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ACS Nano. 2009 Jun 23;3(6):1525-31. doi: 10.1021/nn900171s.

Molecular recognition and supramolecular self-assembly of a genetically engineered gold binding peptide on Au{111}.

Author information

1
Genetically Engineered Materials Science and Engineering Center, Department of Materials Science and Engineering, University of Washington, Seattle, WA 98105, USA.

Abstract

The understanding of biomineralization and realization of biology-inspired materials technologies depends on understanding the nature of the chemical and physical interactions between proteins and biominerals or synthetically made inorganic materials. Recently, combinatorial genetic techniques permit the isolation of peptides recognizing specific inorganic materials that are used as molecular building blocks for novel applications. Little is known about the molecular structure of these peptides and the specific recognition mechanisms onto their counterpart inorganic surfaces. Here, we report high-resolution atomic force microscopy (AFM), molecular simulation (MS), and geometrical docking studies that detail the formation of an ordered supramolecular self-assembly of a genetically engineered gold binding peptide, 3rGBP(1) ([MHGKTQATSGTIQS](3)), correlating with the symmetry of the Au{111} surface lattice. Using simulated annealing molecular dynamics (SA/MD) studies based on nuclear magnetic resonance (NMR), we confirmed the intrinsic disorder of 3rGBP(1) and identified putative Au docking sites where surface-exposed side chains align with both the <110> and <211> Miller indices of the Au lattice. Our results provide fundamental insight for an atomistic understanding of peptide/solid interfaces and the intrinsic disorder that is inherent in some of these peptide sequences. Analogous to the well-established atomically controlled thin-film heterostructure formation on semiconductor substrates, the basis of today's microelectronics, the fundamental observations of peptide-solid interactions here may well form the basis of peptide-based hybrid molecular technologies of the future.

PMID:
19438257
DOI:
10.1021/nn900171s
[Indexed for MEDLINE]

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