超分子化学
单层
自组装
共价键
铰链
肽
化学
色氨酸
环肽
非共价相互作用
超分子组装
材料科学
纳米技术
生物物理学
结晶学
氨基酸
氢键
分子
有机化学
生物化学
晶体结构
生物
工程类
机械工程
作者
Ignacio Insua,Annalisa Cardellini,S. Díaz,Julián Bergueiro,Riccardo Capelli,Giovanni M. Pavan,Javier Montenegro
出处
期刊:Chemical Science
[Royal Society of Chemistry]
日期:2023-01-01
卷期号:14 (48): 14074-14081
被引量:8
摘要
Supramolecular polymerisation of two-dimensional (2D) materials requires monomers with non-covalent binding motifs that can control the directionality of both dimensions of growth. A tug of war between these propagation forces can bias polymerisation in either direction, ultimately determining the structure and properties of the final 2D ensemble. Deconvolution of the assembly dynamics of 2D supramolecular systems has been widely overlooked, making monomer design largely empirical. It is thus key to define new design principles for suitable monomers that allow the control of the direction and the dynamics of two-dimensional self-assembled architectures. Here, we investigate the sequential assembly mechanism of new monolayer architectures of cyclic peptide nanotubes by computational simulations and synthesised peptide sequences with selected mutations. Rationally designed cyclic peptide scaffolds are shown to undergo hierarchical self-assembly and afford monolayers of supramolecular nanotubes. The particular geometry, the rigidity and the planar conformation of cyclic peptides of alternating chirality allow the orthogonal orientation of hydrophobic domains that define lateral supramolecular contacts, and ultimately direct the propagation of the monolayers of peptide nanotubes. A flexible 'tryptophan hinge' at the hydrophobic interface was found to allow lateral dynamic interactions between cyclic peptides and thus maintain the stability of the tubular monolayer structure. These results unfold the potential of cyclic peptide scaffolds for the rational design of supramolecular polymerisation processes and hierarchical self-assembly across the different dimensions of space.
科研通智能强力驱动
Strongly Powered by AbleSci AI