A minimal conformational switching-dependent model for amyloid self-assembly

Progress in Multiscale Modeling of Silk Materials

As a result of their hierarchical structure and biological processing, silk fibers rank among nature's most remarkable materials. The biocompatibility of silk-based materials and the exceptional mechanical properties of certain fibers has inspired the use of silk in numerous technical and medical applications. In recent years, computational modeling has clarified the relationship between the molecular architecture and emergent properties of silk fibers and has demonstrated predictive power in studies on novel biomaterials. Here, we review advances in modeling the structure and properties of natural and synthetic silk-based materials, from early structural studies of silkworm cocoon fibers to cutting-edge atomistic simulations of spider silk nanofibrils and the recent use of machine learning models. We explore applications of modeling across length scales: from quantum mechanical studies on model peptides, to atomistic and coarse-grained molecular dynamics simulations of silk proteins, to finite element analysis of spider webs. As computational power and algorithmic efficiency continue to advance, we expect multiscale modeling to become an indispensable tool for understanding nature's most impressive fibers and developing bioinspired functional materials.

Unveiling the Potential of Polyphenols as Anti-Amyloid Molecules in Alzheimer's Disease

Alzheimer's disease (AD) is a devastating neurodegenerative disease that mostly affects the elderly population. Mechanisms underlying AD pathogenesis are yet to be fully revealed, but there are several hypotheses regarding AD. Even though free radicals and inflammation are likely to be linked with AD pathogenesis, still amyloid-beta (Aβ) cascade is the dominant hypothesis. According to the Aβ hypothesis, a progressive buildup of extracellular and intracellular Aβ aggregates has a significant contribution to the AD-linked neurodegeneration process. Since Aβ plays an important role in the etiology of AD, therefore Aβ-linked pathways are mainly targeted in order to develop potential AD therapies. Accumulation of Aβ plaques in the brains of AD individuals is an important hallmark of AD. These plaques are mainly composed of Aβ (a peptide of 39-42 amino acids) aggregates produced via the proteolytic cleavage of the amyloid precursor protein. Numerous studies have demonstrated that various polyphenols (PPHs), including cyanidins, anthocyanins, curcumin, catechins and their gallate esters were found to markedly suppress Aβ aggregation and prevent the formation of Aβ oligomers and toxicity, which is further suggesting that these PPHs might be regarded as effective therapeutic agents for the AD treatment. This review summarizes the roles of Aβ in AD pathogenesis, the Aβ aggregation pathway, types of PPHs, and distribution of PPHs in dietary sources. Furthermore, we have predominantly focused on the potential of food-derived PPHs as putative anti-amyloid drugs.

Promoted Aggregation of Aβ on Lipid Bilayers in an Open Flowing System

Self-assembly of amyloid-β (Aβ) peptides in nonequilibrium, flowing conditions is associated with pathogenesis of Alzheimer's disease. We examined the role of biologically relevant, nonequilibrium, flowing conditions in the desorption, diffusion, and integration of Aβ-lipid assemblies at the membrane surface using a microchannel connected with microsyringes. A 1,2-dimyristoyl-sn-glycero-3-phosphocholine (DMPC) bilayer was formed on a glass substrate and incubated in Aβ solution under either a quiescent condition (no flow) or flowing condition for 24 h. Although dot-like aggregates (<1 μm) comprising Aβ fibrils formed on the DMPC membrane under the quiescent condition, larger plaque-like aggregates formed under the flowing condition, suggesting that nonequilibrium continuous flow governs the cytotoxicity of Aβ species. We propose that Aβ adsorption on the membrane surface involves spontaneous desorption of Aβ-lipid to form self-assembling aggregates, with this accelerated by surface shear forces. These findings suggest that nonequilibrium, flowing conditions influence inter/intra-molecular Aβ-fibril formation to trigger formation of amyloid plaques.

Computational Model for Studying Breakage-Dependent Amyloid Growth

Amyloid fibrils are typically associated with neurodegenerative diseases. Recent studies have suggested that, similar to prions, many amyloid proteins are infectious in nature and may cause spreading and dissemination of diseases. Typical amyloid infection propagates by recruiting functional proteins into amyloidogenic form and multiplying by breaking the existing fibril. In this study, we model the kinetics of fibril growth through breakage and the subsequent elongation process, similar to the prion infection process. Using kinetic Monte Carlo simulations as well as mathematical counting methods, we show how the measurable quantities like the 50% aggregation time (T₅₀) and the maximum growth rate (V_max) scale with various parameters in the problem. This study has a direct application where it can be used to understand experiments that amplify the minute amount of amyloid seeds present in biological fluid for early detection of human disease. Using the knowledge from our simulations, we can predict the initial seed concentration, known as the filament kinetics.

On the Conformational Dynamics of β-Amyloid Forming Peptides: A Computational Perspective

Understanding the conformational dynamics of proteins and peptides involved in important functions is still a difficult task in computational structural biology. Because such conformational transitions in β-amyloid (Aβ) forming peptides play a crucial role in many neurological disorders, researchers from different scientific fields have been trying to address issues related to the folding of Aβ forming peptides together. Many theoretical models have been proposed in the recent years for studying Aβ peptides using mathematical, physicochemical, and molecular dynamics simulation, and machine learning approaches. In this article, we have comprehensively reviewed the developmental advances in the theoretical models for Aβ peptide folding and interactions, particularly in the context of neurological disorders. Furthermore, we have extensively reviewed the advances in molecular dynamics simulation as a tool used for studying the conversions between polymorphic amyloid forms and applications of using machine learning approaches in predicting Aβ peptides and aggregation-prone regions in proteins. We have also provided details on the theoretical advances in the study of Aβ peptides, which would enhance our understanding of these peptides at the molecular level and eventually lead to the development of targeted therapies for certain acute neurological disorders such as Alzheimer's disease in the future.

Imaging Aβ(1-42) fibril elongation reveals strongly polarised growth and growth incompetent states

The major hallmark of Alzheimer's disease is the deposition of plaques of amyloid fibrils formed from amyloid-β (Aβ) peptides. Kinetic studies have contributed significantly towards a mechanistic understanding of amyloid fibril self-assembly, however dynamic features of the aggregation process cannot be captured using ensemble methods. Here we present an assay for imaging Aβ42 aggregation dynamics at the single fibril level, allowing for the quantitative extraction of concentration and temperature dependent kinetic parameters. From direct observation of elongation using TIRF and super-resolution optical microscopy, we find that Aβ42 fibril growth is strongly polarized, with fast and slow growing ends arising from different elongation rates, but also from a growth incompetent state, which dominates the process at the slow growing end. Our findings reveal the surprising complexity of the Aβ42 fibril elongation reaction at the microscopic level.

Modeling the Effect of Monomer Conformational Change on the Early Stage of Protein Self-Assembly into Fibrils

Filamentous self-assembly of proteins is an important process implicated in a plethora of human diseases and of interest for nanotechnology. Using rate equations, we analyze the early stage of the process in solutions that initially contain fibrillation-passive protein monomers and in which the nascent fibrils are practically insoluble. The analysis is based on a model accounting for the conformational and/or other changes the passive monomers experience to transform themselves into fibrillation-active monomers and thus become fibril nuclei. The model allows exact, comprehensive, and simple mathematical description of the early stage of fibrillation, which reveals the usually neglected role of the nucleation nonstationarity in this stage of fibrillation. We obtain exact and user-friendly expressions for experimentally accessible quantities such as the size distribution of fibrils, their number and mass concentrations, the rate and nonstationary period of fibril nucleation, and the delay time of fibril formation. Analyzing available experimental data, we find that the theory successfully describes the fibrillation time course of pathological and nonpathological ataxin-3, a protein involved in the neurodegenerative disorder spinocerebellar ataxia type-3. The analysis provides mechanistic insight into the reason for the higher fibril nucleation and elongation rates of the pathological ataxin-3.