1. Molecular Modeling of Chemicals Binding to CK2 and to NQO2
Thomas H. Lipscomb
Mentors: Joseph M. Wu, Ph.D.; Tze-chen Hsieh, Ph.D.; Dylan John Bennett
Summer Trainees in Academic Research (STAR) Program
New York Medical College
Summer 2014

2. 2
Table of Contents
Abstract………………………………………………………………………………………….4
1. Introduction……………………………………………………………………………….…5
1.1 Cancer.........................................................................................................................5
1.2 Survival Kinases.........................................................................................................5
1.3 CK2 inhibitors also target NQO2…………………………………………………...5
1.4 Molecular Docking.....................................................................................................6
1.5 Casein Kinase 2 (CK2)...............................................................................................6
1.6 NAD(P)H dehydrogenase, quinone 2 (NQO2)……………………………………...6
1.7 Hypothesis...................................................................................................................7
2. Materials and Methods............................................................................................................7
2.1 Protein Data Bank.......................................................................................................7
2.2 UCSF Chimera………………………………………………………………………7
2.3 UCSF DOCK6............................................................................................................7
2.4 Patchdock....................................................................................................................8
2.5 Firedock……………………………………………………………………………..8
2.6 Human Protein Atlas...................................................................................................8
3. Results………………………………………………………………………………………8
3.1 PDB file comparison UCSF DOCK6……………………………………………….9
3.2 Ranking comparison of inhibitors to choose best CK2, NQO2, and both targeter…9
3.3 Three inhibitors bound to CK2 and NQO2 UCSF DOCK6………………………..10
3.4 Choosing PC-3 over HL-60 and MCF-7…………………………………………...10
4. Discussion………………………………………………………………………………….11
References……………………………………………………………………………………...12
Appendix of Figures……………………………………………………………………………14

3. 3
Abstract
The heterogeneous nature of cancer means that treatment should be personalized so that it works
against particular cancer types. This study focuses on cancers that have resistance to survival
kinase inhibitors. Previous literature (1) raised the possibility that detoxification enzyme
NAD(P)H dehydrogenase, quinone 2 (NQO2) may play a role in the activity/function of casein
kinase 2 (CK2), e.g., resistance to induction of apoptosis in cancers that resist survival kinase
inhibitors. This study investigates whether NQO2 might control the function of CK2 through
direct binding, similar to NQO2 inhibiting the survival kinase RAC-alpha serine/threonine-protein
kinase 1 (AKT1) (2), because CK2 has a pocket similar to the NQO2-binding pocket found in
AKT1. Drugs that co-target NQO2 and CK2 might circumvent the kinase inhibitor resistance of
some cancer types, better than drugs that target either enzyme individually. DOCK6 computer
protein modeling predicted, out of 40 small molecules: the best inhibitor of both (myricetin), the
best CK2 inhibitor (triacetyl resveratrol), and the second best NQO2 inhibitor (piceatannol).
Patchdock and Firedock calculations investigated whether CK2 and NQO2 might bind with each
other and found that: NQO2 binds to the CK2β dimer area of the CK2 tetramer, NQO2 binds most
robustly to CK2β subunit, binding of NQO2 to CK2β subunit increased by adding resveratrol, and
the three best inhibitors decrease CK2α subunit and NQO2 binding through binding to or near the
CK2α subunit Adenosine triphosphate (ATP) site. Future studies may test time- and dose-
dependent effects of the three best candidate inhibitors alone or in combination on apoptosis and
clonogenic survival in prostate cancer cell line PC-3 and other cells.

4. 4
1. Introduction
1.1 Cancer
Cancer is a disease with a constellation of phenotypes. That heterogeneous nature of cancer
suggests that chemotherapy should be personalized so that it works against the particular cancer
type that the patient has. This study focuses on cancers that have resistance to survival kinase
inhibitors.
1.2 Survival Kinases
Survival kinases protect cells from apoptosis and have a role in tumorigenesis (1). Drugs that
inhibit survival kinases are good candidates for killing cancer types that survive by enhancing
survival kinase activity (1). Casein kinase 2 (CK2) is the serine/threonine survival kinase
investigated in this study.
1.3 CK2 inhibitors also target NQO2
A study by James S. Duncan (1) evaluating CK2 inhibitors found that cancer mutants resistant to
CK2 inhibitors were unable to rescue the apoptosis associated with 4,5,6,7-tetrabromo-1H-
benzimidazole (TBBz) and 2-dimethylamino-4,5,6,7-tetrabromo-1H-benzimidazole (DMAT) but
not 4,5,6,7-tetrabromo-1H-benzotriazole (TBB) treatment, suggesting that the two inhibitors
causing the rescue had off-target effects. They used chemoproteomics to reveal that an off-target
of TBBz and DMAT was detoxification enzyme NAD(P)H dehydrogenase, quinone 2 (NQO2)
(1). This result raised the possibility that NQO2 may play a role in the activity/function of CK2,
e.g., resistance to induction of apoptosis. That possibility is explored in this experiment through
molecular docking calculations.
1.4 Molecular Docking
Molecular docking is a calculation which predicts the preferred orientation and position (and twists
bonds as necessary) of one molecule to a second when they are bound to each other to form a
stable complex. Docking identified three categories of inhibitors, targeting CK2, targeting NQO2,
and co-targeting both CK2 and NQO2. The activity of those inhibitors may be validated using
cell-based assays in future work.
1.5 Casein Kinase 2 (CK2)
CK2 is a tetramer that can be arranged as α2β2, αα’β2, or α’2β2 (4).
The α subunits have an ATP-binding kinase domain (KD) catalytic site (1) targeted for binding by
CK2 inhibitors. The α subunits have two distinct isoforms, α and α’, with a global sequence
similarity of about 75%. α and α’ have 30% sequence similarity at the C-terminus. The C-terminal
fragment of α’ is 41 residues shorter than for α (3).
The β subunits are regulatory (4), involve in the assembly and stability of the tetrameric CK2 (4),
and function as the substrate selector and recruiter (4).
Both the subunits have phosphorylation sites (ex: CK2α P-loop), however CK2 activity is not
affected by phosphorylation (4) and have been shown to exist and function individually in cells

5. 5
(4), therefore docking must not only test the CK2 tetramer but also the individual CK2 subunits
separately.
Cells with forced expression of CK2α or CK2αβ resist chemical-mediated apoptosis (5).
Transfection with CK2β did not demonstrate this effect (5).
1.6 NAD(P)H dehydrogenase, quinone 2 (NQO2)
NQO2 exists functionally as a homodimer. NQO2 can chemically modify bound cytotoxic
chemicals into less toxic products (1), so inhibiting NQO2 could increase the toxicity of the
chemotherapy. NQO2 contains a binding pocket located between the two subunits, so computer
modeling analysis requires the use of the homodimer. The hydrophobic cavity of NQO2 and CK2
have similar features. The cavity allows hydrophobic interactions with large bulky side chains to
occur within the ATP binding pocket of CK2 (1).
1.7 Hypothesis
The hypothesis is that perhaps NQO2 controls the function of CK2 through direct binding, similar
to NQO2 inhibiting the survival kinase "RAC-alpha serine/threonine-protein kinase 1 (AKT1) (2),
because CK2 has a pocket similar to the NQO2-binding pocket found in AKT1.
2. Materials and Methods
2.1 Protein Data Bank
Protein structure files for CK2 (1JWH.pdb) and NQO2 (1SG0.pdb) were downloaded from the
Protein Data Bank (PDB) online database (9).
2.2 UCSF Chimera
UCSF Chimera was used to remove phosphoaminophosphonic acid-adenylate ester (ANP) from
the CK2 protein structure file 1JWH.pdb and remove resveratrol from the NQO2 protein structure
file 1SG0.pdb, to make two target files.
2.3 UCSF DOCK6
40 candidate inhibitors were docked with the CK2 target file and separately to the NQO2 target
file, using UCSF DOCK6, the grid scores for each of those dockings were entered into Figure 2,
the grid scores were ranked, and three candidate inhibitors were chosen based on comparing
rankings. Grid scores are force field scores that consist of calculated van der Waals and
electrostatic components that approximate molecular mechanics interaction energies (8), and the
equation that calculates grid scores is shown in reference 8. The first candidate inhibitor was
docked to the CK2 target file and separately to the NQO2 target file using UCSF DOCK6, creating
two protein-inhibitor complexes. The second candidate inhibitor and the third candidate inhibitor
were docked using the same procedure. UCSF DOCK6 also prepared these protein-inhibitor
complexes for protein-protein docking in Patchdock and in Firedock.
2.4 Patchdock
Patchdock docked the CK2-first-inhibitor complex with the NQO2-first-inhibitor complex based
on shape complementarity. The second candidate inhibitor complex and the third candidate
inhibitor complex were docked using the same procedure. The Patchdock inputs were set to CK2

6. 6
(1JWH.pdb) as “receptor”, NQO2 (1SG0.pdb) as “ligand”, clustering RMSD set to 4.0, and
Complex Type set to Default.
2.5 Firedock
Firedock refined the Patchdock docking results by continuing docking calculations based on
chemophysical properties. The Firedock inputs were set to CK2 (1JWH.pdb) as “receptor”, NQO2
(1SG0.pdb) as “ligand”, and solutions to calculate as 100.
2.6 Human Protein Atlas
Protein expression levels of the prostate cancer cell line PC-3, bone marrow promyelocytic cancer
cell line HL-60, and breast cancer cell line MCF-7 from the Human Protein Atlas (10) were
compared to find the best cancer cell line for future research (Figure 5). Protein expression level
data of the non-cancerous version of the cells (Figure 6) from the Human Protein Atlas (10) were
recorded for future comparison.
3. Results
3.1 PDB file comparison UCSF DOCK6
The first protein-protein docking calculations focused on speed, specifically skipping DOCK6
entirely by using pre-made protein files (1JWH.pdb and 1SG0.pdb) from the Protein Data Bank
(9) that were already bound to ANP or trans-resveratrol and submitting them directly to Patchdock
and Firedock. The results of Firedock (Figure 1) suggest that NQO2 binds most robustly to the
CK2β monomer, which makes sense because NQO2 binds to the CK2β dimer area of the CK2
tetramer. The analysis of how binding of ANP and trans-resveratrol, neither, or one or the other
change CK2 and NQO2 binding (in Figure 1) is irrelevant to future work, because it does not
contain the three inhibitors that may be used in future work. Figure 4 is the protein-protein docking
that is relevant to future work.
3.2 Ranking comparison of inhibitors to choose best CK2, NQO2, and both targeter
First the grid scores of the binding of the chemicals and CK2 and the chemicals and NQO2 were
sorted from most negative (best calculated binding) to least negative (worst calculated binding)
and put in a table (Figure 2). However, grid scores (column 1 and column 6) cannot be directly
compared because proteins are different. Then a ranking was created from 1 (best calculated
binding) to 40 (worst calculated binding) for both CK2 and NQO2. Then the NQO2 rank was
subtracted from the CK2 rank to make Figure 3.
The best CK2 targeter needs three characteristics: a good CK2 rank, a bad NQO2 rank, and a large
negative rank difference. The best CK2 targeter (triacetyl resveratrol) had a good CK2 rank 7 and
a bad NQO2 rank 34, so the rank difference was 7 – 34 = -27 and is shown in Figure 3 as the red
bar on the left.
The best co-targeter needs three characteristics: a good CK2 rank, a good NQO2 rank, and a small
magnitude of rank difference. The best co-targeter (myricetin) had a good CK2 rank 3 and a good
NQO2 rank 1, so the rank difference was |3 – 1| = 2 and is shown in Figure 3 as the red bar in the
middle.

7. 7
The best NQO2 targeter needs three characteristics: a bad CK2 rank, a good NQO2 rank, and a
large positive rank difference. The best NQO2 targeter (piceatannol) had a bad CK2 rank 36 and
a good NQO2 rank 19, so the rank difference was 36 - 19 = 17 and is shown in Figure 3 as the red
bar on the right. That second-best NQO2 targeter was chosen, because we could no longer find a
supplier for the chemical that is the best NQO2 targeter: 4-(3,4,5-
trimethoxyphenylethyl)benzenamine (the last bar on the right).
3.3 Three inhibitors bound to CK2 and NQO2 UCSF DOCK6
The second protein-protein docking calculation (Figure 4) focused on relevance to future work by
docking the proteins with the three inhibitors chosen for the future work in Figure 3. The three
inhibitors were docked with CK2 and NQO2, neither, or one or the other. The change in calculated
binding energy of CK2 and NQO2 was recorded in Figure 4. Based on that data, piceatannol and
myricetin are likely to be more potent and specific inhibitors.
3.4 Choosing PC-3 over HL-60 and MCF-7
For future work, a cancer cell line would be chosen. Our lab has three cancer cell lines available:
Prostate cancer PC-3 cells, Bone marrow promyelocytic leukemia HL-60 cells, and breast cancer
MCF-7 cells. Based on protein expression level data (Figure 5) from the Human Protein Atlas
(10), the PC-3 prostate cancer cell line was chosen because it had the highest CK2 expression and
because it is known that CK2 plays an important role in prostate cancer (7). The protein expression
level data of the non-cancerous version of the cells (Figure 6) from the Human Protein Atlas (10)
were recorded for future comparison.
4. Discussion
DOCK6 computer protein modeling predicted, out of 40 small molecules: the best inhibitor of both
CK2 and NQO2 (myricetin), the best CK2 inhibitor (triacetyl resveratrol), and the second best
NQO2 inhibitor (piceatannol).
Patchdock and Firedock calculations investigated whether CK2 and NQO2 might bind with each
other and found that NQO2 binds to the CK2β dimer area of the CK2 tetramer, NQO2 binds most
robustly to CK2β subunit, binding of NQO2 to CK2β subunit increased by adding resveratrol, and
the three best inhibitors decrease CK2α subunit and NQO2 binding through binding to or near the
CK2α subunit ATP site.
The first topic for future study is that NQO2 might act as an intracellular modulator of tumor
survival kinases vis-à-vis CK2. The second topic for future study is that drugs that co-target NQO2
and CK2 might circumvent the kinase inhibitor resistance that some cancers have, better than drugs
that target either enzyme individually. Future studies may test time- and dose-dependent effects
of the three best candidate inhibitors alone or in combination on apoptosis and clonogenic survival
in PC-3 and other cells.

8. 8
References
1. Duncan, J. S., Gyenis, L., Lenehan, J., Bretner, M., Graves, L. M., Haystead, T. A., et al. An
Unbiased Evaluation of CK2 Inhibitors by Chemoproteomics: Characterization of Inhibitor
Effects on CK2 and Identification of Novel Inhibitor Targets. Molecular & Cellular
Proteomics, 7, 1077-1088. Retrieved July 28, 2014, from
http://www.mcponline.org/content/7/6/1077.long
2. Hsieh, T., Lin, C., Bennett, D. J., Wu, E., & Wu, J. M. Biochemical and Cellular Evidence
Demonstrating AKT-1 as a Binding Partner for Resveratrol Targeting Protein NQO2. PLoS
ONE, 9. Retrieved July 29, 2014, from
http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0101070
3. Cozza, G., Meggio, F., & Moro, S. The Dark Side of Protein Kinase CK2 Inhibition. Current
Medicinal Chemistry, 18, 2867-2884. Retrieved August 4, 2014, from
http://www.eurekaselect.com/74490/article
4. Hanif, I. M., & Pervaiz, S. Repressing the Activity of Protein Kinase CK2 Releases
Mitochondria-Mediated Apoptosis in Cancer Cells. Current Drug Targets, 12, 902-908.
Retrieved August 4, 2014, from http://www.eurekaselect.com/73931/article
5. Guo, C., Yu, S., Davis, A. T., Wang, H., Green, J. E., & Ahmed, K. A Potential Role of
Nuclear Matrix-associated Protein Kinase CK2 in Protection against Drug-induced Apoptosis
in Cancer Cells. Journal of Biological Chemistry, 276, 5992–5999. Retrieved August 4,
2014, from http://www.jbc.org/content/276/8/5992.long
6. RCSB Protein Data Bank - RCSB PDB - 1QR2 Structure Summary. (2011, July 13). RCSB
Protein Data Bank - RCSB PDB - 1QR2 Structure Summary. Retrieved August 4, 2014, from
http://www.pdb.org/pdb/explore/explore.do?structureId=1QR2
7. Wang, G., Ahmad, K. A., Unger, G., Slaton, J. W., & Ahmed, K. CK2 Signaling in
Androgen-Dependent and -Independent Prostate Cancer. Journal of Cellular Biochemistry,
99, 382–391. Retrieved August 4, 2014, from
http://onlinelibrary.wiley.com/doi/10.1002/jcb.20847/full
8. Aynechi, T., & Lang, P. (2012, December 27). Tutorial: Generating the Grid. Retrieved
August 30, 2014, from
http://dock.compbio.ucsf.edu/DOCK_6/tutorials/grid_generation/generating_grid.html
9. Protein Data Bank. (n.d.). Retrieved September 1, 2014, from
http://www.rcsb.org/pdb/home/home.do
10. The Human Protein Atlas. (n.d.). Retrieved September 1, 2014, from
http://www.proteinatlas.org/

9. 9
Appendix of Figures
Inhibitors CK2 tetramer
with NQO2
CK2α
with NQO2
CK2β with NQO2
ANP Resveratrol
- - -25.04 -26.58 -35.04
- + -21.40 -33.70 -54.88
+ - -25.04 -26.89 No ATP binding site
+ + -21.40 -20.08 No ATP binding site
Figure 1: Computer modeling of CK2-NQO2 Protein-Protein Docking using Patchdock and
Firedock. Less than -20 means a reasonable probability of interaction. ANP binds competitively
to the ATP site of CK2α, inhibiting CK2α or the entire CK2 tetramer. Resveratrol binds
competitively to the ATP site of CK2α, inhibiting CK2α or the entire CK2 tetramer. NQO2 binds
most robustly to CK2β monomer. Binding of NQO2 to CK2β increased by adding resveratrol.
NQO2 binds to the CK2β dimer area of the CK2 tetramer

10. 10
CK2 Rank NQO2
Grid Score Molecule Name Molecule Name Grid Score
-47.395088 DMAT 1 1 Myricetin -55.250919
-46.20425 Resveratrol-3-O-Glucuronide 2 2
[4-[(E)-2-(3,4,5-
trimethoxyphenyl)vinyl]phenyl]methanamine -55.238911
-45.271542 Myricetin 3 3 Morin -54.174667
-44.961933 Inhibitor VIII 4 4 Resveratrol-3-O-Glucuronide -53.705303
-43.564907 Piceid 5 5 DMAT -53.605484
-43.063278 Resveratrol-3-O-Sulfate 6 6 Quercetin -53.324516
-41.579823 Triacetyl Resveratrol 7 7 Luteolin -52.594341
-40.986191 (E)-4-(3,5-Dinitrostyryl)Phenyl Acetate 8 8 (E)-4-(3,4,5-Trimethoxystyryl)aniline -51.978603
-40.975552 Resveratrol-4'-O-Glucuronide 9 9 Kaempferol -51.531384
-40.853859 Resveratrol-4'-O-Sulfate 10 10 Piceid -51.399097
-40.8531 Baicalein 11 11 Inhibitor VIII -51.348427
-40.544659 Morin 12 12 Fisetin -49.993984
-40.469738 Kaempferol 13 13 Galangin -49.271282
-40.402657
[4-[(E)-2-(3,4,5-
trimethoxyphenyl)vinyl]phenyl]methanamine 14 14 4-(3,4,5-trimethoxyphenylethyl)benzenamine -49.206306
-39.745872 Quercetin 15 15
4-[(E)-2-(3,5-
dimethoxyphenyl)vinyl]phenyl]methanamine -49.018597
-39.141483 Fisetin 16 16 Baicalein -48.337563
-38.389496 Galangin 17 17 (E)-5-(4-Nitrostyryl)benzene-1,3-diol -47.680248
-38.340324 (E)-5-(4-Nitrostyryl)benzo[d][1,3]dioxole 18 18 Genistein -46.644455
-38.214302
4-[(E)-2-(3,5-
dimethoxyphenyl)vinyl]phenyl]methanamine 19 19 Piceatannol -46.118259
-38.180836 Luteolin 20 20 Biochanin A -45.894424
-37.871613 Biochanin A 21 21 (E)-4-(3,5-Dimethoxystyryl)phenol -45.698669
-37.535603 (E)-4-(3,4,5-Trimethoxystyryl)aniline 22 22 (E)-5-(4-Nitrostyryl)benzo[d][1,3]dioxole -45.670696
-37.148319 (E)-5-(4-Nitrostyryl)benzene-1,3-diol 23 23 (E)-4-(3,5-Dimethoxystyryl)aniline -45.475456
-37.016937 (E)-(4-(3,4,5-Trimethoxystyryl)phenyl) 24 24 Resveratrol-3-O-Sulfate -45.437862
-36.930729 (E)-1-(4-chlorostyryl)-3,5-dimethoxybenzene 25 25 trans-Resveratrol -45.299931
-36.795963 (E)-4-(3,4-Dimethoxystyryl)benzenamine 26 26 (E)-4-(2,3,4-trimethoxystyryl)benzenamine -45.149002
-36.789436 (Z)-Resveratrol 27 27 (E)-4-(3,5-Dinitrostyryl)Phenyl Acetate -44.964218
-36.784119 (E)-4-(3,5-Dimethoxystyryl)aniline 28 28 (E)-5-(4-Aminostyryl)benzene-1,3-diol -44.923653
-36.403122 (E)-4-(3,5-Dimethoxystyryl)phenol 29 29 Naringenin -44.262508
-36.400429
(E)-(4(3,5-
Dimethoxystyryl)phenyl)methanamine 30 30 (E)-1-(4-chlorostyryl)-3,5-dimethoxybenzene -44.198097
-36.3256 (E)-4-(2,3,4-trimethoxystyryl)benzenamine 31 31 (E)-4-(3,4-Dimethoxystyryl)benzenamine -44.022881
-36.294876 Genistein 32 32
(E)-4-(2-(benzo[d][1,3]dioxole-5-
yl)vinyl)benzenamine -43.406162
-35.525772 Naringenin 33 33 Resveratrol-4'-O-Sulfate -43.351635
-35.09507 4-(3,4,5-trimethoxyphenylethyl)benzenamine 34 34 Triacetyl Resveratrol -42.33646
-34.151531 TBB 35 35 Resveratrol-4'-O-Glucuronide -41.931175
-33.956631 Piceatannol 36 36
(E)-(4(3,5-
Dimethoxystyryl)phenyl)methanamine -41.81744
-33.691406 TBBz 37 37 (Z)-Resveratrol -41.178276
-33.391792 trans-Resveratrol 38 38 TBBz -41.087536
-33.265179
(E)-4-(2-(benzo[d][1,3]dioxole-5-
yl)vinyl)benzenamine 39 39 TBB -40.97821
-32.318966 (E)-5-(4-Aminostyryl)benzene-1,3-diol 40 40 (E)-(4-(3,4,5-Trimethoxystyryl)phenyl) -39.810394
Figure 2: CK2 and NQO2 grid scores ranked best (rank 1) to worst (rank 40). The most negative
grid score is the best predicted binding of the chemical with the protein. However, grid scores
(column 1 and column 6) cannot be directly compared because proteins are different. Therefore
ranks (column 3 and column 4) were analyzed in the bar graph in Figure 3. The ones highlighted
in red are the three inhibitors that were chosen based on the data in Figure 3.

11. 11
Figure 3: Ranking to find the best Inhibitors of CK2, NQO2, and both (3 total). The purple line
graph is the Figure 1 CK2 grid score minus the Figure 1 NQO2 grid score and the bar graph is the
Figure 1 CK2 ranking minus the Figure 1 NQO2 ranking. In the bar graph, the blue bars are a
negative rank difference, the green bars are a positive rank difference, and the red bars highlight
the top three candidate inhibitors. The top three candidate inhibitor structures are shown above.
0
5
10
15
20
25
30
TriacetylResveratrol
Resveratrol-4'-O-Glucuronide
Resveratrol-4'-O-Sulfate
(E)-4-(3,5-Dinitrostyryl)PhenylAcetate
Resveratrol-3-O-Sulfate
(E)-(4-(3,4,5-Trimethoxystyryl)phenyl)
(Z)-Resveratrol
InhibitorVIII
(E)-(4(3,5-Dimethoxystyryl)phenyl)methanamine
Piceid
Baicalein
(E)-1-(4-chlorostyryl)-3,5-dimethoxybenzene
(E)-4-(3,4-Dimethoxystyryl)benzenamine
DMAT
(E)-5-(4-Nitrostyryl)benzo[d][1,3]dioxole
TBB
Resveratrol-3-O-Glucuronide
TBBz
BiochaninA
Myricetin
Kaempferol
Fisetin
Galangin
4-[(E)-2-(3,5-…
Naringenin
(E)-4-(3,5-Dimethoxystyryl)aniline
(E)-4-(2,3,4-trimethoxystyryl)benzenamine
(E)-5-(4-Nitrostyryl)benzene-1,3-diol
(E)-4-(2-(benzo[d][1,3]dioxole-5-yl)vinyl)benzenamine
(E)-4-(3,5-Dimethoxystyryl)phenol
Morin
Quercetin
[4-[(E)-2-(3,4,5-…
(E)-5-(4-Aminostyryl)benzene-1,3-diol
Luteolin
trans-Resveratrol
(E)-4-(3,4,5-Trimethoxystyryl)aniline
Genistein
Piceatannol
4-(3,4,5-trimethoxyphenylethyl)benzenamine
-27
2
17
Myrecetin Piceatannol Triacetyl Resveratrol

12. 12
Inhibitors Type CK2α with NQO2 CK2β with NQO2
Myricetin ATP-competitive -24.65 -45.12
Piceatannol ATP-competitive -18.58 -49.08
Triacetyl Resveratrol Allosteric near ATP
site
-16.99 -40.00
None n/a -33.53 -33.33
Figure 4: Computer modeling of CK2-NQO2 Protein-Protein Docking using Patchdock and
Firedock: Effects of inhibitors

13. 13
Figure 5: Cancer cell line protein expression levels from Human Protein Atlas (10)
0
1000
2000
3000
4000
5000
6000
7000
Prostate
PC-3
Bone Marrow
HL-60
Breast
MCF-7
NQO2 CK2α1 CK2α2 CK2α3 CK2β

14. 14
NQO2 CK2α1 CK2α2 CK2α3 CK2β
Prostate low medium n/a n/a medium
Bone Marrow low high n/a n/a low
Breast low medium n/a n/a low to med
Figure 6: Non-cancerous cell protein expression levels from Human Protein Atlas (10)