Abstract
Fragment-based drug design (FBDD) involves screening low molecular weight molecules ("fragments") that correspond to functional groups found in larger drug-like molecules to determine their binding to target proteins or nucleic acids. Based on the principle of thermodynamic additivity, two fragments that bind nonoverlapping nearby sites on the target can be combined to yield a new molecule whose binding free energy is the sum of those of the fragments. Experimental FBDD approaches, like NMR and X-ray crystallography, have proven very useful but can be expensive in terms of time, materials, and labor. Accordingly, a variety of computational FBDD approaches have been developed that provide different levels of detail and accuracy.The Site Identification by Ligand Competitive Saturation (SILCS) method of computational FBDD uses all-atom explicit-solvent molecular dynamics (MD) simulations to identify fragment binding. The target is "soaked" in an aqueous solution with multiple fragments having different identities. The resulting computational competition assay reveals what small molecule types are most likely to bind which regions of the target. From SILCS simulations, 3D probability maps of fragment binding called "FragMaps" can be produced. Based on the probabilities relative to bulk, SILCS FragMaps can be used to determine "Grid Free Energies (GFEs)," which provide per-atom contributions to fragment binding affinities. For essentially no additional computational overhead relative to the production of the FragMaps, GFEs can be used to compute Ligand Grid Free Energies (LGFEs) for arbitrarily complex molecules, and these LGFEs can be used to rank-order the molecules in accordance with binding affinities.