Exploring the efficacy of naturally occurring C-C and C-O-C type biflavonoids towards the inhibition of MDM2-p53 interactions

doi:10.21203/rs.3.rs-3973657/v1

P53-MDM2 protein-protein interaction (PPI) is one of the most well-established studied which is involved in human cancer. Most importantly, cell cycle and apoptosis are potentially regulated by the p53 protein. Minute double minute 2 (MDM2), the negative regulator of p53, induces p53 degradation and modulates its tumour-suppressing activity. Regaining p53 function by targeting and inhibiting the p53-MDM2 interaction for the treatment of cancer is a unique approach. In this present study, three C-C type biflavonoids (amentoflavone, robustaflavone and agathisflavone) and three C-O-C type biflavonoids (ochnaflavone, hinokiflavone and delicaflavone) were used as MDM2 inhibitors. Molecular docking and molecular dynamics (MD) simulation studies were done to show the p53-MDM2 inhibitory effect of six naturally occurring biflavonoid-based small molecules and determined the various possible conformations and binding affinity values and investigated the dynamic behaviour of MDM2-biflavonoid complexes. Both the C-C and C-O-C category of biflavonoids potentially inhibit p53-MDM2 interaction by blocking the p53-binding domain of MDM2. From the docking score, one of the C-C type biflavonoid, amentoflavone was found to be the strongest inhibitor i.e., strong binding affinity compared to the reference compound nutlin-3 towards MDM2 protein. MD simulation study showed similar RMSD, RMSF, RoG, and SASA profiles compared to the reference inhibitor nutlin-3, suggesting stability throughout the simulation time. These results indicate naturally occurring biflavonoids might be promising early lead compounds for the development of new anticancer agents targeting p53-MDM2 interaction, which to our knowledge has never been reported to disrupt p53-MDM2 interaction.

P53-MDM2 interaction

MDM2 inhibitors

C-C and C-O-C biflavonoids

Molecular docking

Molecular dynamics (MD) simulation

Anticancer

Transcription factor, p53, is responsible for the development of different types of tumours and ~ 50% of the human cancers are thought to possess a mutated or deleted form of TP53 gene, which encodes p53 [1–2]. The p53 plays a crucial role in controlling several genes expression to regulate different cellular processes, including cell-cycle progression, apoptosis, DNA repair and metabolism [3–4]. Therefore, in cancer biology, targeting p53 is a promising therapeutic strategy due to its tumour-suppressor activity. Compelling evidence has emerged for MDM2 to have a physiologically crucial role in regulating p53, as they are the part of an auto-regulatory feedback loop [5]. It has been found that overexpression of MDM2 inactivates p53-mediated cell-cycle arrest and apoptosis [6]. Consequently, disruption of p53-MDM2 interaction and the reactivation of the tumour-suppression function of p53 are very attractive therapeutic strategies for cancer treatment [7]. In this direction, blocking of p53-MDM2 interaction by small-molecule inhibitors of different scaffolds have been reported and those are in different stages of development [8–9]. However, there has been no such report regarding a single clinically approved agent on the basis of their p53-MDM2 inhibitory potential [10–12].

Flavonoids constitute one of the most widespread and diverse plant-derived natural products with potential applications in medicinal chemistry. The structure of parent flavone moiety (8) is shown in Fig. 1. Biflavonoids belong to a subclass of flavonoid family and a dimetric form of flavonoid [having (C₆-C₃-C₆)₂ system], have become the focus in recent years due to their versatile biological and pharmacological activities, including anti-inflammatory, anti-cancer, anti-microbial, anti-viral and cytotoxic properties [13–14]. Biflavonoids with numbers of hydroxyl, alkoxy, and/or glycosidic groups at various positions have attracted much more attention in recent years in the field of anti-cancer drug development [15–22]. Sui et al. showed in vitro and in vivo anti-lung cancer potential of one of the biflavone, delicaflavone, which induced autophagic cell death via the Akt/mTOR/p70S6K signalling pathway [15]. Viktoria et al. demonstrated the anti-cancer potential of hinokiflavone by targeting the MDM2-MDMX RING domain and have shown that hinokiflavone treatment induced apoptosis in various cancer cell lines by downregulating MDM2 and MDMX expression [17]. Li et al. isolated several bioflavonoids from the male flowers of Ginkgo biloba L. gymnosperm which are reported to show anti-proliferative activities on different cancer lines [18]. Recently, an appreciable number of studies reported anticancer efficacy of amentoflavone, which effectively induced cell-cycle arrest and apoptosis [21–22].

This made us interested to perform the present study involving naturally available C-C type (namely, amentoflavone, robustaflavone and agasthisfavone) and C-O-C type (namely, hinokiflavone, ochnaflavone and delicaflavone) bioflavonoids are shown in Fig. 1. Molecular docking studies were performed to explored the potential inhibitory effect of these plant derived biflavonoids, against the p53-MDM2 interaction. In this study, nutlin-3 (7) was employed as the reference ligand, which is widely studied for its efficacy to inhibit the interaction of MDM2 and p53 PPIs by blocking the p53-binding domain of MDM2 [9]. In the current molecular docking study suggests that the biflavonoids can function as potential inhibitors of p53-MDM2 interaction and interestingly showed higher efficacy over the reference ligand nutlin-3. From the docking score, amentoflavone (1) and hinokiflavone (5) were found to be the strongest inhibitors towards MDM2 protein among C-C and C-O-C type bioflavonoids respectively. In order to analyse the stability, conformational changes and underlying molecular interactions of the candidate biflavonoid inhibitors on protein, the docked complexes of biflavonoids and nutlin-3 bound MDM2 protein were studied in a MD simulation using the GROMACS. Here, in our current study we choose amentoflavone (1) among C-C type and hinokiflavone (5) among C-O-C type biflavonoids as they are giving the higher docking scores. Investigation of the dynamic behaviour of MDM2-Amentoflavone and MDM2-Hinokiflavone through molecular dynamics (MD) simulations showed similar RMSD, RMSF, RoG, and SASA profiles, suggesting stability throughout the simulation time.

Therefore, from the virtual screening followed by molecular docking, molecular dynamics (MD) simulations and ADMET evaluation suggests that the efficacy of such biflavonoid scaffolds to inhibit p53-MDM2 interactions and retaining wild-type p53 function could potentially be applied to treat human cancers.

The computational docking assessment of selected bioflavonoids ligands were carried out through AutoDock Vina interlinked with Molecular Graphics Laboratory (MGL) tools [23–24].

2.1 Preparation of ligands

Molecular docking studies were performed to demonstrate the inhibitory effect of selected bioflavonoids [amentofavone (1), robustafavone (2), agasthisfavone (3), ochnaflavone (4), hinokiflavone (5), and delicaflavone (6)] towards the p53-MDM2 interaction. A well-known p53-MDM2 PPI inhibitor, nutlin-3 (7), was used as reference compound [25]. The ligands three-dimensional structures were retrieved from PubChem database (https://pubchem.ncbi.nlm.nih.gov/) and saved in SDF format. AutoDock tools were used to set the number of torsions for the biflavonoids and p53-MDM2, and vina files were prepared and saved in PDBQT format.

2.2 Preparation of protein

Crystal structure of MDM2 bound to the transactivation domain of p53 (PDB ID: 1YCR) was downloaded from RCSB Protein Data Bank (PDB) [26]. Part structure of p53 and the structure of MDM2 were separated from the PDB file of the p53-MDM2 complex before docking studies and transformed each of them into two separate PDB files with the help of AutoDock software. The protein preparation parameters were then used to prepare the whole structure by removal of water molecules from these PDB files, addition of polar hydrogen atoms and assigning partial charges using Kollman and Gasteiger. The essential docking parameters e.g., the dimensions and the location of the centre of the grid box, chosen for all the docking studies are given in Table S1, in the Supporting information, page S4. The grid spacing was fixed at 0.453 Ǻ in all the cases.

2.3 Molecular docking

At first, molecular docking studies between the retrieved biflavonoid ligands and MDM2 were performed by using AutoDock Vina interlinked with AutoDock MGL tools to get the optimized or most stable docked structure for MDM2-biflavonoid complex. p53 was then allowed to bind as a ligand with the free MDM2 and MDM2-biflavonoid complex to determine the potential of respective biflavonoid to prevent p53 from interacting with the MDM2. The biflavonoids with best confirmations and binding affinity with MDM2 are evaluated by intermolecular interactions. For ligand docking calculations Lamarckian genetic algorithms (GA) modules available with AutoDock were employed. To denote other parameters, default settings of AutoDock programme were selected [23, 27]. Throughout the virtual screening, the lowest energy docked conformation, obtained from the AutoDock vina scoring function, were considered to estimate the preferred binding mode between the ligand (biflavonoids/nutlin-3) and protein (MDM2). The Python Molecular Viewer software (Stefano Forli, Olson Molecular Graphics Laboratory, The Scripps Research Institute, CA, USA) were used to visualized the output of docking interactions.

2.4 Molecular dynamics (MD) simulations

Molecular Dynamics (MD) simulation studies were carried out on energy minimized receptor-ligand complexes of MDM2-nutlin-3 and MDM2-Biflavone by using the Groningen Machine for Chemical Simulation (GROMACS) package (Ver 2022.4) [28]. To do the process, first, we generated topological parameter files for the MDM2 protein with the help of the “pdb2gmx” command. To generate the topological files, the Charmm36-jul2021 force field [29] and TIP3P solvation model [30] were used. Ligand topological and other parameter files were generated by using the Charmm General force field server (CGenFF). A cubic box was generated to solvate the overall complex within the box. For maintenance of periodic boundary conditions, both complexes were set 2.0 nm distance from the cubic box. To neutralize MDM2-nutlin-3 and MDM2-Biflavone complex, 5 Cl⁻ ions were added (optimized structures shown in Fig. S3 in the Supporting information, page S3). After that overall system undergoes energy minimization with the help of the steepest descent algorithm. After that, both NVT and NPT ensembles were used to equilibrate the system for 1000 ps at 300 k temperature. Thereafter, a 40 ns production run in the NPT ensemble was performed for all the complexes.

2.5 Physicochemical properties

Lipinski’s rule [31] was used to assess the physicochemical properties of the DPFDCA possessing effective binding affinity was selected for further analysis of absorption, distribution, metabolism, excretion profiling through Swiss dock (http://www.swissadme.ch/) to predict their drug-like properties. Furthermore, the absorption, distribution, metabolism, excretion and toxicological profile of the compounds were also analysed using admetSAR server (http://lmmd.ecust.edu.cn/admetsar1/predict/) [32].

The MDM2 protein is a negative regulator of p53 and inhibit its transcriptional activity after binding, which in turn favouring unrestrained cell proliferation. Molecular docking studies of selected six bioflavonoids of C-C [amentofavone (1), robustafavone (2), agasthisfavone (3)] and C-O-C linkages [ochnaflavone (4), hinokiflavone (5), and delicaflavone (6)] along with a known inhibitor, nutlin-3 (7) against p53-MDM2 interaction were done to understand the underlying inhibitory mechanism of these biflavonoids.

Phe19, Trp23, and Leu26 amino acid residues at the hydrophobic pockets forming p53 “hot spot triad”, which are known to be participated in the binding interactions with the N-terminal domain of MDM2, which is referred as the p53-binding domain of MDM2 (Fig. S2 in the Supporting information, page S2) [25]. All of the C-C and C-O-C types biflavonoids effectively blocked the p53-MDM2 interactions due to its greater affinity for the p53 binding domain of MDM2, which eventually retarding MDM2 from interacting with p53, and thus, the p53-MDM2 interactions are disrupted. Same type of event is observed in case of the reference compound nutlin-3 (7), which is widely reported for its effectiveness to inhibit the interaction of MDM2 and p53 PPIs by blocking the p53-binding domain of MDM2 in the p53-MDM2 co-crystal (PDB ID: 1YCR) [33] as manifested from the MDM2-nutlin-3 docked structure (Fig. S1 in the Supporting information, page S2). Affinity and the strength of a specific biflavonoid ligand, which binds to the pocket of a target protein, can be expressed in terms of binding energy (ΔG) and the ligand having lower binding energy is preferred as a potential inhibitor.

3.1 Interaction of C-C type biflavonoids for MDM2 at its p53 binding domain

Docked structures MDM2 and C-C type biflavonoids like, amentofavone (1), robustafavone (2), agasthisfavone (3), are shown in Fig. 2(A), Fig. 3(A) and Fig. 4(A) respectively. All of the C-C type biflavonoids were found to bind at the site of MDM2, which was previously occupied by the p53 partially in the MDM2-p53 co-crystal (PDB ID: 1YCR). As shown in Table 1, the binding energy (ΔG) of nutlin-3 for the MDM2 active site was obtained as -7.4 kcal mol⁻¹, while in the case of amentoflavone, the corresponding binding energy (ΔG) was found to be -8.7 kcal mol⁻¹. These results reveal the strong affinity of amentoflavone (1) for MDM2 compared to nutlin-3. Interestingly, amino acid residues, GLN-72 and HIS-96 are found to participate in the interactions with amentoflavone through non-covalent bond formation, like hydrogen bonds (H-bonds) with the binding pockets of MDM2 protein. However, the value of binding energy of robustaflavone (2) and agathisflavone (3) with MDM2, were found to be slightly lower compared to the amentoflavone, although its higher compared to the reference compound nutlin-3. The binding energy (ΔG) of the robustaflavone and agathisflavone for MDM2 as calculated from their optimized docked structure was found to be -8.5 kcal mol⁻¹ and -7.7 kcal mol⁻¹ respectively (Table 1). It was also observed that no hydrogen bond (H-bond) formation between robustaflavone and agathisflavone with MDM2 shown in Fig. 3(A)-(i) and Fig. 4(A)-(i), and this can be the possible reason for its lower binding energy value. From the data of Table 2-4, it is also clear that amino acid residues and the bond distance involved in the docking between the MDM2-amentoflavone complex is different than those involved in the docking between MDM2-robustaflavone and MDM2-agathisflavone.

3.2 Interaction of C-O-C type biflavonoids for MDM2 at its p53 binding domain

MDM2 and C-O-C type biflavonoids like, ochnafavone (4), hinokifavone (5) and delicafavone (6) docked structures, are shown in Fig. 5(A), Fig. 6(A) and Fig. 7(A) respectively. All of the C-O-C type biflavonoids were found to bind at the same site of MDM2 like C-C type biflavonoids. As shown in the respective figures [Fig. 5(A), Fig. 6(A), Fig. 7(A)], the corresponding C-O-C type biflavonoids such as, ochnaflavone, hinokiflavone and delicaflavone, the corresponding binding energies (ΔG) were found to be -8.2, -8.2 and -7.2 kcal mol⁻¹ respectively (Table 1). This also indicates the similar affinity of hinokiflavone and ochnaflavone for MDM2. However, the value of binding energy of hinokiflavone and ochnaflavone with MDM2, were found to be slightly lower compared to the amentoflavone, although its higher compared to the reference nutlin-3. The binding energy (ΔG) of the delicaflavone as calculated from their optimized docked structure was found to be -7.2 kcal mol⁻¹, signifies the comparable affinity of delicaflavone and nutlin-3 towards the MDM2. It was also observed that the delicaflavone did not bind properly at the p53 binding pocket of MDM2 [Fig. 7(B)] and this can be the probable reason for its lower binding efficiency. From the data of Table 5-7, it is also clear that amino acid residues participated in the docking between the MDM2-hinokiflavone and MDM2-ochnaflavone complex are different than those involved in the docking between MDM2-delicaflavone.

3.3 Interaction of C-C or C-O-C type bioflavonoids towards free p53 and MDM2-bounded p53

Molecular docking studies elaborated so far established that both C-C and C-O-C type biflavonoids potentially bind with the MDM2 N-terminal domain but with different binding mode and efficiency. We also performed docking to show how MDM2 interacts with p53 when it is a part of the biflavonoid-MDM2 docked structure and how each biflavonoid interacts with p53 either in their free state or in the complex state with MDM2. We have explored the binding nature of MDM2 with the free p53 separated from MDM2-p53 co-crystal, which is bounded with either C-C or C-O-C type biflavonoid in the most stable docked structures. To validate the results, same PDB file of p53 was also subjected to the docking with free MDM2 protein. The part structure of separated p53 was considered as a rigid ligand by prohibiting the rotations of all the rotatable bonds to retain the structural compatibilities with the parent crystal structure after the docking. The binding energy (ΔG) value of the interaction between free p53 and the free MDM2 as calculated from their optimized docked structure was found to be -18.6 kcal mol⁻¹ (Table S6 in the Supporting information, page S7), which is quite higher. During this docking, free p53 is docked at the same site, where it resided in the co-crystal of p53-MDM2 before its separation (Fig. S2 in the Supporting information, page S2) [26]. It also involved same amino acids which are participating in the hydrogen bonding interactions in the co-crystal of p53-MDM2. Interestingly, for both C-C and C-O-C categories of biflavonoids, the binding energy (ΔG) values were obtained from the docking between MDM2-biflavonoid complex and free p53 are lower compared to the free p53 and free MDM2 docking. Table S15-S20 in the Supporting information, page S10-S12 represents the corresponding docking scores. The binding energy (ΔG) values for all the biflavonoids of C-C and C-O-C categories lies between -10.3 to 11.5 kcal mol⁻¹, which are very much lower compared to the binding energy obtained for the p53 and free MDM2 docking. This significant difference in the binding energy value is because of the fact that individual biflavonoid moiety was occupied at the p53 binding domain of the MDM2 which is considerably retarding the binding of p53 with MDM2. The p53 is retarded to get easy access to MDM2 to bind with it at its actual site due to the steric factor, which driven p53 to orient itself and bind at the different sites. Fig. 2(B), 3(B), 4(B), 5(B), 6(B) and 7(B) represents the possible bonding interactions between the separated structure of p53 and the MDM2-biflavonoid docked structures for both the C-C and C-O-C categories of biflavonoids. Amino acid residues involved in the docking between the free p53 and MDM2-biflavonoid complex for each C-C or C-O-C type biflavonoid are depicted in Table 8-13 respectively. Furthermore, from Fig. 7(A) and (B), it is observed that the delicaflavone (6) binds MDM2 somehow in a different position compared to the other biflavonoid. These may be the plausible reason behind the lower binding efficiency of delicaflavone in comparison to the other biflavonoids towards p53-MDM2 PPIs.

3.4 Drug-likeness prediction and pharmacokinetic properties evaluation

In drug discovery process, at the initial stage predicting the toxicological and pharmacokinetic profiling of any potential lead molecule is an effective method. Here, we investigated the absorption, distribution, metabolism, excretion and toxicological profile [ADMET] profiles of the C-C and C-O-C type biflavonoids and compared with the reference nutlin-3 compound using SwissADME [http://www.swissadme.ch/] [31] and admetSAR web servers [http://lmmd.ecust.edu.cn/admetsar1/predict/] [32]. Predicted results are presented in Tables S2-S3 in the Supporting information, page S5-S6. All the biflavonoids and nutlin-3 satisfied and fulfill the Lipinski’s rule of five [Table S2 in the Supporting information, page S5] as well as exhibited the most desirable pharmacological properties [31-32]. The result of the predicted pharmacokinetic properties of these biflavonoids are reported [Table S3 in the Supporting information, page S6], had satisfactory toxicity status due to their non-carcinogenic and non-mutagenic properties as well as their inability to inhibit the hERG gene encoding the potassium (K⁺) ion channels necessary for the normal electrical activity in the heart. It is also further supported by the fact is that, both the C-C and C-O-C type biflavonoids exhibited acceptable LD50 values [Table S3 in the Supporting information, page S6] and they were found to be in class II on the basis of their oral toxicity which is quite desirable. As revealed, biflavonoids and nutlin-3 [Table S3 in the Supporting information, page S6], are permeable to the human intestinal membrane and Blood Brain Barrier. Importantly, biflavonoids and nutlin-3 lacks the Caco-2 cell permeability. Both the compounds also showed good aqueous solubility. Moreover, compared to nutlin-3, all biflavonoids as lead compound, are non-inhibitor of the renal organic cationic transporter 2 which signifies the total clearance of the body system. From our observations, we can infer that both the C-C and C-O-C biflavonoids may act as lead compound and could be safe to prescribe as treatment option against cancer.

3.5 Molecular dynamics (MD) simulations

In order to analyse the behavioural mechanism i.e., stability, conformational changes and underlying molecular interactions of the candidate biflavonoid inhibitors on protein, the docked complexes of biflavonoids and nutlin-3 (reference compound) bound MDM2 protein were studied in a MD simulation in an explicit water solution for 40 ns using the GROMACS package (Ver 2022.4). Here, in our current study we choose amentoflavone (1) among C-C type and hinokiflavone (4) among C-O-C type biflavonoids as they are giving the higher docking scores. The trajectory plots representing the protein-ligand root mean square deviation (RMSD), the protein root mean square fluctuation (RMSF), the protein radius of gyration (RoG) and the solvent accessible surface area (SASA), are illustrated in Figs. 8-11.

3.5.1 RMSD analysis

RMSD plot of MDM2-Amentoflavone and MDM2-Hinokiflavone complex [Fig. 8 (a-b)], it is clear that from the beginning of the simulation process, both the complexes show higher fluctuation compared to free MDM2 protein and the respective biflavonoid itself. Despite of the deviation, the average RMSD value of the MDM2-Amentoflavone and MDM2-Hinokiflavone complex indicates the stability of the complexes. Although the RMSD value of MDM2-Amentoflavone is higher [2.5±0.5 Å] compared to MDM2-Hinokiflavone complex which remains within 1.5±0.5 Å. On the other hand, from Fig. S4 (a) in the Supporting information, page S3, it is visualized that despite the initial higher fluctuation of the protein-ligand complex, both free MDM2 and MDM2-nutlin-3 complex attains a stable state after 15 ns of MD simulation process and it remains stable till the end of the simulation.

3.5.2 RMSF analysis

One can easily predict the rigid or flexible nature of a particular complex from the Root Mean Square Fluctuation (RMSF) analysis. The RMSF plots of MDM2-Amentoflavone and MDM2-Hinokiflavone complex depicted in Fig. 9 (a-b), shows the initial fluctuations of the protein-ligand complex and thereafter, it remains at 1.5±0.5 Å for a long period of time, which shows the stability of the protein-ligand complex throughout. The RMSF values for free MDM2 and the corresponding biflavonoids remains less than 0.5 Å throughout the simulation. From Fig. S4 (b) in the Supporting information, page S3, it is clear that the RMSF value of the MDM2-Nutlin-3 complex, the RMSF value is about 0.50±0.25Å.

3.5.3 RoG analysis

The compactness of a system is generally measured with the help of radius of gyration (RoG). From the RoG plot of free MDM2 and MDM2-Nutlin-3 complex [Fig. S4 (c) in the Supporting information, page S3], it is clear that like previous observations (i.e., RMSD and RMSF), despite of initial fluctuations after 10 ns of molecular simulation, both free protein and protein-ligand complex indicates the stability of the protein-ligand complex. After 10 ns of simulation, the RoG of the protein-ligand complex (MDM2-Nutlin-3) shows a minimal fluctuation <1.5 Å. It again indicates the stability of the protein-ligand complex, like previous observations. On the other hand, both the MDM2-Amentoflavone and MDM2-Hinokiflavone complexes show some hump peaks, still, the deviation of the complex remains within 3.5±0.5 Å, which indicates its stable nature as a protein-ligand complex [Fig. 10 (a-b)].

3.5.4 SASA analysis

From the Solvent Accessible Surface Area (SASA) analysis, one can easily measure the surface area of a free protein/protein-ligand complex within a solvent system. SASA plot depicted in Fig. 11 (a-b), it is clear that from the beginning of the simulation process, both the MDM2-Amentoflavone and MDM2-Hinokiflavone complexes shows higher fluctuation compared to free biflavonoid, however, it remains stable till the 40 ns simulation process, which indicates the stability of the protein-ligand complex. Similar phenomenon is observed for the free MDM2 and MDM2-Nutlin-3 complex [Fig. S4 (d) in the Supporting information, page S3]. This is in agreement with the previous observations (RMSD, RMSF, and R_OG).

In the present study, rigorous molecular docking based in-silico methods and molecular dynamics (MD) simulations for the efficient identification of novel naturally occurring bio-friendly biflavonoid based small-molecule inhibitors of p53-MDM2 interaction was developed. In this respect, present study showed that six naturally occurring biflavonoids with C-C type [namely, amentofavone (1), robustafavone (2) and agasthisfavone (3)] or C-O-C type [namely, ochnaflavone (4), hinokiflavone (5) and delicaflavone (6)] conjugation, both can be effective for the inhibition of p53-MDM2 interaction, which raised their anti-cancer therapeutic potentials. Among the C-C type biflavonoids, amentoflavone (1) showed relatively higher binding affinity compared to robustaflavone (2) and agathisflavone (3). On the other hand, among the C-O-C type bioflavonoids, hinokiflavone (5) and ochnaflavone (4) showed relatively higher binding affinity compared to delicaflavone (6). Interestingly, both of the C-C and C-O-C type biflavonoids showed higher binding efficiency, except delicaflavone (6), with compared to the reference compound, nutlin-3 (7). Investigation of the dynamic behaviour of MDM2-Amentoflavone and MDM2-Hinokiflavone through molecular dynamics (MD) simulations showed similar RMSD, RMSF, RoG, and SASA profiles, suggesting stability throughout the simulation time. Biflavonoid molecule is primarily binding with the N-terminal of MDM2, acting as MDM2 antagonist, inhibiting its role to interact with p53 protein and there by p53 is freely available which in turn enhancing tumour suppressor activity. This may be responsible for apoptosis induction and can regulate cell cycle progression in the case of damaged DNA and in the case of mutation. The results obtained from this molecular docking and dynamics studies will be valuable for the rational design of novel and potent p53-MDM2 inhibitors based on biflavonoids, preferred to bind on the binding cleft of p53 and MDM2. It is noteworthy to highlight that our selected bioflavonoids had satisfactory toxicity status due to their non-carcinogenic and non-mutagenic properties and met all requirements of Lipinski rule of five and are readily biodegradable. Moreover, biflavonoid bound MDM2 complex was stable over 40 ns MD simulations, and showed similar trajectory profile in comparison with co-crystalized bound MDM2 complex. Therefore, from the virtual screening followed by molecular docking, molecular dynamics (MD) simulations and ADMET evaluation suggests that the efficacy of such biflavonoid scaffolds to inhibit p53-MDM2 interactions and retaining wild-type p53 function could potentially be applied to treat human cancers. In view of all these observations, the biflavonoids can be considered as promising scaffolds for the development of novel anti-cancer agents targeting p53-MDM2 interaction. The plausible mechanism of this inhibitory effect of plant-derived biflavonoids is shown in Scheme 1. To further substantiate it, more task specific biflavonoids with desired structural patterns can be designed and their subsequent synthesis can be done as well as in-vivo/clinical studies will help us to develop potential biflavonoid-based drugs to cure cancer where p53-MDM2 interaction is involved.

Acknowledgements

This work was supported by DSTBT, GoWB, India [Grant No. 1352(Sanc.)/STBT-11012(25)/46/2021-ST-SEC to S.M.] and Research Fund grant [R21-3846200522], the Royal Society of Chemistry, UK. S.K. thank DSTBT, GoWB for his fellowships. S.M. acknowledges Rammohan College, Kolkata for infrastructural facilities.

Author contributions

S.M. (Samiran) designed the study; S.M. (Samiran), S.K, S.M. (Sourav), carried out the study; S.M. (Samiran), S.K., wrote the manuscript; All authors have given approval to the final version of the manuscript.

Conflict of Interest

The authors declare no competing financial interests.

Harris SL, Levine AJ (2005) The P53 pathway: Positive and negative feedback loops. Oncogene 24(17):2899–2908. https://doi.org/10.1038/sj.onc.1208615
Hainaut P, Hollstein M (1999) P53 and human cancer: The first ten thousand mutations. Adv Cancer Res 77:81–137. https://doi.org/10.1016/s0065-230x(08)60785-x
Vousden KH, Lu X (2002) Live or let die: The cell’s response to p53. Nat Rev Cancer 2(8):594–604. https://doi.org/10.1038/nrc864
Brown CJ, Lain S, Verma CS, Fersht AR, Lane DP (2009) Awakening guardian angels: Drugging the P53 pathway. Nat Rev Cancer 9(12):862–873. https://doi.org/10.1038/nrc2763
Wu X, Bayle JH, Olson D, Levine AJ (1993) The p53-mdm-2 autoregulatory feedback loop. Genes Dev 7(7A):1126-1132. https://doi.org/10.1101/gad.7.7a.1126
Chen J, Wu X, Lin J, Levine AJ (1996) MDM-2 inhibits the G1 arrest and apoptosis functions of the p53 tumor suppressor protein. Mol Cell Biol 16(5):2445–2452. https://doi.org/10.1128/MCB.16.5.2445
Chène P (2003) Inhibiting the p53–MDM2 interaction: An important target for cancer therapy. Nat Rev Cancer 3(2):102–109. https://doi.org/10.1038/nrc991
Wang S, Zhao Y, Aguilar A, Bernard D, Yang CY (2017) Targeting the MDM2–p53 protein–protein interaction for new cancer therapy: Progress and challenges. Cold Spring Harb Perspect Med 7. https://doi.org/10.1101/cshperspect.a026245
Zhao Y, Aguilar A, Bernard D, Wang S (2014) Small-molecule inhibitors of the MDM2–p53 protein–protein interaction (MDM2 inhibitors) in clinical trials for cancer treatment. J Med Chem 58(3):1038–1052. https://doi.org/10.1021/jm501092z
Zhu H, Gao H, Ji Y, Zhou Q, Du Z, Tian L, Jiang Y, Yao K, Zhou Z (2022) Targeting p53–MDM2 interaction by small-molecule inhibitors: Learning from MDM2 inhibitors in clinical trials. J Hematol Oncol 15(1):91. https://doi.org/10.1186/s13045-022-01314-3
Iancu-Rubin C, Mosoyan G, Glenn K, Gordon RE, Nichols GL, Hoffman R (2014) Activation of p53 by the MDM2 inhibitor RG7112 impairs thrombopoiesis. Exp Hematol 42(2):137-145. https://doi.org/10.1016/j.exphem.2013.11.012
Hassin O, Oren M (2022) Drugging p53 in cancer: One protein, many targets. Nat Rev Drug Discov 22(2):127–144. https://doi.org/10.1038/s41573-022-00571-8
Kim HP, Park H, Son KH, Chang HW, Kang SS (2008) Biochemical Pharmacology of biflavonoids: Implications for anti-inflammatory action. Arch Pharm Res 31(3):265–273. https://doi.org/10.1007/s12272-001-1151-3
He X, Yang F, Huang X (2021) Proceedings of chemistry, pharmacology, pharmacokinetics and synthesis of biflavonoids. Molecules 26(19):6088. https://doi.org/10.3390/molecules26196088
Sui Y, Yao H, Li S, Jin L, Shi P, Li Z, Wang G, Lin S, Wu Y, Li Y, Huang L, Liu Q, Lin X (2016) Delicaflavone induces autophagic cell death in lung cancer via AKT/mTOR/p70S6K signaling pathway. J Mol Med 95(3):311–322. https://doi.org/10.1007/s00109-016-1487-z
Goossens J-F, Goossens L, Bailly C (2021) Hinokiflavone and related C–O–C-type biflavonoids as anti-cancer compounds: Properties and mechanism of action. Nat Prod Bioprospect 11(4):365–377. https://doi.org/10.1007/s13659-021-00298-w
Ilic VK, Egorova O, Tsang E, Gatto M, Wen Y, Zhao Y, Sheng Y (2022) Hinokiflavone inhibits MDM2 activity by targeting the MDM2-MDMX Ring Domain. Biomolecules 12(5):643. https://doi.org/10.3390/biom12050643
Li M, Li B, Xia ZM, Tian Y, Zhang D, Rui WJ, Dong JX, Xiao FJ (2019) Anticancer effects of five biflavonoids from ginkgo biloba L. male flowers in vitro. Molecules 24(8):1496. https://doi.org/10.3390/molecules24081496
Huang H, Hao J, Pang K, Lv Y, Wan D, Wu C, Ma Y, Yang X, Zhang WK (2020) A biflavonoid‐rich extract from selaginella moellendorffii hieron. induces apoptosis via STAT3 and AKT/NF‐ΚB signalling pathways in laryngeal carcinoma. J Cell Mol Med 24(20):11922–11935. https://doi.org/10.1111/jcmm.15812
Ren M, Li S, Gao Q, Qiao L, Cao Q, Yang Z, Chen C, Jiang Y, Wang G, Fu S (2023) Advances in the anti-tumor activity of Biflavonoids in selaginella. Int J Mol Sci 24(9):7731. https://doi.org/10.3390/ijms24097731
CHEN CH, HUANG YC, LEE YH, TAN ZL, TSAI CJ, CHUANG YC, Tu HF, Liu TC, Hsu FT (2020) Anticancer efficacy and mechanism of Amentoflavone for sensitizing oral squamous cell carcinoma to cisplatin. Anticancer Res 40(12):6723–6732. https://doi.org/10.21873/anticanres.14695
CHEN WT, CHEN CH, SU HT, YUEH PF, HSU FT, CHIANG IT (2021) Amentoflavone induces cell-cycle arrest, apoptosis, and invasion inhibition in non-small cell lung cancer cells. Anticancer Res 41(3):1357–1364. https://doi.org/10.21873/anticanres.14893
Trott O, Olson AJ (2009) Autodock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 31(2):455–461. https://doi.org/10.1002/jcc.21334
Morris GM, Goodsell DS, Halliday RS, Huey R, Hart WE, Belew RK, Olson AJ (1998) Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function, Journal of Computational Chemistry. 19(14):1639-1662. https://doi.org/10.1002/(SICI)1096-987X(19981115)19:14%3C1639::AID-JCC10%3E3.0.CO;2-B
Vassilev LT, Vu BT, Graves B, Carvajal D, Podlaski F, Filipovic Z, Kong N, Kammlott U, Lukacs C, Klein C, Fotouhi N, Liu EA (2004) In vivo activation of the p53 pathway by small-molecule antagonists of MDM2. Science 303(5659):844–848. https://doi.org/10.1126/science.1092472
Kussie PH, Gorina S, Marechal V, Elenbaas B, Moreau J, Levine AJ, Pavletich NP (1996) Structure of the MDM2 oncoprotein bound to the p53 tumor suppressor transactivation domain. Science 274(5289):948–953. https://www.science.org/doi/10.1126/science.274.5289.948
Sanner MF (1999) Python: a programming language for software integration and development. J Mol Graph Model 17(1):57-61.
Berendsen, H JC, Van Der Spoel D, Van Drunen R (1995) GROMACS: A Message-Passing Parallel Molecular Dynamics Implementation. Comput. Phys. Commun 91:43-56. https://doi.org/10.1016/0010-4655(95)00042-E
Huang J, Rauscher S, Nawrocki G, Ran T, Feig M, De Groot BL, Grubmüller H, MacKerell AD (2017) CHARMM36m: An Improved Force Field for Folded and Intrinsically Disordered Proteins. Nat Method 14(1):71-73. https://doi.org/10.1038/nmeth.4067
Lee S, Tran A, Allsopp M, Lim JB, Hénin J, Klauda JB (2014) CHARMM36 United Atom Chain Model for Lipids and Surfactants. J Phys Chem B 118(2):547-556. https://doi.org/10.1021/jp410344g
Lipinski CA (2004) Lead- and drug-like compounds: The rule-of-five revolution. Drug Discov Today Technol 1(4):337–341. https://doi.org/10.1016/j.ddtec.2004.11.007
Cheng F, Li W, Zhou Y, Shen, J, Wu Z, Liu G, Lee PW, Tang Y (2012) admetSAR: a comprehensive source and free tool for assessment of chemical ADMET properties . J Chem Inf Model 52(11):3099-3105. https://doi.org/10.1021/ci300367a
Anil B, Riedinger C, Endicott JA, Noble ME (2013) The structure of an MDM2–nutlin-3a complex solved by the use of a validated MDM2 surface-entropy reduction mutant. Acta Crystallogr D Biol Crystallogr 69(Pt 8):1358–1366. https://doi.org/10.1107/S0907444913004459

Table 1. Binding affinity and number of possible conformation of ligands (Biflavonoids-MDM2 docking)

Test drug/ligands	No. of Conformations/Orientations	DG (Gibb’s free energy) (kcal mol^-1)
Nutlin-3 (7)	09	-7.40
Amentoflavone (1)	09	-8.70
Robustaflavone (2)	09	-8.50
Agathisflavone (3)	09	-7.70
Ochnaflavone (4)	09	-8.20
Hinokiflavone (5)	09	-8.20
Delicaflavone (6)	09	-7.20

Table 2. Detail interactions of amentoflavone with amino acid residues (active site) of MDM2 protein

Hydrophobic interactions:

Index	Bond Type	Amino acid Residue	Bond distance (Å)
1.	Pi-Sigma	LEU-54(A)	3.98688
2.	Pi-Alkyl	ILE-99(A)	5.01898
3.	Pi-Alkyl	LEU-54(A)	4.62731
4.	Pi-Alkyl	ILE-61(A)	5.46277
5.	Pi-Alkyl	VAL-93(A)	4.9189
6.	Pi-Alkyl	VAL-93(A)	4.48794
7.	Pi-Alkyl	VAL-93(A)	4.9931

Hydrogen Bond Interactions:

Index

Amino acid residue

Bond distance (Å)

Distance of H-A (in Å)

Distance of D-A (in Å)

Donor angle (degree)

Donor atom

Acceptor atom

1.

HIS-96(A)

2.80

3.60

93.678

Amino acid

HIS-96(A)-(H atom)

Ligand

(O-H)

2.

GLN-72(A)

3.86

3.31

3.86

118.37

Ligand

(O-H)

Amino acid

GLN-72(A)-(=O)

Table 3. Detail interactions of robustaflavone with amino acid residues (active site) of MDM2 protein

Hydrophobic interactions:

Index	Bond Type	Amino acid Residue	Bond distance (Å)
1.	Pi-Pi Stacked	HIS-96(A)	5.00656
2.	Pi-Pi Stacked	HIS-96(A)	4.55254
3.	Pi-Pi Stacked	TYR-100(A)	3.95576
4.	Amide-Pi Stacked	LEU-54(A), PHE-55(A)	4.43011
5.	Amide-Pi Stacked	LEU-54(A), PHE-55(A)	4.03565
6.	Pi-Alkyl	LEU-54(A)	5.38429
7.	Pi-Alkyl	ILE-99(A)	5.41305
8.	Pi-Alkyl	VAL-93(A)	5.29991
9.	Pi-Alkyl	LEU-54(A)	5.24448
10.	Pi-Alkyl	LEU-54(A)	5.28303

Table 4. Detail interactions of agathisflavone with amino acid residues (active site) of MDM2 protein

Hydrophobic interactions:

Index	Bond Type	Amino acid Residue	Bond distance (Å)
1.	Pi-Pi stacked	HIS-96(A)	5.3823
2.	Pi-Pi stacked	HIS-96(A)	4.4502
3.	Pi-Pi stacked	TYR-100(A)	3.87724
4.	Pi-Sigma	LEU-54(A)	3.53074
5.	Amide-Pi-Stacked	LEU-54(A), PHE-55(A)	5.25281
6.	Pi-Alkyl	VAL-93(A)	4.91014
7.	Pi-Alkyl	LEU-54(A)	4.93605
8.	Pi-Alkyl	LEU-54(A)	4.99455
9.	Pi-Alkyl	ILE-99(A)	5.2034

Table 5. Detail interactions of ochnaflavone with amino acid residues (active site) of MDM2 protein

Hydrophobic interactions:

Index	Bond Type	Amino acid Residue	Bond distance (Å)
1.	Pi-Alkyl	ILE-61(A)	5.28297
2.	Pi-Alkyl	VAL-93(A)	4.5213
3.	Pi-Alkyl	VAL-93(A)	5.00304
4.	Pi-Alkyl	ILE-99(A)	4.99331
5.	Pi-Alkyl	ILE-61(A)	5.46069
6.	Pi-Alkyl	VAL-93(A)	4.64663
7.	Pi-Alkyl	VAL-93(A)	4.98446
8.	Pi-Alkyl	LYS-94(A)	4.31513
9.	Pi-Alkyl	LYS-94(A)	3.7847

Hydrogen Bond Interactions:

Index	Amino acid residue	Bond distance (Å)	Distance of H-A (in Å)	Distance of D-A (in Å)	Donor angle (degree)	Donor atom	Acceptor atom
1.	GLN-72(A)	3.15	3.15	3.55	106.35	Ligand (O-H)	Amino acid GLN-72(A)-(=O)
2.	LYS-94(A)	2.74	2.74	3.29	116.42	Ligand (O-H)	Amino acid LYS-94(A)-(=O)
3.	HIS-96(A)	3.28	3.28	4.01	129.39	Amino acid HIS-96(A)-(=N)	Ligand (H atom)

Electrostatic interactions:

Index	Bond Type	Amino acid Residue	Bond distance (Å)
1.	Pi-Cation	LYS-94(A)	4.90042

Table 6. Detail interactions of hinokiflavone with amino acid residues (active site) of MDM2 protein

Hydrophobic interactions:

Index	Bond Type	Amino acid Residue	Bond distance (Å)
1.	Pi-Pi Stacked	HIS-96(A)	4.49368
2.	Pi-Pi Stacked	HIS-96(A)	4.11027
3.	Pi-Pi Stacked	TYR-100(A)	4.04772
4.	Pi-Pi Stacked	TYR-104(A)	4.81289
5.	Pi-Pi T-Shaped	TYR-100(A)	4.77968

Hydrogen Bond Interactions:

Index

Amino acid residue

Bond distance (Å)

Distance of H-A (in Å)

Distance of D-A (in Å)

Donor angle (degree)

Donor atom

Acceptor atom

1.

HIS-96(A)

2.96

3.37

106.78

Ligand

(=O)3

Amino acid

HIS-96(A)- (=O)2

Electrostatic interactions:

Index	Bond Type	Amino acid Residue	Bond distance (Å)
1.	Pi-Cation	HIS-96(A)	4.84619

Table 7. Detail interactions of delicaflavone with amino acid residues (active site) of MDM2 protein

Hydrophobic interactions:

Index	Bond Type	Amino acid Residue	Bond distance (Å)
1.	Pi-Sigma	VAL-93(A)	3.58109
2.	Pi-Alkyl	MET-62(A)	4.52915

Hydrogen Bond Interactions:

Index	Amino acid residue	Bond distance (Å)	Distance of H-A (in Å)	Distance of D-A (in Å)	Donor angle (degree)	Donor atom	Acceptor atom
1.	GLN-71(A)	3.04	3.04	3.40	101.92	Amino acid GLN-71(A)-(=N)	Ligand (O-H)
2.	GLN-72(A)	1.82	1.82	3.88	152.57	Ligand (O-H)	Amino acid GLN-72(A)-(=O)
3.	HIS-73(A)	3.30	3.30	4.09	135.42	Amino acid HIS-73(A)-(=N)	Ligand (O-H)
4.	LYS-94(A)	3.31	3.31	3.88	117.11	Amino acid LYS-94(A)-(N–H)	Ligand (O-H)

Other interactions:

Index	Bond Type	Amino acid Residue	Bond distance (Å)
1.	Carbon Hydrogen Bond	HIS-73(A)	2.93318

Table 8. Amino acid residues involved in detail interactions of amentoflavone-MDM2 complex with free p53

Hydrophobic interactions:

Index	Bond Type	Amino acid Residue	Bond distance (Å)
1.	Pi-Sigma	LEU-54(A)	3.98688
2.	Pi-Sigma	TRP-23(B)	3.72403
3.	Pi-Pi T-shaped	TRP-23(B)	5.08016
4.	Pi-Alkyl	ILE-99(A)	5.01898
5.	Pi-Alkyl	LEU-54(A)	4.62731
6.	Pi-Alkyl	LEU-22(A)	5.36232
7.	Pi-Alkyl	LEU-26(A)	5.33769
8.	Pi-Alkyl	ILE-61(A)	5.46277
9.	Pi-Alkyl	VAL-93(A)	4.9189
10.	Pi-Alkyl	VAL-93(A)	4.48794
11.	Pi-Alkyl	PRO-27(B)	5.23307
12.	Pi-Alkyl	VAL-93(A)	4.9931
13.	Pi-Alkyl	PRO-27(B)	4.47779

Hydrogen Bond Interactions:

Index

Amino acid residue

Bond

Distance (Å)

Distance of H-A (in Å)

Distance of D-A (in Å)

Donor angle (degree)

Donor atom

Acceptor atom

1.

GLN-72(A)

3.31

3.86

118.37

Ligand

(O-H)

Amino acid

GLN-72(A)-(=O)

2.

HIS-96(A)

2.81

3.20

93.678

Amino acid

HIS-96(A)-(H atom)

Ligand

(O-H)

Other interactions:

Index	Bond Type	Amino acid Residue	Bond distance (Å)
1.	p-Stacking	TRP-23(B)	5.08 (Angle- 80.28)

Table 9. Amino acid residues involved in detail interactions of robustaflavone-MDM2 complex with free p53

Hydrophobic interactions:

Index	Bond Type	Amino acid Residue	Bond distance (Å)
1.	Pi-Pi Stacked	TYR-100(A)	3.95576
2.	Pi-Pi Stacked	HIS-96(B)	5.00656
3.	Pi-Pi Stacked	HIS-96(B)	4.55254
4.	Amide-Pi Stacked	LEU-54(A), PHE-55(A)	4.43011
5.	Amide-Pi Stacked	LEU-54(A), PHE-55(A)	4.03565
6.	Amide-Pi Stacked	GLU-28(A), ASN-29(A)	4.47752
7.	Pi-Alkyl	LEU-54(A)	5.38429
8.	Pi-Alkyl	ILE-99(A)	5.41305
9.	Pi-Alkyl	VAL-93(A)	5.29991
10.	Pi-Alkyl	LYS-24(B)	4.9842
11.	Pi-Alkyl	LEU-54(A)	5.24448
12.	Pi-Alkyl	LYS-24(B)	4.48972
13.	Pi-Alkyl	LEU-54(A)	5.28303

Other interactions:

Index	Bond Type	Amino acid Residue	Bond distance (Å)
1.	Carbon Hydrogen Bond	GLU-28(B)	3.3435
2.	p-Stacking	TYR-100(A)	3.96 (Angle- 3.86)

Table 10. Amino acid residues involved in detail interactions of agathisflavone-MDM2 complex with free p53

Hydrophobic interactions:

Index	Bond Type	Amino acid Residue	Bond distance (Å)
1.	Pi-Sigma	LEU-54(A)	3.53074
2.	Pi-Sigma	LEU-26(B)	3.50271
3.	Pi-Pi Stacked	TYR-100(A)	3.87724
4.	Pi-Pi Stacked	HIS-96(A)	5.3823
5.	Pi-Pi Stacked	HIS-96(A)	4.4502
6.	Amide-Pi Stacked	LEU-54(A), PHE-55(A)	5.25281
7.	Pi-Alkyl	VAL-93(A)	4.91014
8.	Pi-Alkyl	LEU-26(A)	5.12838
9.	Pi-Alkyl	LEU-54(A)	4.93605
10.	Pi-Alkyl	LEU-22(B)	4.64491
11.	Pi-Alkyl	LEU-54(A)	4.99455
12.	Pi-Alkyl	ILE-99(A)	5.2034

Other interactions:

Index	Bond Type	Amino acid Residue	Bond distance (Å)
1.	p-Stacking	TYR-100(A)	3.88 (Angle- 7.61)

Table 11. Amino acid residues involved in detail interactions of ochnaflavone-MDM2 complex with free p53

Hydrophobic interactions:

Index	Bond Type	Amino acid Residue	Bond distance (Å)
1.	Pi-Pi T-shaped	TRP-23(B)	4.67601
2.	Pi-Pi T-shaped	TRP-23(B)	5.06008
3.	Pi-Alkyl	ILE-61(A)	5.28297
4.	Pi-Alkyl	VAL-93(A)	4.5213
5.	Pi-Alkyl	VAL-93(A)	5.00304
6.	Pi-Alkyl	ILE-99(A)	4.99331
7.	Pi-Alkyl	LEU-26(B)	4.80939
8.	Pi-Alkyl	ILE-61(A)	5.46069
9.	Pi-Alkyl	VAL-93(A)	4.64663
10.	Pi-Alkyl	VAL-93(A)	4.98446
11.	Pi-Alkyl	LEU-22(B)	5.06191
12.	Pi-Alkyl	LYS-94(A)	4.31513
13.	Pi-Alkyl	LEU-22(B)	4.51131
14.	Pi-Alkyl	LYS-94(A)	3.7847

Hydrogen Bond Interactions:

Index	Amino acid residue	Bond distance (Å)	Distance of H-A (in Å)	Distance of D-A (in Å)	Donor angle (degree)	Donor atom	Acceptor atom
1.	GLU-17(B)	2.73	2.73	3.25	116.41	Amino acid GLU-17(B))(=O)3	Ligand (O-H)
2.	GLN-72(A)	3.15	3.15	3.55	106.35	Ligand (O-H)	Amino acid GLN-72(A)-(=O)1
3.	LYS-94(A)	2.74	2.74	3.29	116.42	Ligand (O-H)	Amino acid LYS-94(A)-(=O)
4.	HIS-96(A)	3.28	3.28	4.01	129.39	Amino acid HIS-96(A)-(=N)	Ligand (O-H)

Electrostatic interactions:

Index	Bond Type	Amino acid Residue	Bond distance (Å)
1.	Pi-Cation	LYS-94(A)	4.90042

Other interactions:

Index	Bond Type	Amino acid Residue	Bond distance (Å)
1.	p-Stacking	TRP-23(B)	5.06 (Angle- 75.36)
2.	p-Stacking	TRP-23(B)	4.68 (Angle- 75.49)

Table 12. Amino acid residues involved in detail interactions of hinokiflavone-MDM2 complex with free p53

Hydrophobic interactions:

Index	Bond Type	Amino acid Residue	Bond distance (Å)
1.	Pi-Pi Stacked	TYR-104(A)	4.81289
2.	Pi-Pi Stacked	TYR-100(A)	4.04772
3.	Pi-Pi Stacked	HIS-96(A)	4.04772
4.	Pi-Pi Stacked	HIS-96(A)	4.04772
5.	Pi-Pi T-shaped	TYR-100(A)	4.77968

Hydrogen Bond Interactions:

Index

Amino acid residue

Bond distance (Å)

Distance of H-A (in Å)

Distance of D-A (in Å)

Donor angle (degree)

Donor atom

Acceptor atom

1.

SER-20(B)

2.55

2.92

102.86

Amino acid

SER-20(B)-(=N)

Ligand

(O-H)

Other interactions:

Index	Bond Type	Amino acid Residue	Bond distance (Å)
1.	p-Stacking	TYR-100(A)	4.78(Angle- 75.94)
2.	p-Stacking	TYR-100(A)	4.05 (Angle- 12.25)

Electrostatic interactions:

Index	Bond Type	Amino acid Residue	Bond distance (Å)
1.	Pi-Cation	HIS-96(A)	4.84619

Table 13. Amino acid residues involved in detail interactions of delicaflavone-MDM2 complex with free p53

Hydrophobic interactions:

Index	Bond Type	Amino acid Residue	Bond distance (Å)
1.	Pi-Sigma	VAL-93(A)	3.58109
2.	Pi-Sigma	LEU-22(B)	3.89485
3.	Pi-Pi T-shaped	TRP-2(B)	5.52679
4.	Pi-Alkyl	MET-62(A)	4.52915

Hydrogen Bond Interactions:

Index	Amino acid residue	Bond distance (Å)	Distance of H-A (in Å)	Distance of D-A (in Å)	Donor angle (degree)	Donor atom	Acceptor atom
1.	GLN-71(A)	3.04	3.04	3.40	101.92	Amino acid GLU-71(A)-(=N)	Ligand (O-H)
2.	GLN-72(A)	1.82	1.82	2.72	152.57	Ligand (O-H)	Amino acid GLN-72(A)-(=O)1
3.	HIS-73(A)	3.30	3.30	4.09	135.42	Amino acid HIS-73(A)-(=N)	Ligand (=O)
4.	LYS-94(A)	3.31	3.31	3.88	117.11	Amino acid LYS-94(A)-(N–H)	Ligand (=O)

Electrostatic interactions:

Index	Bond Type	Amino acid Residue	Bond distance (Å)
1.	Pi-Anion	GLU-17(B)	4.97592
2.	Pi-Anion	GLU-17(B)	3.93502
3.	Pi-Anion	GLU-17(B)	3.26295

Scheme 1 is available in the Supplementary Files section.

No competing interests reported.

Scheme1.jpg
Scheme 1. Schematic representation of the plausible route of naturally occurring biflavonoids mediated inhibition of p53-MDM2 protein-protein interactions. MDM2 oncoprotein downregulate p53 protein expression through the induction of ubiquitination mediated proteosome degradation pathway. Biflavonoid-based inhibitors disrupt the p53-MDM2 interaction, resulting in accumulation of p53 and should activate p53 tumour-suppressor activity in tumour cells that express wild-type p53.
SupplementaryInformations.doc

Exploring the efficacy of naturally occurring C-C and C-O-C type biflavonoids towards the inhibition of MDM2-p53 interactions

Status:

Version 1

Abstract

Figures

Introduction

Methodology

2.1 Preparation of ligands

2.2 Preparation of protein

2.3 Molecular docking

2.4 Molecular dynamics (MD) simulations

2.5 Physicochemical properties

Results and Discussion

Conclusion

Declarations

References

Tables

Scheme 1

Additional Declarations

Supplementary Files

Status:

Version 1