The mechanism of self-association of human γ-D crystallin from molecular dynamics simulations

Brudar, Sandi; Hribar-Lee, Barbara

doi:10.1016/j.molliq.2023.122461

Cited by 3 publications

(1 citation statement)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The advantages of MD modeling are its ability to capture atomistic-level details and its incorporation of motion dynamics [14][15][16][17][18][19][20][21][22][23]. Advances in simulations and machine learning emerging over the past decade [24] can help to reveal details at the level of the amino acids, and can guide drug discovery [25,26]. For a review of the computer simulation approach and how to identify aggregation-prone regions, see Agrawal et al [27].…”

Section: Background On Modeling the Forces In Protein Solutionsmentioning

confidence: 99%

Protein Association in Solution: Statistical Mechanical Modeling

Vlachy,

Kalyuzhnyi,

Hribar-Lee

et al. 2023

Biomolecules

Self Cite

View full text Add to dashboard Cite

Protein molecules associate in solution, often in clusters beyond pairwise, leading to liquid phase separations and high viscosities. It is often impractical to study these multi-protein systems by atomistic computer simulations, particularly in multi-component solvents. Instead, their forces and states can be studied by liquid state statistical mechanics. However, past such approaches, such as the Derjaguin-Landau-Verwey-Overbeek (DLVO) theory, were limited to modeling proteins as spheres, and contained no microscopic structure–property relations. Recently, this limitation has been partly overcome by bringing the powerful Wertheim theory of associating molecules to bear on protein association equilibria. Here, we review these developments.

show abstract

Section: Background On Modeling the Forces In Protein Solutionsmentioning

confidence: 99%

Protein Association in Solution: Statistical Mechanical Modeling

Vlachy,

Kalyuzhnyi,

Hribar-Lee

et al. 2023

Biomolecules

Self Cite

View full text Add to dashboard Cite

show abstract

Advanced computational approaches to understand protein aggregation

Ghosh,

Biswas,

Radhakrishna

2024

Biophysics Reviews

View full text Add to dashboard Cite

Protein aggregation is a widespread phenomenon implicated in debilitating diseases like Alzheimer's, Parkinson's, and cataracts, presenting complex hurdles for the field of molecular biology. In this review, we explore the evolving realm of computational methods and bioinformatics tools that have revolutionized our comprehension of protein aggregation. Beginning with a discussion of the multifaceted challenges associated with understanding this process and emphasizing the critical need for precise predictive tools, we highlight how computational techniques have become indispensable for understanding protein aggregation. We focus on molecular simulations, notably molecular dynamics (MD) simulations, spanning from atomistic to coarse-grained levels, which have emerged as pivotal tools in unraveling the complex dynamics governing protein aggregation in diseases such as cataracts, Alzheimer's, and Parkinson's. MD simulations provide microscopic insights into protein interactions and the subtleties of aggregation pathways, with advanced techniques like replica exchange molecular dynamics, Metadynamics (MetaD), and umbrella sampling enhancing our understanding by probing intricate energy landscapes and transition states. We delve into specific applications of MD simulations, elucidating the chaperone mechanism underlying cataract formation using Markov state modeling and the intricate pathways and interactions driving the toxic aggregate formation in Alzheimer's and Parkinson's disease. Transitioning we highlight how computational techniques, including bioinformatics, sequence analysis, structural data, machine learning algorithms, and artificial intelligence have become indispensable for predicting protein aggregation propensity and locating aggregation-prone regions within protein sequences. Throughout our exploration, we underscore the symbiotic relationship between computational approaches and empirical data, which has paved the way for potential therapeutic strategies against protein aggregation-related diseases. In conclusion, this review offers a comprehensive overview of advanced computational methodologies and bioinformatics tools that have catalyzed breakthroughs in unraveling the molecular basis of protein aggregation, with significant implications for clinical interventions, standing at the intersection of computational biology and experimental research.

show abstract

Biophysical Principles Emerging from Experiments on Protein–Protein Association and Aggregation

Hribar-Lee,

Lukšič

2024

Annual Review of Biophysics

View full text Add to dashboard Cite

Protein–protein association and aggregation are fundamental processes that play critical roles in various biological phenomena, from cellular signaling to disease progression. Understanding the underlying biophysical principles governing these processes is crucial for elucidating their mechanisms and developing strategies for therapeutic intervention. In this review, we provide an overview of recent experimental studies focused on protein–protein association and aggregation. We explore the key biophysical factors that influence these processes, including protein structure, conformational dynamics, and intermolecular interactions. We discuss the effects of environmental conditions such as temperature, pH and related buffer-specific effects, and ionic strength and related ion-specific effects on protein aggregation. The effects of polymer crowders and sugars are also addressed. We list the techniques used to study aggregation. We analyze emerging trends and challenges in the field, including the development of computational models and the integration of multidisciplinary approaches for a comprehensive understanding of protein–protein association and aggregation. Expected final online publication date for the Annual Review of Biophysics, Volume 53 is May 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

show abstract

The mechanism of self-association of human γ-D crystallin from molecular dynamics simulations

Cited by 3 publications

References 25 publications

Protein Association in Solution: Statistical Mechanical Modeling

Protein Association in Solution: Statistical Mechanical Modeling

Advanced computational approaches to understand protein aggregation

Biophysical Principles Emerging from Experiments on Protein–Protein Association and Aggregation

Contact Info

Product

Resources

About