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β-Amyloid Fibril Structures, In Vitro and In Vivo

  • Robert TyckoEmail author
Chapter
Part of the Research and Perspectives in Alzheimer's Disease book series (ALZHEIMER)

Abstract

Since 1998, a great deal of progress has been made towards determining and understanding the molecular structures of amyloid fibrils, including fibrils formed by the β-amyloid peptide that is associated with Alzheimer’s disease. Much of this progress has resulted from solid state nuclear magnetic resonance (NMR) measurements, which provide experimental constraints on molecular conformations and interatomic distances without requiring solubility or crystallinity. In general, amyloid fibrils are polymorphic, meaning that fibrils formed by a given peptide or protein can have multiple, distinct molecular structures, depending on the precise conditions under which the fibrils grow. From solid state NMR, electron microscopy, and other measurements, we have developed two detailed molecular structural models for fibrils formed by the 40-residue wild-type β-amyloid (Aβ1–40) peptide. These two Aβ1–40 fibril polymorphs share a common, parallel β-sheet organization and contain similar peptide conformations but differ in overall symmetry and in other structural aspects. We have also identified and characterized a surprising antiparallel β-sheet structure in metastable fibrils formed by a disease-associated mutant, D23N-Aβ1–40, which reveals how similar sets of interactions can stabilize both parallel and antiparallel β-sheets within amyloid fibrils. We are currently extending our structural studies to β-amyloid fibrils that develop in human brain tissue, with the goal of testing whether variations in fibril structure correlate with variations in severity, progression rate, or other characteristics of Alzheimer’s disease.

Keywords

Amyloid Fibril Solid State Nuclear Magnetic Resonance Fibril Structure Amyloid Disease Mature Fibril 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This work was supported by the Intramural Research Program of the National Institute of Diabetes and Digestive and Kidney Diseases, a component of the U.S. National Institutes of Health. I thank present and past members of my research group, including Drs. Oleg Antzutkin, Yoshitaka Ishii, John Balbach, Nathan Oyler, Jerry Chan, Aneta Petkova, Anant Paravastu, Kent Thurber, Junxia Lu, and Wei Qiang, for their many contributions to this work. I also thank Prof. Stephen C. Meredith of the University of Chicago for collaborating on several aspects of this work.

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© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Laboratory of Chemical PhysicsNational Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of HealthBethesdaUSA

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