Co-Aggregation of S100A9 with DOPA and Cyclen-Based Compounds Manifested in Amyloid Fibril Thickening without Altering Rates of Self-Assembly.

The amyloid cascade is central for the neurodegeneration disease pathology, including Alzheimer's and Parkinson's, and remains the focus of much current research. S100A9 protein drives the amyloid-neuroinflammatory cascade in these diseases. DOPA and cyclen-based compounds were used as amyloid modifiers and inhibitors previously, and DOPA is also used as a precursor of dopamine in Parkinson's treatment. Here, by using fluorescence titration experiments we showed that five selected ligands: DOPA-D-H-DOPA, DOPA-H-H-DOPA, DOPA-D-H, DOPA-cyclen, and H-E-cyclen, bind to S100A9 with apparent Kd in the sub-micromolar range. Ligand docking and molecular dynamic simulation showed that all compounds bind to S100A9 in more than one binding site and with different ligand mobility and H-bonds involved in each site, which all together is consistent with the apparent binding determined in fluorescence experiments. By using amyloid kinetic analysis, monitored by thioflavin-T fluorescence, and AFM imaging, we found that S100A9 co-aggregation with these compounds does not hinder amyloid formation but leads to morphological changes in the amyloid fibrils, manifested in fibril thickening. Thicker fibrils were not observed upon fibrillation of S100A9 alone and may influence the amyloid tissue propagation and modulate S100A9 amyloid assembly as part of the amyloid-neuroinflammatory cascade in neurodegenerative diseases

Modulation of γ-Secretase Activity by Multiple Enzyme-Substrate Interactions: Implications in Pathogenesis of Alzheimer's Disease

BACKGROUND: We describe molecular processes that can facilitate pathogenesis of Alzheimer's disease (AD) by analyzing the catalytic cycle of a membrane-imbedded protease γ-secretase, from the initial interaction with its C99 substrate to the final release of toxic Aβ peptides. RESULTS: The C-terminal AICD fragment is cleaved first in a pre-steady-state burst. The lowest Aβ42/Aβ40 ratio is observed in pre-steady-state when Aβ40 is the dominant product. Aβ42 is produced after Aβ40, and therefore Aβ42 is not a precursor for Aβ40. The longer more hydrophobic Aβ products gradually accumulate with multiple catalytic turnovers as a result of interrupted catalytic cycles. Saturation of γ-secretase with its C99 substrate leads to 30% decrease in Aβ40 with concomitant increase in the longer Aβ products and Aβ42/Aβ40 ratio. To different degree the same changes in Aβ products can be observed with two mutations that lead to an early onset of AD, ΔE9 and G384A. Four different lines of evidence show that γ-secretase can bind and cleave multiple substrate molecules in one catalytic turnover. Consequently depending on its concentration, NotchΔE substrate can activate or inhibit γ-secretase activity on C99 substrate. Multiple C99 molecules bound to γ-secretase can affect processive cleavages of the nascent Aβ catalytic intermediates and facilitate their premature release as the toxic membrane-imbedded Aβ-bundles. CONCLUSIONS: Gradual saturation of γ-secretase with its substrate can be the pathogenic process in different alleged causes of AD. Thus, competitive inhibitors of γ-secretase offer the best chance for a successful therapy, while the noncompetitive inhibitors could even facilitate development of the disease by inducing enzyme saturation at otherwise sub-saturating substrate. Membrane-imbedded Aβ-bundles generated by γ-secretase could be neurotoxic and thus crucial for our understanding of the amyloid hypothesis and AD pathogenesis

The Binding of Different Substrate Molecules at the Docking Site and the Active Site of γ-Secretase Can Trigger Toxic Events in Sporadic and Familial Alzheimer’s Disease

Pathogenic changes in γ-secretase activity, along with its response to different drugs, can be affected by changes in the saturation of γ-secretase with its substrate. We analyze the saturation of γ-secretase with its substrate using multiscale molecular dynamics studies. We found that an increase in the saturation of γ-secretase with its substrate could result in the parallel binding of different substrate molecules at the docking site and the active site. The C-terminal domain of the substrate bound at the docking site can interact with the most dynamic presenilin sites at the cytosolic end of the active site tunnel. Such interactions can inhibit the ongoing catalytic activity and increase the production of the longer, more hydrophobic, and more toxic Aβ proteins. Similar disruptions in dynamic presenilin structures can be observed with different drugs and disease-causing mutations. Both, C99-βCTF-APP substrate and its different Aβ products, can support the toxic aggregation. The aggregation depends on the substrate N-terminal domain. Thus, the C99-βCTF-APP substrate and β-secretase path can be more toxic than the C83-αCTF-APP substrate and α-secretase path. Nicastrin can control the toxic aggregation in the closed conformation. The binding of the C99-βCTF-APP substrate to γ-secretase can be controlled by substrate channeling between the nicastrin and β-secretase. We conclude that the presented two-substrate mechanism could explain the pathogenic changes in γ-secretase activity and Aβ metabolism in different sporadic and familial cases of Alzheimer’s disease. Future drug-development efforts should target different cellular mechanisms that regulate the optimal balance between γ-secretase activity and amyloid metabolism

The Binding of Different Substrate Molecules at the Docking Site and the Active Site of γ-Secretase Can Trigger Toxic Events in Sporadic and Familial Alzheimer’s Disease

Pathogenic changes in γ-secretase activity, along with its response to different drugs, can be affected by changes in the saturation of γ-secretase with its substrate. We analyze the saturation of γ-secretase with its substrate using multiscale molecular dynamics studies. We found that an increase in the saturation of γ-secretase with its substrate could result in the parallel binding of different substrate molecules at the docking site and the active site. The C-terminal domain of the substrate bound at the docking site can interact with the most dynamic presenilin sites at the cytosolic end of the active site tunnel. Such interactions can inhibit the ongoing catalytic activity and increase the production of the longer, more hydrophobic, and more toxic Aβ proteins. Similar disruptions in dynamic presenilin structures can be observed with different drugs and disease-causing mutations. Both, C99-βCTF-APP substrate and its different Aβ products, can support the toxic aggregation. The aggregation depends on the substrate N-terminal domain. Thus, the C99-βCTF-APP substrate and β-secretase path can be more toxic than the C83-αCTF-APP substrate and α-secretase path. Nicastrin can control the toxic aggregation in the closed conformation. The binding of the C99-βCTF-APP substrate to γ-secretase can be controlled by substrate channeling between the nicastrin and β-secretase. We conclude that the presented two-substrate mechanism could explain the pathogenic changes in γ-secretase activity and Aβ metabolism in different sporadic and familial cases of Alzheimer’s disease. Future drug-development efforts should target different cellular mechanisms that regulate the optimal balance between γ-secretase activity and amyloid metabolism

The Binding of Different Substrate Molecules at the Docking Site and the Active Site of γ-Secretase Can Trigger Toxic Events in Sporadic and Familial Alzheimer’s Disease

Pathogenic changes in γ-secretase activity, along with its response to different drugs, can be affected by changes in the saturation of γ-secretase with its substrate. We analyze the saturation of γ-secretase with its substrate using multiscale molecular dynamics studies. We found that an increase in the saturation of γ-secretase with its substrate could result in the parallel binding of different substrate molecules at the docking site and the active site. The C-terminal domain of the substrate bound at the docking site can interact with the most dynamic presenilin sites at the cytosolic end of the active site tunnel. Such interactions can inhibit the ongoing catalytic activity and increase the production of the longer, more hydrophobic, and more toxic Aβ proteins. Similar disruptions in dynamic presenilin structures can be observed with different drugs and disease-causing mutations. Both, C99-βCTF-APP substrate and its different Aβ products, can support the toxic aggregation. The aggregation depends on the substrate N-terminal domain. Thus, the C99-βCTF-APP substrate and β-secretase path can be more toxic than the C83-αCTF-APP substrate and α-secretase path. Nicastrin can control the toxic aggregation in the closed conformation. The binding of the C99-βCTF-APP substrate to γ-secretase can be controlled by substrate channeling between the nicastrin and β-secretase. We conclude that the presented two-substrate mechanism could explain the pathogenic changes in γ-secretase activity and Aβ metabolism in different sporadic and familial cases of Alzheimer’s disease. Future drug-development efforts should target different cellular mechanisms that regulate the optimal balance between γ-secretase activity and amyloid metabolism

Structural Analysis of the Simultaneous Activation and Inhibition of γ-Secretase Activity in the Development of Drugs for Alzheimer’s Disease

Significance: The majority of the drugs which target membrane-embedded protease γ-secretase show an unusual biphasic activation–inhibition dose-response in cells, model animals, and humans. Semagacestat and avagacestat are two biphasic drugs that can facilitate cognitive decline in patients with Alzheimer’s disease. Initial mechanistic studies showed that the biphasic drugs, and pathogenic mutations, can produce the same type of changes in γ-secretase activity. Results: DAPT, semagacestat LY-411,575, and avagacestat are four drugs that show different binding constants, and a biphasic activation–inhibition dose-response for amyloid-β-40 products in SH-SY5 cells. Multiscale molecular dynamics studies have shown that all four drugs bind to the most mobile parts in the presenilin structure, at different ends of the 29 Å long active site tunnel. The biphasic dose-response assays are a result of the modulation of γ-secretase activity by the concurrent binding of multiple drug molecules at each end of the active site tunnel. The drugs activate γ-secretase by facilitating the opening of the active site tunnel, when the rate-limiting step is the tunnel opening, and the formation of the enzyme–substrate complex. The drugs inhibit γ-secretase as uncompetitive inhibitors by binding next to the substrate, to dynamic enzyme structures which regulate processive catalysis. The drugs can modulate the production of different amyloid-β catalytic intermediates by penetration into the active site tunnel, to different depths, with different flexibility and different binding affinity. Conclusions: Biphasic drugs and pathogenic mutations can affect the same dynamic protein structures that control processive catalysis. Successful drug-design strategies must incorporate transient changes in the γ-secretase structure in the development of specific modulators of its catalytic activity

Mechanism and kinetics of CBDA decarboxylation into CBD in hemp

Cannabidiol (CBD) is a pharmacologically active ingredient for use in medical, cosmetic, and food products. CBD forms from cannabidiolic acid (CBDA) with the process of decarboxylation by heating cannabis (Cannabis sativa L.) material. During the production of CBD-rich material, decarboxylation should be performed in precise conditions regarding temperature and time. The experiments were performed by heating hemp samples at 100, 110, 120, 130, and 140 °C for 180 min. Materials were sampled every 20 min and cannabinoid content was analyzed using HPLC, followed by calculation of kinetic parameters. Experimental results showed an exponential reduction of CBDA in the samples during heating. CBD simultaneously increased, and after a specific point, CBD started degrading. The optimal conditions were 140 °C for 30 min. At the level of molecular orbitals, reaction steps, and reaction coordinates, along with the corresponding changes in molecular energy, the molecular mechanism of CBDA decarboxylation and CBD formation was described. Computational analysis has confirmed that the mechanism of CBDA decarboxylation is a direct beta-keto acid pathway. The course of CBDA decarboxylation depends on the time, temperature, and chemical composition of the sample