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Evaluation of the Impact of Protein Aggregation on Cellular Oxidative Stress in Yeast

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Computational studies of protein aggregation: methods and applications.

Alex Morriss-Andrews¹, Joan-Emma Shea

¹Department of Physics and.

Annual Review of Physical Chemistry

|February 5, 2015

Summary

This review explores theoretical studies on protein aggregation, focusing on computational models for amyloid fibril formation. Understanding these processes is crucial for diseases like Alzheimer's and functional fibril roles.

Keywords:

amyloid fibrils coarse-grained models enhanced sampling methods molecular dynamics simulations replica exchange molecular dynamics systematic coarse graining

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Published on: September 12, 2019

Area of Science:

Biochemistry and Molecular Biology
Computational Biology
Biophysics

Background:

Protein aggregation is the self-assembly of soluble proteins into supramolecular structures.
Amyloid fibrils, rich in beta-sheet content, are common end products of protein aggregation.
This process is implicated in diseases such as Alzheimer's disease and has functional roles in some organisms.

Purpose of the Study:

To review theoretical studies on protein fibril formation.
To emphasize computational models and methods used in studying protein aggregation.

Main Methods:

Focus on theoretical and computational approaches.
Review of common computational models and simulation techniques.
Analysis of studies investigating the mechanisms of fibril formation.

Main Results:

Theoretical studies provide insights into the complex pathways of protein aggregation.
Computational models are essential tools for dissecting the molecular mechanisms of fibril formation.
A variety of computational methods are employed to understand the kinetics and thermodynamics of aggregation.

Conclusions:

Computational modeling is key to understanding protein aggregation and amyloid fibril formation.
Theoretical investigations aid in elucidating disease mechanisms and potential therapeutic targets.
Further development of computational methods will enhance our understanding of these critical biological processes.