Contribution

In our preliminary studies, we investigated the structure of Aβ (β-amyloid protein) associated with Alzheimer's disease (AD) and employed virtual screening to identify potential Aβ inhibitors. Molecular dynamics simulations were performed on the optimized compounds to evaluate their binding stability within the Aβ protein and to observe their inhibitory effects on Aβ aggregation. Through this approach, we identified a lead compound, WGX-50.

The chemical structure of WGX-50 features a conjugated amide bond and contains two aromatic rings (phenyl rings), which confer strong antioxidant properties. This allows WGX-50 to scavenge free radicals and prevent lipid peroxidation. Structurally, WGX-50 resembles many polyphenolic flavonoids, such as curcumin. Curcumin contains two symmetric aromatic rings, each with a hydroxyl group (-OH) and a methoxy group (-OCH₃), which endow it with potent antioxidant capacity, enabling it to scavenge free radicals and inhibit lipid peroxidation.

To further study the anti-aging and antioxidant capabilities of WGX-50, we combined experiments and computational simulations. In experiments, we used the model organism nematodes, through survival analysis, RNA-seq experiments, etc. In computational simulations, we found proteins in metabolic pathways related to aging, and used molecular docking and molecular simulations to study the binding mode, dynamic structural changes, and detailed interactions. We also studied the mechanism of WGX-50's reaction to OH free radicals through DFT calculations, including key transition state structures and activation energy barriers.

There are some key takeaways when using C. elegans as a model for survival analysis in antioxidant and anti-ROS experiments.

1. experimental design and sample size: Ensure sufficient sample size for statistical analysis. Too small a sample size for survival analysis may result in insignificant statistical results, affecting the reliability of the conclusion. There needs to be an appropriate sample size balance between different groups. addressing aversion: we used statistical methods to determine the number of nematodes required per group. a common sample size is around 50-100 nematodes per group to ensure sufficient statistical power.

2. Selection of ROS inducer concentration: Select an appropriate oxidative stress inducer and an appropriate concentration. If the concentration of the inducer is too high, it will cause large-scale acute death and mask the antioxidant ability of the C. elegans; if the concentration is too low, it may be difficult to show the antioxidant effect.

Solution: optimize the concentration of the inducer through preliminary experiments to find the dose that induces moderate oxidative stress in order to distinguish the differences in survival rates between different treatment groups.

3. Criteria for determining C. elegans death: The premise of survival analysis is to correctly determine the survival status of nematodes. Death of nematodes may not be easily observed immediately, and some nematodes may experience temporary inhibition of movement. Solution: use a microscope to carefully observe the C. elegans for movement responses, or use a light touch to determine whether it is alive or dead. For long-term survival experiments, we used fluorescently labeled nematode death dyes to assist in judgment.

In addition, C. elegans age and synchronization, methods of analyzing survival data, and the timing and frequency of survival records will all affect.

Figure: WGX-50 extends lifespan in N2 C. elegans at 20 °C (b, n > 280 worms per condition). **** p < 0.0001.

When conducting RNA interference experiments on C. elegans, attention should be paid to the selection of RNAi methods, the selection of experimental strains, the preparation of bacterial solution and IPTG induction, and the handling and transfer of C. elegans will affect the experimental results.
There are also precautions. Evaluation of RNAi effects:
Microscope observation: After RNAi, the phenotypic changes of the target gene should be evaluated, such as developmental defects, motor ability, behavioral changes, etc.

F1 generation observation: The silencing of some genes may require observation of the phenotype of the offspring.Quality of NGM plates: Ensure that the pH value and composition of NGM plates are stable to avoid affecting nematode growth and RNAi effects. Time selection of RNAi: The silencing effect of different genes may require different times. Before starting the experiment, it is recommended to consult the literature or conduct a small-scale time gradient experiment to determine the optimal RNAi treatment time. Validity of dsRNA expression: The expression of bacterial dsRNA may be uneven, so strain verification can be performed before the experiment, such as detecting dsRNA expression by RT-PCR.

The findings demonstrate that, in contrast to OP50, the WGX-50 treated survival gain was not reduced by RNAi in any of these genes. After being treated with WGX-50, none of the groups of F44E5.4/.5- or F59B2.12-RNAi worms were found to have substantially different survival rates from the control. Therefore, we deduced that WGX-50-mediated lifespan is not caused by these new genes. Note that although RNAi sri-40 and K09C6.9 tested significant, the differences in median survival were not substantial enough to support a plausible causal relationship.

In density functional theory (DFT) calculations, the first thing to do is the conformation search. Only when the low-energy conformations are effectively found in the chemical space is it meaningful. Then the reaction mechanism is constructed. The reaction of free radicals generally involves three mechanisms: SET, HAT and RAF, which involve calculating the transition state and reaction energy barrier of the reaction between these molecules and free radicals.

In the selection of DFT functionals, we chose M06-2X or ωB97X-D, optimized the structure of the reactants, and then we used Fukui function calculations and bond dissociation energy (BDE) calculations to determine the active sites of free radical attack. The Gaussian keyword “opt” was used to search for the transition state structure, and then the reaction path was scanned (IRC calculation) to find the corresponding reactants and products. The entire process used the implicit solvent model using the scrf=smd keyword, and the thermodynamic quantities were calculated to obtain the activation energy barrier.

Figure: DFT calculation of WGX-50 reacted with •OH radicals. (a) Fukui function analysis results of isosurfaces value of ±0.006 (blue, negative; green, positive). The TS structure of HAT reaction (b, c) and RAF reaction (d, e) were shown. (f) The relative free energy profile was calculated. Energies are in kcal/mol.

Molecular simulation, such as molecular docking, molecular dynamics simulations is a state-of-the-art technology that simulates the properties of molecules via the computer platform. Molecular dynamics simulation is an ideal tool to explore the dynamics process, validating and extending the results of molecular docking. Molecular simulation methods are able to accurately and visually predict the binding modes at atom level, shedding light on the mechanism details which are difficult to capture by in vitro experiments. They can also assist the redesign of functional biomolecules with higher efficiency and quickly screen bioactive compounds from large databases with millions of molecules.

By simulating the interaction between wgx-50 molecules and proteins in metabolic pathways over a period of time and observing important non-covalent interactions in molecular recognition such as hydrogen bonds and hydrophobic interactions, we can understand the binding process, binding sites, binding modes and post-binding stability of the two. The whole process requires docking first to obtain the initial structure of the complex, and then construct the topological structure of the complex through the selection and construction of the force field. The most important step in MD simulation is generating topology (i.e., giving the corresponding force field parameters to the molecules, including atom type, bond length, bond angle, and dihedral angle, which are the basis of MD simulation). We run the MD simulation in parallel to obtain the trajectory for subsequent analysis.

The representative conformation after cluster analysis during MD simulations showed that the key active residues F22, I26, L107, F138, V150 and F170 of HSP90 interacted with WGX-50. To gain a detailed landscape of the binding pattern of WGX-50, a popular NCI analysis, named as independent gradient model-based Hirshfeld partition of molecular density (IGMH), was conducted at the basis of the typical structure. The obvious π−π stacking between fragments is analyzed by IGMH. As results show, the aromatic rings of WGX-50 formed face-to-face shape π−π stacking with side chain of F138. Meanwhile, the T-shape π−π stacking interactions were formed between WGX-50 and the side chain of F22.Topological analysis based on the quantum theory of atoms in molecules is also a valuable method to study intermolecular interactions. We also observed that the methoxy group of WGX-50 formed the C-H---O hydrogen bond with CG2 atom of V186. We extended the AIM analysis to the reduced density gradient model to visualize the NCI pattern. Multiple BCPs between the sulfur atom and neighboring F22–I26–L107–F138–V150-F170 residues were calculated.

Figure: Noncovalent interaction analysis of WGX-50-HSP90 complex. (a) The key residues of HSP90 that interacted with WGX-50 were shown as sticks. Hirshfeld partition of molecular density (IGMH) analysis (b), and quantum theory of atoms in molecules (AIM) analysis of the complex(c). The blue region represents attractive van del Waals. Orange spheres show the BCPs. (d) Color-filled RDG scatter plot in the above model calculations.