Beschreibung:
Abstract: Towards reducing the timeframe and the high attrition rate in small-molecule drug discovery, there is growing interest in integrating experimental data and computational methods to decipher the molecular mechanisms through which bioactive compounds interact with their target proteins. The goal of this dissertation is to develop and apply several of these data-intensive integrative approaches. In the first study, an update was made for StreptomeDB, a chemogenomics database describing the physicochemical and biological properties of metabolites originating from bacteria of the genus Streptomyces. Substantial improvements were made over its forerunners, especially in terms of data content (~2500 new metabolites added) and interoperability (hyperlinks to several spectral, (bio)chemical and chemical vendor databases, and to a genome-based metabolite prediction server). Next, a novel pharmacophore-based target prediction tool was developed, named ePharmaLib. It was retrospectively validated using StreptomeDB metabolites. As proof-of-concept, ePharmaLib predictions were complemented with bioassay experiments to identify the human purine nucleoside phosphorylase as a hitherto unknown protein target of the metabolite called neopterin. In another study, an in-depth structural and statistical analysis was carried out using the solved 3D structures of aromatic-cage-containing proteins complexed with their cationic ligands. As a followup, the scope of the aforementioned study was expanded to include ligands forming ππ or hydrophobic contacts with aromatic cages. Ultimately, the collected data set was integrated into a web database named AroCageDB. In the fifth study, the solved 3D structures of covalent protein–ligand complexes were manually expertly annotated from the Protein Data Bank and assimilated into a dedicated web database named CovPDB. Lastly, in the sixth study, was carried out an integrative drug repurposing approach based on computational modeling and in vitro enzymatic assays, to repurpose CovPDB serine targeted covalent inhibitors. This led to the identification of the phenylbororonic acid BC-11, as a nanomolar covalent inhibitor of the human transmembrane protease serine 2, while it exhibited a unique selectivity profile for serine proteases ascribable to its boronic acid warhead. Moreover, BC-11 showed significant inhibition of SARS-CoV-2 (Omicron variant) spike pseudotyped particles in a cell-based entry assay, thus serving as a good starting point for further structural optimization to develop novel COVID-19 antivirals