Abstract:

Due to the limited number of approved antibiotics for Yersinia pestis, the bacterium responsible for the plague epidemic known as the Black Death, the discovery of small-molecule inhibitors targeting essential bacterial enzymes is critical. One such enzyme is ketoacyl-acyl carrier protein reductase (FabG), which plays a key role in the biosynthesis of fatty acids responsible for maintaining the integrity of the bacterial cell envelope. This study integrates computational and biochemical methods to identify potential FabG inhibitors. An X-ray crystallography structure of the target protein (PDB ID: 5CEJ) was used alongside an artificially predicted model generated via ChimeraX and AlphaFold. Using both structures allowed us to evaluate the utility and accuracy of computational models in drug discovery, especially in future cases where no experimental structure is available. The 5CEJ structure, which lacks a native substrate, was aligned using the ligand acetoacetyl-coenzyme A (CAA) from Bacillus sp. FabG (PDB ID: 4NBU). In a validation docking (GOLD, CCDC), a control library of energy-minimized ligands from LipPrep (Schrödinger) was used. Following validation, compound libraries were screened for binding affinity against both the experimental and predicted structures. GOLD scores ranged from 60–102 for 5CEJ and 40–80 for the AlphaFold model. Docking results were visualized in PyMOL (Schrödinger). FabG was successfully cloned, expressed, and purified using standard biochemical techniques. Future work would focus on enzymatic assays to evaluate whether the top-scoring compounds inhibit FabG activity in vitro.

Speaker:

Catalina Colling, University of Texas at Austin

Catalina Colling is a 2025 graduate of the University of Texas at Austin, where she earned a Bachelor of Science and Arts in Biology. Her independent project focused on early-stage drug discovery, combining computational screening with biochemical validation to more efficiently identify potential inhibitors of target proteins.