Fragment-Based Approaches Identified Tecovirimat-Competitive Novel Drug Candidate for Targeting the F13 Protein of the Monkeypox Virus

Abstract

Monkeypox is a serious public health issue in tropical and subtropical areas. Antivirals that target monkeypox proteins might lead to more effective and efficient therapy. The F13 protein is essential for the growth and maturation of the monkeypox virus. F13 inhibition might be a viable therapeutic target for monkeypox. The in silico fragment-based drug discovery method for developing antivirals may provide novel therapeutic options. In this study, we generated 800 compounds based on tecovirimat, an FDA-approved drug that is efficacious at nanomolar quantities against monkeypox. These compounds were evaluated to identify the most promising fragments based on binding affinity and pharmacological characteristics. The top hits from the chemical screening were docked into the active site of the F13 protein. Molecular dynamics simulations were performed on the top two probable new candidates from molecular docking. The ligand-enzyme interaction analysis revealed that the C2 ligand had lower binding free energy than the standard ligand tecovirimat. Water bridges, among other interactions, were shown to stabilize the C2 molecule. Conformational transitions and secondary structure changes in F13 protein upon C2 binding show more native three-dimensional folding of the protein. Prediction of pharmacological properties revealed that compound C2 may be promising as a drug candidate for monkeypox fever. However, additional in vitro and in vivo testing is required for validation.

Keywords: F13 protein; fragment-based drug design; molecular dynamics simulation; monkeypox virus; tecovirimat.

Figure 1

The predicted structure of MPXV-F13 protein using the AlphaFold, embedded in the ChimeraX program.

Figure 2

The identified top three hits of each fragment after virtual screening against library developed using fragment replacement.

Figure 3

The three-dimensional interactions and orientation of (A) ST246 and newly identified compounds from fragment 1, i.e., (B) C1 (C) C2 and (D) C3, (E) C4 (F) C5, and (G) C6.

Figure 4

Root mean square deviation and fluctuation (RMSD/F) of individual ligands and Cα of protein residues. (A) Compounds generated from fragment 1 in complex with the F13 protein. (B) individual RMSDs of ST-246, C1, C2, and C3. (C) RMSF plots analysis of fragment 1-based compounds. (D) Compounds generated from fragment 2 in complex with the F13 protein, and (E) individual RMSDs of ST-246, C4, C5, and C6, and (F) square fluctuations of fragment 2-based compounds after 400 ns of simulation.

Figure 5

The plots show the results of (A) radius of gyration (Rg), (B) solvent accessible surface area (SASA), and (C) hydrogen bond occupancy analysis during the simulation time of 400 ns per system. The line graph has been enriched with color-coded legends for each graph.

Figure 6

Probability distribution of root means square deviation (RMSD) and radius of gyration in 2D space. The data from molecular dynamics (MD) runs were integrated into each model, i.e., (A”) ST-246 (B”) C2, and (C”) C3. (A’–C’) show the most preferred orientation of protein and ligand in state I and state II. (A–C) show the active site of view of ST-246, C2, and C3 in the active site of MPXV-F13 protein, respectively.

Figure 7

Secondary structure and conformational analysis of ST-246 and new candidate drug C2 against MPXV-F13 protein. (A) Percentage secondary structure elements of ST246, C2, and C3-bound to F13 protein of MPXV. (B) ST246 propagation throughout the trajectory. (C) C2 bound MPXV at regular intervals of time. (D) Comparison of secondary structures of ST246-bound MPXV-F13 protein, and (E) comparison of secondary structures of C2-bound MPXV-F13 protein using cartoon representation.

Figure 8

The molecular mechanics, generalized born, and surface area solvation energies of ST246 (blue), C2 (orange), and C3 (gray) in complex with F13 protein of MPXV.

Figure 9

Scatter plot generated for the first two principal components via python scripts using built-in matplotlib and NumPy libraries for (A) ST246-bound MPXV F13 protein, (B) F13-C2 complex, and (C) C3-bound F13 protein along with (D) the percentage contributions of 10 principal components.