Methods that accurately predict protein–ligand binding strengths can be important for structure-based drug design(SBDD). In recent years, advances in binding energy prediction techniques and high-speed computing have enabled the ability to routinely use these methods in the discovery and optimization of drugs. Specifically, many computational methods currently used in this context are based on molecular mechanics force fields that often have deficiencies in describing the quantum mechanical (QM) aspects of molecular binding. For example, molecular docking methods using computationally inexpensive scoring functions and the more rigorous Molecular Mechanics Poisson-Boltzmann Surface Area (MM/PBSA) or Molecular Mechanics Generalized Born Surface Area (MM/GBSA) methods. In order to avoid the short comings inherent in the use of classical force field, the Molecules-in-Molecules(MIM) fragmentation QM method developed to estimate the binding affinities.¹ In fact, MIM has been not only been shown to identify molecular interactions arising from polarization effects,² it also exhibits comparable or superior accuracy to docking-based scoring functions^3,4 and MM/GBSA.⁵ Although the MIM avoids the short-comings of classical force-fields, it neglects the dynamic and solvation aspects of ligand binding. Among the various methods to calculate protein–ligand binding affinities, alchemical free energy perturbation (FEP) calculations performed by way of explicitly solvated molecular dynamics simulations (FEP/MD) provide a thermodynamically rigorous and complete description of the binding event and should in turn yield highly accurate predictions. In order to explore the influence of QM effects and dynamic/solvation effects, MIM and FEP methods were used to calculate the binding energies of several ligand datasets. The results of this work with respect to accuracy and interpretation will be discussed in this presentation.

Jon Erickson

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