The Glyco-Code Molecular Template Instructively Guides Skeletal and Neural Development, Tissue Regeneration and...

Glycosaminoglycans encode instructional information that cells can decipher to regulate tissue development, repair responses, regeneration and recovery of tissue function following trauma or disease. This information has been termed the glyco-code and is a road map for tissue development and repair. This review shows that a clearer understanding of the glyco-code will support improved strategies in repair biology, particularly in skeletal and neural repair, regeneration, and therapeutic biomaterial design. Emerging therapeutic approaches for peripheral nerve repair increasingly combine multimodal delivery, microenvironmental control, and biomimetic structural support; however, the electroconductive properties of glycosaminoglycans have not yet been fully incorporated into these strategies. This gap is clinically important because neurological disorders affect more than 40% of the global population, representing approximately three billion people across conditions such as stroke, migraine, Alzheimer's disease, and diabetic neuropathy. Nanoparticle-engineered platforms and glyco-code-driven biomaterials may, therefore, provide future regenerative therapies that integrate glycosaminoglycan structure, charge, ligand-binding capacity, informational functions, and electroconductive control of resident cell populations to drive neural recovery. Multifunctional glycosaminoglycan interactions with a diverse range of binding proteins orchestrate tissue form and function in a diverse range of tissues in health and disease and have been widely used in tissue engineering protocols in repair biology. Tissue atlases, advanced glycomics, and molecular dynamics binding studies are now identifying roles for glycosaminoglycan binding proteins in therapeutic repair processes at the single-cell level, aiding in the identification of molecular targets for more effective recovery of tissue function. DynamicBind and SurfDock are deep learning methods that accurately demonstrate state-of-the-art performance in understanding the docking of potential therapeutic drugs with prospective targets, identifying cryptic pockets in unseen protein targets, and accelerating the development of small molecules for previously undruggable targets, potentially expanding the horizons of computational drug discovery with glycan structures.