Finding the optimal docking pipeline to identify novel ligands for a target of interest is challenging. Our project aims to develop a data-driven pipeline to find new hits for energy-coupling factor (ECF) transporters through the optimization of various structure-based virtual screening workflows.
Energy-coupling factor transporters (ECFTs) are novel, promising antimicrobial targets which mediate micronutrient transport into the cell. Absence of co-crystallized protein-ligand complexes and its cryptic binding pocket make ECFT a challenging target. However, previous computational work at the Helmholtz-Institute for Pharmaceutical Research Saarland (HIPS) and collaboration partners revealed an opening pocket in ECFT for pantothenate , which is used as basis for this study.
To virtually screen a large in house or freely available data sets, we develop a structure-based, data-driven pipeline. In this approach we identify an efficient docking pipeline for the target of interest to propose promising novel antibacterial agents, which can be tested further.