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Updated: Jun 27, 2025

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

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

Published on: June 23, 2018

Advanced computational approaches to understand protein aggregation.

Deepshikha Ghosh¹, Anushka Biswas², Mithun Radhakrishna

¹Department of Biological Sciences and Engineering, Indian Institute of Technology (IIT) Gandhinagar, Palaj, Gujarat 382355, India.

Biophysics Reviews

|April 29, 2024

Summary

This summary is machine-generated.

Computational methods and bioinformatics tools are revolutionizing the study of protein aggregation. These techniques offer crucial insights into disease mechanisms and aid in developing therapeutic strategies for conditions like Alzheimer's and Parkinson's.

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Characterizing Individual Protein Aggregates by Infrared Nanospectroscopy and Atomic Force Microscopy

Characterizing Individual Protein Aggregates by Infrared Nanospectroscopy and Atomic Force Microscopy

Published on: September 12, 2019

Area of Science:

Molecular Biology
Computational Biology
Bioinformatics

Background:

Protein aggregation is central to neurodegenerative diseases like Alzheimer's and Parkinson's, as well as cataracts.
Understanding the complex dynamics of protein aggregation presents significant challenges in molecular biology.
Precise predictive tools are essential for deciphering protein aggregation mechanisms.

Purpose of the Study:

To review the role of computational methods and bioinformatics in understanding protein aggregation.
To highlight the application of molecular simulations in studying aggregation pathways.
To explore the integration of computational approaches with experimental data for therapeutic development.

Main Methods:

Molecular Dynamics (MD) simulations (atomistic and coarse-grained) including advanced techniques like replica exchange, Metadynamics (MetaD), and umbrella sampling.
Markov state modeling for analyzing chaperone mechanisms in cataract formation.
Bioinformatics, sequence analysis, structural data analysis, machine learning, and artificial intelligence for predicting aggregation propensity.

Main Results:

MD simulations provide microscopic insights into protein interactions and aggregation pathways in diseases.
Computational tools are indispensable for predicting aggregation-prone regions and protein aggregation propensity.
Advanced computational methods elucidate disease-specific aggregation mechanisms, such as in Alzheimer's and Parkinson's disease.

Conclusions:

Computational methods and bioinformatics have catalyzed breakthroughs in understanding protein aggregation.
The synergy between computational approaches and empirical data is key to developing therapeutic strategies.
This review underscores the impact of computational biology on unraveling the molecular basis of protein aggregation and its clinical implications.