In Silico Analysis and Docking Study on a Potential Wild-Type, Tumor Suppressor Protein P53 Activator using Thymoquinone

Ahnaf Khan

Student, Thomas Jefferson High School for Science and Technology, Alexandria, Virginia


p53 is a potent tumor suppressor protein that is frequently mutated or inactivated in almost 50% of all human cancers. These statistics, as well as essential functions of p53 in maintaining normal cell cycle and apoptosis, impose the urge to extend new methods for making p53 a target for new molecular-based therapies. The purpose of this study was to analyze the anti-cancer potential of thymoquinone (TQ) on wild-type (wt) p53 through a computational molecular docking study. In particular, the study focused on the N-terminal, transactivation domain of wt p53 (Residues 1-63), a site to which MDM2, a well-known inhibitor of p53, binds. For this study, the phytochemical TQ extracted from black seed (Nigella sativa) was used as the ligand for molecular interaction, to examine how well it can inhibit p53-MDM2 binding; TQ’s structure was obtained from the PubChem database. The structure of the N-terminal domain of molecular target p53 was obtained from PDB database (PDB ID: 2LY4, Chain B). Known p53 activators Nutlin-3B, RITA, and Serdemetan and the phytochemicals Curcumin, Phenethyl Isothiocyanate, Kaempferol, and Sulforaphane were also tested for their respective capacities to inhibit p53-MDM2 binding (hence, activate wt p53) as part of a comparative study to validate TQ’s anti-cancer abilities. Computational docking analyses were performed using AutoDock 4.2 based on energy scoring options available. TQ showed optimum binding affinity with the molecular target (N-terminal domain of p53) with a binding energy of -4.45 kcal/mol as well as 6 hydrogen bond contacts as compared to all the other standards in this study, except Nutlin-3B (-4.50 kcal/mol). These results indicate that TQ should be considered as a potential drug for reactivating wt 53 and treating cancerous cells in which wt p53 activity is suppressed.

KEYWORDS: Thymoquinone, Nigella Sativa, Phytochemicals, wild-type tumor suppressor protein p53, Molecular Docking, N-terminal transactivation domain of p53


Cancer is a complex, deadly disease characterized by uncontrolled growth, division, and metastasis of abnormal cells; the aberrant nature of these cancer cells occurs as a result of genetic mutations, exposure to carcinogens, oncogene activation, and tumor suppressor inactivation among various other environmental and internal factors1. Due to the various number of ways cancer can progress, this disease has become a global health issue and the second leading cause of death in the United States2. The statistics and widespread prevalence of cancer on a global scale impose the urge to utilize methods for treating and addressing this disease. Hence, there is increasing interest to implement novel cost-effective strategies for cancer prevention.

One increasingly popular approach is chemoprevention, which involves the administration of a synthetic, natural or biological agent to reduce or impede the occurrence of cancer progression. The potential value of this approach has been demonstrated through studies on breast, prostate and colon cancer3. With increasing understanding of the biology of such chemical agents, there have been considerable efforts to explore and discover such therapeutic compounds that can reduce the threat of cancer.

Specifically, naturally-occurring chemicals have been credited for their anti-cancer properties, due to their well-observed ability to target multistep/multistage pathways involved in cell cycle arrest and apoptosis; some of these compounds are extracted from plants (called phytochemicals)4. Along with targeting processes involved in cell-cycle arrest and apoptosis, phytochemicals can also upregulate critical genes that help to either delay or reduce the possibility of carcinogenesis5. Recent studies have indicated that phytochemicals may also have anti-cancer effects against mice cancer models and cancers caused by carcinogens, irradiations and carcinogenic metabolites6. Thus, the usage of phytochemicals for cancer treatment and prevention may be a more promising, cost-effective alternative than other used methods.

One potential molecular target for cancer-based therapy by phytochemicals can be p53, which is one of the most important tumor suppressors in cells. As a transcription factor, p53 functions as a sensor to cellular, environmental, and metabolic stresses that could potentially transform healthy cells into cancerous cells7. In response to such stresses, p53 can induce cell cycle arrest followed by DNA repair, senescence or apoptosis, depending on the amount of cellular damage8. Whenever p53 becomes dysfunctional and/or fails to activate within a cell, failure to repair DNA damage can occur which can cause genomic instability (comment 2). This inability, in turn, can make a cell more susceptible to tumorigenesis. Moreover, cells with low levels of p53 have a higher chance of evading apoptotic pathways and crucial cell cycle checkpoints, allowing them to proceed to the cancerous phase9, 10.

In most cancer cells, p53 is frequently mutated; numerous studies have indicated that p53 is mutated in almost 50% of all human cancers11. Since many proteins, transcription factors, and cell regulatory pathways are connected to p53’s activity, mutations in p53 compromises all these critical regulatory pathways, a hallmark for the onset of cancer. These mutations are oftentimes caused by missense point mutations, with most mutations occurring in the DNA-binding domain of p53; these mutations impair p53’s ability to bind as well as repair damaged DNA, which makes cells more prone to becoming cancerous12, 13. In other cancers that carry wild-type (wt) p53 (or non-mutated p53), murine double minute 2 (MDM2) or its homolog MDMX, which is a E3 ubiquitin ligase, contributes to p53 instability by targeting it for proteasome-mediated degradation14.

The MDM2/MDMX regulator inhibits p53 activity by binding to its N-terminal, transactivation domain17; specifically, the interaction between p53 and MDM2 involves four critical hydrophobic residues (PHE 19, LEU 22, TRP 23 and LEU 26) located on p53’s transactivation domain18, which could potentially be the target of small molecule inhibitors11. This binding causes p53 to be inactivated and then later degraded, lowering p53 levels significantly within the cells, which increases the chances for tumorigenesis, facilitating cancer progression17. Impeding the binding between wt p53 and its inhibitors MDM2 and MDMX is an important goal in restoring the anti-cancer potential of wt p53.

Since p53 is critical to maintaining normal cell cycle and homeostasis, it is an appealing target for mechanism-driven anti-cancer drug discovery. Many therapeutic approaches for correcting p53’s ability mostly involve changing p53 conformation from mutant to wt p53, a strategy termed as mutant p53 reactivation15. This approach usually takes place for halting cancer cells with mutated p53. However, a relatively new therapeutic approach is based on upregulating wt p53 activity that has been lowered in cancer cells – an approach known as wild-type p53 activation14. Specifically, this method usually involves inhibiting the interaction between p53 and MDM2/MDMX15, 16.

The purpose of this study is to find an effective phytochemical drug that can impede the p53-MDM2/MDMX binding process. In order to attempt such a study, interactions between the protein (N-terminal, transactivation domain of p53) and the potential ligands have to be analyzed. Both the ligand and target protein’s binding site are critical components in understanding the protein-ligand structure as well as the effects of the protein-ligand interactions on the protein’s biological activity. The goal of ligand-protein docking is to forecast the predominant binding complex(es) of a ligand with a protein of known three-dimensional structure19, 20.

Molecular docking is an essential tool for structural molecular biology and computer-assisted drug design. It is utilized to predict the binding orientation of small ligands (drug candidates) to their biomolecular target (i.e. protein, carbohydrate and nucleic acid) with the aim to determine their tentative binding constraints21; this tool also analyzes the interaction properties between ligand and protein22. Once a compound is docked, it is scored using mathematical models. Scoring estimates the chemical interactions, such as binding strength and energy state, between the ligand and protein to rank the effectiveness of the drug candidate being scored23.

These estimates establish raw data for the rational drug designing (structure-based-drug development) of new agents with better efficacy and more specificity or for determining the suitability of a known chemical compound, such as a phytochemical21. The molecular docking tool to be used in this study is AutoDockTools, which is a software that effectively analyzes the structural and binding interaction characteristics (such as binding energy in kcal/mol) between any ligand and protein receptor. Other necessary bioinformatics tools for this study include RCSB Protein Data Bank, the PubChem Database, SWISS-PDB viewer, metaPocket, PatchDock, and PyMOL.

Thus, this study planned to evaluate the interaction and suitability of action between thymoquinone (TQ) with wt p53 (target protein). Thymoquinone (TQ) is the biologically active constituent of the volatile oil of black seed (Nigella sativa), which has been shown to exert antineoplastic, anti-tumor/anti-cancer, and anti-inflammatory effects24.

Nigella sativa, commonly known as black cumin, is an annual flowering plant native to Mediterranean and South Asian countries. Studies had shown that the biological, curative activity of Nigella sativa seeds is mainly attributed to its essential oil component TQ, which comprises approximately 54% of the seed oil25. Some studies have tested this compound for its therapeutic effect on many diseases including inflammation, cancer, sepsis, atherosclerosis, and diabetes26. These studies have revealed many different modes of action of thymoquinone; however, there is still insufficient data to provide conclusive evidence of its efficacy against inflammation and cancer26, 27.

TQ is known to induce apoptosis by p53-dependent and p53-independent pathways in cancer cell lines28. However, the precise mechanism by which TQ interacts with p53 is not clearly known, as there are have been no prior scientific studies that have elucidated such. Therefore, in this study, it is hypothesized that TQ interacts with wt p53 by binding to the protein’s N-terminal, transactivation domain to potentially inhibit wt p53-MDM2/MDMX binding. Thus, the study was planned to evaluate the interaction and suitability of action between TQ (ligand) with wt p53 (target protein) to determine the extent of TQ’s anti-cancer potential on cancer cells with wt p53.

A comparative study was conducted with seven other chemical compounds to support TQ’s anti-cancer potential. Three of the chemicals used for the comparative study were RITA, Nutlin-3B, and Serdemetan, which are all known wt p53 reactivators11. The other four chemicals used for comparison were curcumin, phenethyl isothiocyanate, kaempferol, and sulforaphane, which are all phytochemicals that have been shown to induce cell growth inhibition and/or cell death through interaction with p53; the exact mechanisms by which these phytochemicals interact with p53 is not precisely known29.


Ligand Selection

The 3D chemical structures for TQ and the other chemicals were obtained from the PubChem database30. The structures were downloaded as MOL SDF files which were converted to PDB and PDBQT files using OpenBabel 2.3.131.

Target Protein Selection

Since the N-terminal Domain of wt p53 was the domain of interest in this study, search for the target protein was focused on finding the structure for this portion of the protein (Residues 1-63). Search for the target protein was conducted on the RCSB Protein Data Bank (PDB) website32. Initially, the Protein Feature View page of PDB entries for p53 was brought up on the RCSB website to examine possible PDB entries. The one that fulfilled the Residue 1-63 sequence requirement was PDB entry 2LY4 (Residues 14-61) and was downloaded as a PDB file. While this PDB file did not encompass the entire N-terminal transactivation domain of wt p53, it did contain the necessary residues involved in p53-MDM2 binding (i.e. PHE 19, LEU 22, TRP 23 and LEU 26)18; thus, 2LY4 was deemed sufficient for this study. The PDB file was later edited using AutoDockTools33, and only the chain B was chosen; according to the summary page for 2LY4, chain B contained the p53 residues; the use of Chain A was unnecessary for this study.

Energy Minimization

Energy minimization was applied to the protein using SWISS-PDB viewer. The purpose of the energy minimization is to remove very high energy configurations within the protein that would lead to perturbation and instability of the simulation34. Initially, all accessible residues of the protein were selected. The default minimization preferences were used. Energy was then minimized by selecting the “Energy Minimization” option under the “Tools” tab.

Active Site Prediction

The active site of the target was determined using metaPocket 2.035. Using default parameters, the top three favorable binding sites were examined and reported by metaPocket. The residues in the most favorable binding site were PRO27, GLU28, ASN29, ASN30, VAL31, LEU32, SER33, PRO34, PRO36, ASP42, LEU43, MET44, LEU45, and SER46.

Molecular Docking Analysis

The ligand molecules were docked to the target protein using AutoDock (Version 4.2.6) and AutoDockTools33. This computational ligand-target docking approach was utilized to analyze the structural and binding interaction between the N-terminal, transactivation domain of wt p53 and each ligand. The target protein was opened in AutoDockTools as a PDB file and chain A was deleted under the “Edit” tab. Next, all water molecules were deleted, all hydrogen bonds (H-Bonds) were added, and non-Polar Bonds were merged, and then Gasteiger charges were computed and added to the molecule. The total Gasteiger charge for the receptor was -9.0003. After editing the main target molecule, the ligand (TQ) was read into AutoDockTools as a PDBQT file and its root detected.

The grid maps representing the target protein were created using the options under the “Grid” tab and then were calculated using the “Run AutoGrid” option. After the grid maps had been calculated, the target protein and ligand molecule were prepared for the actual docking analysis using the options under the “Docking” tab and then docking was carried out using the “Run AutoDock” option. After running, AutoDock would return results for the top 5 ligand-target binding complexes; these results included displays for the top 5 complexes as well as interaction binding energy for each complex.

Ligand Parameters

The best ligand-target structure from the docked structures was chosen based on the lowest energy and the highest number of hydrogen bond contacts formed between the target and ligand.

Other Bioinformatics Tools


PatchDock works with an algorithm for molecular docking with two molecules of any type: proteins, Deoxyribonucleic acid (DNA), peptides, and drugs, as the inputs. The output obtained is a list of potential complexes sorted by geometric shape complementarity criteria36, 37. This approach was used to analyze how suitable each of the ligands was to the target protein based on their respective geometries. The output page displayed a list of geometric scores (in descending order) for each possible mode of binding between ligand and protein alongside corresponding PDB solution files for the ligand-protein complexes.


PyMOL is an open-source visualization tool used in viewing chemical compounds as well as their interactions with other compounds (i.e. target proteins). It can display high-quality 3D images of small chemical compounds and biological macromolecules such as proteins38. The PDB files for all the different ligand-target complexes obtained from PatchDock and the SDF files for the various ligands used in this study were viewed using PyMOL. Figure 1 displays the chemical structures of the 8 different ligands. Figure 2 shows the ligand-target structures for each of the 8 different ligands bound to the target protein.

Figure 1. Chemical structures of (a) Thymoquinone, (b) Nutlin-3B, (c) RITA, (d) Curcumin, (e) Phenethyl Isothiocyanate, (f) Kaempferol, (g) Sulforaphane, and (h) Serdemetan.

Figure 2. Docked conformations of the N-terminal transactivation domain of wt p53 with (a) Thymoquinone, (b) Nutlin-3B, (c) RITA, (d) Curcumin, (e) Phenethyl Isothiocyanate, (f) Kaempferol, (g) Sulforaphane, and (h) Serdemetan.


The protein target (N-terminal, transactivation domain of wt p53) with PDB code 2LY4 used for this study was found to contain 48 amino acid residues in it (Residues 14 – 61), which contains all the necessary residues that have been shown to participate in p53-MDM2/MDMX binding18.

Among the top 3 binding sites obtained from metaPocket, the first displayed site was highly conserved and the most favorable site for docking. The residues in the active site of the target protein were found to be PRO27, GLU28, ASN29, ASN30, VAL31, LEU32, SER33, PRO34, LEU35, PRO36, ASP42, LEU43, MET44, and SER46.

Table 1 displays the physiochemical and pharmacophore properties of the eight different ligands. From the table, it is clear that TQ obeys Lipinski’s Rule of 543. All the other ligands also follow Lipinski’s Rule of 5, except Nutlin-3B; nonetheless, Nutlin-3B is still shown to be an effective drug in inhibiting binding between wt p53 and MDM2/MDMX39.

Hydrogen bonds (H-bonds) are formed between the atoms on the residues (many of which are in the active site of the target protein) and the atoms on the different ligands. The number of contacts between each ligand and the target protein is the number of H-bonds formed between the ligand and target protein. Prior studies have indicated the higher the number of H-bond interactions (i.e. the number of ligand-receptor contacts), the better the interaction between each ligand and the target protein. Table 2 displays the properties and nature of the interaction between the ligands and the target protein; all of this data was collected using AutoDockTools, which tests the binding interactions between each ligand and the target protein.

For each of the eight different ligands tested in the study, the ligand-target complex conformation that exhibited the lowest binding energy score (kcal/mol) is the one that is most stable, energetically and chemically speaking. The top 5 binding energy scores for all ligand-target complexes per each ligand were calculated and displayed using AutoDock and AutoDockTools. The highest binding energy scores for each ligand interaction with wt p53’s N-terminal, transactivation domain are shown in Table 3.

The geometric shape complementarity scores for the interaction between the target protein and the eight different ligands were obtained from the PatchDock server; these scores are listed alongside the AutoDock binding energy scores in Table 3. These scores demonstrate how well the ligand and target are suitable for binding to each other, based on purely geometric shape calculations rather than chemical/energetic calculations. The higher the score, the more geometrically suitable the ligand is for binding with the target protein.

Table 1. Physiochemical and pharmacophore properties of Ligands

1MF is Molecular Formula; 2MW is Molecular Weight in g/mol; 3LogP is Measure of Lipophilicity; 4HDC is Hydrogen Bond Donor Count; 5HAC is Hydrogen Bond Acceptor Count

Table 2. Interaction Results of TQ and other ligands with target molecule p53 (Number Receptor Contacts and Names of Residues involved in Contact Region)


Table 3. Interaction Results of TQ and other ligands with target molecule (Geometric Shape Complementarity Scores and Binding Energy Scores)


In silico molecular docking is one of the most powerful techniques in discovering novel ligands for target proteins of known structure, which in this study is wt p53’s N-terminal, transactivation domain, and thus play a vital role in structure-based drug design and drug prediction37. Molecular docking is highly pertinent to the field of computer-based drug design, which screens any small molecule or compound inputted by orienting and scoring them in potential, favorable binding sites of the target protein. As a result, novel ligands for receptors of known structure are designed or proposed as drugs, and their interaction energies are calculated using the scoring functions38. Sometimes, scoring functions may also be constituted as geometric shape complementarity scores, as obtained from PatchDock33, 34.

Prior studies have proposed that compounds should possess certain physiochemical and pharmacophore properties to be accepted as possible drugs39. These properties were formulated by Lipinski et al. in 1997. It is necessary to evaluate drug-likeness or to determine if a chemical compound with a certain pharmacological or biological activity has properties that would make it a likely effective drug40. It has been suggested that any pairwise combination of the following conditions: Molecular weight >500, LogP >5, H-bonds donors >5, and H-bonds acceptors >10, may result in compounds with diminished permeability40. As displayed in Table 1, TQ perfectly obeys all 5 conditions of Lipinski’s rule of 5 and is considered to have excellent absorption and permeability. The other chemicals displayed also follow the rule of 5, except Nutlin-3B; nonetheless, numerous studies have shown that Nutlin-3B is an active drug in inhibiting binding between wt p53 and MDM2/MDMX18.

One of the hallmarks of suitable binding interactions between a ligand and target protein is the number of receptor contacts made, preferably active site contacts for optimum binding. Specifically, the number of contacts made between the ligand and target is the number of H-bonds formed between the atoms of the receptor molecule (in this case 2LY4) and the ligand. The greater the number of H-bonds formed, usually the better the interaction and binding between the two compounds. Table 2 depicts the number of receptor contacts made between the target protein (N-terminal transactivation domain of wt p53) and the different ligands (Thymoquinone, Nutlin-3B, RITA, Curcumin, Phenethyl Isothiocyanate, Kaempferol, Sulforaphane, and Serdemetan).

Strong binding interaction properties are some other indicators of optimal ligand-target binding. Thus, molecular docking tools (i.e. AutoDock) play crucial roles in elucidating the extent to which certain ligands binds suitably to a prescribed target. Specifically, AutoDock gives docking scores for each ligand’s capacity to bind with a certain target; the more negative the docking score (measured in kcal/mol), the more effective, stable the ligand-target complex is41.

Another molecular docking tool for ascertaining ligand-target interactions is PatchDock; given a PDB file input for both the receptor molecule and designated ligand molecule for that trial, PatchDock returns a list of scores based on purely geometric shape complementarity between the inputted ligand and receptor molecules, alongside PDB solution files displaying each ligand-target complex corresponding to each score. The higher the score, the better the conformation between the ligand and target based on their respective geometries33, 34.

Table 3 displays the results of interaction between TQ and the other ligands with the target molecule (N-terminal, transactivation domain of wt p53); it displays the binding energy scores of interaction as obtained from AutoDock as well as geometric shape complementarity scores as obtained from PatchDock.

From Table 2, it is clear that TQ exhibits a high number of contacts (6) with the target protein, a number in par with the other ligands shown in the table. The residues involved in contact between TQ and the target protein are LEU26, PRO27, GLU28, ASN29, VAL31, and LEU35 (one of the residues is in the contact site between p53 and MDM2). Likewise, as shown in Table 3, TQ displays an AutoDock binding energy score of -4.45 kcal/mol. This energy score is visibly smaller than most of the other scores shown by the other ligands in Table 3, such as those of RITA (-4.35 kcal/mol), Curcumin (-3.33 kcal/mol), Phenethyl Isothiocyanate (-3.56 kcal/mol), Kaempferol (-4.33 kcal/mol), Sulforaphane (-3.30 kcal/mol), and Serdemetan (-2.76 kcal/mol). However, TQ’s binding energy score was slightly higher than that of Nutlin-3B (-4.50 kcal/mol); however, Nutlin-3B has already been shown to be an effective inhibitor of p53-MDM2 binding. Thus, these observations may be used as evidence to support the potential usage of TQ as a drug to inhibit binding between p53 and MDM2/MDMX as described in this study.

Interestingly, however, TQ exhibits a shape complementarity score (2354) with p53 as shown in Table 3 that is relatively lower than most of the scores shown for the other ligands, such as that of Nutlin-3B (4778), RITA (3260), Curcumin (4266), Kaempferol (3036), and Serdemetan (3828). This apparent distinction may support the idea there is perhaps no correlation between energy binding scores and shape complementarity scores. In this circumstance, binding energy score is the more reliable score to use for effectively judging the quality of usage of a new compound, not the shape complementarity score. Nonetheless, the shape complementarity scores are used to validate that the respective shapes of the ligand and target protein are suitable for binding with each other.

The energy value (-4.45 kcal/mol) and the number of contacts (6 H-bonds) for TQ obtained in this present in silico docking study, when compared to the values of the other compounds surveyed in this study, confirms that a strong binding affinity exists between TQ and the target protein, which is the N-terminal transactivation domain of wt p53 (PBD ID: 2LY4, chain B). Considering these values and the residues involved in TQ’s binding to p53, TQ should be considered as a candidate inhibitor of p53-MDM2 binding, and thus a potentially important wt p53 activator. TQ’s potential ability to activate wt p53 in cancer cells could help to induce cell cycle arrest and apoptosis, coinciding with in vivo results25. Nonetheless, unlike any previous studies, this research study specifically focused on the binding affinity and specificity of interaction with wild-type p53, particularly with the binding site involved in p53-MDM2 binding. Thus, it is essential to perform docking experiments and comparative studies of known drugs to validate a ligand’s potential ability to act on a target protein, and hence, be accepted as another potentially effective drug in the fight against disease progression.


From this study, TQ could be considered as an effective phytochemical in up-regulating a crucial protein (p53) responsible for maintaining cell homeostasis and repairing damages to the cells that could potentially induce tumorigenesis, based on its suitable binding interactions with this target protein. TQ could be also be an effective inhibitor of p53-MDM2 binding, considering that TQ binds with a residue important to conformation between the two proteins. Considering the results of this study, TQ should be considered as a possible drug for controlling cancer. Further in silico research is necessary to test how well TQ, along with other potential ligands, can interact with other target proteins involved in carcinogenesis. One potential future study that can be done is to test the ability of such ligands to inhibit MDM2/MDMX activity. Perhaps a vital question that needs to be answered is how this phytochemical drug will interact with cells and protein in vivo as opposed solely to in silico. Further testing on TQ’s effects on DNA damage within normal and cancerous cells can be one area of further study. Thus, more importantly, more in vivo research needs to be conducted, especially on animal model systems, to observe how well new drugs interact with target proteins, to determine the actual effects a new drug or ligand has on an organism’s overall health, as well as to set the dosage of safety levels. This type of research should be done to explore new promising methods of cancer suppression, to lower health costs for cancer treatment, and to ensure better health among humans worldwide.


I would like to thank Dr. Andrea Cobb, Director of Biotechnology and Life Sciences Laboratory at Thomas Jefferson High School for Science and Technology, for reviewing and commenting on the contents of my study. I would also like to thank Dr. Wafik El-Deiry of University of Pennsylvania’s Perelman School of Medicine for offering his comments on this study, and for providing valuable suggestions for future study.


  1. American Cancer Society. Cancer Facts & Figures 2016. Atlanta: American Cancer Society; 2016
  2. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2016. CA Cancer J Clin. 2016;66:7–30
  3. Steward WP, Brown K. Cancer chemoprevention: A rapidly evolving field. Br J Cancer. 2013;109:1–7
  4. Neergheen VS, Bahorun T, Taylor EW, Jen LS, Aruoma OI. Targeting specific cell signaling transduction pathways by dietary and medicinal phytochemicals in cancer chemoprevention. Toxicology. 2010;278:229–241
  5. Kwon KH, Barve A, Yu S, Huang MT, Kong AN. Cancer chemoprevention by phytochemicals: potential molecular targets, biomarkers and animal models.Acta Pharmacol Sin. 2007 Sep;28(9):1409-21. Review. PubMed PMID: 17723174.
  6. Kwon KH, Barve A, Yu S, Huang MT, Kong AN. Cancer chemoprevention by phytochemicals: Potential molecular targets, biomarkers and animal models. Acta Pharmacol Sin 2007;28(9):1409-21
  7. Hong B, van den Heuvel AP, Prabhu VV, Zhang S, El-Deiry WS. Targeting tumor suppressor p53 for cancer therapy: strategies, challenges and opportunities. Curr Drug Targets. 2014;15:80–89
  8. Duffy MJ, Synnott NC, McGowan PM, Crown J, O'Connor D, Gallagher WM. p53 as a target for the treatment of cancer. Cancer treatment reviews. 2014;40(10):1153–60
  9. Ryan K.M., Phillips A.C., Vousden K.H. Regulation and function of the p53 tumor suppressor protein. Curr. Opin. Cell Biol. 2001; 13:332–337.
  10. Liu J., Zhang C., Hu W., Feng Z. Tumor suppressor p53 and its mutants in cancer metabolism. Cancer Lett 2013; pii: S0304-3835(13)00863-X
  11. Yu X, Narayanan S, Vazquez A, Carpizo DR. Small molecule compounds targeting the p53 pathway: are we finally making progress? Apoptosis. 2014; 19(7):1055–1068.
  12. Freed-Pastor WA, Prives C. Mutant p53: one name, many proteins. Genes Dev. 2012; 26:1268–1286. 
  13. Goldstein I, Marcel V, Olivier M, Oren M, Rotter V, Hainaut P. Understanding wild-type and mutant p53 activities in human cancer: new landmarks on the way to targeted therapies. Cancer Gene Ther(2011) 18:2–11.10.1038/cgt.2010.63
  14. Wang Z., Sun Y. Targeting p53 for novel anticancer therapy. Transl. Oncol. 2010;3:1–12.
  15. Estrada-Ortiz N., Neochoritis C. G. & Dömling A. How to design a successful p53-mdm2/x interaction inhibitor: A thorough overview based on crystal structures. Chem. Med. Chem. (2015)
  16. Kojima K, Ishizawa J, Andreeff M. Pharmacological activation of wild-type p53 in the therapy of leukemia. Exp Hematol. 2016 Sep;44(9):791-8.
  17. Selivanova G. Wild-type p53 reactivation: from lab bench to clinic. FEBS Lett. 2014 Aug 19;588(16):2628-38.
  18. Shangary S, Wang S. Small-molecule inhibitors of the MDM2-p53 protein-protein interaction to reactivate p53 function: a novel approach for cancer therapy. Annu Rev Pharmacol Toxicol. 2009;49:223-41.
  19. Morris GM, Lim-Wilby M. Molecular docking. Methods Mol Biol. 2008;443:365-82.
  20. Ramírez D, Caballero J. Is It Reliable to Use Common Molecular Docking Methods for Comparing the Binding Affinities of Enantiomer Pairs for Their Protein Target? Int J Mol Sci. 2016 Apr 20;17(4). pii: E525.
  21. De Ruyck J, Brysbaert G, Blossey R, Lensink MF. Molecular docking as a popular tool in drug design, an in silico travel. Adv Appl Bioinform Chem. 2016 Jun 28;9:1-11.
  22. Ferreira LG, Dos Santos RN, Oliva G, Andricopulo AD.Molecular docking and structure-based drug design strategies. Molecules. 2015 Jul 22;20(7):13384-421.
  23. Meng XY, Zhang HX, Mezei M, Cui M. Molecular docking: a powerful approach for structure-based drug discovery.Curr Comput Aided Drug Des. 2011 Jun;7(2):146-57.
  24. Gali-Muhtasib H, Roessner A, Schneider-Stock R.Thymoquinone: a promising anti-cancer drug from natural sources. Int J Biochem Cell Biol. 2006;38(8):1249-53.
  25. Banerjee S, Padhye S, Azmi A, Wang Z, Philip PA, Kucuk O, Sarkar FH, Mohammad RM. Review on molecular and therapeutic potential of thymoquinone in cancer. Nutr Cancer. 2010;62(7):938-46.
  26. El-Dakhakhany M. Studies on the chemical constitution of Egyptian N. sativa L. seeds. Planta Med 1963; 11: 465–70.
  27. Woo CC, Kumar AP, Sethi G, Tan KH. Thymoquinone: potential cure for inflammatory disorders and cancer.Biochem Pharmacol. 2012 Feb 15;83(4):443-51.
  28. Nithya G, Sakthisekaran D. In Silico Docking Studies On The Anti-Cancer Effect Of Thymoquinone On Interaction With Pten -A Regulator Of Pi3k/ Akt Pathway. Asian J Pharma Clin Res, 2015;1: 192-195.
  29. Wang H, Khor TO, Shu L, et al. Plants Against Cancer: A Review on Natural Phytochemicals in Preventing and Treating Cancers and Their Druggability.Anti-cancer agents in medicinal chemistry. 2012;12(10):1281-1305.
  30. Kim S, Thiessen PA, Bolton EE, Chen J, Fu G, Gindulyte A, Han L, He J, He S, Shoemaker BA, Wang J, Yu B, Zhang J, Bryant SH. PubChem Substance and Compound databases. Nucleic Acids Res.2016 Jan 4; 44(D1):D1202-13. Epub 2015 Sep 22
  31. O'Boyle NM, Banck M, James CA, Morley C, Vandermeersch T, Hutchison GR. Open Babel: An open chemical toolbox. J Cheminform 2011; 3 :33. URL:
  32. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, et al. The Protein Data Bank. Nucleic Acids Res 2000;28(1):235-42
  33. Michel F. Sanner. Python: A Programming Language for Software Integration and Development. J. Mol. Graphics Mod., 1999, Vol 17, February. pp57-61
  34. Guex N, Peitsch MC. SWISS-MODEL and the Swiss-PdbViewer: An environment for comparative protein modeling. Electrophoresis 1997;18(15):2714-23.
  35. Huang B. MetaPocket: a meta approach to improve protein-ligand binding site prediction. OMICS. 2009 Aug;13(4):325-30.
  36. Duhovny D, Nussinov R, Wolfson HJ. Efficient Unbound Docking of Rigid Molecules. In Gusfield et al., Ed. Proceedings of the 2'nd Workshop on Algorithms in Bioinformatics(WABI) Rome, Italy, Lecture Notes in Computer Science 2452, pp. 185-200, Springer Verlag, 2002
  37. Schneidman-Duhovny D, Inbar Y, Nussinov R, Wolfson HJ. PatchDock and SymmDock: servers for rigid and symmetric docking. Nucl. Acids. Res. 33: W363-367, 2005
  38. DeLano WL. The PyMOL Molecular Graphics System. San Carlos: DeLano Scientific; 2002. URL:
  39. Van Maerken T, Rihani A, Van Goethem A, De Paepe A, Speleman F, Vandesompele J. Pharmacologic activation of wild-type p53 by nutlin therapy in childhood cancer. Cancer Lett. 2014 Mar 28;344(2):157-65.
  40. Brooijmans N, Kuntz ID. Molecular recognition and docking algorithms. Annu Rev Biophys Biomol Struct 2003;32:335-73.
  41. Irwin J, Lorber DM, McGovern SL, Wei B, Shoichet BK. Docking and drug discovery. In: Technical Proceedings of the International Conference on Computational Nanoscience and Nanotechnology: Computer Aided Drug Design, Northwestern University US, Nanotech. Vol. 2. 2002. p. 50-1.
  42. Johnson DE, Wolfgang GH. Predicting human safety: Screening and computational approaches. Drug Discov Today 2000; 5:445-54.
  43. Lipinski CA, Lombardo F, Dominy BW, Feeney PJ. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 2001;46(1-3):3-26.
  44. Muhammad SA, Fatima N. In silico analysis and molecular docking studies of potential angiotensin-converting enzyme inhibitor using quercetin glycosides. Pharmacogn Mag. 2015 May;11(Suppl 1):S123-6.


Active Site: A region on an enzyme that binds to a protein or other substance during a reaction

Apoptosis: Process of programmed cell death that occurs in multicellular organisms.

Cancer: Class of diseases involved in abnormal cell growth with the potential to invade or spread to other parts of the body.

Carcinogen: A substance capable of causing cancer in living tissue

Carcinogenesis: The initiation and progression of cancer formation

Cell Cycle: Series of events that take place in a cell leading to its division and duplication of its DNA to generate two daughter cells

Chemoprevention: Efforts to prevent or delay the development of cancer by taking medicines, vitamins, or other agents

Inhibitor: A substance that binds to a protein or enzyme and decrease that protein/enzyme’s activity

Ligand: Ion or molecule that binds to a central metal atom to form a complex.

Lipinski’s Rule of 5: Used to evaluate drug-likeness or determine if a chemical compound with a certain pharmacological or biological activity has properties that would make it a likely orally active drug in humans.

Missense Point Mutation: Single nucleotide change in DNA that results in a codon that codes for a different amino acid, hence leading to a protein with a different structure than a protein encoded by the non-mutated DNA

Molecular Docking: Key tool in structural molecular biology and computer-assisted drug design; it is used to predict the predominant binding mode(s) of a ligand with a protein of known three-dimensional structure

Murine Double Minute 2 (MDM2): An important negative regulator of the p53 tumor suppressor

Oncogene: A gene that in certain circumstances can transform a cell into a tumor cell

Pharmacophore: Part of a molecular structure that is responsible for a particular biological or pharmacological interaction that it undergoes

Phytochemical: Any of various biologically active compounds found in plants

Thymoquinone: A biologically active constituent of volatile oil of black seed (Nigella sativa), which has been shown to exert antineoplastic, anti-tumor/anti-cancer, and anti-inflammatory effects

Tumor Suppressor Protein: Protein encoded by a tumor suppressor gene that protects a cell from one step on the path to cancer.

Wild-type: A strain, gene, or characteristic that prevails among individuals in natural conditions, as distinct from mutant type



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