Molecular interaction and MD-simulations: investigation of Sizofiran as a promising anti-cancer agent targeting eIF4E in colorectal cancer

Abstract

CRC has a major global health impact due to high mortality rates. CRC shows high expression of eukaryotic translation initiation factor (eIF4E) protein, the rapid development of lung, bladder, colon, prostate, breast, head, and neck cancer is attributed to the dysregulation of eIF4E making an important target for treatment. Targeting eIF4E-mediated translation is a promising anti-cancer strategy. Many organic compounds that inhibit eIF4E are being studied clinically. The compound Sizofiran has emerged as a promising eIF4E inhibitor candidate, but its exact mechanism of action is unclear. In an effort to close this discrepancy by clarifying the mechanism of the interactions between phytochemical substances and eIF4E, molecular docking and dynamics studies were conducted. Molecular docking studies found Sizofiran (− 12.513 kcal/mol) has the most affinity eIF4E binding energy out of 93 phytochemicals, 5 current drugs, and 4 known inhibitors. This positions it as a top eIF4E inhibitor candidate. An alignment of eIF4E protein sequences from multiple pathogens revealed that the glutamate103 interacting residues are evolutionarily conserved across the different eIF4E proteins. Further insights from 100 ns of MD simulations supported Sizofiran having superior stability and eIF4E inhibition compared to reference compounds. Designed Sizofiran-related compounds showed better activity than the current drugs such as Camptosar, Sorafenib, Regorafenib, Doxorubicin, and Kenpaullone, indicating strong potential to suppress CRC progression by targeting eIF4E. This research aims to significantly aid development of improved eIF4E-targeting drugs for cancer treatment.

Graphical abstract

Showing the Graphical abstract of the complete study.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-024-00206-3.

Introduction

CRC stands as the third most prevalent cancer in both sexes, ranking second in terms of mortality rates. Globally, it accounts for 10% and 9.2% of all cancer cases in men and women, respectively, causing over 500,000 deaths annually (Siegel et al. 2020). CRC emerges from the abnormal division and growth of colon cells, forming polyps that may either be malignant or cancerous (Ikwu et al. 2020). The exact cause of these abnormal cell divisions remains incompletely understood, but various risk factors have been identified, including age, race, family history, and a sedentary lifestyle (Simon et al. 2016). CRC is more frequently diagnosed in individuals aged 50 and above, as well as those who indulge in high levels of tobacco, alcohol, and high-fat diets. Moreover, persons with pre-existing health conditions such as obesity and diabetes are more susceptible to this cancer. Approximately a quarter of CRC cases are attributed to hereditary factors (Simon et al. 2016; Araghi et al. 2019; Ikwu et al. 2020).

In human cells, the rapid development of lung, bladder, colon, prostate, breast, head, and neck cancer is attributed to the overexpression or dysregulation of eIF4E (PDB ID: 5EI3) (Romagnoli et al. 2021; Chuet al. 2016; Bhat et al. 2015; Carroll and Borden 2013). An upregulation observed of eIF4E in CRC, with its expression frequency (EF) in cancer tissues surpassing that in normal adjacent tissues (Chen et al. 2023; Niu et al. 2014). Moreover, eIF4E had a significant impact on the growth of CRC organoids (Ruan et al. 2020).

The precise upregulation of eIF4E leads to increased translation of mRNAs linked to tumor growth and invasion, making it a crucial target for tumor therapy (Dong et al. 2008; Chen et al. 2023; Yang et al. 2016). It demonstrated that diminishing the levels of these translation factors, traditionally regarded as housekeeping genes, does not disrupt normal development. However, it is significantly associated with carcinogenesis, underscoring the critical involvement of eIF4E activity in cancer development (Xu et al., 2020). Recent studies have also indicated that eIF4E has the potential to inhibit obesity and fatty liver induced by a high-fat diet. Consequently, investigating the interplay among obesity, carcinogenesis, and eIF4E may emerge as a novel avenue in the exploration of eIF4E (Chen et al. 2023; Bitterman and Polunovsky 2015).

The precise mechanism underlying tumor occurrence, development, and metastasis mediated by eIF4E remains poorly understood, necessitating further attention and study regarding the relationship between eIF4E and tumors. Ultimately, utilizing eIF4E as a molecular target for tumor therapy and establishing models incorporating multiple anti-tumor drugs in combination could offer novel approaches to enhance efficacy and reduce side effects. For instance, combining trametinib and pazopanib, umbilisib, and carfilzomib, as well as patamine A and silvestrol may present opportunities for achieving synergistic anti-tumor effects (Romagnoli et al. 2021). In rapid, eIF4E Serving as a highly sensitive biomarker in tumor detection, it is poised to be employed for early diagnosis and prognostic assessment, thereby presenting expansive possibilities for advancing the treatment of malignant tumors (Romagnoli et al. 2021).

Phytochemicals and their derivatives found in plants offer promising avenues to enhance treatment efficacy in cancer patients while mitigating adverse reactions (Choudhari et al. 2020). Extensive research indicates that numerous plant-derived chemicals possess anticancer properties; however, their potential application in cancer treatment remains unclear. This study focuses on 93 phytochemical compounds out of Sizofiran from S. commune possess high binding potential with the eukaryotic translation initiation factor (eIF4E) receptor protein, including five current medication(Doxorubicin, Regorafenib, Kenpaullone, Sorafenib, and Camptosar) and inhibitors(7-methyl-GpppA, 7-methyl-7,8-dihydroguanosine-5'-diphosphate, 7-methyl-guanosine-5'-triphosphate, and Dihydro-alpha-ergocryptine). Integrated in-silico techniques were employed to detect inhibitory effects. The investigation of eIF4E, facilitated by its overexpression, may lead to the identification of novel and effective approaches for treating CRC.

Methodology

Target protein retrieval and preparation

The eukaryotic translation initiation factor eIF4E protein 3-D structure (PDB: 5EI3) was obtained in PDB format from the PDB database (https://www.rcsb.org/). Protein structure from the PDB proved unsuitable for molecular docking in its original form (Ramírez and Caballero 2018). Consequently, the protein structure was refined using the Protein Preparation Wizard in the Schrodinger suite 2019 (https://www.schrodinger.com/). With Glide (Schrodinger), a protein preparation wizard application, modifications such as bond order assignment, hydrogen addition, metal treatment, overlap detection and water molecule deletion up to five axes away from the structure can be carried out. Furthermore, OPLS_2005 was used to minimize RMSD up to 0.30 Å (Shivakumar et al. 2010). Molecular docking using receptor grids helps the ligand attach to an attainable configuration. The partial charge cutoff Van der Waals radius scaling is 0.25 Å, and the scaling factor is 1.0 Å. Receptor Grid was constructed by adding predicted ligand binding site residues. In Glide, other parameters were specified as defaults (Friesner et al. 2006; Repasky et al. 2007).

Target proteins grid generation

Potential molecular docking investigations require the creation of a defined grid-box on the protein active site. Accordingly, using the Receptor Grid Generation wizard, a smaller secondary box was constructed by default and centered around the possible binding site, and the active site was defined as an enclosing box (36 × 36 × 36 Å) at the centroid of the active site (Samykannu et al. 2019a, b). Van der Waals radii were scaled using default values, with a scaling factor of 1.0 Å and a cutoff point of partial atomic charge of less than 0.25. There were no established limits or rotatable groupings.

Creation of phytochemical library

A total of 93 phytochemical compounds were obtained from recently published literature (Ashraf 2020) these were combined with the reference compounds of current drugs (such as Doxorubicin, Regorafenib, Kenpaullone, Sorafenib, and Camptosar) and inhibitors (7-methyl-GpppA, 7-methyl-7,8-dihydroguanosine-5'-diphosphate, 7-methyl-guanosine-5'-triphosphate, and Dihydro-alpha-ergocryptine). These were then chosen for molecular docking and simulation studies against the eIF4E (PDB ID: 5EI3) receptor. The compounds 2D and 3D structures were obtained in structure data file (SDF) format from the PubChem database, which is accessible online at https://pubchem.ncbi.nlm.nih.gov. These compounds were loaded into the Schrodinger suite project table of Maestro 8.5. The Ligprep 2.2 application option was utilized to perform energy minimization, conformational analysis and ligand preparation, with the results being exported in the SDF format (Release 2017; Yadav et al. 2022). The ligand molecules for the molecular docking investigations were these optimized structures (Shivakumar et al. 2010; Samykannu et al. 2020).

Molecular docking

A key component of structure-based drug design is molecular docking, which is the binding orientation of small compounds to their protein targets to forecast the small molecules affinity and activity (Kandakatla and Ramakrishnan 2014). The active site of eIF4E (PDB ID: 5EI3) was determined using the Schrodinger suite's SiteMap module (Halgren 2009). The SiteMap has discovered the highest-ranked possible receptor binding locations. Crop site maps were selected at a distance of 4 Å from the nearest site point for active site generation calculations. The Glide module was used to perform molecular docking for the receptor-ligand docking (extra precision mode) (Schrodinger Release 2020a, b). The protein grid box was flexibly docked with the prepared and optimized ligands. Based on their respective binding affinities, the ligands were ranked using the top Glide Score (G-Score) and H-Bond formation. The GLIDE module XP visualizer examines the particular ligand–protein interaction (Schrodinger Release 2020a, b; Ahmad et al., 2023). After the docking created the schematic two-dimensional depiction of the docking results.

Molecular dynamics simulation of receptor-ligand complex

The CHARMM36 force field was used in the GROMACS software (Van Der Spoel et al. 2005) molecular dynamics simulations for both the protein alone and protein–ligand complexes. Prior to treating the orientation of hydrogen bond network systems, Van der Waals, and long-range electrostatic interactions within the system, the docked complex was cleaned and optimized (Kawata & Mikami 2001). All covalent bonds were then treated using the LINCS algorithm (Hess et al. 1997), and the water geometry was treated using the SETTLE algorithm (Miyamoto & Kollman 1992). To verify and assess the behavior, 100 ns of a MD simulation was run at 1 atm pressure and 300 K temperature, incorporating the NPT ensemble and a recording interval of 100 ps. This produced 1000 reading frames for each complex independently (McDonald 1972). After that, a leapfrog integrator was used to do MD simulation production on the entire system for 100 ns. Protein and Protein complex coordinates and energy were recorded in their corresponding trajectories at intervals of two ps, and the xmgrace tool was used for analysis. The number of hydrogen bonds, solvent accessible surface area (SASA), radius of gyration (Rg), root mean square fluctuation (RMSF), and root mean square deviation (RMSD) were all determined using the simulated trajectories.

Results and discussion

Molecular docking studies

Sizofiran, Rosmarinic Acid, Kaempferol, Procyanidin, and Epigallocatechin Gallate were the five bioactive compounds out of 93 that had the highest glide score, ranging from–12.5 to–10.0 kcal/mol, according to the results of the molecular docking analysis (Table 1). Compounds interactions with the protein target (PDB ID: 5EI3) produced a range of binding potentials based on glide score. The high glide score for reference compounds of inhibitors 7-methyl-GpppA (Table 2 and Fig. 3A, B) and current drugs of doxorubicin (Table 3 and Fig. 2A, B) showed–11.31 & − 7.59 kcal/mol, respectively, while Sizofiran (− 12.5 kcal/mol) and Rosmarinic Acid (− 11.48 kcal/mol) had the best glide score results among the evaluated compounds. The eIF4E is where the possible bioactive substances as therapeutic candidates bind, as indicated by the optimum binding position (Fig. 1A, B). A substantial contribution to the overall energy of the contact was made by amino acids, according to the 2D schematics (Figs. 1B, 2B and 3B), which also showed their importance in the pattern of interactions between the proteins and ligands. Additionally, the hydrogen bonding greatly increased the binding energy and mode, which is crucial for determining the specificity with which ligands bind to receptors, designing drugs for chemical and biological processes, molecular recognition, and biological activity (Anandan et al. 2022; Vijayababu et al. 2019; Rasul et al. 2022a, b).

Table 1.

H-bond interacting residues of phytochemical compounds with eIF4E

Table 2.

H-bond interacting residues of Inhibitors of CRC with eIF4E

Fig. 3.

Multiple sequence alignment of eIF4E protein group of receptor

Table 3.

H-bond interacting residues of current available drugs of CRC with eIF4E

Fig. 2.

A and B H-bond interacting residues

Fig. 1.

Binding mode of eIF4E with bioactive compounds. 1A, 2A and 3A &1B, 2B and 3B) 3D & 2D–representation of eIF4E protein-Sizofiran, Doxorubicin and 7-methyl-GpppA complex, Stick represents the Sizofiran, Doxorubicin and 7-methyl-GpppA inhibitor respectively. The grey colour represents the hydrogen bond interaction, while a green represents conventional hydrogen bond

Sizofiran optimal docked complex with eIF4E receptor had a highest glide score of–12.5 kcal/mol. This complex was firmly bound through conventional hydrogen bonds, interacting strongly with Sizofiran inhibitors at residues Gly13, Ala18, Phe28, Val29, Asp30, Glu31, Tyr32, Asp33, Ala83, Asn85, Lys117, Leu120, Serl22, and Lys147. Our hypothesis was that Sizofiran created a hydrogen and hydrophobic contact with the surrounding residues depicted in Fig. 1B after becoming deeply locked into the binding site. Remarkably, Phe28, Val29, Tyr32, and Leu120 hydrophobic residues interacted with Sizofiran.

The binding energies of the inhibitor (7-methyl-GpppA;7-methyl-7,8-dihydroguanosine-5'-diphosphate;7-methyl-guanosine-5'-triphosphate;Dihydro-alpha-ergocryptine) and current drugs (Doxorubicin; Regorafenib; Kenpaullone; Sorafenib and Camptosar) were observed to be between the glide score of − 11.31 to − 3.82 kcal/mol, and − 7.59 to − 1.18 kcal/mol respectively, with the correspondingly active amino acid residues.

Doxorubicin created conventional hydrogen bond, van der waals contact with the surrounding residues depicted in Fig. 2B after becoming deeply locked into the binding site. Remarkably, Lys54, Trp56, Trp102, Gu103 and Arg157 hydrophobic residues interacted with Doxorubicin.

Furthermore, the reference compounds were discovered to exhibit lower glide score of 7-methyl-GpppA–11.31 kcal/mol and doxorubicin–7.59 kcal/mol when compared to the natural phytochemical compound of Sizofiran–12.5 kcal/mol with the same eIF4E protein. Due to their superior binding potential of Sizofiran complex when compared with the reference molecule, the docking results was validated using Pymol (ref), superimpose the sizofiran complex with co crystalized structure observed the RMSD valve in the both complex similar to the range of 1.56 Å. Which indicate sizofiran complex binding was similar to co-crystallised structure. Further the MD simulation investigations were carried out using the combination of Sizofiran and the eIF4E receptor.

Contact map analysis

Asn50, Lys54, Trp56, Asp90, Pro100, Trp102, Glu103, Arg112, Asn155, Arg157, Lys162, His200, Thr203, Ala204 and Lys206 residues of eIF4E were involved in hydrogen bond formation with bioactive compounds. Glu103 is the key residue which involved in most of the interactions. Contact map of the hydrogen bond formation shown in Fig. 2 (A and B). In multiple sequence analysis of eIF4E, key residue Glu103 exists in conserved region, revealed the secondary structure conservation (Fig. 3).

Multiple pathogen analysis of eIF4E receptor

Multiple sequence alignment was done to determine the conserved interaction residue of Glu103 from the bioactive chemicals in other significant eIF4E receptor. A literature review was conducted to investigate eIF4E protein receptors, which include 3M93 (Ascaris suum), 2WMC (Pisum sativum), 5EI3 (Homo sapiens), 6YLR (Mus musculus), 5ABX (Caenorhabditis elegans), and 5ME6 (Cucumis melo). BlastAlign was utilized to perform multiple sequence alignment, which was evaluated by ESPript (Robert and Gouet 2014). As a result, the alignment in Fig. 3 was considered to be almost (< 50%) in conserved regions. The red-highlighted residues in ESPript flat Figure are same and similar, which is exciting. The semi-conserved and conserved portions are indicated by text in red, respectively. The highlighted box in Fig. 3 illustrates how the residues of eIF4E interact with Sizofiran. Six of the nine interaction residues in the eIF4E group of receptors are located in the same exact location.

MD simulation analysis

In order to investigate the interaction and stability of the protein–ligand complex in an aqueous system for a simulation time of 100 ns, the most promising docked bioactive compounds, Sizofiran, along with a reference compound of doxorubicin and 7-methyl-GpppA in complex with the eIF4E receptor were also subjected to MD simulation. The geometric properties of the protein–ligand complex were obtained from the simulation study, which also included the variation in the receptor alone and the three complexes were shown in Fig. 4 the analysis of RMSD (Root Mean Square Deviation), RMSF (Root Mean Square Fluctuation), Rg (Radius of Gyration), and SASA (Solvent Accessible Surface Area). The structural variations and stability of the simulation systems were examined through analysis of these calculations. The average distance between the atoms of overlaid protein and ligand structures over time is computed using the RMSD of atomic locations (Pradeep et al. 2021; Prasad et al. 2021; Anandan et al. 2022; Rasul et al. 2022a, b).

Fig. 4.

The MD simulation trajectories from 100 ns simulation time represents the (A) RMSD plot, (B) RMSF plot, (C) RG plot and (D) SASA plot (Black: 5EI3 alone, Red: 5EI3-7-methyl-GpppA complex, Blue: 5EI3- Sizofiran complex, and Green: 5EI3- Doxorubicin complex

Although the eIF4E receptor-Sizofiran complex RMSD plot indicated that it reached equilibrium at 45–100 ns and deviations of 0.15–0.47 nm, the eIF4E receptor alone indicated that it reached equilibrium approximately at 85–100 ns time scale with larger deviations and the remaining showed the stable trajectory simulation with maximum deviation in 0.15–0.67 nm RMSD range. Proteins structural flexibility is reserved for comparing the eIF4E protein complex with Sizofiran to the reference drugs of doxorubicin and 7-methyl-GpppA, the latter is in free form. After some initial oscillations, the ligand molecule Sizofiran attached to the eIF4E receptor reached equilibrium, indicating that this molecule has fewer variations than the others.

The protein structural locations that deviate most or least from the mean are the focus of the RMSF. Additionally, by figuring out root mean square distances in relation to the rotational center. Only the terminal ends and loop sections showed oscillations in the RMSF plots, which suggested that the interactions between the complexes were stable. With the exception of 200–218 residues, the protein complexed with Sizofiran showed fewer variations, suggesting that it was a stable molecule. In a system of proteins, the Rg is the root mean square distance between each atom and its center of mass (Lobanov et al. 2008). Throughout the simulation, the capacity form and folding of the trajectory were examined using the Rg plot at each time step. When complexed with Sizofiran, the eIF4E showed a pattern of Rg values with a variation of 1.7–1.6 nm. To gain a deeper understanding of the complex surface area alterations, the solvent-accessible surface area (SASA) of the simulation complex was investigated. A greater SASA denotes the complex extension of surface volumes, while a lower SASA denotes its truncated nature (Chen and Panagiotopoulos 2019).

The surface area of the hydrophobic core produced by interactions between proteins and ligands is computed using the SASA. Comparing the eIF4E-Sizofiran complex to other complexes, divergence SASA values were found. Furthermore, an evaluation of the hydrogen bond within the biological system is necessary to ascertain the bonding and structural alterations within the complex. The complex shows a stable trend throughout, with the simulation systems hydrogen bond serving as the definition of the complex stability (Chikalov et al. 2011; Samykannu et al. 2019a, b).

Throughout the simulation, the hydrogen bonds made during molecular docking are assessed. The investigation focused exclusively on the intermolecular hydrogen bonds that exist between the Sizofiran complex and the eIF4E protein. In contrast to doxorubicin and 7-methyl-GpppA, the Fig. 5 demonstrated that the quantity of hydrogen bonds created throughout simulation runs was consistent with the molecular docking analysis, with only a small number of bonds being broken and repaired simultaneously. The MD simulation confirmed the steadiness of the docked complexes over the simulated trajectories at 100 ns time scale and verified the stability of Sizofiran, Doxorubicin, and 7-methyl-GpppA within the interaction cavity of eIF4E protein (PDB: 5EI3) in humans (Muhammad et al. 2022).

Fig. 5.

The number H-bond variation of trajectories from 100 ns simulation time represents the Black: Red: 5EI3-7-methyl-GpppA complex, Blue: 5EI3- Sizofiran complex, and Green: 5EI3- Doxorubicin complex

Through molecular dynamics analysis, it was determined that eIF4E had a great affinity for Sizofiran, and that the hydrogen bond pattern of Sizofiran, together with hydrophobic and van der Waals interactions, provided substantial support for eIF4E superior binding. Therefore, it might be a better molecule for identifying a lead therapeutic candidate against eIF4E, on the other hand, more experimental research is required to corroborate these results.

Discussion

Derivatives of natural compounds have shown to be crucial in the search for new drugs, particularly for infectious and cancerous illnesses (Atanasov et al. 2021; Choudhari et al. 2020). The natural products may be enriched with several biologically active substances due to the high number of H-bond donors and acceptors, low estimated octanol–water partition coefficients, and favorable molecular rigidity (Atanasov et al. 2021; Choudhari et al. 2020). We have looked at the Sizofiran analogues in an attempt to find novel natural molecule compounds that could be potential therapeutic candidates against eIF4E (CRC). Previous investigations have indicated that Sizofiran may be able to treat multiple forms of cancer (Meng et al. 2016; Mansour et al. 2012).

Sizofiran therapeutic anti-cancer effects have been demonstrated in rats and mice (Chaichian et al. 2020; Mizuhira et al. 1985). According to a study, Sizofiran increases T cell and Langerhans cell infiltration (a type of antigen-presenting cell for T cell responses) (Nakano et al. 1996; Katz et al. 1985). As a result, Sizofiran can be used to treat advanced cervical cancer and extend patient survival (Gorai et al. 1992). Infiltration of ILC or Langerhans cells not only improves T cell response but also improves the local response to radiation therapy for cervical cancer, demonstrated an additional role for Sizofiran (Nakano et al. 1996; Shimizu et al. 1992). Sizofiran is a polysaccharide with strong anticancer properties that targets solid S-180 tumors (Meng et al. 2016; Kikumoto et al. 1971). Schizophyllan is useful in suppressing stomach cancer in humans, and it may also increase the length of survival for those with head and neck cancer (Borchers et al. 1999). Schizophyllan is also thought to function as a biological response modulator in vivo and to boost immune responses (Nemoto et al. 1994).

Motivated by these assertions, we have analyzed the potential candidate chemical using Sizofiran analogs. One chemical was found to be the best by the molecular dock scores, and its stability in the protein binding pocket was further investigated. According to the MDS, Sizofiran has been able to attach to eIF4E binding pocket with the help of numerous essential residues. Specifically, the chemical has established a persistent contact that is effective against the eIF4E–Sizofiran complex with the residues GLU103, TRP102, ASN155, ARG122, LYS162, and THR203. The X-ray crystal structure (PDB ID:5EI3) and the previously published research showed interactions with these residues. It's interesting to note that the TRP102, GLU103 and THR203 residues were also noted in a prior investigation (Soukarieh et al. 2016). Furthermore, as shown in Fig. 2, Sizofiran has established multiple important interactions with essential residues from different subsites.

The MD Simulations has confirmed that Sizofiran is effective against eIF4E. These findings show that Sizofiran has occupied the binding site firmly throughout the simulation. The plots of hydrogen bond distance indicate that during the simulations evolution, the residue ASN155 has remained firmly attached to the Sizofiran. From 35,000 ps on, the crucial residue GLU103 generated a more robust connection. These results offer strong support for Sizofiran status as a likely eIF4E inhibitor. In addition, the stability analysis conducted using RMSD and Rg specifically indicated that, in comparison to the reference compounds, the protein–ligand complex was relatively compact and stable as shown in Fig. 4. It is noteworthy that the protein has produced a higher number of favorable conformations than unfavorable ones, indicating that the interaction with the ligand must have occurred through these conformations. All of these results point to the possibility of using sizofiran for eIF4E receptor. Additionally, throughout the simulation run, the chemical substance interacted with the critical residues GLU103, TRP102, ASN155, ARG122, LYS162, and THR203, respectively, to location itself at the binding pocket. The MDS data also demonstrate that the RMSD and Rg results were reasonably consistent throughout the simulation run, explaining how the ligand was accommodated within the binding pocket. These findings bolster the use of the Sizofiran analogue as an antiviral and for CRC malignancies. The present study comprehensive computational analysis, which aimed to identify a potential natural molecule counterpart, identified Sizofiran as a potential dual inhibitor with anticancer properties. Moreover, this substance could serve as a starting point for the creation of novel inhibitors.

Conclusion

The current work investigated the structural insights into potential binding mechanisms of drug-like bioactive compounds of Sizofiran against eIF4E by utilizing integrated approaches of Molecular Docking and MD simulation studies. Sizofiran and Rosmarinic acid were shown to have a higher binding affinity among the screened compounds relative to the standard inhibitors of 7-methyl-GpppA and medications of doxorubicin. The eukaryotic translation initiation factor protein and Sizofiran complexes were shown to be extremely stable in the biological system, as demonstrated by the MD simulation study conducted for up to 100 ns. When the eIF4E protein sequences from various pathogens were aligned and compared, it was found that the amino acid residues that interact with Glu103 are present at the same positions in all the eIF4E proteins. This suggests these Glu103-interacting residues have an important conserved function in the eIF4E proteins from these different organisms. Based on the results of this study, it can be argued that Sizofiran is the most promising therapeutic medication options for the treatment of colorectal cancer. They have been shown to exhibit the most inhibitory capacity against the eIF4E receptor when compared to reference compounds. As such, they may be regarded as potential candidates for clinical and pre-clinical trials, which will shape clinical therapy in the future. This structural information can be used in a near future to design novel and more specific eIF4E-targeting molecules with inhibitory potential, thus having an impact on the mRNA translation process.

Supplementary Information

Below is the link to the electronic supplementary material.

Abbreviations

CRC

Colorectal cancer

eIF4E

Eukaryotic translation initiation factor

MDS

Molecular dynamics simulation

RMSD

Root mean square deviation

RMSF

Root mean square fluctuation

Rg

Radius of gyration

SASA

Solvent accessible surface area

Author contributions

Gopinath: Structurebased virtual screening, Molecular docking, Manuscript preparation. Nandhini: Molecular Dynamics and Editing. Jeyakumar: Idea generation, Review & Editing, and Overall supervision.

Data Availability

Not Applicable.

Declarations

Conflict of interest

The authors declare no competing interests.