peptideassembled

Peptideassembled refers to materials and structures formed through the self-assembly of peptides into higher-order, supramolecular architectures. In this context, peptide assembly is driven by noncovalent interactions such as hydrogen bonding, electrostatic attractions, hydrophobic effects, aromatic stacking, and, in some cases, metal coordination. The resulting assemblies include nanofibers, nanotubes, hydrogels, vesicles, and composite materials that can function as scaffolds, carriers, or functional surfaces.

Design principles: Short peptides, usually 5–20 amino acids, are chosen to bias secondary structure and assembly. Amphiphilic designs pair a hydrophobic tail with a charged or polar headgroup to promote water-driven organization. Peptide amphiphiles, beta-sheet–forming sequences, and other motifs can be engineered to present bioactive signals on their surfaces, enabling interactions with cells and tissues.

Common platforms and forms include peptide hydrogels formed by a network of self-assembled fibers, nanofibers for scaffolds, nanotubes for encapsulation, and peptide-based nanoparticles. Hybrid materials can be created by co-assembling peptides with polymers, inorganic nanoparticles, or metal ions, enabling multifunctional properties.

Applications span tissue engineering and regenerative medicine, controlled drug delivery, wound healing, vaccines and immunotherapies, biosensing, and catalytic or responsive materials.

Characterization and properties: Transmission electron microscopy (TEM), atomic force microscopy (AFM), scanning electron microscopy (SEM), and small-angle scattering reveal morphology; circular dichroism and FTIR identify secondary structure; rheology measures mechanical properties of hydrogels.

Challenges include achieving reproducible assembly under physiological conditions, controlling kinetics, ensuring stability and biocompatibility, manufacturing at scale, and managing potential immunogenicity. Ongoing advances in design rules, high-throughput screening, and computational modeling aim to improve predictability and translation.