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A Protocol for the Design of Protein and Peptide Nanostructure Self-Assemblies Exploiting Synthetic Amino Acids

  • Nurit HaspelEmail author
  • Jie Zheng
  • Carlos Aleman
  • David Zanuy
  • Ruth Nussinov
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1529)

Abstract

In recent years there has been increasing interest in nanostructure design based on the self-assembly properties of proteins and polymers. Nanodesign requires the ability to predictably manipulate the properties of the self-assembly of autonomous building blocks, which can fold or aggregate into preferred conformational states. The design includes functional synthetic materials and biological macromolecules. Autonomous biological building blocks with available 3D structures provide an extremely rich and useful resource. Structural databases contain large libraries of protein molecules and their building blocks with a range of sizes, shapes, surfaces, and chemical properties. The introduction of engineered synthetic residues or short peptides into these building blocks can greatly expand the available chemical space and enhance the desired properties. Herein, we summarize a protocol for designing nanostructures consisting of self-assembling building blocks, based on our recent works. We focus on the principles of nanostructure design with naturally occurring proteins and synthetic amino acids, as well as hybrid materials made of amyloids and synthetic polymers.

Key words

Nanostructures Self-assembly Peptide-based nanodesign Synthetic amino acids Beta-helical proteins Computational nanodesign Amyloid peptides Hybrid materials 

Notes

Acknowledgements

J.Z. thanks for financial supports from the National Science Foundation (CAREER Award 0952624, 1510099, and 1607475) and Alzheimer Association—New Investigator Research Grant (2015-NIRG-341372), and National Natural Science Foundation of China (NSFC-21528601). The calculations were carried out in part on the UMass Boston research cluster. This work has been funded in whole or in part with Federal funds from the National Cancer Institute, National Institutes of Health, under contract number HHSN261200800001E. The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government. This research was supported (in part) by the Intramural Research Program of the NIH, National Cancer Institute, Center for Cancer Research.

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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Nurit Haspel
    • 1
    Email author
  • Jie Zheng
    • 2
  • Carlos Aleman
    • 3
    • 4
  • David Zanuy
    • 3
  • Ruth Nussinov
    • 5
    • 6
    • 7
  1. 1.Department of Computer ScienceThe University of Massachusetts BostonBostonUSA
  2. 2.Department of Chemical and Biomolecular EngineeringThe University of AkronAkronUSA
  3. 3.Departament d’Enginyeria Química, E. T. S. d’Enginyeria Industrial de BarcelonaUniversitat Politècnica de CatalunyaBarcelonaSpain
  4. 4.Center for Research in Nano-EngineeringUniversitat Politècnica de CatalunyaBarcelonaSpain
  5. 5.Department of Human Genetics and Molecular Medicine, Sackler School of Medicine, Sackler Inst. of Molecular MedicineTel Aviv UniversityTel AvivIsrael
  6. 6.Basic Science ProgramLeidos Biomedical Research, Inc.FrederickUSA
  7. 7.Cancer and Inflammation ProgramNational Cancer InstituteFrederickUSA

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