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Amyloid Fibrils

Amyloid Fibrils

Amyloid fibrils are aggregates of misfolded proteins. Under most circumstances, misfolded proteins are either refolded by chaperone proteins or degraded by the proteasome. However, in the case of a mutation or a disease, these proteins can accumulate to form large clusters and often further assemble to form elongated fibers, called fibrils.
Amyloid deposits were observed as early as 1639 in the liver and the spleen. In 1854, Rudolph Virchow performed iodine staining,...

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Decoding Solubility Signatures From Amyloid Monomer Energy Landscapes.

Home
Decoding Solubility Signatures From Amyloid Monomer Energy Landscapes.

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Decoding Solubility Signatures from Amyloid Monomer Energy Landscapes.

Patryk Adam Wesołowski¹, Bojun Yang², Anthony J Davolio³

¹Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K.

Journal of Chemical Theory and Computation

|February 24, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

Researchers explored amyloid monomer energy landscapes to understand Alzheimer's disease protein misfolding. They identified specific structural features, like exposed hydrophobic residues, linked to reduced solubility and aggregation-prone states.

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Area of Science:

Biophysics
Computational Chemistry
Neuroscience

Background:

Protein misfolding and aggregation are central to neurodegenerative diseases like Alzheimer's.
Amyloid monomers' energy landscapes influence their stability and propensity to misfold.
Solubility is a critical factor in protein aggregation.

Purpose of the Study:

To investigate the energy landscapes of amyloid monomers.
To identify structural features correlated with reduced solubility and aggregation.
To elucidate the thermodynamics and kinetics of amyloid monomer behavior.

Main Methods:

Utilized the UNOPTIM program and Cambridge energy landscape framework.
Employed single-ended transition state searches and discrete path sampling to build kinetic transition networks.

Applied graph convolutional networks for structural analysis and solubility trend identification.

Main Results:

Identified specific energy minima associated with low solubility and aggregation-prone states.
Highlighted key residues, such as Phe19, that drive reduced solubility through structural collapse.
Quantified energy landscapes and investigated kinetics, including first passage times between states.

Conclusions:

Specific structural features of amyloid monomers dictate their solubility and aggregation propensity.
Understanding these energy landscapes offers insights into Alzheimer's disease pathogenesis.
Findings may guide the development of novel therapeutic strategies targeting protein misfolding.