Computational Approaches in Targeting Monkeypox Virus:

A Focus on Phytochemical Inhibition of Profilin-like Protein A42R

 

Afsana Amin Shorna¹, Farhana Rahman¹, Md. Shakil Ahmed¹, Rabeya Basri¹,

Mahbuba Haque¹, Tania Khan¹, Most. Arzu Banu², Md. Atiqur Rahman^1,3*

¹Department of Pharmacy, University of Development Alternative (UODA),

Dhanmondi, Dhaka 1209, Bangladesh.

²Department of Biotechnology and Genetic Engineering,

University of Development Alternative (UODA), Dhanmondi, Dhaka1209, Bangladesh.

³Professor, Department of Pharmacy, University of Development Alternative (UODA),

Dhanmondi, Dhaka1209, Bangladesh.

 

ABSTRACT:

Monkeypox, a viral infection caused by the Monkeypox virus (MPXV), poses a significant public health threat. To identify potential antiviral metabolites against MPXV, we focused on the monkeypox profilin-like protein, crucial for viral replication. Twenty metabolites from various classes were retrieved from PubChem for molecular dynamics simulations. The top three molecules—Melongoside N, CID-4483043, Avenacosid A, CID-267363, and Melongoside P, CID- 131750951 demonstrated the best binding affinity for Profilin-like Protein A42R (PDB-4QWO). These ligands displayed stable interactions and minimal structural fluctuations during simulations, as indicated by favorable RMSD, RMSF, Rg, SASA, MolSA, and PSA results. The ligands maintained acceptable conformational stability with RMSD values within 1–3 Å, showing minimal structural changes. The ligands exhibited stable interactions with specific protein residues, indicating consistent and limited local alterations in the protein structure. Throughout a 250 ns simulation, the ligands maintained the protein's compactness, with average Rg values suggesting no major structural changes. Ligand complexes displayed typical van der Waals surface areas and polar interactions, supporting their stable interaction with the target protein. These ligands show promise as antiviral agents against monkeypox, with in-silico findings providing valuable insights for drug design. However, further experimental validation is crucial to advancing these ligands toward tangible antiviral therapeutics. This study contributes vital information to the computational drug discovery field, emphasizing interdisciplinary approaches for effective viral infection control.

 

 

 

INTRODUCTION:

Monkeypox virus (MPXV) belongs to the Orthopoxvirus genus and shares close genetic ties with other human pathogens, such as Variola major virus (VARV), responsible for smallpox, Cowpox virus (CPXV), and Vaccinia virus (VACV).¹ The virus's discovery traces back to 1958 when an outbreak occurred among macaques imported from Singapore into Denmark for polio-vaccine research.²

Human monkeypox manifests with symptoms such as headache, fever, and flu-like symptoms, followed by the emergence of distinct pox lesions shortly after the onset of symptoms.^3,4 Although the majority of patients recover within two to four weeks, monkeypox can be a life-threatening disease, with case-fatality ratios of 10.6% for infections caused by MPXV clade 1 strains (formerly recognized as the Central African clade) and 3.6% for infections by MPXV clade 2 and 3 strains (formerly identified as the West African clade).^5,6

 

Given the absence of established clinical treatments for MPXV infection, the application of computational chemical biology techniques becomes crucial, especially during MPX outbreaks.⁷ In particular scenarios, such as these outbreaks, computational methods like molecular docking, virtual screening,⁸ and artificial intelligence^9,10 (referred to as "dry method" research) play a significant role in Traditional Chinese Medicine (TCM) for preventing and treating infectious diseases. These computational approaches are essential for reducing reliance on animal models in pharmacological research, aiding in the rational design of novel and safe drug candidates, repurposing existing drugs, and providing support to medicinal chemists and pharmacologists throughout the drug discovery process.¹¹

 

Two key proteins, the MPXV F8-A22-E4 DNA polymerase holoenzyme¹² and profilin-like protein A42R,¹³ have been identified as potential targets for screening drugs that can impede MPXV replication and proliferation. The A42R protein that is encoded by the gp153 locus of MPXV has significant amino-acid sequence identity to eukaryotic cell profilin proteins.¹⁴ Proteins from the profilin family are actin-binding proteins that participate in the assembly and regulation of F-actin^15,16,17. This is facilitated by ongoing efforts to decipher protein structures, guiding the exploration of structure-based drug discovery methodologies.

 

METHODS AND MATERIALS:

Receptors and ligands selection:

To explore potential antiviral metabolites against the monkeypox virus, our focus centered on the monkeypox profilin-like protein, a crucial element in viral replication and assembly.^18,19 A set of 20 metabolites in the SDF format, spanning various classes, was acquired from the PubChem database through this approach. These metabolites, which have undergone prior scrutiny for their antiviral properties.²⁰ PubChem, a comprehensive database, catalogs chemical compounds along with their responses to biological experiments.²¹

 

For additional molecular dynamics simulation investigations, only the top three molecules Melongoside N, Avenacosid A, and Melongoside P that demonstrated the best binding affinity (least binding energy) for the Profilin-like Protein A42R (PDB-4QWO) were chosen.

 

Protein Preparation and Generation of Receptor Grids:

The Profilin-like Protein A42R (PDB-4QWO) of the Monkeypox Virus is the protein that is being studied in this study. The protein's structure was in the PDB using the RCSB Protein Data Bank (https://www.rcsb.org/),²² and it was prepared for use in molecular docking using Maestro Desmond's Protein Preparation Wizard version 12.5 (Schrodinger Version 2020-3 Schrodinger, LLC, New York, NY, 2020). The following criteria were applied in order to get the desired outcomes: assign bond orders, use the CCD database, add hydrogens, form zero-order bonds to metals, form disulfide bonds, fill in missing side chains and loops using prime, fixed cap termini, delete waters beyond 5 from heat groups; and generate heat states of pH 7.0 2.0 using Epik. Assign bond orders, make use of the CCD database, add hydrogens, and generate zero were the evaluation criteria used to assess the outcomes. The H-bond was assigned to PROPKA pH level 7.0 and the devaluation was confined to RMAD 0.30 utilizing the converging heavy atom using the refine tab and the OPLS3e force field. The receptor grid was then created by focusing on the protein's natural ligand.²³

 

Ligand Compounds’ Preparation:

The open-source PubChem database was used to obtain all of the three-dimensional (3D) structures of the phytochemicals in SDF format (SDF).²⁴ Using LigPrep, all ligand structures were created for molecular docking. We used the Epik Ionizer to make them smaller and the OPLS3e force field to make them smaller to make things simpler. Each structure could have a maximum of 32 conformers, and its RMSD was 1.0A.

 

Molecular Docking and Visualization:

The PDB structures of all protein-ligand complexes were retrieved for post-docking analysis using the extra precision (XP) technique of molecular docking. All protein-ligand complexes were docked using the Schrödinger Version 2021-2: Maestro, Schrödinger, LLC, New York, NY, 2020-3. Using Ligplot+ version 2.2, the non-covalent interactions (polar and hydrophobic) between protein-ligand complexes were examined. This visualization tool was made incredibly effective by the Java interface (Java SE Runtime Environment 8u271)^8,9 which only permitted the concatenated PDB files produced by the Maestro application. Also, the compactness of structural bonding, polar and non-polar interaction bonds in complexes, as well as other pertinent properties, were confirmed using the 64-bit version of Discovery Studio Visualizer.^25,26,27

 

Molecular Dynamic Simulation:

Molecular dynamics (MD) simulation was used to understand the binding stability, fluctuation, conformational changes, and kinetic behavior of desired compounds to the targeted protein. A 100 ns MD simulation was carried out using Schrodinger's "Desmond v3.6 Software" (academic version in Linux environment), which was then utilized to assess the MD simulation of the target-ligand complex structure using the OPLS-2005 force field.²⁸ In order to maintain a preset volume with orthorhombic periodic bounding boxes spaced apart by ten, a predefined TIP3P water technique was developed for this framework. Electrical charges were electrically neutralized by the addition of the proper ions, such as Na+ and Cl with a salt concentration of 0.15 M. The system framework was reduced and relaxed using the technique carried out employing force field constants OPLS3e offered inside the Desmond package after creating solvency protein systems comprising a ligand complex. All Nose-Hoover temperature and isotropic technique-based isothermal-isobaric ensemble (NPT) assemblies were maintained at 300 K and one atmospheric pressure (1,01325 bar), with 50 PS capture intervals and an efficiency of 1.2 kcal/mol.²⁹ The SID module of the Schrodinger package was created to assess the quality of the MD simulation and the simulation event. Using the root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), hydrogen bonding interaction, solvent accessible surface area (SASA), the radius of gyration (Rg), MolSA, polar surface area (PSA), and protein-ligand interaction, the stability of the protein-ligand complex structure was evaluated from the simulation trajectories.^30,31

 

RSMD Analysis:

The RMSD formula is used to calculate the average conformational changes during a given time period between a protein structure's backbone (the target) and the ligand (the reference), as compared to the reference time.³⁰ Prior to calculating the system's RMSD utilizing the atom selection during the MD simulation, the reference frame backbone and protein frames were first aligned. The RMSD value is calculated using Equation (1) with the time frame x to the MD simulation for 100 ns.

 

  …….… (1)

Thus, N stands for the chosen number of atoms, while tref designates the reference time. After superimposing the frame x on the reference frame, r' determines where the selected atoms are located within it, and tx specifies recording intervals.

 

RMSF Analysis:

Similar to RMSD, RMSF measures the flexibility of a single residue, or how much it bends during a simulation, as opposed to representing changes in position over time for all structures. Given the number of AA residues I for a protein throughout the MD simulation, the RMSF may be obtained using Equation (2) across a 100 ns simulation time.³¹

 

  ………. (2)

 

Here, the trajectory time is written as T; the given or reference time is written as tref; r' denotes the position of the chosen atoms in frame I after superimposing the reference frame; and (>) is the average square distance over the chosen atoms in the residue.

 

RG Analysis:

The arrangement of its atoms along its axis determines the radius of gyration (Rg) of a protein-ligand interaction system. The computation of Rg is one of the most important indicators to look for when forecasting a macromolecule's structural functioning since it shows variations in complex compactness over time.³²

 

SASA, PSA, MolSA Analysis:

The accessibility of AAs in a protein to solvents has important consequences. Its solvent-accessible surface area is the portion of a protein that is in contact with the solvent. It is estimated for the natural form and is based on the three-dimensional structure of the protein molecule. Protein interactions frequently come with significant structural changes. We looked into the connections between the conformational changes that happen when proteins interact and the relationships between protein structures. The molecular surface area and the van der Waals surface area were both calculated using a 1.4 probe radius (MolSA).³³

 

RESULTS:

Confirmation of Ligand Affinity:

Different ligands, accompanied by their corresponding PubChem IDs, were subjected to molecular docking simulations, generating dock scores that serve as indicators of the binding affinity of each ligand to the target protein Profilin-like Protein A42R. Lower dock scores are indicative of stronger binding interactions between the ligands and the protein. The molecular docking results, as depicted in Table 1, confirm the strong binding affinities of Melongoside N, Avenacosid A, and Melongoside P with the Profilin-like Protein A42R with docking score -10.21 (Kcal/mol), -9.06 (Kcal/mol) and -8.95 (Kcal/mol) respectively (Table 1, 2). The diverse chemical structures of these phytochemicals played a crucial role in their respective interactions, as evidenced by the favorable docking scores.^34,35,36,37

Table 1: Various ligands and their respective PubChem IDs along with the corresponding dock scores obtained from molecular docking simulations. Dock scores reflect the binding affinity of each ligand to the target protein Profilin-like Protein A42R, with lower scores indicating stronger binding.

Sl. No.	Ligands Name	PubChem ID	Dock Score
01	Melongoside N	4483043	-10.21
02	Avenacosid A	267363	-9.06
03	Melongoside P	131750951	-8.95
04	Graecunin E	156783	-8.92
05	Melongoside H	3826176	-8.51
06	Gracillin	159861	-8.03
07	Methylprotogracillin	171348	-7.88
08	Melongoside O	131750948	-7.86
09	Balanitoside	150711	-7.36
10	Trigonelloside B	181281	-7.27
11	Aculeatiside A	159012	-7.25
12	Graecunin G	156784	-6.94
13	Melongoside F	192242	-6.91
14	Dioscin	119245	-6.80
15	Melongoside G	131752997	-6.52
16	Melongoside B	11827970	-4.58
17	Formosanin C	21603986	-4.08
18	Picroside 1	6440892	-3.98
19	Quadrangularin A	5318096	-3.56
20	Pallidol	484757	-3.27

 

Table 2: Interaction between phytochemicals and targeted receptor the Profilin-like Protein A42R (PDB-4QWO) with docking score is tabulated.

Ligands Name	Docking Score	H-Bond Interaction	Hydrophilic Bonds Interaction
Melongoside N (PubchemID-4483043)	-10.21 (Kcal/mol)	Ser73(B), Asp76(B), Thr79(B), Arg119(A), Arg115(A), Tyr118(A), Ala81(A), Ser73(A), Agr114(A), Arg115(B)	Tyr118(B), Thr71(A), Thr79(A)
Avenacosid A (PubchemID-267363)	-9.06 (Kcal/mol)	Asp76(A), Arg115(B), Glu83(B), Arg114(B),	Tyr118(A), Tyr118(B), Thr71(B), Glu83(A), Arg114(A), Arg115(A)
Melongoside P (PubChem- 131750951)	-8.95 (Kcal/mol)	Asp76(B), Thr79(B), Arg119(A), Arg114(A), Arg115(B), Tyr118(A), Ala81(A)	Ser73(B), Arg115(A), Thr71(B), Thr71(A), Glu83(A), Thr79(A), Ser73(A), Tyr118(B)

 

Visualization of Post-Docking Protein-Ligand Interactions: The interactions between the three specified ligands and the target protein in this study were investigated using the BIOVIA Discovery Studio Visualizer and Ligplot+ Version 2.2 tools. Ligplot+ Version 2.2 was employed to analyze the interactions, primarily hydrophobic and noncovalent, within all the docked complexes, as outlined in Tables 1 and 2, and depicted in Figure 1.

 

Figure 1: The interaction between compound Melongoside N, Avenacosid and Melongoside P with the Profilin-like Protein A42R (PDB-4QWO) is depicted. The left side represents 3D, and the right represents 2D complex protein–ligand interaction.

Melongoside N demonstrated a notably stable interaction, characterized by 10 hydrogen bonds (H bonds) with Ser73(B), Asp76(B), Thr79(B), Arg119(A), Arg115(A), Tyr118(A), Ala81(A), Ser73(A), Agr114(A), Arg115(B), and 3 hydrophilic bonds with Tyr118(B), Thr71(A), Thr79(A). Avenacosid A exhibited 4 hydrogen bonds with Asp76(A), Arg115(B), Glu83(B), Arg114(B), and 6 hydrophilic bonds with Tyr118(A), Tyr118(B), Thr71(B), Glu83(A), Arg114(A), Arg115(A). Melongoside P displayed 7 hydrogen bonds with Asp76(B), Thr79(B), Arg119(A), Arg114(A), Arg115(B), Tyr118(A), Ala81(A), and 8 hydrophilic bonds with Ser73(B), Arg115(A), Thr71(B), Thr71(A), Glu83(A), Thr79(A), Ser73(A), Tyr118(B)^38,39,40 (Table 2 and Fig 1).

 

RMSD Analysis:

The acceptable range for the average change in the root mean square deviation (RMSD) of the protein-ligand interaction typically falls within 1–3˚A. An RMSD number larger than this range indicates a significant alteration in the protein structure.²⁷ To evaluate the conformational changes in the desired protein during a 100 ns molecular dynamics (MD) simulation, RMSD values were calculated for the complexes formed with four ligand compounds, specifically, Melongoside N CID-4483043, Avenacosid A CID-267363, Melongoside P CID- 131750951. Similar findings were presented by Partha et al., 2022 and Subasri et al., 2016.^38,40,41

 

 

Figure 2: The RMSD value of the selected three compounds Melongoside N, Avenacosid A, and Melongoside P with the Profilin-like Protein A42R (PDB-4QWO) are represented by a blue, orange, and grey color, respectively.

 

RMSF Analysis:

The root mean square fluctuation (RMSF) is a valuable metric for characterizing local alterations within the protein chain when specific ligand chemicals interact with particular residues. In this context, RMSF values for the compounds Melongoside N, Avenacosid A, and Melongoside P with the Profilin-like Protein A42R (PDB-4QWO) were calculated. This analysis aims to investigate how the attachment of specific ligand compounds to particular residual positions influences the structural flexibility of the protein. The results are visually presented in Figure 3. Partha et al., 2022 and Subasri et al., 2016^38,40 also showed the similar result.

 

 

Figure 3: Showing the RMSF values extracted from protein residues Cα atoms of the complex structure. The RMSF value of the selected three compounds Melongoside N, Avenacosid A, and Melongoside P with the Profilin-like Protein A42R (PDB-4QWO) are represented by a blue, orange, and grey color, respectively. Top of Form

 

Radius of Gyration (Rg) Analysis:

The arrangement of atoms along the axis defines the radius of gyration (Rg) in a protein-ligand interaction system. Rg calculation is a crucial indicator for anticipating the structural dynamics of a macromolecule, reflecting variations in complex compactness over time. In this study, the stability of Melongoside N (CID-4483043), Avenacosid A (CID-267363), and Melongoside P (CID-131750951) in interaction with the target protein was examined in terms of Rg throughout a 250 ns simulation duration, as illustrated in Figure 6. The average Rg values for the compounds CID-4483043, CID-267363, and CID-131750951 were 8, 7.4, and 8.8, respectively. These values suggest that the binding site of the protein does not undergo major structural changes when the ligand compounds are bound, indicating a stable interaction over the simulation period. The findings have shown as similar as Partha et al., 2022.³⁸

 

 

Figure 4: The radius of gyration (Rg) of the protein-ligand interaction was calculated using a 100 ns simulation, where blue, orange, and grey represent the selected three ligand compounds Melongoside N, Avenacosid A, and Melongoside P in contact with the Profilin-like Protein A42R (PDB-4QWO), respectively.

Analysis of SASA, MolSA, and PSA:

The solvent-accessible surface area (SASA) plays a crucial role in regulating the arrangement and activities of biological macromolecules. Amino acid residues on a protein's surface often function as active sites or interact with other molecules and ligands, providing insights into a molecule's solvent-like behavior (hydrophilic or hydrophobic) and the components of protein-ligand interactions.

 

The SASA values for the protein complexes with CID-4483043, CID-267363, and CID-131750951 were calculated and are depicted in Figure 5. The SASA values for the three compounds CID-4483043, CID-267363, and CID-131750951 averaged between 500 to 1200Å, with CID-131750951 showing an average of 900 Å. These values indicate that, in the complex systems, amino acid residues were exposed to a significant amount of the selected ligand molecules.

 

The molecular surface area (MolSA) is equivalent to the van der Waals surface area calculated with a probe radius of 1.4. All ligand complexes, including CID-4483043, CID-267363, and CID-131750951, exhibited typical van der Waals surface areas in us in-silico investigation, as illustrated in Figure 6.

 

 

Figure 5: The SASA value of the selected three compounds Melongoside N, Avenacosid A, and Melongoside P with the Profilin-like Protein A42R (PDB-4QWO) are represented by a blue, orange, and grey color, respectively.

 

 

 

Figure 8: Bar graphs represent the ligand-protein interaction of the Profilin-like Protein A42R (PDB-4QWO) with the ligands Melongoside N (PubChem ID-4483043), Avenacosid A (PubChem ID-267363), and Melongoside P (PubChem ID-131750951), respectively.

DISCUSSION:

The investigation into potential antiviral metabolites against the monkeypox virus focused on the Profilin-like Protein A42R, crucial for viral replication and assembly. Ligands such as Melongoside N, Avenacosid A, and Melongoside P were identified for their potential inhibitory effects on the protein. Molecular docking and computational analysis were employed, leveraging computational methodologies like molecular docking, virtual screening, and artificial intelligence. The identified ligands demonstrated favorable interactions with the target protein, suggesting their potential as inhibitors.

 

The molecular dynamics simulations, including RMSD, RMSF, and Rg analyses, provided insights into the stability, flexibility, and compactness of the protein-ligand complexes over time. The ligands Melongoside N, Avenacosid A, and Melongoside P exhibited stable interactions, minimal structural fluctuations, and consistent compactness during the simulations.

 

Further analyses, including SASA, MolSA, and PSA assessments, shed light on the solvent-accessible surface area, molecular surface area, and polar surface area, respectively. The ligand-protein complexes displayed favorable characteristics, indicating significant exposure of amino acid residues to the ligand molecules.

 

The results collectively suggest that the identified ligands have potential antiviral properties against the monkeypox virus. The in-silico methods employed in this study provide a valuable foundation for further experimental validation and drug development efforts. The comprehensive computational analysis enhances our understanding of the protein-ligand interactions, paving the way for rational drug design strategies targeting the monkeypox virus.

 

CONFLICTING INTEREST:

We, the authors of the research article hereby declare that there is no conflict of interest among the authors regarding the publication of this manuscript.

 

ABBREVIATIONS:

MPXV: Monkeypox virus; MDS: Molecular dynamic simulation; NSPs: Nonstructural proteins Rg: Radius of gyration; RMSD: Root-mean-square deviation; RMSF: Root-mean-square fluctuation; SASA: Solvent-accessible surface area.

 

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Received on 24.04.2024      Revised on 13.08.2024 Accepted on 11.11.2024      Published on 17.12.2024 Available online on December 23, 2024 Asian Journal of Pharmaceutical Research. 2024; 14(4):355-362. DOI: 10.52711/2231-5691.2024.00056 ©Asian Pharma Press All Right Reserved
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