(2024) 18:141
Alharbi et al. BMC Chemistry
https://doi.org/10.1186/s13065-024-01251-x

BMC Chemistry

RESEARCH

Open AccessMarine fungal diversity unlocks
potent antivirals against monkeypox
through methyltransferase inhibition revealed
by molecular dynamics and free energy
landscapeAzzah S. Alharbi1,2*, Sarah A. Altwaim1,2, Mai M. El‑Daly1,3, Ahmed M. Hassan1, Ibrahim A. AL‑Zahrani1,3,
Leena H. Bajrai1,4, Isra M. Alsaady1,3, Vivek Dhar Dwivedi5,6* and Esam I. Azhar1,3

Abstract
The escalating threat posed by the Monkeypox virus (MPXV) to global health necessitates the urgent discovery
of effective antiviral agents, as there are currently no specific drugs available for its treatment, and existing inhibitors are hindered by toxicity and poor pharmacokinetic profiles. This study aimed to identify potent MPXV inhibitors
by screening a diverse library of small molecule compounds derived from marine fungi, focusing on the viral protein
VP39, a key methyltransferase involved in viral replication. An extensive virtual screening process identified four promising compounds—CMNPD15724, CMNPD28811, CMNPD30883, and CMNPD18569—alongside a control molecule.
Rigorous evaluations, including re-docking, molecular dynamics (MD) simulations, and hydrogen bond analysis, wereconducted to assess their inhibitory potential against MPXV VP39. CMNPD15724 and CMNPD30883, in particular,
demonstrated a superior binding affinity and stable interactions within the target protein’s active site throughout the MD simulations, suggesting a capacity to overcome the limitations associated with sinefungin. The stabilityof these VP39-compound complexes, corroborated by MD simulations, provided crucial insights into the dynamic
behavior of these interactions. Furthermore, Principal Component Analysis (PCA) based free energy landscape assessments offered a detailed understanding of the dynamic conformational changes and energetic profiles underlying
these compounds’ functional disruption of VP39. These findings establish CMNPD15724, CMNPD28811, CMNPD30883,
and CMNPD18569 as promising MPXV inhibitors and highlight marine fungi as a valuable source of novel antiviral
agents. These compounds represent potential candidates for further experimental validation, advancing the development of safer and more effective therapeutic options to combat this emerging viral infection.
Keywords Monkeypox virus, Marine Fungi, Screening, Docking and molecular Simulation*Correspondence:
Azzah S. Alharbi
asalharbi3@kau.edu.sa
Vivek Dhar Dwivedi
vivek_bioinformatics@yahoo.com
Full list of author information is available at the end of the article© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0
International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as longas you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if
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Alharbi et al. BMC Chemistry(2024) 18:141

Introduction
Monkeypox virus (MPXV) is a zoonotic viral infectionthat has garnered attention as it emerges as a global
health threat. Bearing significant homology to the Variola
virus, the causative agent of smallpox, MPXV has been
newly implicated in outbreaks revealing its pathogenic
potential [1]. At the heart of these pathogenic processes
is the viral methyltransferase VP39, a replicase enzyme
essential for mRNA capping, a process required to cap
viral mRNAs to stabilize them [2], and conceal them fromthe immune system [3, 4]. Understanding the molecularmechanics of VP39 in the epidemiology and pathogenesis of Monkeypox is vital to the development of the next
generation of antiviral therapeutics.
The MPXV is an Orthopoxvirus belonging to the Poxviridae family [5], which possesses a complicated double-stranded DNA genome [6]. This genome encodes avariety of proteins, including the methyltransferase VP39[7], which is essential to the viral life cycle. The role of
VP39 in the mRNA capping process is crucial for preventing viral mRNA degradation and ensuring efficienttranslation [4]. This distinct process distinguishes thevirus from its host and opens the door to focused therapeutic interventions. Understanding the genetic architecture of the virus, as well as the specific role of VP39 in
the virus’s replication machinery, is critical for the development of effective antiviral medications. Because of its
specific involvement in the MPXV, VP39 is an appealing
target for antiviral medication research. Inhibiting VP39
may impair the virus’s capacity to multiply and eludedetection by the host immune system. This method holds
the potential for creating specialized antiviral medicineswith few off-target impacts on host cells. The development of VP39 inhibitors necessitates a thorough understanding of the enzyme’s structure and role in the viral
replication cycle. Targeting VP39 may not only impede
viral replication but also increase the virus’s visibility to
the immune system, giving a dual mode of action against
Monkeypox. Recent Monkeypox outbreaks, characterized by close contact and respiratory droplets [8], haveunderlined the need for better therapies. The virus’s
global expansion, with instances recorded on various
continents, emphasizes the importance of developing targeted antiviral therapies. Because of its crucial role in thevirus’s life cycle, the enzyme VP39 emerges as a key target
in this attempt. Inhibiting VP39 effectively could result ina considerable reduction in viral replication, assisting in
the control and management of Monkeypox epidemics.
Understanding the epidemiology of Monkeypox, including transmission mechanisms and the function of VP39
in pathogenicity, is critical for developing targeted andeffective antiviral medicines. With their unique ability
to create a diverse variety of bioactive chemicals, marine

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fungi represent an untapped resource for antiviral medication discovery [9]. Adapted to the harsh maritime
environment, these organisms have evolved to generate secondary metabolites with a wide range of chemical
structures and biological functions [9]. Recent studies
have begun to look into the possibility of these chemicals
as sources of novel antiviral medicines [10]. The chemical
diversity of marine fungi-derived chemicals represents apromising pathway for the discovery of new treatments,
notably in the fight against developing viral infections.
Silhan and his team conducted a study on designingsinefungin analogs (Fig. 1) [2]. This study demonstrated
that the inhibitor TO1119 features a tiny iodine atom atthe 7-deaza position. This compound causes minimal
disruption to the water network within the binding site,
resulting in an IC50 of 128 nM. Despite its relatively small
substituent, TO1119 effectively binds within the watercavity adjacent to the sinefungin binding groove, illustrating the importance of subtle modifications for maintaining inhibitory activity. In contrast, inhibitors such as
TO427, TO494, TO500, TO504, and TO507 possess large
aromatic or heterocyclic substituents at the 7-deaza position. These modifications lead to a significant rearrangement of the water molecules in the binding site. These
inhibitors demonstrate markedly improved efficacy compared to sinefungin, with IC50 values ranging from 0.08
to 0.17 μM. Notably, TO500 includes a benzimidazole
heterocycle capable of forming a hydrogen bond, which
contributes to its effective inhibition profile. These structural changes enhance the binding affinity and inhibitory
potency, making them promising candidates for antiviraldrug development [2].
We used a computational drug discovery strategy toidentify potential inhibitors of Monkeypox methyltransferase VP39 from a library of marine fungus compounds
in this study [11]. Virtual screening with Lipinski’s ruleof five, re-docking, molecular dynamics simulations, freebinding energy calculations, and PCA-based free energy
landscape analysis were all part of our process. This thorough approach enabled us to uncover interesting compounds that could block VP39, opening up new avenues
for the development of effective Monkeypox antiviraltreatments. Not only do our findings advance antiviralresearch, but they also highlight the potential of marine
fungi as a rich source of novel medicinal compounds.

Materials and methods
Data collection, virtual screening and re‑docking

This study focused on computational techniques to discover possible inhibitors of Monkeypox methyltransferase VP39. We utilized a Marine Fungi Compounds
library, focusing on compounds that would demonstrate
a putative inhibitory effect against VP39 [12]. The process

Alharbi et al. BMC Chemistry(2024) 18:141

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Fig. 1 Structures of previously reported sinefungin (SFG) analogs with potential inhibitory effects against Mpox methyltransferase VP39

involved virtual screening with Lipinski’s rule of five filters, followed by re-docking of the selected compounds.
The initial step in our methodology was the collection oftarget protein and ligand data. The 3D structure of Monkeypox methyltransferase VP39 was retrieved from theProtein Data Bank (PDB) with the PDB ID 8B07 [2]. The
PDB is a widely recognized archive for the 3D structuresof major biological molecules [13]. The Marine Fungi
Compounds library was downloaded from the Comprehensive Marine Natural Products Database (CMNPD)[14]. This database provides a large number of compounds, each with unique structures and characteristics,
making them good candidates for virtual screening. The
VP39 protein was prepared for docking using the Dock
Prep function in the Chimera program [15]. This preparation involves many key steps such as the insertion ofhydrogen atoms to ensure the right ionization and tautomeric states of amino acids, the removal of water molecules to simulate an anhydrous environment, and theassignment of Kollman charges to the protein. These
procedures were necessary for accurately the docking ofligands with the protein. The MtiOpenScreen webserver
was deployed for virtual screening [16]. This tool allows
us to apply Lipinski’s rule of five as a filter. Lipinski’s rule
is vital for assuring the drug-likeness of the compounds
by analyzing factors such as molecular weight, lipophilicity, and hydrogen bond donors and acceptors [17]. We

conducted a thorough screening of the Marine Fungi
Compounds library against the VP39 protein structure.
The screening procedure was automated, allowing us toefficiently examine 330 compounds obtained after applying Lipinski’s rule (Supplementary Table S1). For thisscreening, the center coordinates (X = − 3.5, Y = 18.0
and Z = − 33.38) and grid size (X = 20, Y = 20 and Y = 20).
The upshot of this screening was the selection of four
compounds that displayed the highest docking scores,
indicating a substantial potential for interaction with
the VP39 protein. Following the virtual screening, a redocking process was done to further validate the results.
For this, we used the AutoDock Vina [18] plugin within
the Chimera interface [15]. Similar methodologies wereused in recent research [19, 20]. This plugin is noted
for its quickness and accuracy in docking simulations.
The selected four compounds, together with a reference molecule for comparison, were re-docked against
the VP39 protein. This phase was crucial to confirm theinitial binding affinities and to acquire a more complete
understanding of the interaction between the chemicals
and the protein. We verified that the re-docking circumstances were consistent with the first screening to retain
the validity of our comparisons. The re-docking method
not only corroborated our previous findings but also gavefurther insights into the binding mechanisms and orientations of the compounds within the VP39 active region.

Alharbi et al. BMC Chemistry(2024) 18:141

This knowledge is essential for understanding how thesesubstances could affect the action of the VP39 enzyme.
Molecular dynamic simulation

We conducted 200 ns Molecular Dynamics (MD) simulations and trajectory analysis to study the interactions
between Monkeypox methyltransferase VP39 with the
top four scoring putative inhibitors identified as virtual
screening and redocking [21]. Our methodology includes
the utilization of the CHARMM—36 force field [22, 23]
within the GROMACS version 2023.3 [24, 25] simulation
software, focused on four selected chemical complexes
and one contol molecule. The MD simulations begin
with the creation of the VP39-inhibitor complexes. Eachcomplex containing a ligand was placed in the center of a
cubic simulation box. The complexes were solvated with
TIP3P water [26], guaranteeing a buffer distance of atleast 1 nm from the protein to the border of the box. To
simulate physiological ionic strength, sodium and chloride ions were introduced, neutralizing the system andobtaining a 0.15 M concentration. Before the dynamics,
an energy minimization phase was required to remove
steric conflicts and geometric inconsistencies. This
was achieved using the steepest descent minimization
method [27], continuing until the maximal force was lessthan 1000 kJ/mol/nm. This procedure guaranteed thatthe systems were appropriately relaxed for the upcoming
MD simulations after that we performed equilibration in
two phases—NVT followed by NPT. In the NVT phase,
each system was equilibrated at 310 K using a V-rescale
thermostat. This was followed by the NPT phase, where
the pressure was maintained at 1 bar using the Parrinello-Rahman barostat [28]. Each phase lasted 100 ps,
allowing the systems to establish a stable temperatureand pressure. The production MD simulations were doneusing the CHARMM force field [22]. We ran simulations
for a time scale sufficient to capture the relevant dynamic
characteristics of the complexes, generally lasting 200 ns.
During this interval, the temperature and pressure were
controlled as per the equilibration phases, and periodic boundary conditions were applied in all directions
to resemble an endless system. The obtained trajectories were evaluated for several metrics including RMSD,
RMSF, hydrogen bonding patterns, and binding interactions. RMSD gave insights into the general stability of thecomplexes, while RMSF revealed the flexibility of individual residues in the protein. Additionally, analysed thequantity and length of hydrogen bonds between VP39
and the inhibitors to assess the strength and specificityof the interactions. PCA was utilized to deconstruct the
MD trajectories into primary motions. This study helps
in detecting the significant conformational changes during the simulation. The free energy landscapes were built

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based on these primary components, offering a visual
representation of the conformational states explored by
the protein–ligand complexes. These landscapes allowed
us to pinpoint stable conformations and transitional
states, affording vital insights into the dynamic behaviorof the complexes.
Principal component analysis based on free energy
landscape and structural analysis

The PCA was conducted for MD simulation trajectoriesof the VP39 complexes. This selection attempted to capture the most relevant dynamic interactions between theprotein and the inhibitors. We focused on the backbone
atoms of the VP39 protein to underline the key motions
controlling ligand binding. Utilizing the gmx sham module within GROMACS [24, 25], we retrieved the principal
components (PCs) from the trajectory data. This procedure required reducing the multidimensional dataset todetermine the fundamental axes of motion. PCA showed
substantial conformational changes and patterns within
the protein–ligand interactions, providing insight into
the dynamic features of binding. For generating the scattered plot based on the PC1 and PC2 obtained the ‘gmx
anaproj’ command was utilized [29]. The PCs served
as coordinates for creating the free energy landscapes.
Using Python’s Matplotlib tool, we plotted these PCs
against each other to depict the energy states traversedby the complexes during the simulations [30, 31]. The
landscapes displayed the distribution of structural states,with valleys suggesting stable low-energy conformations.
The free energy landscapes were studied to determine thestability and flexibility of the VP39 complexes. Key observations included identifying stable conformational basins
and transition states, which are indicative of the binding
affinities and specificities of the inhibitors. This approachallowed us to find energetically favorable binding modes
and to forecast the probable efficacy of the inhibitors.
The extracted structures were superimposed and visualized in UCSF Chimera [32].

Results
Virtual screening

Virtual screening is a computational-based drug discovery approach utilized for the identification of possible
therapeutic compounds from the enormous database ofchemical compounds. Virtual screening aims to prioritize
chemical compounds that are expected to exhibit biological activity against a target molecule, such as a protein
linked to a disease, this will assist in lowering the quantity of chemicals that require experimental testing, saving time and money in the process. Virtual screening was
utilized in this study to identify the four natural chemicals derived from the Marine Fungi Compounds libraryAlharbi et al. BMC Chemistry(2024) 18:141

following the virtual screening, the top compounds werechosen based on binding energy. For the screening purpose, a total of 4683 compounds library was selected,
based on the binding score obtained in the virtual screening, the top four compounds named CMNPD15724,
CMNPD28811, CMNPD30883, and CMNPD18569
with binding scores of − 8.60 kcal/mol, − 7.63 kcal/
mol, − 7.53 kcal/mol, and − 7.52 kcal/mol respectively
were chosen for further study along with one control(sinefungin).
Redocking and interaction analysis

Redocking is a technique used in the computationalbased drug discovery strategy to evaluate the precision of
the molecular docking protocol and investigate the significant interaction between the ligands and binding site
residues of the protein of interest. During the re-docking
procedure, nine poses for each complex were obtained.

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The one with the highest negative binding energy waschosen to analyze the selected complex’s stability andbinding affinity. Additionally, sinefungin as a control
molecule was used for the comparative study. During the
redocking individual compounds’ obtained good scoressuch as compounds CMNPD15724, CMNPD28811,
CMNPD30883, and CMNPD18569 with binding scoresof − 9.4 kcal/mol, − 8.9 kcal/mol, − 9.2 kcal/mol, and− 9.4 kcal/mol respectively and control sinefungin scored-8.9 kcal/mol in the redocking (Center gride was used
X = − 3.5, Y = 18.0 and Z = − 33.38), as illustrated inTable 1. The list of methyltransferase VP39 amino acidresidues involved in various types of molecular interactions with the selected compounds is given in Table 2
and plotted in Fig. 2. The CMNPD15724-protein complex has a strong contact, as evidenced by the formationof eight hydrogen bonds between the chemical and theprotein residues A­ sp138, ­Ser141, ­Val116, and ­Arg97. ­Arg140

Table 1 Illustrating the binding affinity and structure of four selected compounds along with the control molecule
S. No.

Compound name

Binding affinity

1

Methyltransferase VP39- CMNPD15724− 9.4 kcal/mol

2

Methyltransferase VP39-CMNPD28811− 8.9 kcal/mol

3

Methyltransferase VP39-CMNPD30883− 9.2 kcal/mol

4

Methyltransferase VP39-CMNPD18569− 9.4 kcal/mol

5

Methyltransferase VP39- sinefungin (Control)-8.9 kcal/mol

Structure

Alharbi et al. BMC Chemistry(2024) 18:141

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Table 2 Intermolecular interaction for the selected compound docked against methyltransferase VP39
S. no Protein–ligand complex

Hydrogen bondHydrophobic
interaction

π–πSalt Bridge Halogen bondstacking/
Interaction interaction
π–π cation*interaction

1

Methyltransferase VP39CMNPD15724

Asp138,Ser141, ­Val116, ­Arg97­Arg140 ­Asp95

Leu42, ­Val139, ­Ala70, ­Ile67, ­Leu159,­ al116, ­Ile94, ­Phe115
V–2

Methyltransferase VP39CMNPD28811

Asp138

Leu159, ­Val116, ­Phe115, ­Ile94,­Val139, ­Ile67, ­Ala70

Phe115

3

Methyltransferase VP39CMNPD30883

Gly72 ­Gln39

Val116, ­Phe115, ­Leu159, ­Tyr66, ­Ile67, –­Ala70, ­Pro71, ­Leu42, ­Ile75, ­Val139

4

Methyltransferase VP39CMNPD18569

Gly68, ­Asp138, ­Arg140, ­Asp95,­ rg97, and ­Val116A

Leu159, ­Val116, ­Phe115, ­Val139,­Ala70, ­Ile67 and ­Ile94

Phe115

5

Methyltransferase VP39-sinefungin (control)

Asp95, ­Arg140, ­Asp138, ­Try66,­Gln39, ­Gly72
Val116

Ile94, ­Phe115,Val116, ­Leu159, ­Ile75,
L­ eu42, ­Pro71, ­Ala70, ­Ile67, ­Tyr66
and ­Val139

Phe115

Ile94 and ­Val116

Arg97*π–π stacking/π–π cation interaction: This indicates the interactions between aromatic rings (π–π stacking) or between an aromatic ring and a cation (π–π cation)and ­Asp95 also form two hydrogen bonds with A­ sp95 and140
115­Arg , a pi-pi stacking interaction with P­ he , and two
halogen bonds with I­ le94 and ­Val116 and eight hydrophobic interactions with ­Leu42, ­Val139, ­Ala70, ­Ile67, ­Leu159,­Val116, ­Ile94, ­Phe115 (Fig. 2a). These numerous interactions
point to CMNPD15724 having a robust and well-definedbinding mode. Through docking interactions, compound
CMNPD28811 displays significant binding affinity,
including one hydrogen bond with ­Asp138 and two pi-pi
stacking interactions with P­ he115 and seven hydropho159
bic interactions with ­Leu , ­Val116, ­Phe115, ­Ile94, ­Val139,­Ile67 and A­ la70 (Fig. 2c). These interactions imply that
CMNPD28811 adopts a unique orientation relative to theprotein, which contributes to its high binding potential.
The CMNPD30883-protein complex interacts in a variety of ways, including two hydrogen bonds with G­ ly7239
97
and ­Gln , a salt bridge with ­Arg and ten hydrophobic interactions with ­Val116, ­Phe115, ­Leu159, ­Tyr66, ­Ile67,­Ala70, ­Pro71, ­Leu42, ­Ile75, ­Val139 (Fig. 2e). This broad set ofcontacts suggests that CMNPD30883 operates in a flexible binding mechanism that allows it to adapt to different protein conformational states. G­ ly68, ­Asp138, ­Arg140,
95
97
116
­Asp , ­Arg , and ­Valform seven hydrogen bondsin the CMNPD18569 complex V­ al116 formed a doublebond (Fig. 2g). Furthermore, ­Phe115 interacts via one
pi-pi stacking and seven hydrophobic bonds with L­ eu159,
116
115
139
70
67
94
­Val , ­Phe , ­Val , ­Ala , ­Ile and ­Ile . These interactions imply that CMNPD18569 has a highly structured
binding mechanism, which may contribute to its specificity and selectivity. The control complex forms eighthydrogen bonds with ­Asp95, ­Arg140, ­Asp138, ­Try66, ­Gln39,
and ­Gly72 residues, as well as a double hydrogen bondwith ­Val116, one Pi-Pi stacking with P­ he115 and eleven
94
115
hydrophobic interaction ­Ile , ­Phe , ­Val116, ­Leu159, ­Ile75,­Leu42, ­Pro71, ­Ala70, ­Ile67, ­Tyr66 and V­ al139. This differential binding pattern indicates that the control compound

has a different conformation and orientation than the
CMNPD compounds. Overall, the examination of theseinteractions reveals the varied binding mechanisms andaffinities of CMNPD compounds, providing important
insights into their possible therapeutic applications.
Molecular dynamic simulation

Bioinformatics extensively utilizes molecular simulation to examine the structural behaviours and interplaysof biomolecules like proteins and their ligands, focusing
on atomic and molecular detail [34, 35]. The four mostpromising results were selected for molecular dynamics (MD) simulation, aiming to investigate the complex’s
stability and adaptability. To analyze the protein–ligand
complex’s interactions, the Root Mean Square Deviation(RMSD) and Root Mean Square Fluctuation (RMSF) of
both the protein and ligand were calculated throughout
200 ns. The first and last pose of the MD simulation isshown in Figure S1.
RMSD and RMSF analysis

Root Mean Square Deviation (RMSD) is a key metric inmolecular dynamics (MD) simulations that measures
the average deviation of a protein–ligand complex fromits initial position over time. This metric provides valuable insights into the stability of the complex, as lower
RMSD values generally indicate greater stability andless deviation from the starting structure. In this study,
RMSD values for the selected marine fungal compounds(CMNPD15724, CMNPD28811, CMNPD30883, and
CMNPD18569) were analysed over a 200 ns simulation
period to assess their stability in complex with the VP39
protein as shown in Fig. 3. Compound CMNPD15724
exhibited an average RMSD of approximately 0.225 nm
over the simulation period. The RMSD remained below
0.25 nm for the first 100 ns, indicating stable interactions

Alharbi et al. BMC Chemistry(2024) 18:141

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Fig. 2 The representation of molecular interactions in three dimensions and two dimensions for the ligands CMNPD15724 (a, b), CMNPD28811 (c,d), CMNPD30883 (e, f), CMNPD18569 (g, h), and (i, j) sinefungin (control). These structures were generated using free academic maestro [33]Alharbi et al. BMC Chemistry(2024) 18:141

Page 8 of 19

Fig. 3 The MPV and marine fungal compounds’ RMSD plots are as follows: a CMNPD15724; b CMNPD28811; c CMNPD30883; d CMNPD18569;
and e sinefungin (Control)

between the compound and the protein. After 100 ns,
there were minor fluctuations, with RMSD values consistently below 0.2 nm. These minor fluctuations suggest that while the complex is largely stable, it undergoes
slight conformational adjustments to maintain optimal
interactions within the binding site. Overall, the low

RMSD indicates a stable binding configuration throughout the simulation. While the CMNPD28811 average
RMSD for this compound was about 0.18 nm. During
the initial 100 ns, the complex showed slight variations in
RMSD, likely due to structural adjustments of the protein
as it accommodates the ligand. However, these variations

Alharbi et al. BMC Chemistry(2024) 18:141

were minimal, and the complex quickly reached a stableequilibrium that was maintained for the rest of the 200 ns
period. The low and stable RMSD values reflect a robust
binding conformation, suggesting that CMNPD28811
forms a strong and stable interaction with the VP39
protein. On the other hand compound CMNPD30883
demonstrated the highest stability among the tested
compounds, with an average RMSD of around 0.15 nm.
Although there were major fluctuations in the RMSD
values up to 125 ns, the complex stabilized significantly
afterwards, with RMSD values settling at approximately
0.1 nm. The initial fluctuations indicate that the compound and the protein undergo substantial conformational changes to achieve a stable binding mode. Once
this stable configuration is reached, the strong and stable interactions are reflected in the low RMSD values,
making CMNPD30883 the most stable complex in thisstudy. Compound CMNPD18569 displayed more significant fluctuations with an average RMSD of approximately 0.23 nm throughout the simulation. The higher
degree of fluctuation suggests that the binding mode of
CMNPD18569 is more flexible, potentially allowing theligand to adapt to different conformational states of theprotein. While this flexibility can be advantageous insome contexts, it also indicates a less rigid binding interaction compared to the other compounds. The control
compound, sinefungin, maintained an average RMSD
of around 0.075 nm, demonstrating high stability. The
RMSD values for sinefungin were consistently ≤ 0.05 nm
until 175 ns, with only minor fluctuations between 0.1
and 0.05 nm at specific intervals. This high stability provides a useful baseline for comparison. The fact that all
selected compounds exhibited RMSD values within acomparable range to the control indicates that they form
stable interactions with the VP39 protein. Among thetested compounds, CMNPD30883 stands out due to its
superior stability, as evidenced by its consistently low
RMSD values after the initial stabilization period. Overall, the RMSD analysis suggests that the selected marinederived compounds form stable interactions with the
VP39 protein, with CMNPD30883 demonstrating themost promising stability profile. This stability is crucial
for the potential development of these compounds aseffective inhibitors in therapeutic applications.
Root Mean Square Fluctuation (RMSF) is a measureused in molecular dynamics simulations to assess theflexibility and mobility of individual residues within a protein structure over time. RMSF values are calculated foreach residue in a protein based on its fluctuation aroundan average position during simulation trajectories. Lower
RMSF values indicate less fluctuation, suggesting higher
rigidity or stability of those residues. Higher RMSF values indicate greater flexibility or movement of residues.

Page 9 of 19

The compound CMNPD15724 exhibited the Low RMSF
values of greater than 0.4 Å. Indicate high stability andlimited flexibility of residues in the VP39 protein–ligand
complex. This indicates the compound stabilizes the
VP39 protein structure, with residues showing minimal
movement. It suggests strong interactions and a potentially rigid binding mode. The compound CMNPD28811
exhibited slightly higher RMSF values within a low rangeof less than 0.5 Å suggesting limited flexibility and stable interactions. The protein–ligand complex remains
stable, with residues showing slight flexibility but still
maintaining a predominantly stable binding interaction.
While compound CMNPD18569 exhibited the Lowest RMSF value among all compounds which is 0.35 Å.
Indicates superior stability and minimal movement ofprotein residues. This compound likely forms very stableinteractions with VP39, with residues showing minimal
fluctuation. It suggests a strong and rigid binding mode,
advantageous for being a potent inhibitor. On the other
hand compound CMNPD30883 exhibited slightly higher
RMSF values of less than 0.4 Å. Indicate some flexibility of residues. This compound shows flexibility in certain residues, which might be advantageous for binding
to dynamic regions of the protein. However, comparedto others, it suggests a slightly less rigid binding mode.
The Control Compound exhibited the low RMSF valuesof 0.4 Å which indicates the stable interaction with VP39
protein. Sinefungin maintains stable interactions similar
to the selected compounds, indicating minimal structuralchanges and high stability. Overall or average protein
RMSF Values of below 0.5 nm across all simulations wereobserved. Indicate minimal fluctuation and high structural integrity of the VP39 protein. Consistent low RMSF
values across all complexes and the control suggest thatthese compounds do not induce significant structuralchanges in VP39 (Fig. 4). This stability supports theirpotential as effective inhibitors.
Hydrogen bond analysis

Hydrogen bond interaction analysis in molecular simulation acts as an essential marker of the strength ofinteractions between natural chemicals and their target proteins, which aids in the understanding of thestructural and dynamic properties of biomolecules at
the atomic level. The number of hydrogen bonds wasdetermined to understand and confirm the compound’sbinding stability after the MD simulation. Based on ourfindings as shown in Fig. 5, During the 200 ns molecular dynamics simulations, the examination of hydrogenbond formation revealed varied patterns among the fourchemicals. CMNPD15724 had an impressive capacity
to maintain a high number of hydrogen bonds throughout the simulation, reaching a maximum of four bonds

Alharbi et al. BMC Chemistry(2024) 18:141

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Fig. 4 The RMSF plots for the MPV and marine fungal compounds are as follows: a CMNPD15724; b CMNPD28811; c CMNPD30883; d
CMNPD18569; and e sinefungin (control)

around 100 ns and maintaining this level until the simulation ended (Fig. 5a). This shows that the molecular
structure of CMNPD15724 is especially conducive to thecreation of stable hydrogen bonds. CMNPD28811 had amore moderate hydrogen bond profile, with an average of
two to three bonds throughout the simulation (Fig. 5b).
This shows that CMNPD28811’s molecular structureis less prone to generating strong hydrogen bonds than
CMNPD15724’s. The hydrogen bond formation pattern

in CMNPD30883 was fascinating. The molecule firstcreated two hydrogen bonds, which lasted until roughly
120 ns. The number of hydrogen bonds increased tofour in the final 80 ns of the simulation, indicating a considerable shift. This shows that during the simulation,
CMNPD30883 experiences a structural rearrangementthat improves its hydrogen bonding capabilities (Fig. 5c).
A dynamic hydrogen bonding characteristic was also
observed in CMNPD18569. Within the first 20 ns of theAlharbi et al. BMC Chemistry(2024) 18:141

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Fig. 5 Number of hydrogen bonds present in the Protein docked with Fungi natural compounds, i.e., a CMNPD15724, b CMNPD28811, c
CMNPD30883, d CMNPD18569 and e sinefungin (control) during the 200 ns MD simulation

simulation, the molecule created four hydrogen bonds.
However, for the remainder of the simulation, this number was reduced to two and three bonds. This showsthat early in the simulation, CMNPD18569 undergoes astructural rearrangement that initially promotes hydrogen bond formation but subsequently destabilizes thesebonds (Fig. 5d). While the control complex had ten toeleven hydrogen bonds up to 90 ns and reduced to sevento eight hydrogen bonds between the 90 ns to 100 ns
and 100 ns to 200 ns ten to eleven hydrogen bonds again

formed which indicates the compound undergoes structural rearrangement during simulation.
Principal component analysis

PCA (Principal Component Analysis) is a statistical approach for identifying patterns in a collection ofobservations that is particularly effective for evaluating
molecular dynamics (MD) simulations. It minimizes data
dimensionality by identifying the system’s most essential
directions of motion, known as principal components,

Alharbi et al. BMC Chemistry(2024) 18:141

which correspond to the aggregate motions of the atoms.
By focusing on these primary components, the dynamics of the system may be visualized and understood
more easily. PCA has various advantages for evaluating
MD simulations, including dimension reduction, identification of collective motions, and dynamic display.
It is commonly used for data preparation, covariancematrix calculation, eigenvalue decomposition, projection onto principal components, analysis, interpretation,
protein dynamics analysis, protein folding research, andligand binding studies. PCA is an extremely adaptableand powerful method for assessing molecular dynamics
simulations, reducing data dimensionality, and displaying system dynamics. It has numerous applications inmolecular science and is becoming increasingly popular
as a tool for assessing MD simulations. "We generated a
2D projection of the trajectory using the first and second
eigenvectors (PC1 and PC2) to visualize the structuraldynamics of the complexes during the MD simulations"outlines how PCA was used to visualize the dynamic
behaviour of molecular complexes during MD simulations. The intricate structural changes can be viewed in
two dimensions by projecting the high-dimensional trajectory onto the first two principal components (PC1 andPC2), which encapsulate the system’s most significant
collective motions. The ability of PC1 and PC2 to capture
the most significant structural alterations is critical for
accurately reflecting the complexes’ dynamics. This efficiency is due to the nature of PCA, which prioritizes the
motion directions that contribute the most to the overallvariation in the system’s behaviour. During the MD simulations, the 2D projection of the trajectory onto PC1 andPC2 provides a clear and succinct picture of the structural dynamics of the complexes. Researchers can use
this visualization to identify significant conformationalchanges, assess complex stability, and obtain insights into
the mechanisms behind their function. More dispersion
on the graph indicates greater conformational diversity,emphasizing the link between dispersion and conformational diversity. When the data points on the PC projection are spread out more widely, it shows that the system
has explored a greater range of conformational states
during the simulation. The complex "CMNPD30883"
demonstrated the least conformational dispersion, suggesting that it maintained a relatively stable structure
throughout the simulation (Fig. 6c). In contrast, the compounds CMNPD15724 (Fig. 6a), CMNPD28811 (Fig. 6b),and sinefungin complex (Fig. 6e) displayed notable conformational diversity, indicating that these complexes
explored a wider range of structural conformations
during the simulation. These complexes had a wider
dispersion of data points on the PC projection, indicating that they transitioned more easily between different

Page 12 of 19

conformational states. In summary, the compounds
CMNPD15724 (Fig. 6a) and CMNPD30883 (Fig. 6c) demonstrated the least conformational variation, suggesting ahigher degree of structural stability, while the other complexes exhibited more prominent diverse structural fluctuations. This illustrates the difference between the more
conformationally varied CMNPD28811 (Fig. 6b) andsinefungin (control) complexes (Fig. 6e) and the comparatively stable CMNPD30883 (Fig. 6c) and CMNPD15724(Fig. 6a) complexes.
Free energy landscapeThe free energy landscape analysis offers a comprehensive view of the energy distribution and the conformational dynamics within a biomolecular system duringmolecular dynamic simulations. This multi-dimensional
representation reveals various accessible states andenergy levels over time. It is instrumental in examining
different conformations and energy minima that a protein–ligand complex may adopt during its dynamic functioning [36]. During principal component analysis, thefirst and second principal components (PC1 and PC2)
were utilized to construct three-dimensional free energy
diagrams. This landscape is crucial for identifying transitions between distinct conformations, stable states, andenergy barriers, where peaks represent energy barriersand valleys indicate stable states [37]. Understanding the
kinetics and thermodynamics of the system is key to deciphering vital interactions, conformational shifts, and theoverall stability of the biomolecular complex. Principal
Component Analysis (PCA) is a widely used techniquein this context, helping to simplify the data into informative representations of the free energy environment. Thisapproach aids in studying biological processes like protein–ligand interactions and conformational changes inmacromolecules [38]. The 3D projections graphically represent dynamic conformational changes occurring during
the simulation, leading to the formation of a low-energy
structure resembling a narrow funnel. The presenceof deep blue areas on the larger energy map indicatesregions of lowest energy, showing that protein structures
reach their lowest energy levels at specific times during
the experiment. These areas are marked by dark blue inthe broader energy landscape, highlighting local energy
minima, particularly evident in the complexes. [39]. Theenergy minima or states that molecules assume can be
categorized into “narrow basin” and “wide basin” types.
Typically, molecules situated in a wide basin demonstrate
greater stability compared to those in a narrow basin.
As illustrated in Fig. 7, the complexes CMNPD15724,
CMNPD28811, CMNPD18569, along with the control,
display comparatively wide basins. This implies they havemultiple accessible states and undergo transitions more

Alharbi et al. BMC Chemistry(2024) 18:141

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Fig. 6 PCA analysis of the MD simulation trajectories a VP39-CMNPD15724, b VP39-CMNPD28811, c VP39-CMNPD30883, and d VP39-CMNPD18569,
e VP39-sinefungin (control)

often. On the other hand, the compound CMNPD30883
is characterized by narrow basins, suggesting it has deepenergy wells and experiences fewer transitions. The
study also encompassed gathering structural data forstates with minimum, moderate, and maximum energy
levels, allowing for a detailed examination of the complex interactions and conformational changes withinmolecular complexes. The varied interaction patternsAlharbi et al. BMC Chemistry(2024) 18:141

Page 14 of 19

Fig. 7 PCA-based Free energy landscape analysis. a VP39-CMNPD15724, b VP39-CMNPD28811, c VP39-CMNPD30883, d VP39-CMNPD18569, and e
VP39-sinefungin (control)and multiple conformational states uncovered in thisinvestigation provide critical insights into the dynamic
molecular dynamics driving these interactions. Notably,
the observed behaviour points to a stable binding configuration. The lowest energy state, serving as the reference,
consistently registered at 0 kJ/mol across all complexes,
while the relative maximum energy state varied between
16 kJ/mol and 20 kJ/mol. This numerical assessment of
the energy landscape underscores the subtle and dynamic
nature of energy profiles associated with these complex
molecular interactions.
Exploration of the reference molecule’s free energy
landscape reveals the existence of two different conformations, each with equivalent Gibbs free energy levels.

This observation suggests that the molecule’s stability
may be compromised, implying that it can easily transition between these two forms. The presence of theseenergetically comparable conformations may affectthe molecule’s biological activity and ability to interact with target proteins. The free energy landscape of
CMNPD18569 shows a moderate global minimum
reflecting the less stable conformation, as well as a secondary local minimum with a somewhat lower Gibbs
free energy (Fig. 7d). This shows that the molecule may
have a slightly less stable shape than the fundamental one. This secondary minimum may be a transition
state or an intermediate conformation along a chemical
pathway. While CMNPD30883 free energy landscapeAlharbi et al. BMC Chemistry(2024) 18:141

exhibits a single global minimum, indicating the existence of a single stable configuration (Fig. 7c). Thisshows that, when compared to the sinefungin (control)(Fig. 7e) and CMNPD18569 (Fig. 7d), CMNPD30883(Fig. 7c) has a well-defined structure and is less prone
to conformational changes. CMNPD30883 stability
could be related to structural properties or interactions with solvent molecules. CMNPD28811’s energy
landscape includes a major global minimum as well as
one secondary local minimum with slight reductions inGibbs free energy. This shows that when CMNPD28811
interacts with its receptor, it may adopt a less stable
shape (Fig. 7b). These secondary minima could be binding modes or intermediary states during the binding
process. These different conformations may affect themolecule’s affinity and selectivity for its target receptor. Unlike CMNPD15724, which displays a dominantprimary minimum on its free energy landscape, there
is also a noticeable secondary minimum with a slightly
higher Gibbs free energy. This indicates that the molecule could potentially adopt another conformationthat is somewhat less stable, as depicted in Fig. 7a). At
last, the energy landscape of CMNPD30883 (Fig. 7c) is
characterized by a prominent global minimum, indicating a primary stable conformation. An additional
local minimum is observed, with a higher Gibbs freeenergy than the original one, indicating a less stableconformation. This implies that The VP39 methyltransferase’s free energy landscape when docked with (a)CMNPD15724; (b) CMNPD28811; (c) CMNPD30883;(d) CMNPD18569; and (e) sinefungin (control). Preferentially assumes a particular shape, but it may potentially exist in a less stable configuration under certain
conditions. This secondary minimum’s presence may
have ramifications for the molecule’s biological function or interactions with other molecules.
It is important to mention that the minimum energy
state, which serves as a reference point, was consistently
determined to be 0 kJ/mol in all complexes. The thermodynamic free energy landscape of the analyzed chemical complexes, including a control compound, provides
accurate information regarding their molecular dynamics
and stability characteristics. A notable characteristic of
this landscape is the existence of a uniform relative maximum energy state inside these complexes, which remains
within a restricted range of 14 to 20 kJ/mol.
The similar energy threshold seen in all complexes
indicates a possible uniformity in structural or functional characteristics, such as molecular interactions or
stability. Furthermore, it is worth noting that all of thecomplexes exhibit a stable conformation at energy levels below 2 kJ/mol, as seen by the presence of a dark blue
color on the landscape.

Page 15 of 19

Our study focused on analyzing the lower energy statesof selected ligand–protein complexes. Lower energy
states indicate more stable conformations, as systems
favor energetically favorable (lower energy) arrangements. From these analyses, three stable poses wereextracted for each complex. However, an exception wasnoted for the compound CMNPD18569, which exhibited
only two stable poses at lower energy states, suggesting
a potentially less flexible interaction with its protein target or a constrained conformational landscape. To further our analysis, we constructed the superimposition
structure as shown in Fig. 8, wherein the stable poses
identified from the docking studies were aligned with
their initial (reference) poses. Superimposition is a critical step in understanding the extent of conformationalchanges that compounds undergo upon binding to theirtargets. It provides a visual and quantitative measureof these changes, which can be crucial for assessing thebinding mode and the possible biological implications ofthese interactions. The quantitative aspect of this analysis
was addressed by calculating the overall RMSD for eachsuperimposed structure. Our findings revealed distinct
RMSD values for the complexes studied, reflecting thediversity in conformational variation upon binding. The
compound CMNPD15724 in complex with target protein
showed an overall RMSD of 0.772 nm (Fig. 8a), suggesting a moderate degree of structural deviation from theinitial pose. Similarly, CMNPD28811 complexed with theprotein exhibited an overall RMSD of 0.784 nm, indicating a comparable level of structural change (Fig. 8b). In
contrast, CMNPD30883 displayed a notably lower RMSD
of 0.721 nm in its superimposed structure, pointing to acloser alignment with its initial pose and possibly a morefavorable binding conformation (Fig. 8c). Conversely,
CMNPD18569 exhibited a significantly higher Overall
RMSD of 0.852 nm (Fig. 8d), suggesting substantial structural change upon binding. For comparative purposes,
a control complex was also analyzed, yielding an overall
RMSD of 0.838 nm (Fig. 8e). These RMSD values provide
a foundational understanding of the structural dynamics
within these compound-protein interactions. By quantifying the deviations from initial to binding poses, we gain
insights into the conformational flexibility of the compounds, their potential binding affinities, and the specificity toward their protein targets.
ADMET analysis

To predict the toxicity and safety of the selected compounds we have also performed the ADMET analysis by using the ADMETLab 2.0 [40, 41] as shownin Table S2. The ADMET profiles of the compounds
CMNPD15724, CMNPD28811, CMNPD30883, and
CMNPD18569 are highly promising. All compounds

Alharbi et al. BMC Chemistry(2024) 18:141

Page 16 of 19

Fig. 8 Superimposed representation of a CMNPD15724, b CMNPD28811, c CMNPD30883, d CMNPD18569, and e sinefungin (control). Where
the a1, b1, c1, d1 and control e1 are the superimposed (aligned) structure of (a2, a3, a4), (b2, b3, b4), (c2, c3, c4), (d2, d3, d4) and (e2, e3, e4)respectively

exhibit moderate to high lipophilicity (LogP values),
enhancing their potential for cellular membrane permeability. Their LogD values are balanced, indicating agood equilibrium between hydrophilicity and lipophilicity. Particularly, CMNPD28811 and the Control compound show high Human Intestinal Absorption (HIA),
suggesting excellent potential for oral bioavailability.
The compounds CMNPD30883 and CMNPD28811 also
demonstrate significant blood–brain barrier permeability (BBB), which is indicative of potential central
nervous system activity. Regarding distribution, thecompounds have favorable protein binding percentages (PPB), which ensures efficient transport within the
bloodstream, and their volumes of distribution (VDss)
indicate widespread distribution throughout the body.
In terms of metabolism, the compounds exhibit inhibitory activity against several CYP450 enzymes, particularly CMNPD15724 and CMNPD30883, which might
help in avoiding rapid metabolism. CMNPD15724
and CMNPD18569 show strong inhibitory effects on
CYP1A2 and CYP3A4, suggesting potential for sustained plasma levels. CMNPD28811 demonstrates
favorable metabolic stability across multiple CYP

substrates. The compounds also exhibit reasonable
clearance rates (CL) and elimination half-lives (T12),
indicating manageable dosing frequencies. The toxicity
profiles of these compounds are quite favorable, with
CMNPD15724, CMNPD30883, and CMNPD18569
showing low Ames toxicity, suggesting a low mutagenic risk. CMNPD15724 and CMNPD28811 show
no significant hepatotoxicity or potential for DrugInduced Liver Injury (DILI). Additionally, mostcompounds have acceptable skin sensitization andrespiratory toxicity profiles. Furthermore, the compounds CMNPD15724 and CMNPD30883 score highon natural product-likeness, indicating they are derived
from or similar to natural products, which often correlates with better biocompatibility. All the compounds
adhere to Lipinski’s Rule of Five, suggesting good oral
bioavailability. Overall, the compounds CMNPD15724,
CMNPD28811, CMNPD30883, and CMNPD18569
exhibit favorable ADMET properties, including goodabsorption, distribution, metabolic stability, and low
toxicity profiles, making them promising candidates forfurther development in drug discovery.

Alharbi et al. BMC Chemistry(2024) 18:141

Discussion
In this study, we conducted a comprehensive analysis
of four marine-derived compounds (CMNPD15724,
CMNPD28811, CMNPD30883, and CMNPD18569) andtheir interactions with the Monkeypox virus (MPXV)
methyltransferase VP39 using various computational
techniques. The reference compound sinefungin wasused as a control to benchmark the results. Our methods included virtual screening, redocking, molecular
dynamics (MD) simulations, root mean square deviation(RMSD), root mean square fluctuation (RMSF), radiusof gyration (Rg), and principal component analysis(PCA) based free energy landscape analysis [38, 42].
Virtual screening was performed on a library of marine
fungi compounds, resulting in the identification of thefour top-scoring compounds [43]. These compounds
exhibited significant docking scores indicating strong
potential interactions with the VP39 protein [44]. The
redocking process further validated these interactions.
The binding scores for the compounds were as follows: CMNPD15724 (− 9.4 kcal/mol), CMNPD28811(− 8.9 kcal/mol), CMNPD30883 (− 9.2 kcal/mol),
CMNPD18569 (− 9.4 kcal/mol), and sinefungin(− 8.9 kcal/mol). Hydrogen bonds and hydrophobic
interactions were critical in stabilizing the VP39-compound complexes. CMNPD15724 formed eight hydrogenbonds with key residues such as Asp138, Ser141, Val116,
and Arg97, and several hydrophobic interactions withresidues including Leu42, Val139, and Ile94. Similarly,
CMNPD28811 showed significant hydrophobic interactions and hydrogen bonds, contributing to its binding
stability. CMNPD30883 and CMNPD18569 also demonstrated a variety of interactions, indicating flexible andstructured binding mechanisms respectively. Sinefungin,
the control, showed a different binding pattern, which
provided a comparative baseline for the studied compounds. The 200 ns MD simulations provided insights
into the dynamic behavior and stability of the VP39compound complexes [21]. RMSD analysis revealed that
CMNPD30883 maintained a lower RMSD (0.721 nm)
compared to CMNPD15724 (0.772 nm), CMNPD28811(0.784 nm), and CMNPD18569 (0.852 nm), indicatingthat CMNPD30883 had the most stable binding configuration. The control, sinefungin, exhibited an RMSD of
0.838 nm. RMSF analysis indicated the flexibility of individual residues in the protein. The compounds displayed
different RMSF profiles, with CMNPD30883 showing
the least fluctuation, correlating with its stable binding
observed in the RMSD analysis. The Rg values suggested
that all compounds maintained a compact structure
throughout the simulation, with CMNPD30883 again
showing the least variation. PCA was utilized to reducethe dimensionality of the MD trajectories, focusing on

Page 17 of 19

the most significant motions. The free energy landscape(FEL) derived from the PCA revealed that CMNPD30883
occupied a single stable conformational state, while othercompounds showed multiple minima, indicating a rangeof binding conformations. This stability of CMNPD30883
was consistent with its low RMSD and RMSF values, suggesting a robust inhibitory activity. The binding site of
VP39 involves critical residues such as Asp138, Ser141,
Val116, Arg97, and Phe115. Hydrogen bond analysis
showed that these residues played significant roles instabilizing the complexes. For example, CMNPD15724
formed stable hydrogen bonds with Asp138 and Ser141,
essential for its binding stability. Hydrophobic interactions with residues like Val116 and Ile94 were also crucial in maintaining the binding configuration of thecompounds. The hydrogen bond patterns observed during the simulations highlighted the importance of theseinteractions in disrupting VP39’s function. Compounds
that consistently formed critical hydrogen bonds, such as
CMNPD15724 and CMNPD30883, showed higher stability and potential efficacy as inhibitors. Hydrophobic
interactions further supported the stability and specificity of the binding, particularly for CMNPD28811 and
CMNPD30883, which showed extensive hydrophobic
contacts. The article “Discovery and Structural Characterization of Monkeypox Virus Methyltransferase VP39
Inhibitors”, this article presents crystal structures of the
VP39 methyltransferase in complex with sinefungin andother discovered inhibitors. Sinefungin, a known panMTase inhibitor, binds to the VP39 methyltransferase
in a conserved manner, similar to its binding in SARSCoV-2 nsp14 MTase [2]. The inhibitors identified in theirstudy, including TO507, contain five-membered rings
crucial for their binding and inhibitory action. Similarly,
our study focuses on identifying inhibitors derived frommarine fungi utilizing virtual screening and molecular
dynamics simulations to identify four promising compounds: CMNPD15724, CMNPD28811, CMNPD30883,
and CMNPD18569. These compounds also feature fivemembered rings, contributing to their effective binding
to the VP39 methyltransferase. The similarity in structural features, particularly the presence of five-memberedrings, underscores the importance of this structural motif
in the inhibitors’ efficacy. Both sets of compounds demonstrate a conserved binding pattern, indicating thatthese rings facilitate the interaction with the VP39 methyltransferase’s active site. Additionally, the inhibitors
identified in their work exhibit cross-viral efficacy, showing activity against SARS-CoV-2 nsp14 MTase, suggesting their broad-spectrum antiviral potential. This aspect
highlights the potential for the marine-derived inhibitors
to be evaluated for similar cross-viral activity, expanding
their utility beyond MPXV.

Alharbi et al. BMC Chemistry(2024) 18:141

Conclusions
This study is based on in-depth computational analysis
to identify and evaluate potential inhibitors of the Monkeypox virus (MPXV) methyltransferase VP39, a critical
enzyme for viral replication. Using a diverse library ofmarine fungi-derived small molecules, we screened andidentified four promising compounds: CMNPD15724,
CMNPD28811, CMNPD30883, and CMNPD18569.
These compounds were subjected to rigorous evaluations including re-docking, molecular dynamics (MD)simulations, and ADMET analysis to determine theirinhibitory potential and pharmacokinetic profiles. Our
virtual screening and re-docking processes highlighted
CMNPD15724 and CMNPD30883 as exhibiting superior binding affinities and stable interactions within the
VP39 active site. MD simulations confirmed the stability of these interactions, with CMNPD30883 showing
the highest stability with an average RMSD of around0.15 nm. ADMET analysis further indicated that
CMNPD28811 and CMNPD30883 possess significant
blood–brain barrier permeability, suggesting potential
central nervous system activity, while CMNPD15724
and CMNPD18569 demonstrated strong inhibitory
effects on CYP1A2 and CYP3A4, supporting theirpotential for sustained plasma levels and favorable
metabolic stability. The combined findings from ouranalyses underscore the potential of CMNPD15724,
CMNPD28811, CMNPD30883, and CMNPD18569 as
potent MPXV inhibitors. These compounds not only
exhibit strong binding and stable interactions withthe target enzyme but also possess favorable ADMET
properties, including good absorption, distribution,
metabolic stability, and low toxicity profiles. This study
highlights the untapped potential of marine fungi as asource of potential antiviral agents and paves the way
for further experimental validation and development ofthese compounds into effective therapeutic options forcombating MPXV infections.
Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s13065-​024-​01251-x.
Additional file 1.
Acknowledgements
Not applicable.
Author contributions
Conceptualization, A.S.A., S.A.A., M.M.E.D., A.M.H., I.A.A.Z., L.H.B., I.M.A., V.D.D.
and E.I.A.; Data curation, A.S.A., S.A.A. and V.D.D.; Formal analysis, A.S.A., S.A.A.
and V.D.D.; Methodology, A.S.A., S.A.A., M.M.E.D., A.M.H., I.A.A.Z., L.H.B., I.M.A.,
V.D.D. and E.I.A.; Validation, A.S.A., S.A.A., M.M.E.D., A.M.H., I.A.A.Z., L.H.B., I.M.A.,
V.D.D. and E.I.A.; Visualization, A.S.A., S.A.A., M.M.E.D., A.M.H., I.A.A.Z., L.H.B., I.M.A.,
V.D.D. and E.I.A.; Writing—original draft, A.S.A. and V.D.D.; Writing—review andediting, All the authors.

Page 18 of 19

Funding
This research was funded by the Deanship of Scientific Research (DSR) at King
Abdulaziz University, Jeddah, Saudi Arabia, through an institutional funding
program, project number (IFPIP: 636–140-1443). The funders had no role instudy design, data collection and analysis, decision to publish, or preparationof the manuscript.
Availability of data and materials
The datasets used and/or analysed during the current study are available fromthe corresponding author on reasonable request.

Declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Author details
1
Special Infectious Agents Unit – BSL3, King Fahd Medical Research Center,
King Abdulaziz University, 21362 Jeddah, Saudi Arabia. 2 Department of ClinicalMicrobiology and Immunology, Faculty of Medicine, King Abdulaziz University,
21362 Jeddah, Saudi Arabia. 3 Department of Medical Laboratory Sciences,
Faculty of Applied Medical Sciences, King Abdulaziz University, 21362 Jeddah, Saudi Arabia. 4 Department of Biochemistry, Faculty of Sciences, King
Abdulaziz University, 21362 Jeddah, Saudi Arabia. 5 Center for Global HealthResearch, Saveetha Institute of Medical and Technical Sciences, Saveetha
Medical College and Hospitals, Saveetha University, Chennai 605102, India.
6
Bioinformatics Research Division, Quanta Calculus, Greater Noida 201310,
India.
Received: 10 May 2024 Accepted: 19 July 2024

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