Molecular Recognition Insights of Sialic Acid Glycans by Distinct Receptors Unveiled by NMR and Molecular Modeling

All cells are decorated with a highly dense and complex structure of glycan chains, which are mostly attached to proteins and lipids. In this context, sialic acids are a family of nine-carbon acidic monosaccharides typically found at the terminal position of glycan chains, modulating several physiological and pathological processes. Sialic acids have many structural and modulatory roles due to their negative charge and hydrophilicity. In addition, the recognition of sialic acid glycans by mammalian cell lectins, such as siglecs, has been described as an important immunological checkpoint. Furthermore, sialic acid glycans also play a pivotal role in host–pathogen interactions. Various pathogen receptors exposed on the surface of viruses and bacteria are responsible for the binding to sialic acid sugars located on the surface of host cells, becoming a critical point of contact in the infection process. Understanding the molecular mechanism of sialic acid glycans recognition by sialic acid-binding proteins, present on the surface of pathogens or human cells, is essential to realize the biological mechanism of these events and paves the way for the rational development of strategies to modulate sialic acid-protein interactions in diseases. In this perspective, nuclear magnetic resonance (NMR) spectroscopy, assisted with molecular modeling protocols, is a versatile and powerful technique to investigate the structural and dynamic aspects of glycoconjugates and their interactions in solution at the atomic level. NMR provides the corresponding ligand and protein epitopes, essential for designing and developing potential glycan-based therapies. In this review, we critically discuss the current state of knowledge about the structural features behind the molecular recognition of sialic acid glycans by different receptors, naturally present on human cells or pathogens, disclosed by NMR spectroscopy and molecular modeling protocols.