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Programmed Self-Assembly of Hierarchical Nanostructures through Protein-Nanoparticle Coengineering.

Rubul MoutGulen Yesilbag TongaLi-Sheng WangMoumita RayTrinava RoyVincent M Rotello
Published in: ACS nano (2017)
Hierarchical organization of macromolecules through self-assembly is a prominent feature in biological systems. Synthetic fabrication of such structures provides materials with emergent functions. Here, we report the fabrication of self-assembled superstructures through coengineering of recombinant proteins and nanoparticles. These structures feature a highly sophisticated level of multilayered hierarchical organization of the components: individual proteins and nanoparticles coassemble to form discrete assemblies that collapse to form granules, which then further self-organize to generate superstructures with sizes of hundreds of nanometers. The components within these superstructures are dynamic and spatially reorganize in response to environmental influences. The precise control over the molecular organization of building blocks imparted by this protein-nanoparticle coengineering strategy provides a method for creating hierarchical hybrid materials.
Keyphrases
  • machine learning
  • high resolution
  • deep learning
  • protein protein
  • binding protein
  • risk assessment
  • mass spectrometry
  • human health
  • tissue engineering
  • neural network
  • cell free