Journal of Molecular Graphics and Modelling 83 (2018) 42e52
Contents lists available at ScienceDirect
Journal of Molecular Graphics and Modelling
journal homepage: www.elsevier.com/locate/JMGM
Structural dynamics and quantum mechanical aspects of shikonin
derivatives as CREBBP bromodomain inhibitors
Sarmistha Mitra a, Raju Dash b, c, *
a
Department of Pharmacy, University of Chittagong, Chittagong, 4331, Bangladesh
Molecular Modeling & Drug Design Laboratory (MMDDL), Pharmacology Research Division, Bangladesh Council of Scientific & Industrial Research
(BCSIR), Chittagong, 4220, Bangladesh
c
Department of Biochemistry and Biotechnology, University of Science & Technology Chittagong, Chittagong, 4202, Bangladesh
b
a r t i c l e i n f o
a b s t r a c t
Article history:
Received 29 November 2017
Received in revised form
16 April 2018
Accepted 23 April 2018
Available online 4 May 2018
The Proteins involved in the chemical modification of lysine residues in histone, is currently being
excessively focused as the therapeutic target for the treatment of cell related diseases like cancer. Among
these proteins, the epigenetic reader, CREB-binding protein (CREBBP) bromodomain is one of the most
prominent targets for effective anticancer drug design, which is responsible for the reorganization of
acetylated histone lysine residues. Therefore, this study employed an integrative approach of structure
based drug design, in combination with Molecular Dynamics (MD) and QM/MM study to identify as well
as to describe the binding mechanism of two shikonin derivatives, acetylshikonin and propionylshikonin
as inhibitors of CREBBP bromodomain. Here induced fit docking strategy was employed to explore the
important intrinsic interactions of ligands with CREBBP bromodomain, consistently molecular dynamics
simulation with two different methods and binding energy calculations by MM-GBSA and MM-PBSA
were adopted to determine the stability of intermolecular interactions between protein and ligands.
The results showed that both these derivatives made direct contacts with the important conserved
residues of the active site, where propionylshikonin demonstrated stronger binding and stability than
acetylshikonin, according to molecular dynamics simulation and binding free energy calculations.
Further, QM/MM energy calculation was employed to study the chemical reactivity of the propionylshikonin and also to describe the mechanism of non bonded interactions between the propionylshikonin
and CREBBP bromodomain. Though this study demands in vitro and in vivo experiments to evaluate the
efficiency of the compound, these insights would assist to design more potent CREBBP bromodomain
inhibitor, guiding the site of modification of propionylshikonin moiety for designing selective inhibitors.
© 2018 Elsevier Inc. All rights reserved.
Keywords:
CREBBP
Bromodomain
Molecular dynamics
Propionylshikonin
MM-PBSA
MM-GBSA
QM/MM
1. Introduction
Chromatin, being a combination of histone protein and DNA,
contains the entire genomic DNA in eukaryotic cells [1]. “Epigenetics” is a process which stands for heritable changes in gene
expression but no amendment in DNA sequence [2]. The best
known epigenetic processes are DNA methylation [3], histone
protein's phosphorylation, acetylation, methylation, ubiquitination
and sumoylation all of which can play a great role in cancer
development [4]. Among them, lysine acetylation is one of the main
* Corresponding author. Molecular Modeling and Drug Design Laboratory, Pharmacology Research Division, Bangladesh Council of Scientific & Industrial Research
(BCSIR) Chittagong, 4220, Bangladesh.
E-mail address: rajudash.bgctub@gmail.com (R. Dash).
https://doi.org/10.1016/j.jmgm.2018.04.014
1093-3263/© 2018 Elsevier Inc. All rights reserved.
modifications occurring in histone N-terminals tails, is known to be
linked with activation of transcription through opening of chromatin architecture and with gene transcription [5]. The lysine
acetyalation is established, modulated and interpreted by the
several epigenetic enzymes and protein-protein interaction domains. Some enzymes are known as “writer”, catalyze the addition
of post translational modifications. By the enzymes, called epigenetic “eraser”, post translational modification are removed. The
domains that mediate post translational modifications recognition
are called epigenetic “reader” [6e8]. Among the reader domains,
Bromodomains (BRDs) have an important role in the targeting of
chromatin-modifying enzymes and other proteins to specific sites,
read the acetyl marks in histone tails and regulate the gene transcription [9]. These are proteins with acetyl-lysine binding modules; function as the principal mediators of molecular recognition
S. Mitra, R. Dash / Journal of Molecular Graphics and Modelling 83 (2018) 42e52
of acetylated chromatin [10]. “Bromodomain” is generally used to
describe the bromodomain and extra-terminal (BET) family of human bromodomain proteins (BRD2, BRD3, BRD4). BRD-containing
proteins have been reported to be involved in disease processes
such as cancer, inflammation, cardiovascular diseases and viral
replication [9,11]. From previous works, it was established that BET
proteins are highly related to the genetical modification in cancer,
directly regulating the expression of certain cancer-related genes,
such as c-MYC. Preventing the BET proteins from binding at the
MYC locus with BET inhibitors leads to a reduction in cell proliferation thus may be used as anti-cancer therapeutics [12e14].
Among the other bromodomains, one of the primary transcriptional regulators cAMP response element-binding protein
(CREB) binding protein (CREBBP or CBP) bromodomain, which
plays significant roles in the transcriptional activation of human
cells [15]. The architecture of CREBBP protein consists of a HAT
domain, a CREB binding domain, several zinc finger domains, a
plant homology domain (PHD) and a bromodomain (BRD). Literature revealed that the bromodomain of CREBBP is one of the major
considerations in anticancer drug target [16e19], involved several
major biological functions like DNA replication and repair, genomic
stability, cell cycle regulation and cell growth. Furthermore, various
oncology-relevant transcription factors like cMYB, c-MYC and p53
are found to be amended by it [20e24]. Experiments done by
Conery and Picaud group [15,25] showed that the bromodomain of
CREBBP has better druggabilty than the other domains, inhibiting
by small molecule leads to the enhancement the traditional
chemotherapy by modulation intractable expression of oncogenic
transcriptional factors [26].
In recent year, the shikonin derivatives, mainly isolated from the
root extract of Lithospermum erythrorhizon have been reported to
have anti-cancer, anti-inflammatory, wound healing, anti-viral and
a wide range of biological effects [27,28]. Studies suggested that
derivatives of shikonin are more effective than shikonin as well as
less toxic and also have favorable pharmacokinetic and pharmacodynamic profiles in vivo [29]. Till to date, there are more published studies representing the anticancer activity of shikonin and
its derivatives [30e35], however, little is known relating the activity of shikonin derivatives on bromodomains and other areas of
study in epigenetics. The present study is concerned to evaluate
CREBBP bromodomain inhibition by shikonin derivatives. For that,
seventeen shikonin derivatives (Supplementary file 1) have been
selected for virtual screening against CREBBP bromodomain using
Glide extra precision docking and MM-GBSA binding energy
calculation methods, where R-enantiomer of each compound has
been considered (as described in Wang et al. [36]) for analysis.
Among these derivatives, acetylshikonin and propionylshikonin
have obtained the best binding energy (Supplementary file 1). The
present study is therefore aimed to unveil the structural and molecular properties of acetylshikonin and propionylshikonin as inhibitors of CREBBP bromodomain. The investigations were
accomplished by induced fit docking, classical MD simulations and
two different binding energy calculations approaches. In addition,
QM/MM calculation was also performed to describe the binding
mode of the ligands at electronic level.
2. Methods
2.1. Dataset and ligand preparation
The structures of two compounds were retrieved from PubChem
database, i.e. acetylshikonin and propionylshikonin and then the 3D
structures were built by using Ligand preparation wizard in Maestro
10.1 [39] with an OPLS_2005 force field. Their ionization states were
€dinger Suite.
generated at pH 7.0 ± 2.0 using Epik 2.2 in Schro
43
2.2. Protein preparation
Three dimensional crystal structure of the bromodomain of
human CREBBP (PDB id: 5I86, Chain: A,: Resolution: 1.05 Å, R-Value
Free: 0.161 and R-Value Work: 0.139) was downloaded in pdb
format from the protein data bank [37]. After that, the structure was
prepared and refined using the Protein Preparation Wizard of
€dinger-Maestro v10.1. Bond orders and charges were
Schro
imposed, hydrogens were combined to heavy atoms and all waters
were removed. By optimizing the protein at neutral pH, readjustment of some thiol and hydroxyl groups, amide groups of asparagines, glutamines and imidazole ring of histidines, protonation
states of histidines, aspartic acids and glutamic acids were done.
Employing force field OPLS_2005, minimization was executed
setting maximum heavy atom RMSD to 0.30 Å.
2.3. Induced fit docking
Induced Fit Docking (IFD) was performed using the module
€dinger-Maestro v10.1 [38]. In this
Induced Fit Docking of Schro
docking procedure, both ligands were docked into the target protein (PDB ID: 5I86) with a constrained minimization process and for
generation of centroid of the residues 0.18 Å was chosen and the
box size was automatically generated. After that, a soften potential
glide docking was performed; in which, side chains were trimmed
automatically based on B-factor and ligand and receptor with van
der Waals scaling of 0.50 and 0.70, respectively. The generated
number of poses were set to be 20. In the docking simulation, the
closest residues to the ligand (within 5 Å of ligand pose) were kept
flexible in prime refinement and during the process, the side chains
were further optimized. Glide redocking process was further
introduced for the ligand having the best pose within 30.0 kcal/mol.
In the induced-fit receptor structure, the ligands were appropriately docked and for each output pose, the results yielded an IFD
score. For further consideration, the pose having the lowest IFD
score for each ligand was selected.
2.4. Molecular dynamics (MD) simulation
MD simulations of the protein-ligand complexes obtained from
induced fit docking; were performed by YASARA Dynamic software.
Each complex was first cleaned and hydrogen bond network was
optimized. A cubic simulation cell was generated with periodic
boundary condition. Applying the AMBER14 [40,41] force field, the
atoms of each complex were typed. Assignment of the parameters
of each molecule was done through AutoSMILES [42] algorithms,
where unknown organic molecules are fully automatically parameterized by the calculation of semi-empirical AM1 Mulliken point
charges [42] with the COSMO solvation model, assigning of
AM1BCC [43] atom and bond types, and also assigning GAFF [44]
(General AMBER Force Field) atom types and remaining force field
parameters.
By using the transferable intermolecular potential3 points
(TIP3P) water model the simulation box was solvated; maintaining
the density of 0.997 g/L. During the solvation, pKa (acid dissociation
constant) values of titratable amino acids of the protein were
calculated. Each simulation system consisted of 62521 ± 10 atoms.
An energy minimization protocol was used to remove the conformational stress, where the initial structure was energy minimized
by steepest descent without electrostatic interaction. After that, the
structure was subsequently relaxed by steepest descent minimization and subjected to full potential energy minimization for 5000
cycles, until convergence was reached. The energy was improved by
less than 0.05 kJ/mol per atom during 5000 steps. MD simulations
were carried out using the PME methods to describe long-range
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S. Mitra, R. Dash / Journal of Molecular Graphics and Modelling 83 (2018) 42e52
electrostatic interactions at a cut off distance of 8 Å at physiological
conditions (298 K, pH 7.4, 0.9% NaCl) [45]. A multiple time step
algorithm together with a simulation time step interval of 2.50 fs
was chosen [46]. At a constant pressure and Berendsen thermostat,
MD simulations were performed for 100 ns long and MD trajectories were saved every 25 ps for further analysis. Using YASARA
structure built in macros, VMD [47] and Bio3D [48] software, the
resulting trajectories were subjected to analyze for the stability by
various evaluative measures viz. RMSD (Root Mean Square Deviation), RMSF (Root Mean Square Fluctuation), Radius of gyration and
protein backbone comparisons. After that, MM-PBSA (Molecular
mechanics-PoissoneBoltzmann Surface Area) binding free energies
of all snapshots were calculated with YASARA software using the
following formula,
Binding Energy ¼ EpotRecept þ EsolvRecept þ EpotLigand þ EsolvLigand EpotComplex - EsolvComplex
Here, YASARA built in macros was used to calculate MM-PBSA
binding energy, using AMBER14 as a Force Field, where resulting
more positive energies indicate the better binding.
For calculating MM-GBSA binding free energy analysis, additional 30 ns MD simulations were performed with OPLS_3 force
field by DESMOND software. The details of the method have been
described in Supplementary file 2.
2.5. Prime MM-GBSA analysis
Binding free energy was calculated to rescore and for choosing
the top hits from the candidate ligands. In prime MM-GBSA
method, the calculation of binding energy was performed by
combining OPLSAA molecular mechanics energies (EMM), an SGB
solvation model for polar solvation (GSGB) and a non-polar solvation term (GNP) composed of the non-polar solvent accessible
surface area and van der Waals interactions [49]. Here, as the
source in Prime MM-GBSA simulation, the glide pose viewer file of
the best conformation was given. For modeling directionality of
hydrogen-bond and p-stacking interactions, the dielectric solvent
model VSGB 2.0 [50] was used to apply empirical corrections.
Keeping the protein chain flexible [51e55], minimizing approach is
applied as sampling methods. The analysis denotes more excellent
binding by more negative binding energy.
The overall free energy of binding:
DGbind ¼ Gcomplex e (Gprotein þ Gligand), where
G ¼ EMM þ GSGB þ GNP
2.6. QM/MM calculation
In order to analyze the molecular interactions of ligand-protein
complex at the electronic label, hybrid quantum mechanical/molecular mechanical (QM/MM) calculations had been employed. For
QM/MM calculations, the QSITE program of Schrodinger suites was
used. For the QM region, this module used JAGUAR [56] program
and the IMPACT molecular modeling code for the MM region. As
there were covalent connections between the QM and MM regions,
frozen orbitals were used to mediate an interface between the two
regions [57] during the calculation. The calculation of the interactions between QM and MM parts had been performed through
van der Waals interactions among QM and MM atoms and the
electrostatic interactions between MM point charges and the QM
wave function. The MM region was treated with OPLS-AA force field
[58], while for QM region density functional theory was used with
the 6-31G*/LACVP* basis set, B3LYP density functional and “Ultrafine” SCF accuracy level (iacc ¼ 1, iacscf ¼ 2) [59]. All calculations
were converged before reaching the limit; the highest number of
iterations was set to 100 cycles and the root mean squared change
in density matrix elements was less than the criterion of 5.0 10 6.
3. Results and discussion
3.1. Induced-fit docking analysis
Many proteins undergo side chain or backbone movements, or
both, upon ligand binding so the presumption of a rigid receptor
always can seldom model the actual binding mechanism. Receptor
alters its binding site due to these changes, so that is more closely
conforms to the shape and binding mechanism of the ligand. This is
one of the main complicating factors in structure-based drug
design, which is often termed as “induced fit” [60]. To study the
binding mechanism of the selected ligands, the IFD protocol was
used.
Bromodomains are composed with four a-helices (aZ, aA, aB,
aC), constructed by ~110 residues, which are connected by interhelical loops, is also termed as bromodomain fold. Experimental
studies reported that the N-acetyl group of lysine residues binds in
most bromodomain by forming hydrogen bonds with ASN1168
residue of large hydrophobic pocket, which is attached by two loop
regions named ZA and BC. Furthermore, the acetyl group of lysine
residues is also seen to form water-mediated hydrogen bond with
another conserved residue TYR1125, where the interaction is made
through the phenolic hydroxyl group to acetyl carbonyl of lysine
[26,61e64]. Other studies concluded that compound, which may
act as an inhibitor is proved to have electrostatic attractions between the conserved ASN1168, TYR1125, PRO1110 side chains and also
hydrophobic interactions with ILE1122, LEU1120 and VAL1174 residues
are critical for inhibitor binding and inhibition [24,65,66].
From the docking results (Table 1), it was found that compound
acetylshikonin, formed hydrogen bonds with the ASN1168 and
PRO1110 residues in the active site of the protein. In these two bonds,
the corresponding distances are 2.29 Å and 2.23 Å, respectively
(Table 2), where both of these hydrogen bonds are contributed by
naphthazarin ring of acetylshikonin. As shown in Fig. 1, this ring
was also formed hydrophobic interactions with this ligand by
forming pi-alkyl bonds with VAL1115 (4.21 Å), VAL1174 (4.42 Å),
ALA1164 (5.33 Å), VAL1115 (4.31 Å), ALA1164 (4.58 Å) residues on the
ZA loop. Two other alkyl hydrophobic interactions were also seen
with the side chain of LEU1120 at the ZA loop with 4.47 Å and 3.96 Å
bond distances respectively.
While another ligand, propionylshikonin, here the naphthazarin
ring was also found to have H-bonds with TYR1125 with bond distance of 2.55 Å; VAL1115 with 2.79 Å; ASN1168 with 2.10 Å; PRO1110
with 2.76 Å, GLN1113 with 2.77 Å and MET1160 with 2.18 Å (Fig. 2).
Furthermore, another important residue ARG1173 formed hydrogen
bond with oxygen atom of propionyl moiety having bond length of
2.72 Å. The ligand was also interacted with ILE1122 (3.96 Å), ILE1122
(4.22 Å), PRO1110 (4.49 Å) by forming alkyl hydrophobic bonds and
with TYR1125 (4.39 Å), TYR1167 (5.21 Å), TYR1167 (4.21 Å), PRO1110
(5.45 Å), VAL1115 (4.66 Å), VAL1174 (4.85 Å), VAL1115 (4.62 Å) residues
it formed pi-alkyl bonds. The side chain TYR1125 was also involved
with two hydrophobic pi-pi interactions to ligand with the bond
distance of 5.41 Å and 5.89 Å, respectively.
From the above study, it is found that acetylshikonin binds with
CREBBP bromodomain through interacting with ASN1168, PRO1110
residues by means of hydrogen bonds and the hydrophobic bond
with VAL1174 residue. In case of propionylshikonin, the ligand
formed polar interaction with ASN1168, TYR1125 residues and hydrophobic bonds with ILE1122 and VAL1174. These finding denotes
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S. Mitra, R. Dash / Journal of Molecular Graphics and Modelling 83 (2018) 42e52
Table 1
Docking simulations analysis and protein-ligand binding free energy values of CREBBP bromodomain and the shikonin derivatives.
Compound name Docking score (kcal/
mol)
Acetylshikonin
Propionylshikonin
a
b
8.14
9.85
IFD score (kcal/
mol)
273.02
273.48
Glide score (kcal/
mol)
Glide emodel (kcal/
mol)
8.15
9.86
59.62
88.33
MM/PBSA Binding Energya (kcal/ MM/GBSA Binding Energyb (kcal/
mol)
mol)
9.80
27.65
54.92
59.58
Average binding free energy calculated in 100ns MD simulation using AMBER14 force field.
Average binding free energy calculated in 30ns MD simulation using OPLS_3 force field.
Table 2
Binding site analysis of acetylshikonin and propionylshikonin with CREBBP Bromodomain.
Compound name
Hydrogen bonds (Distance in Å)
Hydrophobic bonds (Distance in Å)
Alkyl- Alkyl
Pi-alkyl
Pi-Pi
Acetylshikonin
ASN1168(2.29)
PRO1110(2.23)
LEU1120(4.47)
PRO1120(5.25)
LEU1120(3.96)
Propionylshikonin
VAL1115(2.79)
TYR1125 (2.55)
ASN1168 (2.10)
ARG1173 (2.72)
PRO1110 (2.76)
GLN1113(2.77)
MET1160(2.18)
ILE1122(3.96)
ILE1122(4.22)
PRO1110(4.49)
VAL1115(4.21)
VAL1174(4.42)
ALA1164(5.33)
VAL1115(4.31)
ALA1164(4.58)
TYR1125(4.39)
TYR1167(5.21)
TYR1167(4.21)
PRO1110(5.45)
VAL1115(4.66)
VAL1174(4.85)
VAL1115(4.62)
TYR1125(5.41)
TYR1125(5.89)
Fig. 1. Molecular interactions between acetylshikonin and CREBBP bromodomain. Here, the three dimensional representation is in hydrophobic surface view, and in 2D view, green
dot lines represent hydrogen bond and pink for pi-alkyl interaction.
that both the ligands bind to the active site of bromodomain and
have the chance to act as inhibitors.
3.2. Molecular dynamics (MD) simulation
In order to understand and evaluate the interactions of the
ligand molecules with the CREBBP bromodomain, we carried out
100 ns of MD simulation for each protein-ligand complex and
ligand free protein. Two main parameters, which were analyzed
throughout the simulations, were Root Mean Square Deviation
(RMSD) and Root Mean Square Fluctuations (RMSF).
RMSD value of the protein backbone of the complexes were
calculated individually and plotted by comparing with the initial
trajectory snapshot of protein structure. In the plot, the smaller
difference of RMSDs represents the higher stability of protein
conformation. As shown in Fig. 3a, the RMSD values for the protein
backbone of acetylshikonin complex were found to fluctuate
around 3 Å, while 2 Å for protein backbone of propionylshikonin.
Interestingly, both these complexes were stabled during the simulations, after a structural drifting at 22 ns In a comparative
manner, the protein backbone of acetylshikonin was less rigid than
propionylshikonin, indicating the greater flexibility. While the
ligand free CREBBP showed larger conformational changes; represented RMSD around 1.5 Å for 20 ns, however afterward significant
fluctuations were observed. The major structural drifting was
observed at 41 ns, which caused the RMSD raise to 3.6 Å and
remained stable subsequently.
In contrast, RMSD values of two ligands are also plotted together
and from the Fig. 3b, it is clearly seen that the RMSD value of
acetylshikonin is much higher than the RMSD value of
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Fig. 2. Molecular interactions between propionylshikonin with CREBBP bromodomain. Here, the three dimensional representation is in hydrophobic surface view, and in 2D view,
green dot lines represent hydrogen bond and pink for pi-alkyl interaction. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version
of this article.)
Fig. 3. Time evolution of root-mean-square deviations (RMSDs) of backbone atoms (C,
Ca, and N) for a) protein and b) ligand of each docked complex. Here, orange and ash
colors denote acetylshikonin, propionylshikonin complexes, respectively, while green
line indicates apo-CREBBP bromodomain. (For interpretation of the references to color
in this figure legend, the reader is referred to the Web version of this article.)
propionylshikonin. Though the RMSD of acetylshikonin was ranged
from 0.3 to 2 Å, this compound showed different level of fluctuations in several intervals. The Fig. 3b clearly describes that at initial
position, the RMSD of acetylshikonin fluctuated to 2 Å and
remained stable for 22 ns, after the curve sudden fall to 0.6 Å and
continued to around 1.5 Å till to end of the simulation with several
large fluctuations. On the contrary, RMSD of another ligand, propionylshikonin demonstrates stability in the active site during the
simulation, as the level of RMSD within the range from 0.5 to 1.8 Å.
For describing the local changes along the protein chain, RMSF
(Root Mean Square Fluctuation) is used in this study (Fig. 4a). On
this plot, peaks demonstrate the areas of the protein that fluctuated
most in the entire simulation period. The overall fluctuations of the
RMSF of the ligands were found from a range of 1e8 Å throughout
Fig. 4. a) Ligand induced thermodynamic behaviour of protein during the simulations.
The plots described structural changes of protein by means of Root Means Square
Fluctuations (RMSF). Here, green, orange and ash colors denote apo-CREBBP bromodomain, acetylshikonin, propionylshikonin complexes respectively. The baseline below
describes the secondary structure contents (Red color for alpha helix and green for
loops) of CREBBP bromodoamin, calculated by VMD software, where the alpha helixes
were seen in the regions of 1087e1094, 1096e1099, 1026e1028, 1135e1143,
1150e1167, 1174e1189, 1191e1196 respectively. b) Comparative Root Mean Square
Fluctuations (RMSF) of the active site residues of acetylshikonin, propionylshikonin
and apo-CREBBP bromodomain. (For interpretation of the references to color in this
figure legend, the reader is referred to the Web version of this article.)
the simulation. In all cases, the loop regions were found to show
more fluctuation than the area of alpha helix of the proteins.
Highest RMSF was observed at the regions of ZA loop, ALA1110 to
ASP1120, which was around 4 Å. At this region, acetylshikonin
showed large RMSF difference than the others, which may due to
the interactions of ligand to active site residues, located in the loop.
S. Mitra, R. Dash / Journal of Molecular Graphics and Modelling 83 (2018) 42e52
To justify this statement, RMSF of ligand binding residues were
further analyzed and rendered in Fig. 4b in a comparative manner.
As can be seen in Fig. 4b, acetylshikonin interacting residues produced higher RMSF compared with other. Moreover, three main
residues including Pro1110, Gln1113, Val1115 and TYR1125 showed high
fluctuations, which confirms the flexibility of the loop ZA and it
may undergoes different conformational changes. For better understanding, RMSD and Rg (Radius of Gyration) of the two loops
that connected with the binding site, ZA and BC have been calculated and illustrated in Fig. 5a&b. The Rg value is an indicator of
protein flexibility describes the compactness, where high value
indicates loose packing. According to the Fig. 5a, the Rg of ZA loop
was seen to fluctuate more in acetylshikonin complex, while it was
47
stabilized in propionylshikonin complex. In case of BC loop (Fig. 5b),
both apo and propionylshikonin complex were seen to stable over
the time, however, the fluctuations in Rg are more greater for
acetylshikonin. The RMSD analysis for both these loops also supports the result from Rg, where the results of RMSD are directly
correlated with the results of RMSD calculated from total protein.
This observation also clearly concluded that flexibility of these
loops directly modulate the overall flexibility of protein. Furthermore, significant differences in the fluctuations of ZA loop were
also observed by the closer view of principal component analysis
(PCA), where flexibility of this loop in acetylshikonin complex was
associated with the changes in pocket shape and volume of CREBBP
bromodomain. From the previous analysis of ligand fluctuation
Fig. 5. Loop conformational changes, i.e. a) ZA loop (PRO1110 to TYR1125), b) BC loop (ASN1168 to SER1172), by means of Radius of gyration (Rg) and RMSD. The green line represents the
apo-CREBBP, while orange and ash colors denote acetylshikonin, propionylshikonin complexes respectively. c) The first principal component of dynamics from bromodomain
simulations calculated by Bio3D and imaged by VMD software. From left to right, the apo-CREBBP, acetylshikonin and propionylshikonin are shown in tube, colored from red to blue
indicating increasing timestep. Here enlarge tube represent the flexibility. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version
of this article.)
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S. Mitra, R. Dash / Journal of Molecular Graphics and Modelling 83 (2018) 42e52
(Fig. 3b), the flexibility of acetylshikonin was seen to change
dramatically, which may due to the rotation of acetyl moiety and
this rotation may cause the conformational changes of the loops
because of the molecular interactions between the ligand and
residues of these loops. Thus, higher RMSD and RMSF were obtained for acetylshikonin. The Fig. 6 showed the number of H-bonds
formed between the ligand and residues of ligand binding site of
protein for each complex. As shown in Fig. 6 a&b, maximum
hydrogen bonds were formed by propionylshikonin during the
simulation, while acetylshikonin showed minimum interactions
(Table 2). For the detailed understanding, we calculated percentage
of H-bond occupancy, showed in Fig. 6b. Fig. 6c shows the percentage of H-bond occupancy, where each ligand involved acting as
H-bond donor and the residues in the catalytic site as H-bond
acceptor. As shown in Fig. 6c, acetylshikonin formed several H-
bonds with ASN1168, TYR1125, GLN1113 residues during the simulation. Throughout the H-bond formation, acetylshikonin contributed as an acceptor and showed less than 15% occupancy with
ASN1168, TYR1125, GLN1113 residues. However, as a donor, acetylshikonin shows less percentage of H-bond occupancy through the
simulation, only 1.13% for GLN1113. Compared with acetylshikonin,
propionylshikonin formed H-bonds with ASN1168, TYR1125 residues
of the active site, where this ligand showed 40.88% of H-bond occupancy with ASN1168 as H-bond acceptor. In addition, water
mediated hydrogen bonds were also observed for both complexes
in 30 ns simulations by OPLS_3 force field (Figure S3,
Supplementary file 2). In both complexes, active site residues
including PRO1110, GLN1113, TYR1125, ARG1173, VAL1174 and ASN1163
were seen to form water mediated hydrogen bonds with the ligands. It is noteworthy to mention that the TYR1125 residue which
formed water mediated hydrogen bonds with the acetyl group of
lysine residue [26,61e64] was also formed similar interactions with
both ligands, where propionylshikonin showed more interactions
than acetylshikonin. In acetylshikonin complex, MET1133 and
ASN1163 residues formed water mediated hydrogen bonds more
than 20% and 30% of the simulation time. ARG1173 residue was also
formed water bridges with both ligands, however, less than 20% of
the simulation time. In addition, the results of RMSD, RMSF and
molecular interactions were also calculated (Data are shown in
Supplementary File 2), where the results were found to be
consistent with the results of 100 ns simulations.
The outcomes from both these simulations clearly conclude that
propionylshikonin formed more stable complex with CREBBP bromodomain, however, binding energy is one of the main concern in
protein ligand complex, which was analyzed and discussed in
further section.
3.3. Binding free energy evaluation
Fig. 6. Total number of hydrogen bonds formed between the protein and ligand in
complex state, during the simulation. Here two figures at the upper position represent
the number of intermolecular H-bond formed in a) propionylshikonin, b) acetylshikonin complexes respectively. And, the rest of the figure in the bottom depicts the
percentage occupancy of hydrogen bond contacts formed between the ligands and
active site residues. Figure c) shows the time evolution of the occupancy of the listed
hydrogen bonds, where each ligand acts as acceptor.
As stated before in methods sections, MD simulation of each
complex was run for 100 ns in YASARA software, with the force
field of AMBER14. During simulations, snapshots were saved every
25 ps, therefore total 4000 snapshots were generated in 100 ns
simulation and all were considered for MM-PBSA calculation. The
results of MM-PBSA binding energy for two complexes are plotted
against time and shown in Fig. 7a. According to the analysis,
propionylshikonin-CREBBP complex obtained highest average
positive binding energy of 27.65 kcal/mol, while acetylshikonin
showed the less binding energy of 9.80 kcal/mol (Table 1), which
indicates weak binding. It should be noted that more positive MMPBSA binding energy designated as the better binding of protein
ligand complex. In the same way, in MM-GBSA calculation, propionylshikonin showed tighter binding to the target structure
compared to acetylshikonin, where the binding energy
was 59.58 kcal/mol. However, acetylshikonin demonstrated less
favored binding with the CREBBP bromodomain ( 54.92 kcal/mol).
In MM-GBSA analysis, more negative binding energy represents
strong binding.
As can be seen in Fig. 7, the binding energy of acetylshikonin was
fluctuated more in both different simulations (MM-PBSA and MMGBSA), while in both cases the binding energy of propionylshikonin
was in steady level. It is further noteworthy to state that the acetylshikonin showed high ligand RMSD, which means that acetylshikonin had undergone to different conformations in the binding
site of target protein during simulation that caused the formation
and breakdown of molecular interactions with the cavity residues
and thus resulted in fluctuation of binding energy. The results of
binding energy calculation are also consistent with the ligand
RMSD profile.
49
S. Mitra, R. Dash / Journal of Molecular Graphics and Modelling 83 (2018) 42e52
Fig. 7. : Binding free energy calculations of acetylshikonin and propionylshikonin
complexes during the molecular dynamics simulation. Binding energy was evaluated
with two different methods, a) MM-PBSA and b) MM-GBSA, respectively. Here, orange
and ash colors denote acetylshikonin, propionylshikonin complexes respectively. In
MM-PBSA analysis, binding energy calculated from trajectory of 100 ns molecular
dynamics simulation with AMBER14 force field, where positive energy indicates better
binding. In contrast, MM-GBSA binding energy calculated from the snapshots of 30 ns
molecular dynamics simulation with OPLS_3 force field, where more negative binding
energy denotes better binding. (For interpretation of the references to color in this
figure legend, the reader is referred to the Web version of this article.)
3.4. QM/MM calculation
Concurrent with the investigation, QM/MM calculation has been
performed on propionylshikonin and propionylshikonin-CREBBP
bromodomain complex, as it demonstrated the better binding
free energy in both MM-GBSA and MM-PBSA analysis. Application
of Quantum Mechanics (QM) may provide a deeper understanding
of molecular systems, where the basic physical quantities are
described as a function of the electronic structure. With the
development of advanced computational facilities (both in terms of
software and hardware), QM based methods have become the
center of attraction in the study of biomolecules. However, applications of QM methods are limited to small sized systems but the
development of polarization force fields [67,68], variants of second
generation pair-additive force fields [69], have improved the results
of MM approach.
In this study, QM/MM calculations have been executed on three
different stages of propionylshikonin, i) QM/MM on propionylshikonin in TIP3P solvent system (Fig. 8a), where ligand was treated
with QM and solvent by MM; ii) QM/MM on propionylshikonin in
the binding with CREBBP protein in the TIP3P solvent system
(Fig. 8b), here only ligand was subjected in QM region and rest of
the portions were considered in MM region; iii) QM/MM on propionylshikonin in binding with CREBBP protein, considering
VAL1115, TYR1125, ASN1168, ARG1173, PRO1110, GLN1113, MET1160 residues and the propionylshikonin in QM region (Fig. 10a) while
resting of the portion in MM.
During the QM optimization, the calculation of frontier molecular orbitals energies of propionylshikonin by means of HOMO and
LUMO was carried out to elucidate the conformational energy of
the ligand at the higher level of theory and also protein reorganization ability. Table 3 describes the HOMO and LUMO energies of
the ligand in first two different stages; i.e. in solvent and in binding
to the protein, the band gap between HOMO and LUMO was also
calculated. Interestingly, the ligand showed larger band gap of
0.10552 eV in solvent system, however, the gap was seen to
decrease when the ligand binds with the protein, as described in
Table 3. The increase of band gap between HOMO and LUMO
described the less chemical reactivity of a molecule; while the
lower band gap denotes higher chemical reactivity, which is mostly
considered in drug designing applications [70e72]. Furthermore,
Fig. 9 represents the molecular orbital maps of HOMO and LUMO,
describing the sites of ligand as electron donor and acceptor. Fig. 9
described the HOMO maps of propionylshikonin, where the ring
containing hydroxyl group of naphthazarin part of propionylshikonin represented the most electron-sufficient area and also in
the heteroatom as oxygen. In contrast, the electron acceptors regions are defined by LUMO maps indicate the electron-transfer
ability of ketone groups of naphthazarin part mostly. From
Table 3
HOMO and LUMO energies of the orbital, calculated form QM/MM simulation of two
different systems.
System
Propionylshikonin
Propionylshikonin - CREBBP
HOMO (eV)
LUMO (eV)
0.20501
0.23789
0.09949
0.13815
Band Gap (eV)
0.10552
0.09974
Fig. 8. Solvated system of propionylshikonin in two different stages, i.e. a) propionylshikonin, b) propionylshikonin in binding with CREBBP domain. Here, transferable intermolecular potential3 points (TIP3P) water model has been used for building solvation system for each stage.
50
S. Mitra, R. Dash / Journal of Molecular Graphics and Modelling 83 (2018) 42e52
Fig. 9. The geometries of propionylshikonin after QM/MM optimization. a) The electron density distributions of HOMO, and LUMO for propionylshikonin, and b) The electron
density distributions of HOMO, and LUMO for propionylshikonin in binding with CREBBP bromodomain. The negative energy is represented by blue color, while red color for
positive energy. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
induced fit docking, the maximum number of electrostatic interactions between protein and ligand were formed by this naphthazarin part and in both MD simulations similar phenomenon was
also observed. These findings are in agreement with the results
from molecular docking and MD simulations, describe how propionylshikonin contributes in binding to CREBBP bromodomain.
To gain more structural insights, we subjected the
propionylshikonin-CREBBP complex with solvent system for QM
optimization, including the VAL1115, TYR1125, ASN1168, ARG1173,
PRO1110, GLN1113, MET1160 residues along with the ligands (Fig. 10a).
The final optimized complex was obtained and all possible
hydrogen bonds between the residues have been analyzed through.
It has been revealed that ASN1168 residue, which is mostly
contributed in lignad binding in docking and MD simulations, also
formed hydrogen bond after QM optimization with a distance of
2.21 Å (Fig. 10b). Another important residue TYR1125 formed strong
hydrogen bonds with the oxygen atom of ketone group of naphthazarin ring with corresponding distance of 1.93 Å MET1160 residue in the active site represented medium hydrogen bonds with
the ligand, where the respective distance was 2.33 Å, while PRO1110
formed two weak hydrogen bonds with 2.56 Å and 3.16 Å bond
distances, respectively. Furthermore, the ketone group of propionyl
Fig. 10. Propionylshikonin-CREBBP bromodomain complex for QM/MM calculation, where a) the QM part was consisted with the stick represented residues and the right side,
Figure b) demonstrates the binding interactions of propionylshikonin-CREBBP bromodomain after QM/MM calculation. In Figure b), green dot lines describes H-bond between the
only residues treated in QM region. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
S. Mitra, R. Dash / Journal of Molecular Graphics and Modelling 83 (2018) 42e52
moiety showed hydrogen bond with the guanidinium of the
ARG1173 side chain with a bond distances of 2.08 Å. It should be
noted that the selectivity CREBBP bromodomain inhibitor is served
by the polar interaction with the ARG1173 side chain [65,66,73,74].
Though propionylshikonin contributed small portion of electrostatic interaction with the ARG1173 side chain throughout MD
simulations, according to the QM/MM calculation, the modification
of functional group at the same position of propionyl moiety could
provide strong electrostatic interaction with this side chain and
thereby selectivity can be achieved.
4. Conclusion
In this study, an attempt has been made to study the binding
potentiality of shikonin derivatives, as they are less toxic than
shikonin. Using comprehensive molecular dynamics simulations
and binding energy calculations, the study concluded that propionylshikonin has the strong binding to the CREBBP bromodomain.
Furthermore, propionylshikonin is more reactive when binds with
bromodomain, which is explained by the smaller band gap between the HOMO and LUMO. In addition, the QM/MM calculation
revealed, modifications of propionyl moiety of propionylshikonin
could be enabled to obtain selective CREBBP bromodomain inhibition. Therefore, these findings are valuable for the designing of
target specific new CREBBP bromodomain inhibitor from propionylshikonin and shikonin derivatives and will provide a suitable
starting point for further development as well as detailed in vitro
and in vivo analysis.
Acknowledgements
We the authors are heartily grateful to Mr. S M Zahid Hosen,
Scientific Officer, Molecular Modeling and Drug Design Laboratory,
Pharmacology Research Division, BCSIR laboratories Chittagong, for
providing the facilities to study Molecular Dynamics Simulations.
Conflicts of interest
All authors declare that they have no competing interests.
Appendix A. Supplementary data
Supplementary data related to this article can be found at
https://doi.org/10.1016/j.jmgm.2018.04.014.
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