Model

To predict potential targets of WGX-50, we used online machine learning (ML) platform SwissTargetPrediction (http://swisstargetprediction.ch/) both for human and mice molecules. Then we checked WGX-50 via SEA in ChEMBL 20 library setting the affinity threshold at 5 nM. More deeply, both extended connectivity fingerprints (ECFP, by rdkit) and maximum tanimoto coefficient (Tc) parameters were used for target prediction that involved searching database ChEMBL (https://www.ebi.ac.uk/chembl/) for proteins of which the ligands share similar structural features as WGX-50. ECFP was utilized to represent chemical structure of the query. Tc was employed as similarity metric for comparing fingerprints. For identified targets, a lower p-value suggests more significant association between the compound and target.

We predicted targets of WGX-50 based on its structural similarity to known proteins includes receptors, enzymes, and others. Interestingly, as predicted using SwissTargetPrediction, hsp-90aa1 and hsp-b1 are among the potential targets, of which, however, the probability scores were all around 0.1. In following analyses, hsp-90aa1 and hsf-1 were revealed among putative targets (p-val < 1.0×10-6) using ECFP fingerprints searching methods. HSP90 has been targeted to develop senolytics.[1]

Since molecular system is usually composed of a large number of particles, it is impossible to determine the properties of these complex systems using analytical methods. Molecular Dynamics (MD) simulation can avoid this problem; therefore, it is a powerful research method for exploring microscopic interactions and mechanisms.[2] Molecular docking is a technology developed based on the “lock and key” theory, which was able to identify the best binding modes between a series of small molecules and their receptors. MD simulation is a technique based on classical Newton mechanics to simulate molecular motion on atomic scale in computers.[3]

We conducted MD simulations to elucidate whether WGX-50 has the predicted actions against HSP90. MD simulations were performed using Amber 22 and AmberTools 22 packages.[4] The RMSD fluctuates between 1.5 and 2.5 Å (Figure 7, a), which is an indicative of a relatively stabilized WGX-50-HSP90 system during the simulation. This binding capacity stables evenly in a dynamic environment as shown in Rg plots (Figure 7, b), showing uniform levels of compactness with minimal unbinding events. RMSF results revealed that the complex was stabilized by WGX-50 binding, only regions of 45-60, 98-102, 125-150 were in relatively higher fluctuations than the others (Figure 7, c). The binding energy was calculated to be -31.3 ± 1.7 kcal/mol providing conclusive evidence on the binding stability.

Figure 1: The conformation structure analysis of wgx50-enzyme system. (a): Root mean square deviation (RMSD) of the backbone carbon atoms of WGX-50-HSP90 complex. The MD simulations were run in parallel for 3 times. (b): The compactness of the complex was measured by the radius of gyration (Rg). (c): RMSF analysis.

The representative conformation after cluster analysis during MD simulations showed that the key active site residues F22, I26, L107, F138, V150 and F170 of HSP90 interacted with WGX-50 (Figure 2, a). 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 (Figure 2, b), 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. It was intuitive that a vast green oral oval region exited between the aromatic rings.

Plots of the sign(λ₂)ρ(r) function mapped on the isosurface that demonstrate the regions of the negative function around F22, F138 residues, shown by green color, indicated the attractive nature of these interactions. Topological analysis based on the quantum theory of atoms in molecules (QTAIM) is also a valuable method to study intermolecular interactions (Figure 2, c). The topological parameters such as electron density ρ, Laplacian ∇²ρ, kinetic (G), potential (V) and total energy density (H) at bond critical points (BCPs) could be employed to determine the type of interactions and estimate the strength of hydrogen bonds. if ∇²ρ of BCP is positive, the interaction is a closed-shell type, such as ionic bonds, hydrogen bonds, halogen bonds, etc. In this case, the positive value for these four interactions indicates their essence of closed-shell interaction. The hydrogen bonds were estimated to be −5.8, -4.3 and -7.2 kcal/mol, respectively.

We also observed that the methoxy group of WGX-50 formed the C-H---O hydrogen bond with C_G2 atom of V186. We extended the AIM analysis to the reduced density gradient (RDG) model to visualize the NCI pattern. Multiple BCPs between the sulfur atom and neighboring F22–I26–L107–F138–V150-F170 residues and the sign(λ₂) r-dependence of RDG were calculated. Laplacian contour plots of electron density also support the topological patterns of the wgx50-HSP90 interactions, the vdW interactions derived from the methylene groups of F22–I26–L107–F138–V150-F170 (Figure 2, d). Collectively, these data suggest the pharmacological potential of WGX-50 against HSP90, and therefore it possibly is a choice of treatment of aging.

Figure 2: 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. Strong attractive interactions like electrostatic salt bridge are marked with blue color, weak interactions like vdW interaction with green, and steric repulsion in red.

[1].Filipe, H. A. L.; Loura, L. M. S. Molecular Dynamics Simulations: Advances and Applications. Molecules 2022, 27, 2105.

[2].Araki, M.; Matsumoto, S.; Bekker, G. J.; Isaka, Y.; Sagae, Y.; Kamiya, N.; Okuno, Y. Exploring ligand binding pathways on proteins using hypersound-accelerated molecular dynamics. Nat. Commun. 2021, 12 (1), 2793.

[3].Fuhrmann-Stroissnigg, H. et al. Identification of HSP90 inhibitors as a novel class of senolytics. Nat. Commun. 8, 422 (2017).

[4]. Case, D. A.; Belfon, K.; Ben-Shalom, I. Y.; Brozell, S. R.; Cerutti, D. S.; Cheatham, T. E.; Iii; Cruzeiro, V. W. D.; Darden, T. A.; Duke, R. E.; Giambasu, G.; Gilson, M. K.; Gohlke, H.; Goetz, A. W.; Harris, R.; Izadi, S.; Izmailov, S. A.; Kasavajhala, K.; Kovalenko, A.; Krasny, R.; Kurtzman, T.; Lee, T. S.; LeGrand, S.; Li, P.; Lin, C.; Liu, J.; Luchko, T.; Luo, R.; Man, V.; Merz, K. M.; Miao, Y.; Mikhailovskii, O.; Monard, G.; Nguyen, H.; Onufriev, A.; Pan, F.; Pantano, S.; Qi, R.; Roe, D. R.; Roitberg, A.; Sagui, C.; Schott-Verdugo, S.; Shen, J.; Simmerling, C. L.; Skrynnikov, N. R.; Smith, J.; Swails, J.; Walker, R. C.; Wang, J.; Wilson, L.; Wolf, R. M.; Wu, X.; Xiong, Y.; Xue, Y.; York, D. M.; Kollman, P. A. AMBER 2022; University of California: San Francisco, 2022.