Are Granulins Copper Sequestering Proteins?

Granulins (GRN 1-7) are short (~6 kDa), cysteine-rich proteins that are generated upon the proteolytic processing of progranulin (PGRN). These peptides, along with their precursor, have been implicated in multiple pathophysiological roles, especially in neurodegenerative diseases. Previously we showed that GRN-3 and GRN-5 are fully disordered in the reduced form implicating redox sensitive attributes to the proteins. Redox-based modulations are often carried out by metalloproteins in mitigating oxidative stress and maintaining metal-homeostasis within cells. To probe whether GRNs play a role in metal sequestration, we tested the metal binding propensity of the reduced forms of GRNs −3 and − 5 under neutral and acidic pH mimicking cytosolic and lysosomal conditions, respectively. We found, at neutral pH, both GRNs selectively bind Cu and no other divalent metal cations, with a greater specificity for Cu(I). Binding of Cu did not result in a disorder-to-order structural transition but partly triggered the multimerization of GRNs via uncoordinated cystines at both pH conditions. Overall, the results indicate that GRNs −3 and − 5 have surprisingly strong affinity for Cu in the pM range, comparable to other known copper sequestering proteins. The results also hint at a potential of GRNs to reduce Cu(II) to Cu(I), a process that has significance in mitigating Cu-induced ROS cytotoxicity in cells. Together, this report uncovers metal-coordinating property of GRNs for the first time, which may have profound significance in their structure and pathophysiological functions

Dynamics of Protofibril Elongation and Association Involved in Aβ42 peptide Aggregation in Alzheimer\u27s Disease

Background: The aggregates of a protein called, ‘Aβ’ found in brains of Alzheimer’s patients are strongly believed to be the cause for neuronal death and cognitive decline. Among the different forms of Aβ aggregates, smaller aggregates called ‘soluble oligomers’ are increasingly believed to be the primary neurotoxic species responsible for early synaptic dysfunction. Since it is well known that the Aβ aggregation is a nucleation dependant process, it is widely believed that the toxic oligomers are intermediates to fibril formation, or what we call the ‘on-pathway’ products. Modeling of Aβ aggregation has been of intense investigation during the last decade. However, precise understanding of the process, pre-nucleation events in particular, are not yet known. Most of these models are based on curve-fitting and overlook the molecular-level biophysics involved in the aggregation pathway. Hence, such models are not reusable, and fail to predict the system dynamics in the presence of other competing pathways. Results: In this paper, we present a molecular-level simulation model for understanding the dynamics of the amyloid-β (Aβ) peptide aggregation process involved in Alzheimer’s disease (AD). The proposed chemical kinetic theory based approach is generic and can model most nucleation-dependent protein aggregation systems that cause a variety of neurodegenerative diseases. We discuss the challenges in estimating all the rate constants involved in the aggregation process towards fibril formation and propose a divide and conquer strategy by dissecting the pathway into three biophysically distinct stages: 1) pre-nucleation stage 2) post-nucleation stage and 3) protofibril elongation stage. We next focus on estimating the rate constants involved in the protofibril elongation stages for Aβ42 supported by in vitro experimental data. This elongation stage is further characterized by elongation due to oligomer additions and lateral association of protofibrils (13) and to properly validate the rate constants involved in these phases we have presented three distinct reaction models. We also present a novel scheme for mapping the fluorescence sensitivity and dynamic light scattering based in vitro experimental plots to estimates of concentration variation with time. Finally, we discuss how these rate constants will be incorporated into the overall simulation of the aggregation process to identify the parameters involved in the complete Aβ pathway in a bid to understand its dynamics. Conclusions: We have presented an instance of the top-down modeling paradigm where the biophysical system is approximated by a set of reactions for each of the stages that have been modeled. In this paper, we have only reported the kinetic rate constants of the fibril elongation stage that were validated by in vitro biophysical analyses. The kinetic parameters reported in the paper should be at least accurate upto the first two decimal places of the estimate. We sincerely believe that our top-down models and kinetic parameters will be able to accurately model the biophysical phenomenon of Aβ protein aggregation and identify the nucleation mass and rate constants of all the stages involved in the pathway. Our model is also reusable and will serve as the basis for making computational predictions on the system dynamics with the incorporation of other competing pathways introduced by lipids and fatty acids

Dynamics of Protofibril Elongation and Association Involved in Aβ42 peptide Aggregation in Alzheimer\u27s Disease

Background: The aggregates of a protein called, ‘Aβ’ found in brains of Alzheimer’s patients are strongly believed to be the cause for neuronal death and cognitive decline. Among the different forms of Aβ aggregates, smaller aggregates called ‘soluble oligomers’ are increasingly believed to be the primary neurotoxic species responsible for early synaptic dysfunction. Since it is well known that the Aβ aggregation is a nucleation dependant process, it is widely believed that the toxic oligomers are intermediates to fibril formation, or what we call the ‘on-pathway’ products. Modeling of Aβ aggregation has been of intense investigation during the last decade. However, precise understanding of the process, pre-nucleation events in particular, are not yet known. Most of these models are based on curve-fitting and overlook the molecular-level biophysics involved in the aggregation pathway. Hence, such models are not reusable, and fail to predict the system dynamics in the presence of other competing pathways. Results: In this paper, we present a molecular-level simulation model for understanding the dynamics of the amyloid-β (Aβ) peptide aggregation process involved in Alzheimer’s disease (AD). The proposed chemical kinetic theory based approach is generic and can model most nucleation-dependent protein aggregation systems that cause a variety of neurodegenerative diseases. We discuss the challenges in estimating all the rate constants involved in the aggregation process towards fibril formation and propose a divide and conquer strategy by dissecting the pathway into three biophysically distinct stages: 1) pre-nucleation stage 2) post-nucleation stage and 3) protofibril elongation stage. We next focus on estimating the rate constants involved in the protofibril elongation stages for Aβ42 supported by in vitro experimental data. This elongation stage is further characterized by elongation due to oligomer additions and lateral association of protofibrils (13) and to properly validate the rate constants involved in these phases we have presented three distinct reaction models. We also present a novel scheme for mapping the fluorescence sensitivity and dynamic light scattering based in vitro experimental plots to estimates of concentration variation with time. Finally, we discuss how these rate constants will be incorporated into the overall simulation of the aggregation process to identify the parameters involved in the complete Aβ pathway in a bid to understand its dynamics. Conclusions: We have presented an instance of the top-down modeling paradigm where the biophysical system is approximated by a set of reactions for each of the stages that have been modeled. In this paper, we have only reported the kinetic rate constants of the fibril elongation stage that were validated by in vitro biophysical analyses. The kinetic parameters reported in the paper should be at least accurate upto the first two decimal places of the estimate. We sincerely believe that our top-down models and kinetic parameters will be able to accurately model the biophysical phenomenon of Aβ protein aggregation and identify the nucleation mass and rate constants of all the stages involved in the pathway. Our model is also reusable and will serve as the basis for making computational predictions on the system dynamics with the incorporation of other competing pathways introduced by lipids and fatty acids

Design of Peptides With, α,β-Dehydro-Residues: A Dipeptide With a Branched β-Carbon Dehydro-Residue at the (\u3ci\u3ei\u3c/i\u3e+1) Position, Methyl \u3ci\u3eN\u3c/i\u3e-(benzyloxycarbonyl)-α,β-Didehydrovalyl-L-Tryptophanate

The structure of the title peptide, C25H27N3O5, has been determined and its conformation analysed. Values of the standard peptide torsion angles are φ(1) = -44.2 (3)°, ψ(1) = 135.9 (2)°, φ(2) = -141.6 (2)° and ψ(T/2) = 168.0 (2)°. The crystal structure is stabilized by an intermolecular hydrogen bond, with an N···O distance of 2.919 (3)Å, which is formed between screw-axis-related NH and CO groups of dehydrovaline residues

A modified Stokes-Einstein equation for Aβ aggregation

<p>Abstract</p> <p>Background</p> <p>In all amyloid diseases, protein aggregates have been implicated fully or partly, in the etiology of the disease. Due to their significance in human pathologies, there have been unprecedented efforts towards physiochemical understanding of aggregation and amyloid formation over the last two decades. An important relation from which hydrodynamic radii of the aggregate is routinely measured is the classic Stokes-Einstein equation. Here, we report a modification in the classical Stokes-Einstein equation using a mixture theory approach, in order to accommodate the changes in viscosity of the solvent due to the changes in solute size and shape, to implement a more realistic model for A<it>β</it> aggregation involved in Alzheimer’s disease. Specifically, we have focused on validating this model in protofibrill lateral association reactions along the aggregation pathway, which has been experimentally well characterized.</p> <p>Results</p> <p>The modified Stokes-Einstein equation incorporates an effective viscosity for the mixture consisting of the macromolecules and solvent where the lateral association reaction occurs. This effective viscosity is modeled as a function of the volume fractions of the different species of molecules. The novelty of our model is that in addition to the volume fractions, it incorporates previously published reports on the dimensions of the protofibrils and their aggregates to formulate a more appropriate shape rather than mere spheres. The net result is that the diffusion coefficient which is inversely proportional to the viscosity of the system is now dependent on the concentration of the different molecules as well as their proper shapes. Comparison with experiments for variations in diffusion coefficients over time reveals very similar trends.</p> <p>Conclusions</p> <p>We argue that the standard Stokes-Einstein’s equation is insufficient to understand the temporal variations in diffusion when trying to understand the aggregation behavior of A<it>β</it>42 proteins. Our modifications also involve inclusion of improved shape factors of molecules and more appropriate viscosities. The modification we are reporting is not only useful in A<it>β</it> aggregation but also will be important for accurate measurements in all protein aggregation systems.</p

A Modified Stokes-Einstein Equation for A Beta Aggregation

Background: In all amyloid diseases, protein aggregates have been implicated fully or partly, in the etiology of the disease. Due to their significance in human pathologies, there have been unprecedented efforts towards physiochemical understanding of aggregation and amyloid formation over the last two decades. An important relation from which hydrodynamic radii of the aggregate is routinely measured is the classic Stokes-Einstein equation. Here, we report a modification in the classical Stokes-Einstein equation using a mixture theory approach, in order to accommodate the changes in viscosity of the solvent due to the changes in solute size and shape, to implement a more realistic model for A beta aggregation involved in Alzheimer\u27s disease. Specifically, we have focused on validating this model in protofibrill lateral association reactions along the aggregation pathway, which has been experimentally well characterized. Results: The modified Stokes-Einstein equation incorporates an effective viscosity for the mixture consisting of the macromolecules and solvent where the lateral association reaction occurs. This effective viscosity is modeled as a function of the volume fractions of the different species of molecules. The novelty of our model is that in addition to the volume fractions, it incorporates previously published reports on the dimensions of the protofibrils and their aggregates to formulate a more appropriate shape rather than mere spheres. The net result is that the diffusion coefficient which is inversely proportional to the viscosity of the system is now dependent on the concentration of the different molecules as well as their proper shapes. Comparison with experiments for variations in diffusion coefficients over time reveals very similar trends. Conclusions: We argue that the standard Stokes-Einstein\u27s equation is insufficient to understand the temporal variations in diffusion when trying to understand the aggregation behavior of A beta 42 proteins. Our modifications also involve inclusion of improved shape factors of molecules and more appropriate viscosities. The modification we are reporting is not only useful in A beta aggregation but also will be important for accurate measurements in all protein aggregation systems

A Game-Theoretic Approach to Deciphering the Dynamics of Amyloid-Beta Aggregation Along Competing Pathways

Aggregation of amyloid-β (Aβ) peptides is a significant event that underpins Alzheimer\u27s disease (AD). Aβ 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 Aβ 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 Aβ 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 Aβ 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 Aβ 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

Fatty Acid Concentration and Phase Transitions Modulate Aβ Aggregation Pathways

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 FAconcentration 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