An important role of computer aided drug design is to guide modifications of ligands to improve binding to the protein target. Free energy perturbation (FEP) is one of the most rigorous methods to calculate protein-ligand free energy of binding. By characterization of the performance of force field and quantum mechanical (QM) based approaches for a range of pharmaceutical targets and ligands, already for a limited number of systems there were both cases where the dynamical treatment included in FEP was essential and cases where only QM accurately described the interactions, indicating that there is a need for combining the two methods to enable accurate predictions for a wider set of systems. An example of a case where the standard force field description of the interactions was insufficient was for a set of inhibitors with electronically diverse core structures to the Alzheimer target BACE-1. For a specific subset of nitrogen containing heterocyclic core structures the partial charge distribution was poorly described by the standard force field BCC charge model. In a previous study, a QM approach in combination with a model of the protein including only key residues, could accurately quantify the effect of the core variations on affinity (Roos K. et al. J. Chem. Inf. Model. 2014, 54, 818-825). By now including updated ESP fitted partial charges from QM calculations of the studied modelcules, the predicted binding free energies with FEP were significantly improved, resulting in high correlation with experimental affinity. Ongoing work further explores how a QM description can be combined with FEP to describe fundamental ligand-protein interactions, and develop improved feasible methods for combined accurate treatment of entropic/dynamic and electronic polarization effects. A successful composite approach is expected to be able to rank binding affinity for novel molecular structures with improved accuracy, including systems currently outside the scope of pure QM or FEP methods, and guide prioritizing of compounds for synthesis in prospective drug design projects, both in academia and industry.

Exploring physics-based methods for predicting protein-ligand binding

251st National Meeting (2016)

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