Synergistic π-Tunnel Clamps in β-Sheets for Long-Range Dry-State Conduction: Toward Neural Restoration
The ordered arrangement of π-π networks within nanostructures is advantageous for the construction of artificial electronic transport (ETp) systems. Building such structures with biocompatible peptides offers a potential for prescribed structural addressability and enhanced ETp capability with implications for targeted neural tissue engineering. However, creating ordered π-π tunnels in peptide nanostructures composed entirely of natural amino acids presents challenges resulting from the flexible side chains and the free movement of aromatic residues, causing unpredictable orientation. In this study, a novel peptide nanostructure was constructed through rational design and high-throughput screening leveraging hierarchical β-sheets to achieve molecular programmability. Precise regulation of key residues at the aromatic-hydrophilic junctions within the peptide chain facilitated the transition from single interaction forces (hydrophobic or hydrogen bonding) to synergistic forces, enabling the formation of supramolecular clamps during the lateral stacking of β-sheets. The clamps compel the torsion-angle alternation between aromatic residues and the β-plane, increasing the stacking order of aromatic rings and reducing the π-π distance in the optimized RT peptide system. The RT system promotes the formation of an orderly delocalized electron tunnel, achieving dry-state molecular conductivity composed entirely of natural amino acids. Besides ETp, the RT system also provides neural-targeting capability, flexibility, and mechanical strength, allowing it to support axon elongation and neural restoration, serving as an advanced neuro-electronic interface.
Authors
- Yao Yu (ORCID: https://orcid.org/0000-0001-6893-4203)
- Jinge Zhao (ORCID: https://orcid.org/0000-0002-2790-462X)
- Kai Han (ORCID: https://orcid.org/0009-0002-7388-4719)
- Jiaqing Li (ORCID: https://orcid.org/0000-0002-7993-7706)
- Xubiao Peng (ORCID: https://orcid.org/0000-0002-6494-9560)
- Yubin Ke (ORCID: https://orcid.org/0000-0002-4983-7755)
- Jie Cao (ORCID: https://orcid.org/0000-0002-9942-3243)
- Weizhi Wang (ORCID: https://orcid.org/0000-0002-7310-4068)
- Beilei Sun (ORCID: https://orcid.org/0000-0001-6546-5659)
- Xin Wang (ORCID: https://orcid.org/0000-0001-9291-3517)
- Bokai Ma
- Limin Zhang (ORCID: https://orcid.org/0000-0002-0176-1798)
- Bo Wang (ORCID: https://orcid.org/0000-0002-8588-4284)
- Jian Zhang
- Jingtian Cao
- Ying Qi
Institutions
- Beijing Institute of Technology (CN)
- China Spallation Neutron Source (CN)
- First Automotive Works (China) (CN)
- Beijing Academy of Science and Technology (CN)
- Ministry of Industry and Information Technology (CN)
Publication Details
- Journal
- Journal of the American Chemical Society
- Published
- 2025-03-25
- DOI
- https://doi.org/10.1021/jacs.4c16008
- Citations
- 3
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 2.19
Funders
- National Natural Science Foundation of China
- Beijing Institute of Technology Research Fund Program for Young Scholars
- Beijing Municipal Natural Science Foundation