cell detection of three methylations (m62A, m1G and m3U) that destabilize Watson–Crick base pairs. Our method—methylation-sensitive RNA fluorescence in situ hybridization—detects single methylations of rRNA, quantifies antibiotic-resistant bacteria in mixtures of cells and simultaneously detects multiple methylations using multicolor fluorescence imaging., Methylated RNA bases influence many life processes, but current detection methods lack the ability to detect individual methylations in single cells. Here, the authors use fluorescence hybridization probes sensitive to methylation to detect specific epitranscriptomic modifications at the single-cell level.

African and American trypanosomiases are estimated to affect several million people across the world, with effective treatments distinctly lacking. New, ideally oral, treatments with higher efficacy against these diseases are desperately needed. Peroxisomal import matrix (PEX) proteins represent a very interesting target for structure- and ligand-based drug design. The PEX5-PEX14 protein-protein interface in particular has been highlighted as a target, with inhibitors shown to disrupt essential cell processes in trypanosomes, leading to cell death. In this work, we present a drug development campaign that utilizes the synergy between structural biology, computer-aided drug design, and medicinal chemistry in the quest to discover and develop new potential compounds to treat trypanosomiasis by targeting the PEX14-PEX5 interaction. Using the structure of the known lead compounds discovered by Dawidowski et al. as the template for a chemically advanced template search (CATS) algorithm, we performed scaffold-hopping to obtain a new class of compounds with trypanocidal activity, based on 2,3,4,5-tetrahydrobenzo[f][1,4]oxazepines chemistry. The initial compounds obtained were taken forward to a first round of hit-to-lead optimization by synthesis of derivatives, which show activities in the range of low- to high-digit micromolar IC50 in the in vitro tests. The NMR measurements confirm binding to PEX14 in solution, while immunofluorescent microscopy indicates disruption of protein import into the glycosomes, indicating that the PEX14-PEX5 protein-protein interface was successfully disrupted. These studies result in development of a novel scaffold for future lead optimization, while ADME testing gives an indication of further areas of improvement in the path from lead molecules toward a new drug active against trypanosomes., Journal of Chemical Information and Modeling, 61 (10), ISSN:1549-9596, ISSN:0095-2338, ISSN:1520-5142