Aggregation of amyloid β (Aβ) peptides is a significant event that underpins Alzheimer disease (AD) pathology. Aβ aggregates, especially the low-molecular weight oligomers, are the primary toxic agents in AD and hence, there is increasing interest in understanding their formation and behavior. Aggregation is a nucleation-dependent process in which the pre-nucleation events are dominated by Aβ homotypic interactions. Dynamic flux and stochasticity during pre-nucleation renders the reactions susceptible to perturbations by other molecules. In this context, we investigate the heterotypic interactions between Aβ and fatty acids (FAs) by two independent tool-sets such as reduced order modelling (ROM) and ensemble kinetic simulation (EKS). We observe that FAs influence Aβ dynamics distinctively in three broadly-defined FA concentration regimes containing non-micellar, pseudo-micellar or micellar phases. While the non-micellar phase promotes on-pathway fibrils, pseudo-micellar and micellar phases promote predominantly off-pathway oligomers, albeit via subtly different mechanisms. Importantly off-pathway oligomers saturate within a limited molecular size, and likely with a different overall conformation than those formed along the on-pathway, suggesting the generation of distinct conformeric strains of Aβ, which may have profound phenotypic outcomes. Our results validate previous experimental observations and provide insights into potential influence of biological interfaces in modulating Aβ aggregation pathways.
Aggregation of amyloid- β (A β ) peptides is a significant event that underpins Alzheimer's disease (AD). A β aggregates, especially the low-molecular weight oligomers, are the primary toxic agents in AD pathogenesis. Therefore, there is increasing interest in understanding their formation and behaviour. In this paper, we use our previously established results on heterotypic interactions between A β and fatty acids (FAs) to investigate off-pathway aggregation under the control of FA concentrations to develop a mathematical framework that captures the mechanism. Our framework to define and simulate the competing on- and off-pathways of A β aggregation is based on the principles of game theory. Together with detailed simulations and biophysical experiments, our models describe the dynamics involved in the mechanisms of A β aggregation in the presence of FAs to adopt multiple pathways. Specifically, our reduced-order computations indicate that the emergence of off- or on-pathway aggregates are tightly controlled by a narrow set of rate constants, and one could alter such parameters to populate a particular oligomeric species. These models agree with the detailed simulations and experimental data on using FA as a heterotypic partner to modulate the temporal parameters. Predicting spatio-temporal landscape along competing pathways for a given heterotypic partner such as lipids is a first step towards simulating scenarios in which the generation of specific ‘conformer strains’ of A β could be predicted. This approach could be significant in deciphering the mechanisms of amyloid aggregation and strain generation, which are ubiquitously observed in many neurodegenerative diseases.
determination is typically carried out in vitro and requires pure samples of the studied assemblies. Here, we describe an integrative structure determination approach based on in vivo measurements of genetic interactions of point mutants of the assembly proteins. Using the point-mutant epistatic miniarray profiling (pE-MAP) to measure growth phenotypes (Braberg et al. Cell, 2013), we construct phenotypic profiles for point mutations crossed against single gene deletions, other point mutations, and/or chemical or physical perturbations. We quantify the similarity between pE-MAP profiles, allowing us to convert the phenotypic data into upper distance bounds on pairs of mutated residues. We parametrized these distance restraints using a pE-MAP dataset of 305 point mutations in histones H3 and H4 crossed against an array of 1,400 gene deletion alleles. Using integrative structure modeling based on this data, we reconstructed the structure of the H3-H4 dimer with an accuracy better than 3.0 Å RMSD for Ca-atoms. To assess the applicability of the approach, we determined the structures of two additional protein assemblies from pE-Map datasets not used in the parametrization: (1) subunits Rpb1 and Rpb2 of yeast RNA polymerase using a dataset that includes 53 single point mutants and a library of 1,200 gene-deletion alleles, and (2) subunits RpoB and RpoC of a bacterial RNA polymerase using a dataset of 49 point mutations subject to 139 different conditions (e.g., treatments with chemicals and temperature shocks). The resulting model accuracy is comparable to that obtained with chemical cross-linking data. The approach is implemented in our open-source Integrative Modeling Platform software (http://integrativemodeling.org), thus allowing us to model structures based on other types of structural datasets as well as genetic interactions data.
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