MDM2 Case Study: Computational Protocol Utilizing Protein Flexibility Improves Ligand Binding Mode Predictions

Recovery of the P53 tumor suppressor pathway via small molecule inhibitors of onco-protein MDM2 highlights the critical role of computational methodologies in targeted cancer therapies. Molecular docking programs in particular, have become an essential tool in computer-aided drug design by providing a quantitative ranking of predicted binding geometries of small ligands to macro-molecular targets based on binding free energy, and allows for the screening of large chemical libraries in search of lead compounds for cancer therapeutics. In this study, we found improved ligand binding mode predictions of small medicinal compounds to MDM2 using AutoDock and AutoDock Vina while adopting a rigid ligand/flexible receptor protocol. Crystal structures representing small molecule inhibitors bound to MDM2 were selected from the protein data bank and a total of 12 rotatable bonds was supplied to each complex and distributed systematically between the ligand and binding site residues. A docking run was performed for each configuration and evaluated in terms of the top ranked binding free energy and corresponding RMSD from the experimentally known binding site. Results show lowest RMSD values coincide with the ligand having no or few rotatable bonds, while the protein retained all, or the majority of flexibility. Further, we found AutoDock Vina mirrored these results, while requiring substantially less computational time. This study suggests the future implementation of a rigid ligand/flexible receptor protocol may improve accuracy of high throughput screenings of potential cancer drugs targeting the MDM2 protein, while maintaining manageable computational costs. The continued evaluation and optimization of these programs complimented by advanced computer architecture will aide in reducing the cost of cancer drug development, as well as foster new insights into bio-molecular binding processes.