Dissecting the Molecular Mechanisms of the Co-Aggregation of Aβ40 and Aβ42 Peptides: A REMD Simulation Study

Advanced computational approaches to understand protein aggregation

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.

Molecular Insights into the Inhibition and Disaggregation Effects of EGCG on Aβ40 and Aβ42 Cofibrillation

The misfolding and aggregation of amyloid-β (Aβ) peptides play a pivotal role in the pathogenesis of Alzheimer's disease (AD). Aβ40 and Aβ42, the two primary isoforms of Aβ, can not only self-aggregate into homogeneous aggregates but also coaggregate to form mixed fibrils. Epigallocatechin-3-gallate (EGCG), a prominent tea polyphenol, has shown the capability to prevent the self-aggregation of Aβ40 and Aβ42 peptides and disaggregate their homogeneous fibrils. However, its effects on the cofibrillation of Aβ40 and Aβ42 have not yet been explored. Here, we employed molecular dynamic simulations to investigate the effects of EGCG on the coaggregation of Aβ40 and Aβ42, as well as on their mixed fibril. Our findings indicated that EGCG effectively inhibits the codimerization of Aβ40 and Aβ42 primarily by impeding the interchain interaction between the two isoforms. The key binding sites for EGCG on Aβ40 and Aβ42 are the polar residues and aromatic residues, engaging in hydrogen-bond , π-π, and cation-π interactions with EGCG. Additionally, EGCG disaggregates the Aβ40-Aβ42 mixed fibril by reducing its long-range interaction through similar binding sites and interactions as those between EGCG and Aβ40-Aβ42 heterodimers. Our research reveals the comprehensive inhibition and disaggregation effects of EGCG on the cofibrillation of Aβ isoforms, which provides further support for the development of EGCG as an effective antiaggregation agent for AD.

The Inhibition Effect of Epigallocatechin-3-Gallate on the Co-Aggregation of Amyloid-β and Human Islet Amyloid Polypeptide Revealed by Replica Exchange Molecular Dynamics Simulations

Alzheimer's disease and Type 2 diabetes are two epidemiologically linked diseases which are closely associated with the misfolding and aggregation of amyloid proteins amyloid-β (Aβ) and human islet amyloid polypeptide (hIAPP), respectively. The co-aggregation of the two amyloid proteins is regarded as the fundamental molecular mechanism underlying their pathological association. The green tea extract epigallocatechin-3-gallate (EGCG) has been extensively demonstrated to inhibit the amyloid aggregation of Aβ and hIAPP proteins. However, its potential role in amyloid co-aggregation has not been thoroughly investigated. In this study, we employed the enhanced-sampling replica exchange molecular dynamics simulation (REMD) method to investigate the effect of EGCG on the co-aggregation of Aβ and hIAPP. We found that EGCG molecules substantially diminish the β-sheet structures within the amyloid core regions of Aβ and hIAPP in their co-aggregates. Through hydrogen-bond, π-π and cation-π interactions targeting polar and aromatic residues of Aβ and hIAPP, EGCG effectively attenuates both inter-chain and intra-chain interactions within the co-aggregates. All these findings indicated that EGCG can effectively inhibit the co-aggregation of Aβ and hIAPP. Our study expands the potential applications of EGCG as an anti-amyloidosis agent and provides therapeutic options for the pathological association of amyloid misfolding disorders.

Structural insights into the co-aggregation of Aβ and tau amyloid core peptides: Revealing potential pathological heterooligomers by simulations

The self-aggregation of amyloid-β (Aβ) and tau proteins are closely implicated in Alzheimer's disease (AD). Recent evidence indicates that Aβ and tau proteins can cross-interact to form co-aggregates, which aggravates the development of AD. However, their transient heterooligomer conformations and co-aggregation molecular mechanisms are largely unknown. Herein, we utilize replica exchange molecular dynamics simulations to investigate the conformational ensembles formed by the central hydrophobic core of Aβ (Aβ16-22) and each of two fibril-nucleating core segments of tau (PHF6* and PHF6). Both PHF6 and PHF6* are found to co-aggregate with Aβ16-22 into β-sheet-rich heterooligomers. Intriguingly, PHF6 and Aβ16-22 peptides formed closed β-barrels, while PHF6* and Aβ16-22 formed open β-barrels, implying their distinct co-aggregation property. Compared to Aβ16-22-PHF6*, Aβ16-22-PHF6 heterooligomers have higher β-sheet content, and contain longer β-strands and larger β-sheets, indicative of stronger co-aggregation ability of PHF6 with Aβ16-22. Further analyses reveal that hydrophobic and π-π stacking interactions between Y310 of PHF6 and Aβ16-22 are crucial for the closed β-barrel/larger β-sheet formation in Aβ16-22-PHF6 heterooligomers. These results highlight the paramount importance of PHF6 fragment, particularly Y310 residue, as a potential target for inhibiting Aβ-tau co-aggregation, which could help for effective therapeutic design in mitigating Aβ-tau co-aggregation related amyloidogenesis.

Perspective for Molecular Dynamics Simulation Studies of Amyloid-β Aggregates

The cause of Alzheimer's disease is related to aggregates such as oligomers and amyloid fibrils consisting of amyloid-β (Aβ) peptides. Molecular dynamics (MD) simulation studies have been conducted to understand the molecular mechanism of the formation and disruption of Aβ aggregates. In this Perspective, the MD simulation studies are classified into four categories, focusing on the target systems: aggregation of Aβ peptides in bulk solution, Aβ aggregation at the interface, aggregation inhibitor against Aβ peptides, and nonequilibrium MD simulation of Aβ aggregates. MD simulation studies in these categories are first reviewed. Future perspectives in each category are then presented. Finally, the overall perspective is presented on how MD simulations of Aβ aggregates can be utilized for developing Alzheimer's disease treatment.

The stability and dynamics of the Aβ40/Aβ42 interlaced mixed fibrils

The accumulation of fibrillar amyloid-β (Aβ) aggregates in the brain, predominantly comprising 40- and 42-residue amyloid-β (Aβ40 and Aβ42), is a major pathological hallmark of Alzheimer's disease (AD). Aβ40 and Aβ42 naturally coexist in the brain under normal physiological conditions, and their interplay is generally considered to be a critical factor in the progression of AD. In addition to forming homogeneous oligomers and fibrils, Aβ40 and Aβ42 are also reported to co-assemble into hetero-oligomers and interlaced mixed fibrils, as evidenced by solid-state nuclear magnetic resonance spectroscopy (NMR), high molecular weight mass spectrometry and cross-seeding experiments. However, the exact molecular mechanisms underlying these processes remain unclear. In this study, we have used a recently resolved structurally uniform 1:1 mixture of Aβ40/Aβ42 interlaced mixed fibril as a prototype to gain insights into the molecular-level interactions between Aβ40 and Aβ42. We employed fully atomistic molecular dynamics simulation and compared the results with a homogeneous U-shaped Aβ40 fibrillar model. Our simulations using two different force fields provide conclusive evidence that the Aβ40/Aβ42 interlaced mixed fibril is energetically more favorable than the homogeneous Aβ40 fibrillar model. Furthermore, we also show that the increase in stability observed in the mixed model stems primarily from the packing interfaces and the stacking interfaces between C-termini. Our simulation results provide valuable mechanistic insights that are not readily accessible in experiment and could have significant implications for both the pathogenesis of AD and the development of current therapeutic strategies.Communicated by Ramaswamy H. Sarma.