Structural Biology Of Amyloid Fibrils

Author : Kevin Alexander Murray
Publisher :
ISBN 13 :
Total Pages : 186 pages
Book Rating : 4.:/5 (116 download)

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Download or read book Computational Analysis of Amyloid Protein Structure to Identify Novel Pathologies and Therapeutics written by Kevin Alexander Murray and published by . This book was released on 2020 with total page 186 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many diseases are characterized by the pathologic accumulation of aggregated proteins. Known as amyloid, these fibrillar aggregates are present in many neurodegenerative diseases, including Alzheimer's and Parkinson's disease (PD), and amyotrophic lateral sclerosis (ALS). The development and spread of amyloid fibrils within the brain correlates with disease onset and progression, and inhibiting their formation is a possible route towards therapeutic development. Advances in structural biology, namely micro-crystal x-ray diffraction, micro-electron diffraction (MicroED), cryo-electron microscopy (cryoEM) and solid-state NMR spectroscopy (ssNMR) have enabled the determination of amyloid fibril structures to atomic-level resolutions, improving the possibility of structure-based inhibitor design. In Chapter 1, we use these amyloid structures to design inhibitors which bind to the ends of fibrils, "capping" them so as to prevent further growth. Applying recent breakthroughs in de novo protein design, we describe a computational approach to develop mini-protein inhibitors of 35-48 residues which target the amyloid structures of tau, A (found in Alzheimer's disease) and Syn (found in Parkinson's disease). Biophysical characterization of the in silico designed inhibitors shows they form stable folds, with no sequence homology to naturally occurring proteins, and specifically prevent the aggregation of their targeted amyloid-prone proteins in vitro. The inhibitors also prevent the seeded aggregation and toxicity of fibrils in cells. In vivo evaluation reveals their ability to reduce aggregation and rescue motor deficits in C. elegans models of PD and AD. In Chapter 2, we apply a similar design strategy to generate inhibitors of a different form of protein aggregation. Proteins with low complexity segments engage in liquid-liquid phase separation (LLPS) during normal cell processes, but aberrant LLPS leads to the eventual aggregation of such proteins into amyloid fibrils. This presents inhibition of LLPS as another potential therapeutic target. As LLPS appears to be a precursor state to fibrillization, we test if inhibitors targeting an amyloid fibril structure can also reduce LLPS of the same protein. To accomplish this, we design de novo miniprotein inhibitors which target the fibril structure of the FUS low-complexity domain, a protein known to undergo LLPS and eventually aggregate in ALS, frontotemporal dementia (FTD) and other dementias. Several designs are able to reduce FUS LLPS in vitro, as well as FUS fibrilization. Additionally, the same inhibitors reduce stress granule formation in cells, a form of LLPS. Mutations to improve binding to the FUS fibril interface improve stress granule reduction, while steric clashes introduced into the interface abolish the inhibitor effects. The top inhibitor construct iFUS-G specifically inhibits FUS LLPS and has no effect on phase separation of low complexity segments from TDP43 or hnRNPA2, aggregation prone proteins similar in sequence composition to FUS. These findings present a rational design strategy to specifically inhibit the phase separation of low complexity proteins and have implications for the structural underpinnings of protein LLPS. Next, we transition from therapeutically targeting known amyloid proteins to identifying new ones. In addition to FUS, many proteins including hnRNPA1, hnRNPA2, and TDP-43 have been established to undergo aggregation into amyloid-like fibrils through interactions of their low-complexity domains. Mutations in the low-complexity domains of the same proteins can lead to irreversible amyloid aggregation and disease. In Chapter 3 we introduce a computational procedure to identify mutations in low-complexity domains of disease-related proteins that are predicted to increase their propensity for amyloid aggregation. This procedure found several disease-related mutations in a low complexity region of the intermediate filament protein Keratin-8 (KRT8). Atomic structures of wild-type and mutant KRT8 segments confirm the transition of a highly extended strand to a pleated strand capable of amyloid formation. Biochemical analysis of KRT8 reveals the protein forms amyloid aggregates and that the identified mutations promote aggregation. Aggregated KRT8 is found in Mallory-Denk bodies, often observed in the hepatocytes of livers with alcoholic steatohepatitis (ASH). We demonstrate that ethanol promotes KRT8 aggregation, and KRT8 amyloid structures co-crystallize with alcohol. We also observe that KRT8 aggregation can be seeded with ASH patient liver extract, consistent with the amyloid nature of KRT8 aggregates. Lastly, in Chapter 4 we explore structural characteristics that distinguish amyloid proteins known to undergo reversible versus irreversible aggregation. While all amyloid fibrils are primarily composed of repeating layers of beta-sheets, we observe that fibril structures of proteins known to reversibly aggregate have an enrichment of highly extended non-ideal beta-sheets. Quantum calculations of pleated- and extended-beta sheet amyloid structures show that extended backbones decrease the energy required to separate strand pairs. Non-covalent interaction analysis shows that the extended beta-sheets may be stabilized by interactions between the amide proton and carbonyl oxygen of the same residue, known as C5 hydrogen-bonding. These findings identify a key structural element that may regulate reversible amyloid assembly. This body of work offers insight into new ways to therapeutically target protein aggregation, identifies novel amyloid pathologies, and explores the structural underpinnings that may distinguish different forms of amyloid aggregation.