Computational Pipeline for Accelerating the Design of Glycomimetics.

Area of Science:

• Computational chemistry
• Drug discovery
• Glycoscience

Background:

• Rational design of glycomimetic inhibitors is crucial for therapeutic development.
• Existing methods for designing carbohydrate-based ligands are often time-consuming.
• Automating the creation and modeling of analogs can accelerate inhibitor discovery.

Purpose of the Study:

• To introduce a computational pipeline for automated design and modeling of glycomimetic inhibitors.
• To develop and validate a genetic algorithm (GA) for optimizing moiety orientation in receptor binding sites.
• To evaluate different computational approaches for calculating interaction energies of glycomimetics.

Main Methods:

• Assembled virtual library of over 1500 drug-like molecular fragments for grafting onto carbohydrate scaffolds.
• Employed a genetic algorithm (GA) to determine optimal moiety placement within the receptor binding site.
• Utilized molecular dynamics (MD) simulations and various post-MD energy calculation methods (AutoDock Vina-Carb, GBSA/PBSA with AMBER MM force field) for validation.

Main Results:

• The GA achieved an average root-mean-squared deviation (RMSD) of 1.5 Å for grafted moieties compared to crystallographic data.
• Molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) with ligand conformational entropies yielded the highest correlation (R² = 0.67) with experimental binding free energies.
• The computational pipeline demonstrated generalizability beyond glycomimetics to any bound ligand.

Conclusions:

• The developed computational pipeline effectively automates the design and modeling of glycomimetic inhibitors.
• MM-PBSA with conformational entropy is a promising method for accurate interaction energy prediction in glycomimetic design.
• This approach accelerates the rational design of novel carbohydrate-based therapeutics.