Mechanisms of action (MoA) of drugs can be highly complex. Thus, their elucidation requires system-level methodology alongside widely used in silico methods (Mitchell, 2023). In the proposed project, we will investigate the MoA of natural compounds affecting P. aeruginosa and C. albicans. We will build upon the results of in vitro phenotypic study arm of the AIDD program and provide insights into MoA of phenotypically identified anti-infective natural compounds. Our approach will complement research lines aimed at metabolomic profiling and at in silico target identification by applying state-of-the-art chemoproteomic protocols to identify drug targets and binding sites in complex proteomes using limited proteolysis-mass spectrometry (LiP-MS) (Schopper, 2017; Piazza, 2020). In summary, this project will provide a highly valuable platform to tackle diverse aspects relevant for phenotype-directed anti-infective drug discovery: (i) Prediction of MoA by determining proteome alterations (i.e. so-called proteome-signatures) and their integration with metabolomic alterations. (ii) First insights into potential drug binding sites by assessing structurally informative proteolytic fragments using LiP-MS. (iii) Knowledge about potential natural compound toxicity and off-target effects in dependence on concentration and time of application by in silico comparison of LiP-MS data with known host proteomes.

We will capitalize upon our extensive expertise in mass spectrometry-based quantitative proteomics (please see publication list for details) to perform customized chemoproteomics assays and LiP-MS employing our recently purchased state-of-the-art mass spectrometer (timsTOF pro, Bruker). Establishment of these approaches will be achieved by testing known standard-of-care drugs for each pathogen. MoA predictions will be based on natural compound-induced changes in protein abundance and extensive network analysis (e.g. http://wren.hms.harvard.edu/DeepCoverMOA/) comparing obtained proteome signatures and utilizing freely available databases for “essential genes” and “druggability”.