Trans-Cannabitriol as a Dual Inhibition of MPOX Adhesion Receptors L1R and E8L: An In Silico Perspective
1Mohammed VI University of Sciences and Health (UM6SS), Casablanca, Morocco
2Mohammed VI Center for Research and Innovation (CM6RI), Rabat, Morocco
3Department of Emergency, Mohammed V Military Training Hospital, Mohammed V University of Rabat, Rabat, Morocco
4Laboratory of Human Pathologies Biology, Department of Biology, Faculty of Sciences, Mohammed V University, Rabat, Morocco
5Biotechnology lab (MedBiotech), Bioinova Research Center, Medical and Pharmacy School, Mohammed V University in Rabat, Rabat, Morocco
Hanane Abbou, Mohammed VI University of Sciences and Health (UM6SS), 82 403 Casablanca, Morocco. Email: habbou@um6ss.ma; hanane.abbou95@gmail.comAbstract
The re-emergence of monkeypox virus (MPXV) as a global public health concern highlights the urgent need for novel therapeutic strategies targeting viral proteins essential for infection. This study investigates the inhibitory potential of Trans-Cannabitriol (trans-CBT), a minor cannabinoid, against MPXV proteins L1R, H3L, and E8L using an integrative in silico framework. Homology modeling was employed to generate 3D structures of these proteins, followed by molecular docking and 1 µs molecular dynamics (MD) simulations. The trans-CBT demonstrated strong binding affinities for L1R (−10.76 kcal/mol) and E8L (−8.531 kcal/mol), with weaker interactions observed for H3L (−5.739 kcal/mol). Four MD simulations of 1 µs revealed that trans-CBT stabilizes L1R by reducing its flexibility and solvent exposure, potentially inhibiting viral entry into host cells. In contrast, trans-CBT increased the flexibility and conformational changes of E8L, possibly impairing its function in viral attachment and pathogenesis. ADMET and target prediction analyses further supported its drug-likeness and safety, with the absence of strong CB1/CB2 binding suggesting that trans-CBT may exert its antiviral effects independently of classical cannabinoid pathways. These findings provide insights into the diverse mechanisms of action of trans-CBT on MPXV proteins and underscore its potential as a broad-spectrum antiviral agent. While promising, further experimental validation and optimization are necessary to assess the real-world applicability of trans-CBT in combating MPXV infections. This work contributes to the expanding field of cannabinoid-derived antivirals and highlights the importance of exploring under-investigated phytochemicals for therapeutic applications.
Introduction
The re-emergence of MPOX (monkeypox) as a global public health threat, declared a Public Health Emergency of International Concern (PHEIC) by the World Health Organization (WHO), underscores the urgent need for innovative therapeutic strategies.1,2 The monkeypox virus (MPXV), an Orthopoxvirus, exploits host cell adhesion receptors such as L1R, H3L, and E8L, critical structural proteins facilitating viral attachment and entry, to establish infection. Notably, L1R affects particle entry, E8L protein binds chondroitin sulfate on host cells, while H3L facilitates binding to host cells and enhances infectivity.3bibr4-11779322251355315bibr5-11779322251355315bibr6-11779322251355315bibr7-11779322251355315-8
With no specific treatment currently available for MPXV infection, the urgent need for novel inhibitors that disrupt viral adhesion, a critical step in the MPXV lifecycle, remains unmet. Targeting viral adhesion proteins is particularly interesting, as blocking these interactions at the earliest stage of infection could provide a robust antiviral strategy. Recent computational studies have highlighted the potential of phytochemicals as promising MPXV inhibitors, with plant-derived compounds such as punicalagin demonstrating superior binding affinity compared to repurposed drugs.2,9
Multiprotein targeting of these receptors could synergistically block viral entry, a strategy previously attempted by multicomponent vaccines eliciting immunity against multiple viral proteins. 1
This study focuses on L1R and E8L, 2 key MPXV adhesion receptors that facilitate viral attachment and entry into host cells. The dual inhibition of these proteins presents a particularly intriguing approach, as disrupting both could create a synergistic blockade against viral invasion. This concept mirrors the success of multicomponent vaccines designed to elicit immunity against multiple viral proteins. 1 By targeting these receptors simultaneously, we explore a novel strategy that could enhance antiviral efficacy and pave the way for phytochemical-based therapeutics against MPXV.
Cannabinoids, a structurally diverse class of phytochemicals, have garnered attention for their broad-spectrum antibacterial properties and modulatory effects on host-pathogen interactions.9bibr10-11779322251355315bibr11-11779322251355315-12 This class of molecules makes an attractive source to explore in an attempt to find a potential direct antiviral agent against poxviruses, especially minor cannabinoids, which constitute a vastly underexplored subclass of cannabinoids.
Notably, cannabinoids exert their biological effects primarily through modulation of the endocannabinoid system (ECS) via CB1 and CB2 receptors. While CB2-selective cannabinoids offer therapeutic benefits with minimal psychoactivity, CB1 activation in the central nervous system (CNS) can lead to adverse effects such as cognitive impairment, psychoactivity, and dependency.13,14 This dual nature emphasizes both the promise and limitations of cannabinoids in drug development. However, the growing interest in non-psychoactive or minimally psychoactive minor cannabinoids provides an opportunity to harness their therapeutic potential while mitigating CNS-related side effects.15,16
Computational approaches, such as molecular docking and dynamics simulations, have proven instrumental in identifying high-affinity ligands for viral targets, as evidenced by recent successes in repurposing phytochemicals against viruses, such as SARS-CoV-2.17bibr18-11779322251355315-19
The current study investigates the inhibitory potential of major and minor cannabinoids against MPXV cell adhesion proteins through an integrative in silico framework. Leveraging homology modeling, molecular docking, and molecular dynamics (MD) simulations, this work evaluates the minor cannabinoids’ capacity to destabilize viral adhesion mechanisms.
By bridging cannabinoid bioactivity with computational virology, this work aims to expand the therapeutic arsenal against MPXV while illuminating novel structure-function relationships in cannabinoid-derived antivirals.
Methods
Protein structure homology modeling
The SwissModel online server 20 was used to identify the most suitable structures for homology modeling based on the protein sequences, as the 3D structures of the 3 proteins (L1R, H3L, and E8L) are unavailable. Still, the homologous proteins from Vaccinia Virus (VACV), which were identified using SwissModel, have their structures resolved in X-ray and are retrievable via the Protein Data Bank (PDB). 21 These structures were used as templates for homology modeling. The quality of the newly modeled structures was assessed using their respective Ramachandran plots, generated with the PROCHECK22,23 online tool. In addition, a reliability assessment of protein models was conducted by analyzing non-bonded atomic interactions and identifying regions with higher error frequency using the ERRAT 24 online tool. Both tools are accessible via the SAVESv6.1 online server.
Structures’ retrieval and preparation for docking
A list of known minor cannabinoids has been retrieved from Cayman Chemicals, 25 the 3D structures of these molecules were retrieved from PubChem 26 in SDF format. The structures were prepared using the LigPrep module, 27 followed by force field assignment via OPLS4, within the Schrodinger suite. This force field uses quantum mechanics-based training data and extensive experimental validation. This ensures a reliable representation of molecular geometries, partial charges, torsional profiles, and non-bonded interactions. 28 The previously generated 3D structures of the proteins were prepared using the Protein Preparation Wizard in the Schrodinger Maestro Module. 29
Extra precision docking
Extra precision (XP) docking mode in the Schrodinger Glide Module 30 was used to screen the minor cannabinoids against the 3 prepared PDB structures of L1R, H3L, and E8L. The docking grids were generated using a blind docking approach, where a receptor grid box was centered on the centroid of the entire protein structure for each of the 3 targets. The grid box dimensions were set to sufficiently encompass the entire protein surface, allowing for unrestricted ligand binding across the whole structure.
Docking between the minor cannabinoids and the proteins (L1R, E8L, and H3L) was conducted in XP mode with default parameters, including a van der Waals radius scaling factor of 0.8 and a partial charge cutoff of 0.15 for non-polar atoms. No constraints were applied during docking. The ligand poses were scored and ranked based on GlideScore, and for each target, the best-scoring pose was retained. A comparative ranking was used to identify the top-ranking molecule common to all 3 proteins, which was selected for subsequent MD simulations.
Ligand-protein interactions were visualized and analyzed using LigPlot 31 to identify hydrogen bonding, hydrophobic contacts, and key residue interactions contributing to binding affinity.
Molecular dynamics simulation
The molecule showing a high binding affinity with the 3 proteins was selected for MD simulations. The docking-generated complexes were used as the initial conformation for the simulation system preparation using the System Builder wizard in Desmond. 32 The preparation consisted of embedding the L1R, L1R-trans-CBT complex, H3L, H3L-trans-CBT complex, E8L, and E8L-trans-CBT complex in a TIP3P solvent using a Cubic box system with 10 Å padding, neutralizing the systems with Cl− ions, then adding 0.15 M of NaCl concentration to mimic physiological ionic strength. The final simulation box had approximate edge lengths of 76.03 Å.
The generated systems were then subjected to 1 µs MD simulations using OPLS4 force field, which is specifically optimized for proteins and drug-like ligands, including phytochemicals. The multigrator integrator was employed, using the Martyna-Tobias-Klein (MTK) method for pressure coupling and the Nose-Hoover thermostat for temperature regulation. The temperature was set to 300 K, and the pressure to 1.01325 bar, using relaxation times (τ) of 1.0 ps and 2.0 ps, respectively, for thermostat and barostat. A time step of 2 fs was used for bonded and short-range non-bonded interactions, and 6 fs for long-range interactions.
Long-range electrostatics were handled using the u-series method, with a cutoff radius of 9.0 Å for non-bonded interactions. The constraints on bond lengths involving hydrogen atoms were applied using the default Desmond algorithm, with a convergence tolerance of 1 × 10−8 and a maximum of 16 iterations. Initial velocities were randomly assigned from a Maxwell-Boltzmann distribution at 300 K using a fixed seed (2007) to ensure reproducibility.
Simulation trajectories were saved every 500 ps, and system energies were recorded every 1.2 ps. Checkpoint files were written every 240.06 ps, and simulation box dimensions were monitored at 1.2 ps intervals.
Following the completion of MD simulations for each system, the resulting trajectories were analyzed using the Simulation Interaction Diagram (SID) in Maestro. This analysis quantified system dynamics through metrics such as root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (Rg), and solvent-accessible surface area (SASA). The average (Avg) and standard deviation (SD) of each parameter were calculated. The protein’s secondary structure elements (SSEs), including alpha-helices and beta-strands, were tracked throughout the simulation for each residue.
ADMET and human target prediction analysis
To evaluate the drug-likeness and pharmacokinetic profile of trans-Cannabitriol (trans-CBT), in silico ADMET predictions were performed using the SwissADME tool. 33 Input was provided as the canonical SMILES of trans-CBT to predict its physicochemical parameters (molecular weight, H-bond donors/acceptors, rotatable bonds), drug-likeness (Lipinski, Ghose, Veber, Egan, and Muegge rules), pharmacokinetics (gastrointestinal [GI] absorption, blood-brain barrier permeability, and P-glycoprotein interaction), and toxicity risk alerts (PAINS and Brenk filters). The compound’s potential to inhibit major CYP450 isoforms (CYP1A2, 2C19, 2C9, 2D6, 3A4) was also assessed.
To explore likely human macromolecular targets, SwissTargetPrediction 34 was employed. This tool predicts probable protein targets based on 2D and 3D similarity to known ligands.
Results
Protein structure homology modeling and preparation
Using SwissModel, we identified the structures with the highest coverage and identity to L1R, H3L, and E8L and used them as templates to model the 3 proteins (Table 1).
| Protein | Sequence ID | Chosen template |
|---|---|---|
| L1R | M1LBP0 | 1YPY |
| H3L | V9NPG6 | 5EJ0 |
| E8L | Q8V4Y0 | 4E9O |
The quality of the modeled structures was assessed using PROCHECK and ERRAT through Ramachandran plot analysis (Figure 1) and compatibility of the atomic 3D model with its corresponding amino-acid (AA) sequence. The newly modeled structures showed high quality, as their Ramachandran plots show that L1R has 1 Gly outside the optimal and allowed areas, E8L shows 5 AAs, of which 4 are Gly, outside the optimal and allowed regions, and finally, H3L shows 6 AAs outside the same areas. Since more than 90% of residues in each model fall within the optimal and allowed regions, the generated structures are considered valid. The ERRAT assessment showed that the L1R model exhibited a high overall quality factor of 95.38%, indicating excellent structural reliability and minimal local errors. The E8L model scored 87.11%, and the H3L model achieved a score of 81.94%, both of which are considered within the acceptable range for homology-modeled structures. These values suggest that the models are structurally sound.
According to standard PROCHECK and ERRAT evaluation criteria, all 3 models passed the quality check, supporting their suitability for further computational analyses, including docking and MD simulations.
These structures, along with the 3D structures of the minor cannabinoids, were prepared for molecular docking by adding missing hydrogen atoms to ensure structural integrity and proper binding energy calculations.
Molecular docking
Molecular docking is a computational method widely utilized in structural biology and drug discovery to identify molecules with potential binding affinity for specific receptors. This technique offers valuable insights into the binding mechanisms and interactions between ligands and receptors; thus, it plays a crucial role in facilitating the drug discovery process. 35
Following the structure preparations of L1R, H3L, and E8L, XP-docking was performed against the ligands to identify the one with the best-binding affinity (Table 2).
| Compound | Binding energy (kcal/mol) | ||
|---|---|---|---|
| L1R | E8L | H3L | |
| Trans-Cannabitriol | −10.76 | −8.53 | −5.74 |
| Cannabichromene | −10.42 | −5.16 | −4.24 |
| Cannabinodiol | −9.73 | −5.61 | −4.11 |
| Delta 8-Tetrahydrocannabinol | −9.64 | −4.22 | −3.28 |
| Cannabigerol | −9.49 | −5.79 | −3.52 |
| Cannabinol | −9.08 | −5.79 | −2.99 |
| Cannabielsoin | −7.57 | −4.68 | −2.91 |
| Cannabicyclol | −7.33 | −4.44 | −3.19 |
| Cannabitriol | −6.5 | −8.3 | −1.87 |
| Cannabinodivarin | −5.78 | −4.61 | −5.68 |
| Cannabichromenic acid | −5.4 | −7.89 | −4.46 |
| Cannabichromanone | −4.36 | −6.72 | −4.36 |
| Cannabielsoic acid A | −3.87 | −7.94 | −2.42 |
| 5-(1-(4-(Benzo[d]isothiazol-3-yl)piperazin-1-yl)-2-hydroxyethyl)-6-chloroindolin-2-one | −3.65 | −4.81 | −3.65 |
| Cannabicoumaronone | −3.64 | −4.7 | −2.83 |
| Cannabicyclolic acid | −3.55 | −8.57 | −2.34 |
| Dehydrocannabifuran | −3.33 | −4.15 | −3.33 |
| Cannabiripsol | −2.76 | −5.01 | −2.76 |
| Cannabichromevarin | −2.57 | −5.15 | −1.01 |
The docking studies of the minor cannabinoids against L1R, E8L, and H3L revealed a range of binding affinities, as quantified by binding energy (ΔG) in kcal/mol. Among the tested ligands, trans-CBT emerged as the most promising candidate, exhibiting the highest binding affinity with the 3 proteins. Specifically, trans-CBT demonstrated binding energies of −10.76 kcal/mol for L1R, −8.53 kcal/mol for E8L, and −5.74 kcal/mol for H3L. For L1R and H3L, trans-CBT is the best-ranking molecule, as for E8L, it appears to be the second-best-ranking minor cannabinoid, after Cannabicyclolic acid.
Given these results, trans-CBT was selected for further MD simulation studies with L1R, E8L, and H3L. The decision was based on its consistently strong binding affinities across all 3 proteins, suggesting a broad-spectrum potential to disrupt viral membrane fusion processes. Furthermore, the significant binding energy for L1R, a protein essential for viral entry, underscores its potential therapeutic relevance.
Molecular interactions
The docking score of trans-CBT against L1R is −10.76 kcal/mol (Table 2). This high score suggests an optimal interaction between multiple AAs and the selected ligand. Further analysis of the structure resulting from docking revealed hydrophobic interactions between the ligand and several AA (Figure 2) (Ser6, Ile7, Thr10, Val11, Leu14, Thr76, Leu79, Gln83, Tyr86, Val87, Met90, Leu163, Leu166, and Thr167), and hydrogen bonds (HBs) with Leu163 and Thr167 (Figure 2A).
Similarly, E8L and trans-CBT’s docking score is −8.531 kcal/mol (Table 2), with hydrophobic interactions only between Phe56, Arg20, Leu170, Gln230, and Asp228 (Figure 2E).
As for the interaction between H3L and trans-CBT, of which the docking score was −5.739 kcal/mol, Asp127, Ser186, Val13, Asn97, Glu125, Tyr213, Ile156, Tyr181, Val183, and Arg154 formed hydrophobic interactions with the ligand, while Arg97, Glu125, and Tyr213 formed HBs (Figure 2G).
Despite the plethora of interactions trans-CBT was able to establish with the 3 proteins, further validation is needed to assess the ability of these molecular bonds to stay stable throughout time.
Molecular dynamics simulation analysis
The MD simulation is a powerful computational tool for studying the movements of complex macromolecular systems, including biomolecules. It enhances molecular docking models by considering the flexibility of proteins and ligands, leading to a more accurate representation of the binding process and the identification of more effective drug candidates. 36
An important aspect of MD simulations is the analysis of the structural fluctuations of the macromolecule. It can be used to identify key conformational changes that occur upon ligand binding and subsequently provide insights into the molecular basis of ligand-target interactions. 37
L1R
The MD simulation results illustrate a difference in structural fluctuation between L1R and L1R-trans-CBT. The RMSD analysis is a strong metric for evaluating protein structural changes over time. 38
The RMSD values of L1R (Avg = 5.09 (Å), SD = 0.71) are higher than those of the docking complex (Avg = 3.97 (Å), SD = 0.52) (Table 3). This suggests that trans-CBT’s interaction with L1R induces rigidity within the protein, potentially preventing it from interacting with its molecular targets (host cell surface proteins in this case) to achieve its function, which relies on adaptation and recognition 39 (Figure 3A). This fluctuation is further emphasized in the RMSF analysis. It is an important parameter that reflects the structural flexibility of Cα atoms of each residue in its corresponding system. 40 The majority of L1R AA show higher values (Avg = 1.84 (Å), SD = 1.60) in the absence of the ligand compared to when it is bound to L1R (Avg = 1.60 (Å), SD = 1.43) (Table 3 and Figure 3B).
| Parameters | Analyzed structures | |||||||
|---|---|---|---|---|---|---|---|---|
| L1R | L1R-trans-CBT | E8L | E8L-trans-E8L | |||||
| Avg | SD | Avg | SD | Avg | SD | Avg | SD | |
| RMSD (Å) | 5.09 | 0.71 | 3.97 | 052 | 1.83 | 0.29 | 2.08 | 0.37 |
| RMSF (Å) | 1.84 | 1.60 | 1.60 | 1.43 | 0.78 | 0.51 | 0.95 | 0.78 |
| Rg (Å) | 16.03 | 0.19 | 16.09 | 0.14 | 17.59 | 0.07 | 17.57 | 0.08 |
| SASA (Å) | 9522.04 | 273.80 | 9152.64 | 233.07 | 11 500.57 | 165.44 | 11 442.65 | 248.55 |
Concurring with the docking results, the majority of AA demonstrated interactions with trans-CBT throughout the simulation time, such as Ile7, Thr10, Val11, Leu14, Leu79, Tyr86, Val87 Met90, Leu163, Leu166, and Thr167, which was illustrated by lower RMSF values per residue (Figure 3) due to the potential stabilization of these AA by the bonds formed with the ligand. Moreover, additional interactions between trans-CBT and Val170, and to a less impactful degree, Ile174, Ala175, and Val184 were formed (Supplemental Figure S1). The total number of HBs was also monitored over the 1 µs simulation time; it fluctuated between 0 and 4 HBs, but the values were mostly between 1 and 2 HBs (Supplemental Figure S2).
The Rg is an indicator of biomolecule structure compactness. 41 The Rg of L1R (Avg = 16.03 (Å), SD = 0.19) is slightly lower than Rg of L1R-trans-CBT (Avg = 16.09 (Å), SD = 0.14) (Table 3).
A slight increase in this value upon ligand binding could suggest that the protein adopts a slightly more extended conformation when bound to trans-CBT (Figure 3C). However, the change induced by trans-CBT is minimal, indicating that the overall compactness of the protein remains largely unchanged.
The SASA refers to the surface portion of a molecule that can interact with the surrounding solvent. 42 This parameter helps assess the compactness of the studied molecule. L1R has a higher SASA value than L1R-trans-CBT complex, which coincides with the RMSD and RMSF data, further illustrating trans-CBT’s potential to limit the movement of L1R and induce a structurally rigid conformation with a low exposure to solvent (Figure 3D).
Visual analysis of the final frame from the MD simulation alongside the SSE monitoring results, we noticed a significant difference in secondary structure distribution between L1R and L1R bound to the ligand. In the absence of trans-CBT, L1R has 56.99% of its structure as helices (41.30%) and beta-strands (15.69%) (Figure 5A), but the interaction with the ligand induced an increase in SSE, which led to the helices accounting for 43.91%, and beta-strands accounting for 16.28%, raising SSE up to 60.19% of the total structure (excluding loops) (Figure 5B). The AA most impacted by this gain of secondary structures are Asp62, Tyr76 to Gly78, Ala94, Ala95, Asn117, Asp123 to Leu126, and Pro176 to Val179, which have all gained helix, while Cys49, Asn50, Thr144, and Asn145 gained beta-strands (Figure 6A).
E8L
The RMSD values of E8L alone (Avg = 1.83 (Å), SD = 0.29) are lower compared to those of E8L bound to trans-CBT (Avg = 2.08 (Å), SD = 0.37) (Table 3), indicating that CBT causes significant internal motion and subsequent conformational changes in E8L (Figure 4A). This fluctuation is further illustrated in RMSF analysis, where the majority of AA show higher values in the presence of trans-CBT (Avg = 0.95 (Å), SD = 0.78) compared to the values of E8L alone (Avg = 0.78 (Å), SD = 0.51) (Table 3 and Figure 4B).
Similarly to the docking results, Arg20, Phe56, Leu170, Gln230, and Asp228 were indeed interacting with trans-CBT in multiple instances, with Arg20 showing the strongest interaction (more than 4 bonds) from 300 ns to the end of the simulation, and Phe56 interacting with the ligand for the entire duration of the simulation time (Supplemental Figure S3). The HBs count through the simulation time further elucidates the importance of the interactions between trans-CBT and E8L, as the values ranged between 1 and 8, but most of which concentrated around 3 and 6 (Supplemental Figure S4).
The comparison of Rg values between E8L (Avg = 17.59 (Å), SD = 0.7) and E8L-trans-CBT (Avg = 17.57 (Å), SD = 0.08) shows a minimal decrease when E8L is bound to the ligand (Figure 4C). As for SASA, the interaction with the ligand lowers its values (Avg = 11 442.65 (Å), SD = 248.55) compared to E8L alone (Avg = 11 500.57 (Å), SD = 165.44) (Figure 4D).
The SSE monitoring shows a minimal increase in secondary structure (1.29%), as E8L shows 36.17% of SSE (8.03% helices and beta-strands 28.14%), while E8L-trans-CBT has 37.46% (8.80% helices and beta-strands 28.66%). But the total SSE on its own does not provide a full capture of the dynamics the protein goes through during the simulation time; close analysis of the SSE (Figure 5C and D) shows that multiple AAs have lost their SSE in multiple instances of the simulation time, proving that E8L bound to trans-CBT is struggling to find back its original conformation, which is related to the function. Final frame structure analysis further proves this, as we find multiple AAs that lost their SSE (I184 lost beta-sheets, L138 to S140 lost helices) and others that gained SSE (Lys32, Pro33, Phe47, Lys48, Ser88, and Gly89 gained beta-sheets, while Leu156 to Leu159 gained helices) (Figure 6B).
H3L
The MD simulation of H3L and trans-CBT showed very scattered and minimal interactions in concordance with the low docking score initially found in the docking results. Thus, no further or deep analysis was performed on this complex as trans-CBT (although it is the highest-scoring molecule) did not display interesting results (Supplemental Figure S5).
ADMET and human target prediction analysis
The trans-CBT displayed favorable physicochemical characteristics, including a molecular weight of 332.43 g/mol, 4 rotatable bonds, 4 HB acceptors, and 3 donors, all within drug-like ranges. It showed no violations of major drug-likeness filters (Lipinski, Ghose, Veber, Egan, Muegge). The compound was predicted to have high GI absorption and be permeable across the blood-brain barrier (BBB). It is also a P-glycoprotein substrate, which may influence its bioavailability and efflux (Table 4).
| Molecule | Trans-cannabitriol |
|---|---|
| Molecular weight | 332.43 |
| Heavy atoms | 24 |
| Aromatic heavy atoms | 6 |
| Rotatable bonds | 4 |
| H-bond acceptors | 4 |
| H-bond donors | 3 |
| GI absorption | High |
| BBB permeant | Yes |
| Pgp substrate | Yes |
| CYP1A2 inhibitor | No |
| CYP2C19 inhibitor | No |
| CYP2C9 inhibitor | No |
| CYP2D6 inhibitor | Yes |
| CYP3A4 inhibitor | No |
| Lipinski violations | 0 |
| Ghose violations | 0 |
| Veber violations | 0 |
| Egan violations | 0 |
| Muegge violations | 0 |
| Bioavailability score | 0.55 |
| PAINS alerts | 0 |
| Brenk alerts | 0 |
| Leadlikeness violations | 0 |
CYP inhibition analysis showed that trans-CBT is likely a CYP2D6 inhibitor, but not an inhibitor of CYP1A2, 2C19, 2C9, or 3A4, suggesting a low risk of widespread metabolic interactions. The bioavailability score was 0.55, indicating moderate oral availability. Importantly, no PAINS or Brenk alerts were detected, and leadlikeness criteria were met.
SwissTargetPrediction identified the cannabinoid receptors CB1 and CB2 as top-predicted targets, but with low probability scores of 0.31, indicating weak confidence in binding affinity. Other predicted targets were primarily kinases (eg, JAK2, CDK2, PRKCA, MAPK family members), each with a low probability of 0.10 (Supplemental Table S1).
Discussion
The results presented in this study reveal several significant insights into the potential of trans-CBT as an inhibitor for MPXV proteins, specifically L1R and E8L. Through molecular docking and subsequent MD simulations over a 1 µs timescale, trans-CBT exhibited strong binding affinities to L1R and E8L, indicating its potential role in disrupting viral processes.
The MPXV relies on key proteins for host cell entry and infection. The E8L protein, also known as the cell surface binding protein, facilitates viral attachment by interacting with glycosaminoglycans like chondroitin sulfate.43,44 This protein is crucial for host cell attachment and membrane fusion, making it an attractive therapeutic target. The L1R protein, part of a complex of twelve proteins conserved across poxviruses, is essential for the membrane fusion step of viral entry. 45 Inhibiting these proteins could significantly hinder viral replication and spread by preventing the virus from establishing infection in host cells. Without efficient adhesion and membrane fusion, the virus would be unable to initiate its replication cycle, ultimately reducing viral spread and pathogenicity.
These findings are particularly relevant given these proteins’ roles; L1R is essential for viral entry into host cells, while E8L has been identified as important for attachment and pathogenesis. 46
In the case of L1R, MD simulations demonstrated that trans-CBT induces rigidity within the protein, reducing both RMSD and RMSF values compared to the unbound state, suggesting stabilization, which might impede necessary conformational changes required for viral entry. This stabilization effect could potentially block interactions between L1R and host cell surface proteins, thereby inhibiting viral infection. In addition, the reduction in SASA indicates that trans-CBT limits exposure of hydrophobic regions, possibly hindering interactions crucial for viral entry, which relies on interaction with host cell membrane and membrane receptors. 46
For E8L, the interaction with trans-CBT leads to increased internal motion and conformational changes rather than stabilization, contrasting sharply with observations made for L1R. Increased RMSD and RMSF values suggest enhanced flexibility, which may disrupt the protein’s ability to maintain functional conformations needed for its biological role. 47 While total SSE only minimally increased, detailed analysis shows that some residues lost their secondary structure during simulation time, hinting at difficulties for E8L to regain its original conformation post-binding, thus potentially impairing its functionality. 48
Comparatively, the literature supports the notion that identifying specific targets within viruses can lead to more effective therapeutic strategies, highlighting the importance of understanding individual protein behaviors and their responses to potential inhibitors. 49
The ADMET and target prediction results collectively highlight trans-CBT as a drug-like, orally bioavailable phytochemical with a favorable safety profile, supported by its lack of PAINS and toxicophores. The molecule’s high GI absorption and BBB permeability suggest it could exert effects in both peripheral and central compartments. However, despite BBB penetration potential, SwissTargetPrediction estimated only a 31% probability of CB1 and CB2 receptor engagement, which is considered low-to-moderate confidence, and binding probability alone does not imply bioactivity. 34
This result challenges assumptions that BBB permeability equates to cannabinoid receptor activity. These insights reinforce the need for experimental validation to confirm the mechanism of action. Nonetheless, the compound’s clean ADMET profile and oral drug-likeness make it a compelling candidate for further pharmacological and antiviral investigations, particularly in the context of MPXV.
Our results contribute valuable information toward developing novel treatments by demonstrating how trans-CBT interacts differently with various MPXV viral proteins, offering new avenues for antiviral drug discovery. It also sheds light on a cluster of molecules that are little explored, as the overall conversation surrounding cannabinoids tends to center around Tetrahydrocannabinol and Cannabidiol. 50
Experimental validation through biochemical assays will be critical to confirm computational predictions and assess real-world applicability.
Conclusion
The trans-CBT shows promise as an antiviral agent targeting MPXV proteins L1R and E8L. Molecular docking identified trans-CBT as the best-binding ligand for these proteins, with MD simulations revealing distinct effects: it stabilizes L1R by reducing flexibility and solvent exposure, potentially inhibiting viral entry, while it increases E8L’s flexibility and disrupts its conformation, possibly impairing its function. While the findings are encouraging, experimental validation is needed to confirm these effects and optimize trans-CBT for therapeutic use.
Supplemental Material
Acknowledgements
The authors thank Mr Abdellah ABBOU for providing the workstation that made the computational biology calculations possible.