Engineering Peptide Self-Assembly: Modulating Noncovalent Interactions for Biomedical Applications

Abstract

Controlling 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.