Webinar - Expanding the Domain of Applicability of Structure-based Drug Design with IFD-MD

December 10, 2019

Time: 1:00 PM ET 
Speaker: Dr. Ken Borrelli, Senior Principal Scientist and Product Manager at Schrödinger

Many structure-based drug design (SBDD) methods, including free energy perturbation (FEP+), require accurate, atomic-level detail of the target protein in complex with a member of the ligand series being modeled to perform optimally; consequently, the domain of applicability of SBDD is limited by the availability of high-resolution crystal structures. Even when the exact protein-ligand complex structure is not available, highly similar ones may be. IFD-MD is an integral part of workflows that can predict the atomic details of the structure needed for SDBB of the desired ligand series starting from a structure of the target protein with a very different ligand in the binding site or even starting from a structure of a highly homologous protein.

Often the only differences in protein structure between the available structure and the one that is needed for SBDD are the movements of a couple of sidechains or a small loop motion, however, these small changes can have a large impact on the accessible ligand binding modes. IFD-MD uses a combination of docking algorithms, water thermodynamics, empirical scoring functions, implicit solvent force field energies and explicit solvent metadynamics trajectories to explore the motions of the target protein and simultaneously determine the relative energetics of them.

We applied IFD-MD to predicting the binding modes of over 400 published co-crystal structures using the complex structure of a highly dissimilar ligand as a starting point. In cases where binding affinity data were available, we demonstrate the ability to predict retrospectively those binding affinities with FEP+ using the cross-docked binding site conformation produced by IFD-MD, demonstrating that these cross-docked structures are suitable for structure-based drug design. In the second part of this work, we demonstrate the applicability of IFD-MD to the refinement of homology models. IFD-MD reliably identifies protein-ligand binding poses when the sequence identity between the target protein and template is greater than 40%. Last, we show some early examples of the impact of IFD-MD in real-world drug discovery programs, where it is applied in conjunction with FEP+ for prospective prediction of binding affinity for a target where no co-crystals are available. Taken all together, we demonstrate the potential of IFD-MD to increase significantly the impact of structure-based drug design. 
 

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