A Computational Framework for Fucosidic Ligand Discovery: Combining Automated Docking with Molecular Dynamics Simulations

Glycans are abundant and diverse natural products that coat the surface of every cell. The diversity of this “glycocalyx” encodes vast biological information that is deciphered by lectins, a specialized class of Glycan-Binding Proteins (GBPs). Interactions between the glycocalyx and lectins regulate critical immunopathological processes, including bacterial and viral infection, fibrosis, homeostasis and cancer proliferation. Fucosylated glycans such as Lewis and ABO antigens, bearing a unit of L-6-deoxy-galactopyranose, are key players in these processes. For instance, the altered expression of these antigens is a hallmark of many carcinomas and correlates with patient prognosis. Furthermore, many pathogens exploit these glycoconjugates, using lectins with high affinity for fucose to bind human tissues. Modulating or inhibiting these recognition events with glycomimetic ligands represents a promising yet challenging therapeutic strategy, primarily due to the limited druggability of lectins and poor pharmacokinetics of carbohydrate-based ligands. In this context, and to better understand the structural requirements for receptor engagement, an automated multi-target docking workflow was implemented via the Schrödinger Suite Python API, ensuring efficiency, customization and reproducibility. This approach was used to assess the binding potential of a combinatorial library of synthetically accessible fucosides to multiple selected targets. Thorough analysis of the most promising docking poses, further refined through molecular dynamics (MD) simulations, enabled the identification of high-potential chemotypes and alternative binding modes not previously observed for several fucose-binding lectins. These findings provide a structural foundation for the rational design of new glycomimetics and offers candidates for subsequent experimental validation.