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Network Hamiltonian models reveal pathways to amyloid fibril formation

Amyloid fibril formation is central to the etiology of a wide range of serious human diseases, such as Alzheimer’s disease and prion diseases. Despite an ever growing collection of amyloid fibril structures found in the Protein Data Bank (PDB) and numerous clinical trials, therapeutic strategies rem...

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Autores principales: Yu, Yue, Grazioli, Gianmarc, Unhelkar, Megha H., Martin, Rachel W., Butts, Carter T.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7515878/
https://www.ncbi.nlm.nih.gov/pubmed/32973286
http://dx.doi.org/10.1038/s41598-020-72260-8
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author Yu, Yue
Grazioli, Gianmarc
Unhelkar, Megha H.
Martin, Rachel W.
Butts, Carter T.
author_facet Yu, Yue
Grazioli, Gianmarc
Unhelkar, Megha H.
Martin, Rachel W.
Butts, Carter T.
author_sort Yu, Yue
collection PubMed
description Amyloid fibril formation is central to the etiology of a wide range of serious human diseases, such as Alzheimer’s disease and prion diseases. Despite an ever growing collection of amyloid fibril structures found in the Protein Data Bank (PDB) and numerous clinical trials, therapeutic strategies remain elusive. One contributing factor to the lack of progress on this challenging problem is incomplete understanding of the mechanisms by which these locally ordered protein aggregates self-assemble in solution. Many current models of amyloid deposition diseases posit that the most toxic species are oligomers that form either along the pathway to forming fibrils or in competition with their formation, making it even more critical to understand the kinetics of fibrillization. A recently introduced topological model for aggregation based on network Hamiltonians is capable of recapitulating the entire process of amyloid fibril formation, beginning with thousands of free monomers and ending with kinetically accessible and thermodynamically stable amyloid fibril structures. The model can be parameterized to match the five topological classes encompassing all amyloid fibril structures so far discovered in the PDB. This paper introduces a set of network statistical and topological metrics for quantitative analysis and characterization of the fibrillization mechanisms predicted by the network Hamiltonian model. The results not only provide insight into different mechanisms leading to similar fibril structures, but also offer targets for future experimental exploration into the mechanisms by which fibrils form.
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spelling pubmed-75158782020-09-29 Network Hamiltonian models reveal pathways to amyloid fibril formation Yu, Yue Grazioli, Gianmarc Unhelkar, Megha H. Martin, Rachel W. Butts, Carter T. Sci Rep Article Amyloid fibril formation is central to the etiology of a wide range of serious human diseases, such as Alzheimer’s disease and prion diseases. Despite an ever growing collection of amyloid fibril structures found in the Protein Data Bank (PDB) and numerous clinical trials, therapeutic strategies remain elusive. One contributing factor to the lack of progress on this challenging problem is incomplete understanding of the mechanisms by which these locally ordered protein aggregates self-assemble in solution. Many current models of amyloid deposition diseases posit that the most toxic species are oligomers that form either along the pathway to forming fibrils or in competition with their formation, making it even more critical to understand the kinetics of fibrillization. A recently introduced topological model for aggregation based on network Hamiltonians is capable of recapitulating the entire process of amyloid fibril formation, beginning with thousands of free monomers and ending with kinetically accessible and thermodynamically stable amyloid fibril structures. The model can be parameterized to match the five topological classes encompassing all amyloid fibril structures so far discovered in the PDB. This paper introduces a set of network statistical and topological metrics for quantitative analysis and characterization of the fibrillization mechanisms predicted by the network Hamiltonian model. The results not only provide insight into different mechanisms leading to similar fibril structures, but also offer targets for future experimental exploration into the mechanisms by which fibrils form. Nature Publishing Group UK 2020-09-24 /pmc/articles/PMC7515878/ /pubmed/32973286 http://dx.doi.org/10.1038/s41598-020-72260-8 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Yu, Yue
Grazioli, Gianmarc
Unhelkar, Megha H.
Martin, Rachel W.
Butts, Carter T.
Network Hamiltonian models reveal pathways to amyloid fibril formation
title Network Hamiltonian models reveal pathways to amyloid fibril formation
title_full Network Hamiltonian models reveal pathways to amyloid fibril formation
title_fullStr Network Hamiltonian models reveal pathways to amyloid fibril formation
title_full_unstemmed Network Hamiltonian models reveal pathways to amyloid fibril formation
title_short Network Hamiltonian models reveal pathways to amyloid fibril formation
title_sort network hamiltonian models reveal pathways to amyloid fibril formation
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7515878/
https://www.ncbi.nlm.nih.gov/pubmed/32973286
http://dx.doi.org/10.1038/s41598-020-72260-8
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