Exploring conformation of human fatty acid synthase inhibitors using Replica Exchange Molecular Dynamics

Human fatty acid synthase (hFAS) is a homodimer multienzyme complex involved in the lipogenesis and catalysis of long-chain fatty acids. hFAS is overexpressed in cancer cells and enhances tumor growth. A recent study reported a new potent and selective inhibitor of the β-ketoacyl reductase (KR) domain of hFAS, GSK2194693. An x-ray crystal structure of this inhibitor bound to the KR domain provides binding mode information regarding the druggable pocket. In this thesis simulations and analysis of the solution-phase conformational ensembles of four inhibitors of the human fatty acid synthase are required. The ensembles are generated using replica exchange enhanced sampling molecular dynamics approaches for two force fields, and analysed using a combination of dihedral and Cartesian space clustering, and principal components analysis. These ensembles are compared to experimental data derived using nuclear magnetic resonance from C4X Discovery to evaluate the convergence of our data and to analyze the influence of the force field on the quality of the sampling. We find that while the simulations are able to identify all the conformations found by NMR, their relative populations are in less satisfactory agreement. The ligandreceptor complex binding modes were also investigated by first identifying conformations of the four compounds with shape and chemical group similarity using clustering and superimposition methodologies. Then, in a ligand preorganization approach to identify if the solution phase conformations obtained from NMR and REMD bind favourably to the receptor binding pocket, the interactions made with hFAS were evaluated keeping the conformations and the receptor rigid. Potential binding modes for the compounds were generated with consistent interactions. Contacts found in the x-ray structure GSK2194069 were highly conserved in the compounds and additional hydrogen bonds were identified. Thus, this study offers valuable information for future drug development and optimization.