Computational Pipeline for Accelerating the Design of Glycomimetics

To accelerate the rational design of glycomimetic inhibitors, based on derivatization of a carbohydrate ligand, we introduce a computational pipeline that automates the creation and modeling of analogs and computes their interaction energies. Putative glycomimetics are assembled by grafting small drug-like moieties onto the native carbohydrate scaffold in the presence of the receptor protein, with the moieties chosen from a virtual library of more than 1500 molecular fragments, selected for their synthetic accessibility. The method is illustrated for the case of glycomimetics but is generalizable to any bound ligand. A genetic algorithm (GA) was developed to identify the most likely orientation of the appended moieties in the receptor binding site. For validation, curated experimental data sets were assembled from the literature, consisting of 119 glycomimetics, with reported solution binding free energies, including 46 with corresponding high-resolution crystal structures of the glycomimetic complexes. These data sets were subdivided for protocol testing and "real-world" performance validation. The GA search resulted in an average root-mean-squared deviation (RMSD) of 1.5 Å for the added moieties, compared to their crystallographic data. The GA-generated structures were then subjected to molecular dynamics (MD) simulation, and the performance was evaluated for three post-MD approaches to computing interaction energies: the scoring function from AutoDock Vina-Carb, as well as the generalized Born and Poisson-Boltzmann surface area (GBSA/PBSA) implementations within the AMBER molecular mechanical (MM) force field. For the Test data set of structures with reported energies, the highest coefficient of determination (R² = 0.67) was obtained with MM-PBSA when ligand conformational entropies were included. Current limitations of the protocol and experimental data sets are discussed.