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.