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ConspectusControlling self-assembled peptide nanostructures has emerged as a significant area of research, offering versatile tools for developing functional materials for various applications. This Account emphasizes the essential role of noncovalent interactions, particularly in peptide-based materials. Key forces, such as aromatic stacking and hydrogen bonding, are crucial for promoting molecular aggregation and stabilizing supramolecular structures. Numerous studies demonstrate how these interactions influence the phase transitions and the morphology of self-assembled structures. Recent advances in computational methodologies, including molecular dynamics simulations and machine learning, have significantly enhanced our understanding of self-assembly processes. These tools enable researchers to predict how molecular properties, such as hydrophobicity, charge distribution, and aromaticity, affect assembly behavior. Simulations uncover the energetic landscapes governing peptide aggregation, providing insights into the kinetic pathways and thermodynamic stabilities. Meanwhile, machine learning facilitates the rapid screening of peptide libraries, identifying sequences with optimal self-assembly characteristics, and accelerating material design with tailored functionalities.Beyond their structural and physicochemical properties, self-assembled peptide nanostructures hold immense potential in biological applications due to their versatility and biocompatibility. By manipulating molecular interactions, researchers have engineered responsive systems that interact with cellular environments to elicit specific biological responses. These peptide nanostructures can mimic extracellular matrices, facilitating cell adhesion, proliferation, and differentiation. They also show promise in modulating immune responses, recruiting immune cells, and regulating signaling pathways, making them valuable tools in immunotherapy and regenerative medicine. Moreover, their ability to disrupt bacterial membranes positions them as innovative alternatives to conventional antibiotics, addressing the urgent need for solutions to antimicrobial resistance.Despite its promise, peptide self-assembly faces several challenges. The assembly process is highly sensitive to environmental conditions, such as pH, temperature, and ionic strength, leading to variability in the morphology and properties. Furthermore, peptide aggregation can result in heterogeneous and poorly defined assemblies, complicating the reproducibility and scalability. Designing peptides with predictable self-assembly behavior remains a significant hurdle. Looking ahead, integrating computational predictions with experimental validations will be crucial in discovering novel peptide sequences with tailored self-assembly properties. Machine learning, combined with high-throughput screening techniques, will enable the rapid identification of optimal peptide sequences. In situ characterization tools, such as cryoelectron microscopy and advanced spectroscopy, will provide deeper insights into assembly mechanisms, aiding the rational design of peptide materials.As research progresses, the dynamic and reversible nature of noncovalent interactions can be leveraged to create adaptive responsive to environmental stimuli. Self-assembled peptide nanostructures are poised for impactful applications in biomedicine including targeted drug delivery, tissue repair, and advanced therapeutic strategies. Ultimately, these nanostructures represent a powerful platform for addressing complex challenges in biomedicine and beyond, paving the way for transformative breakthroughs in science and technology.
Li et al. (Mon,) studied this question.