Computational exploration and experimental validation to identify a dual inhibitor of cholinesterase and amyloid-beta for the treatment of Alzheimer’s disease

Article abstract of DOI:10.1007/s10822-020-00318-w

The cholinesterases are essential targets implicated in the pathogenesis of Alzheimer’s disease (AD). In the present study, virtual screening and molecular docking are performed to identify the potential hits. Docking-post processing (DPP) and pose filtration protocols against AChE and BChE resulted in three hits (AW00308, HTS04089, and JFD03947). Molecular Mechanics-Generalized Born Surface Area (MM-GBSA) and molecular dynamics simulation analysis affirmed the stability and binding pattern of the docked complex JFD03947, which was further synthesized and evaluated for in vitro cholinesterase inhibition (AChE, IC₅₀ = 0.062 μM; BChE, IC₅₀ = 1.482 μM) activity. The enzyme kinetics study of the JFD03947 against hAChE and hBChE suggested a mixed type of inhibition. The results of thioflavin T-assay also elicited anti-Aβ aggregation activity by JFD03947. Further, biological evaluation of identified compound JFD03947 also showed neuroprotective ability against the SH-SY5Y neuroblastoma cell lines.

Full text of DOI:10.1007/s10822-020-00318-w

Journal of Computer-Aided Molecular Design
https://doi.org/10.1007/s10822-020-00318-w
Computational exploration and experimental validation to identify
a dual inhibitor of cholinesterase and amyloid-beta for the treatment
of Alzheimer’s disease
Manish Kumar Tripathi1 · Piyoosh Sharma1 · Avanish Tripathi1 · Prabhash Nath Tripathi1 · Pavan Srivastava1 ·
Ankit Seth1 · Sushant Kumar Shrivastava1
Received: 11 November 2019 / Accepted: 26 May 2020
© Springer Nature Switzerland AG 2020
Abstract
The cholinesterases are essential targets implicated in the pathogenesis of Alzheimer’s disease (AD). In the present study,
virtual screening and molecular docking are performed to identify the potential hits. Docking-post processing (DPP) and
pose fltration protocols against AChE and BChE resulted in three hits (AW00308, HTS04089, and JFD03947). Molecular
Mechanics-Generalized Born Surface Area (MM-GBSA) and molecular dynamics simulation analysis afrmed the stability
and binding pattern of the docked complex JFD03947, which was further synthesized and evaluated for in vitro cholinesterase
inhibition (AChE, IC50 =0.062 µM; BChE, IC50 =1.482 µM) activity. The enzyme kinetics study of the JFD03947 against
hAChE and hBChE suggested a mixed type of inhibition. The results of thiofavin T-assay also elicited anti-Aβ aggregation
activity by JFD03947. Further, biological evaluation of identifed compound JFD03947 also showed neuroprotective ability
against the SH-SY5Y neuroblastoma cell lines.
Keywords Molecular docking · Molecular dynamics · Virtual screening · Acetylcholinesterase · Butyrylcholinesterase ·
Anti-Aβ aggregation
Introduction
[8]. It is a multifactorial disease that creates a high social
and economic burden worldwide [9, 10].
Alzheimer’s disease (AD) is one of the most fearsome,
sophisticated, and age-associated neurodegenerative disor-
der, which is the most common cause of dementia [1–3].
It is characterized by the decline in cognitive abilities [4],
mood swings, loss of memory [5], disorientation, difculties
with language, and behavioral changes [6, 7]. It is one of the
signifcant public health issues with its maximum prevalence
in developing countries, especially in the elderly population
In the past decades, several hypotheses have been pro-
posed to be involved in AD. Among them, the cholinergic
hypothesis is the most common, which describes the degen-
eration of cholinergic neurons in the brain [11, 12]. The loss
of ACh and subsequently altered cholinergic neurotransmis-
sion adversely afects the human cognitive functions and
causes memory alterations [13]. This hypothesis suggests
that the cognitive functions may be improved by enhancing
the ACh levels or by inhibiting its hydrolysis [14]. To aug-
ment cholinergic neurotransmission, the inhibition of ace-
tylcholinesterase (AChE) is an essential therapeutic strategy
[15–18]. At present, there are only four drugs in the market
to treat this challenging disorder. Among those, three are
AChE inhibitors (donepezil, rivastigmine, and galantamine),
and an NMDA receptor antagonist (memantine). All these
drugs are reported for symptomatic relief in the AD [16],
and they are inefcacious in disease modifcation [19].
Among available drugs, rivastigmine also acts on another
cholinesterase named butyrylcholinesterase (BChE), which
has 65% structural similarity to AChE along with functional
Electronic supplementary material The online version of this
article (https://doi.org/10.1007/s10822-020-00318-w) contains
supplementary material, which is available to authorized users.
* Sushant Kumar Shrivastava
skshrivastava.phe@iitbhu.ac.in
1
Pharmaceutical Chemistry Research Laboratory, Department
of Pharmaceutical Engineering & Technology, Indian
Institute of Technology (Banaras Hindu University),
Varanasi 221005, India
Vol.:(0123456789)
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Journal of Computer-Aided Molecular Design
similarities [20]. This enzyme exists in the central and
peripheral nervous system, in most tissues, along with
intestine, lungs, pancreas, and liver [21]. The inhibition of
BChE is also reported to enhance memory and cognition.
Investigations also confrmed the involvement of BChE
in AD patients having progressive loss of ACh [22, 23].
Several clinical evidence suggested that BChE also plays a
signifcant role in the regulation of ACh in maintaining the
normal cholinergic functions in AD [24, 25]. The activity
of BChE was observed to be elevated during AD [26–28].
Therefore, inhibition of AChE and BChE can be considered
as a potential therapeutic advantage in the advanced and late
phase of AD.
complexes were retrieved for this study. Protein Preparation
Wizard module of the Schrödinger suite was used to prepare
respective protein crystal structures [32]. Structures were pro-
cessed by removal of water molecules, addition of hydrogen
atoms, flling of missing side chains and loops, followed by
restraining minimization, and structure refnement using the
OPLS force feld [33]. Grids were prepared using the Protein
Grid Generation module by defning the box volume of 10
× 10 × 10 Å surrounding active sites of the respective co-
crystallized ligands. The prepared grids and docking protocol
were validated by extracting and re-docking co-crystallized
ligands in respective grid structures. Superposition tool was
used to compare and calculate the RMSD of actual and re-
docked poses of a co-crystallized ligands for AChE and BChE.
AD is a multifaceted disorder interrelated with complex
pathogenic mechanisms like, i.e., decreased level of ACh,
deposition of amyloid β (Aβ), tau protein, and oxidative
stress [29]. The role of AChE is also reported to induce Aβ
formation resulting in the highly toxic AChE-Aβ peptide
complex [30]. The outer gorge of AChE, known as periph-
eral anionic binding site (PAS) that is rich in hydrophobic
amino acid residues, has the property to stimulate the Aβ
aggregation [31]. Thus, AChE inhibitors that interact exclu-
sively with the peripheral anionic site (PAS) also helps in
enhancing the cholinergic transmission and prevent the
pro-aggregating activity of AChE toward Aβ. These mul-
tiple factors in AD pathology need to develop and discover
novel scafolds of next-generation, which could be useful in
disease-modifying therapy by acting through dual inhibition
and reduce the formation of neurotoxic aggregates.
Selection and preparation of ligands
The chemical database of Maybridge Hit fnder (http://www.
maybridge.com/) containing 14,400 compounds (accessed in
October 2018), was used for screening and identifying poten-
tial inhibitors. The downloaded ligands were optimized, and
minimum energy conformers were generated by LigPrep mod-
ule of the Schrödinger 2018-1 using default parameters at pH
7.4.
Structure-based virtual screening
The virtual screening protocols were accomplished using the
Glide module, and potential hits were identifed from the gen-
erated conformers of ligands. Hierarchal fltration protocols
were used to determine the best possible binding poses in the
receptor grid space. A three-stage flter screening process was
used: (i) high throughput virtual screening (HTVS), (ii) stand-
ard precision (SP), and (iii) extra precision (XP) docking with
fltration criteria of 10%, 20%, and 25% in each successive
steps, respectively [34]. Further, the identifed poses were ana-
lyzed by docking-post processing and pose fltration protocols.
Common potential hits against AChE and BChE were identi-
fed and processed for further studies.
The purpose of this study is based on computational
explorations of the Maybridge Hit fnder database containing
14,400 compounds to identify novel hits targeting cholinest-
erases (AChE and BChE). Virtual screening protocols were
employed to screen potential hits that interact with active
site residues of both the enzymes. The identifed ligands
with respective docked poses were screened by Molecular
Mechanics-Generalized Born Surface Area (MM-GBSA),
molecular dynamics, and drug likeliness prediction studies.
The previous fndings intrigued us to synthesize the top hit
(JFD03947), which is characterized by physicochemical and
spectral techniques and evaluated for dual ChE inhibition
(AChE and BChE). Further, its potential for neurotoxicity
and Aβ aggregation inhibition was also evaluated.
Binding free energy calculation
MM-GBSA Prime Module of Schrödinger 2018-1 was used
to evaluate the binding afnity of identifed docked complexes
[35]. The Prime module uses XP docked “out.maez” fle and
rank the ligands based on the calculated binding energy. The
equation used by the prime module to calculate binding energy
is
Materials and methods
Computational studies
ΔGbinding = Gcomplex − (Gprotein + Gligand
)
Protein preparation and grid generation
Briefy, 3D crystal structures of AChE (PDB Code: 4EY7,
Homo sapiens) and BChE (PDB Code: 6EYF, Homo sapiens)
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Journal of Computer-Aided Molecular Design
where Gcomplex, Gprotein, and Gligand are optimized free
energies of the complex, free protein, and free ligand,
respectively.
Chemistry: synthesis and characterization
All reagents and solvents used in the studies were procured
from commercial sources and used without further purifca-
tion. FT-IR spectrum was recorded on Bruker ECO-ATR
1
(Alpha). H NMR (500 MHz) and 13C NMR (125 MHz)
In silico drug likeliness characteristics
spectra were performed using Avance Bruker FT-NMR spec-
The QikProp module of the Schrödinger 2018-1 was used
to predict the drug likeliness characteristics of identifed
potential hits. Some important properties were predicted,
such as molecular weight, numbers of hydrogen bond
donors and acceptors, total solvent accessible surface area
(SASA), brain/blood partition coefcient (QPlogBB), log
of octanol/water partition coefcient (QPlogPo/w), appar-
ent Caco-2 cell permeability (QPPCaco), apparent MDCK
cell permeability (QPPMDCK), binding to human serum
albumin (QPlogKhsa), and Lipinski’s rule of fve.
trometer in DMSO-d6 using TMS as an internal standard.
Synthesis of JFD03947
The ethanolic solution of 1,4-bis(aminomethyl)cyclohexane
(cis- and trans-mixture) (1) (1 mmol) and 2-hydroxybenza-
ldehyde (2) (2 mmol) with 2–3 drops of glacial acetic acid
were refuxed for 4 h. The solution was allowed to cool at
room temperature to aford the solid compound, which was
recrystallized from absolute ethanol to get pure compound
JFD03947 [37].
Yield: 72.8%, Yellow solid; FT-IR (cm− 1): 1606
(–HC=N), 3460 (–OH); 1H NMR (500 MHz, DMSO-d6) δ
1.42–1.54 (m, 6H), 1.76–1.81 (m, 3H), 3.34–3.57 (m, 5H,
–CH2–N=), 6.85–6.90 (m, 4H), 7.30–7.44 (m, 4H), 8.53 (d,
2H, J=16 Hz), 13.75 (s, 2H); 13C NMR (125 MHz, DMSO-
d6) δ 26.50, 30.48, 36.61, 62.47, 117, 118.84, 119.00,
132.05, 132.71, 161.42, 166.36. Anal. C22H26N2O2: C,
75.40; H, 7.48; N, 7.99; Found: C, 75.64; H, 7.42; N, 7.86.
Molecular dynamics simulations
In the drug discovery process, the molecular dynamics
simulation is widely used for microscopic examination of
protein-ligand interactions in an explicit water environ-
ment. Desmond module of Schrödinger Maestro 2018-1
was used for molecular dynamics simulation, and runs
of 100 ns timescale were performed to afrm the bind-
ing stability of docked poses of AW00308, HTS04089,
and JFD03947 on both AChE and BChE. The docked
complexes of donepezil with AChE and rivastigmine
with BChE were also simulated, and obtained results
were used for comparative assessment. Using the system
builder option of Desmond, each docked complexes were
soaked adequately in the TIP3P water molecules through
the orthorhombic water box [36]. The systems were fur-
ther neutralized by adding the counterions and minimized
by steepest descent and LBFGs algorithm using maxi-
mum 2000 iterations with convergence criteria of 1 kcal/
mol/Å. After the system minimization, the production run
of molecular dynamics was carried out with the periodic
boundary condition, a constant number of atoms (N), pres-
sure (P) and temperature (T) NPT ensemble, the tempera-
ture at 300K and 1.013 bars atmospheric pressure. The
non-bonded interaction was computed at the truncation
distance of 9 Å, Particle mesh Ewald (PME) method was
used to calculate electrostatic interaction. The recording
interval energy and trajectories were set to 1.2 ps and 50
ps, respectively. Finally, the molecular dynamics simula-
tion run of docked complexes were performed for 100 ns.
The generated trajectories after complete production runs
were used to generate simulation interaction diagrams, and
the results were analyzed.
Biological evaluation of selected compound
In vitro cholinesterase inhibition assay (hAChE and hBChE)
Ellman assay was performed to determine the ChE inhibi-
tory potential of target compounds JFD03947 [38]. The
hAChE stock solution (EC No. 3.1.1.7, from human eryth-
rocytes) was prepared in HEPES bufer (20 mM; pH 8) and
Triton X-100 (0.1% v/v). The hBChE (EC No. 3.1.1.8, from
human serum) stock solution was prepared in aqueous gela-
tine solution (0.1% w/v). The fve increasing concentrations
of test compounds were prepared in DMSO that preferably
causes 20–80% inhibition (fnal concentration ≤ 1% v/v).
Initially, a test compound (10 µL) was pre-incubated for 10
min with a stock solution of hAChE or hBChE (25 µL; 0.25
U/mL), followed by the addition of 340 µM 5,5-dithio-bis-
(2-nitrobenzoic acid) (DTNB), and 550 µM of ATCI and
BTCI as a substrate for hAChE & hBChE respectively. The
blank readings were taken with all the components except
enzyme to account for the nonenzymatic hydrolysis of sub-
strates. The change in absorbance was recorded at 37 °C for
6 min (wavelength=412 nm) on Multimode Reader (BioTek
Synergy H1M, USA). The rate of the reactions with or with-
out inhibitors was compared, followed by the measurement
of percentage inhibition using the following expression:
[(Vo − Vi)/Vo]×100, where Vi is the rate of reaction with
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Journal of Computer-Aided Molecular Design
inhibitor and Vo is the reaction rate without inhibitor. The
IC50 value of JFD03947 was calculated by non-linear vari-
able slopes of log (inhibitor) vs normalized response (Graph
Pad Prism 5.01) [39]. The assay was performed in triplicate.
Enzyme kinetics study was performed for JFD03947 at
six varied concentrations of ATCI or BTCI (66–400 µM) to
determine the nature of inhibition of hAChE and hBChE.
Three diferent concentrations of the compound JFD03947
(0.030, 0.060, 0.090 µM for hAChE; and 1.0, 1.5, 2.0 µM
for hBChE) were taken for the experiment and evaluated
against all the six varied concentrations of substrates. The
Lineweaver-Burk double reciprocal plot was constructed
between varied substrate concentrations and rates of reac-
tion to identify the type of hAChE or hBChE inhibition
[40]. Finally, the Ki values were measured by generating
the Dixon plots [41]. The assay was performed in triplicate.
by incubation for another 24 h at 37 °C. In each well, 20
µL (5 mg/mL) of MTT reagent was added, and the cells
were incubated for 3 h. The MTT media was discarded and
obtained purple-colored formazan crystals were solubilized
in solvent (100 µL 0.1 N HCl in isopropanol). The absorb-
ance was measured at 570 nm, and the % cell viability was
calculated. Each treatment was executed in triplicate, and
data are presented as a percentage of the control.
The overall workfow used for the hit identifcation and
validation is given in Fig. 1.
Results
Computational studies
Virtual screening and hit identifcation
Aβ aggregation inhibition by thiofavin T assay
The virtual screening workfow of the Schrödinger Maestro
2018-1 was used to screen Maybridge hit fnder database
compounds (14,400 compounds) based on their afnity for
the binding site of cholinesterase enzymes. Docking proto-
cols and prepared grids of enzymes were validated to avoid
the false-positive results of molecular docking. Re-docking
with co-crystallized ligands was performed in the generated
grid. Further, the superposition tool was used to align actual
pose with the docked pose of the respective ligands. RMSD
has often been used to measure the quality of reproduction
of a known (i.e., crystallographic) binding pose by a compu-
tational method, i.e., docking. The RMSD value was found
within the range of less than 2.0 Å, which represented a good
and correct pose. Reported literature suggested that RMSD
value under 2.0 Å are signifcant and acceptable [44, 45].
Thus, the obtained results validated our docking protocol.
The virtual screening workfow selectively flters the
ligands based on their potential to interact with the tar-
get structure. All the generated conformers 18,930 were
screened using virtual screening methods. Based on the
Glide scoring protocol, 95 potential hits were identifed for
both the targets and processed for docking-post processing
and pose fltration.
Test compound JFD03947 was evaluated for their inhibitory
potential against Aβ fbril formation by Thiofavin-T assay
[42]. The stock solution of Aβ (2000 µM) was prepared by
dissolving the protein in ammonium hydroxide (pH≥9.0).
The test compound was prepared in PBS (pH 7.4) and a
small quantity of DMSO (≤1% w/v) to dissolve the com-
pound. The test compound and Aβ were screened in three
diferent ratios, i.e., 10:5, 10:10, and 10:20 µM, respectively.
The experiment was performed in triplicate.
For anti-Aβ aggregation assay, the mixture of Aβ (10 µL,
fnal concentration: 10 µM) in PBS pH 7.4 with or without
inhibitor (10 µL, fnal concentrations: 5 µM, 10 µM, and 20
µM) was incubated (37 °C, 48 h) followed by addition of thi-
ofavin T (180 µL, 5 µM) solution in 50 mM glycine–NaOH
bufer pH 8.0. The fuorescence intensity was measured at
excitation (λex = 450 nm) and emission (λem = 485 nm)
wavelengths. The anti-Aβ aggregatory potential was calcu-
lated as percentage inhibition following an expression: [100
– (Fi/Fo×100)]; and NFI=Fi/Fo. The Fi and Fo are the fuo-
rescence intensities with or without inhibitor, respectively.
Neuroprotective studies on SH-SY5Y cell lines
The neurotoxic liability of test compound JFD03947 on SH-
SY5Y neuroblastoma cell lines was determined by MTT
assay [43]. The protocol of the study was according to lit-
erature with slight modifcation. Condition and medium of
cell lines (density 1×105 cells/wells) were seeded on 96 well
plates and incubated for 24 h at 37 °C in a humidifed atmos-
phere with 5% CO2. After incubation, cells were exposed to
compound JFD0397 at varying concentrations (10, 20, 40,
and 80 µM) and incubated at 37 °C for 24 h. After incuba-
tion, the media containing test compounds were replaced
with fresh media and treated with Aβ (20 µM), followed
Docking-post processing and pose fltration
The 3D crystal structures of AChE and BChE revealed that
active site of AChE and BChE containing fve subsites,
namely, CAS, PAS, oxyanion hole, acyl binding pocket, and
anionic subsite [28, 46]. The CAS, PAS, and Anionic subsite
amino acid residues were considered as most important for
the functionality of enzymes. Thus, the hits showing interac-
tion with the CAS (His447 for AChE and His438 for BChE),
PAS (Trp286 for AChE and Asp70 for BChE) and Anionic
subsite (Trp86, Phe338 for AChE and Trp82, Glu197, Ala
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Journal of Computer-Aided Molecular Design
HTS04089, and JFD03947 were found as the common hits
(Fig. 2).
Binding mode elucidation of hits identifed by virtual
screening (VS)
Binding mode analysis of AW00308 Binding mode analy-
sis of AW00308 with PAS of AChE showed the interaction
of phenyl ring with amino acid residues Tyr72, Tyr124,
Trp286, and Tyr341 under the hydrophobic pocket. Trp286
also showed π–π interaction with the phenyl ring. Whereas
Asp74 amino acid residue of the PAS region showed elec-
trostatic interaction with the phenyl moiety of AW00308.
The docked pose of donepezil also showed a similar type of
interaction in the PAS site (Fig. S1). The nitrogen atom of
the piperazine ring in identifed hit interacted with an ani-
onic subsite Phe338 and Trp86 amino acid residues through
hydrophobic and π–cation interaction. In CAS, His 447 and
Ser203 showed charged interaction with the pyrimidine
ring, comparable to the donepezil where benzyl piperazine
moiety, also showed the charged interaction. In anionic
subsite, phenyl ring showed hydrophobic interaction with
Phe295 and Phe297 residues (Fig. 3a, b). These interac-
tions are similar to the donepezil except for the PAS residue.
Phe295 showed both hydrophobic and hydrogen bonding
interactions in donepezil, whereas the AW00308 showed
hydrophobic interaction only (Fig. S1).
The binding mode analysis of AW00308 with BChE
revealed that allylic hydroxy showed hydrogen bonding
with His438 residues at CAS. Rivastigmine moiety also
showed a similar hydrogen-bonding pattern with His438
residue. At the acyl binding pocket, the phenyl ring showed
hydrophobic interaction with Leu286 and Phe329 residue.
Pyrimidine moiety showed polar interaction with Glu197
and hydrophobic interaction with Trp82 amino acid residue.
In the PAS region, Asp70 showed charged interaction with
phenyl ring, and Tyr332 showed hydrogen bonding with the
nitrogen atom of piperazine moiety of the compound. Dime-
thyl ethanamine moiety of rivastigmine also showed charged
interaction with Asp70 and hydrogen bonding interaction
with Tyr322 amino acid of the PAS site. (Fig. 3c, d).
Binding mode analysis of HTS04089 The binding mode of
HTS04089 was evaluated with both the targeted enzymes.
At the PAS site of AChE, the compound interacted with
Tyr74, Tyr124, Trp286, and Tyr341 through hydrophobic
interactions with the 2-methoxyphenyl moiety. Tyr341 also
formed π–π stacking interaction with this moiety. In the
donepezil docked complex similar to HTS04089 hydropho-
bic interaction was showed by 1,4 indonale-1,1 moiety with
Tyr341, and this moiety also formed π–π stacking interac-
tion with Trp286 residue (Fig. S1). Whereas, Asp74 amino
acid of the PAS region showed charge interaction with the
Fig. 1 Flowchart of the proposed methodology
328 for BChE) were selected as the leading fltration cri-
terion to choose out the signifcant pose (60 hits for AChE
and 45 hits for BChE) of compounds (Supplementary Tables
S1 and S2).
The common hits amongst the screened ligands interact-
ing with both the enzymes at CAS, PAS, and anionic subsite
were manually identifed, and the compounds AW00308,
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Journal of Computer-Aided Molecular Design
Fig. 2 2D structures of identifed hits
2-methoxyphenyl moiety and hydrophobic interaction with
Phe295 and Phe297 amino acid residues of an acyl bind-
ing pocket. The nitrogen atom of the piperidine ring formed
the π–π stacking and π cation interaction with the anionic
subsite Trp86 residue, whereas the piperidine ring showed
hydrophobic interaction with the Trp86 and Phe338 residue.
The indole ring of the identifed lead showed polar inter-
action with His447 and Ser203 amino acid residue at CAS
comparable to the benzyl piperazine moiety of donepezil
(Fig. 4a, b).
bic interactions with anionic subsite amino acid residues
Trp86 and Phe338. Trp86 also showed π–π stacking and
π–cation interaction with o-hydroxy benzylidine moiety.
A similar interaction pattern was also observed in done-
pezil, where Trp86 showed hydrophobic and π–π stacking
interaction with benzyl piperazine moiety and π–cation
interaction with the piperazine moiety. This moiety also
showed hydrophobic and π–cation interaction with the
Phe338 residue (Figs. S1, S2).
Interaction pattern of JFD03947 with acyl binding
pocket amino acid residues Phe295 and Phe297 showed
hydrophobic interaction with the o-hydroxy benzylidine
moiety similar to the 1,4 indonale-1,1 moiety of done-
pezil docked complex interaction, which showed hydrogen
bonding and hydrophobic interactions with Phe295 and
Phe297 amino acid residues (Fig. S1). The oxygen atom
of o-hydroxy benzylidine moiety also formed hydrogen
bonding with Phe295 amino acid residue. The active site
residues of the PAS region Tyr124, Trp286, and Tyr341
showed hydrophobic interaction with JFD03947 (Fig. 5a,
b).
The binding analysis of HTS04089 with CAS of BChE
revealed that the nitrogen atom of the indole ring showed
charged and hydrogen bonding interaction with His438.
These similar interactions were identifed in the CAS region,
identical to the rivastigmine docked pose. At PAS, Asp70
amino acid interacted with the nitrogen atom of piperidine
ring by hydrogen bonding and showed a charged interaction,
whereas 2-methoxyphenyl ring showed hydrophobic interac-
tion with Tyr332. Dimethyl ethanamine moiety of rivastig-
mine also showed the same hydrogen bonding and hydro-
phobic interaction pattern with Asp70 and Tyr332 amino
acid residue. The same interaction pattern was found at ani-
onic subsite. The indole ring showed π cationic interaction
and π–π stacking interaction with Trp82 residue, whereas the
piperidine ring showed hydrophobic interaction with Ala328
amino acid residue (Table 1). At the acyl binding pocket,
amino acids Phe329 showed hydrophobic interaction with
the piperidine ring. (Fig. 4c, d).
The docked pose of JFD03947 at BChE revealed that it
formed the polar interaction with His438 residues of the
CAS site, while rivastigmine showed charged interaction.
The JFD03947 showed charged, and the dimethyl ethan-
amine moiety of rivastigmine also showed salt bridge inter-
actions with Asp70 and hydrophobic interaction with Tyr332
were observed in both rivastigmine and JFD03947 at PAS.
Further, o-hydroxybenzylidine moiety of JFD03947 showed
their interaction with Trp82 amino acid residues through
π–π stacking and π–cation interaction and hydrogen bonding
with Glu197 amino acid residues. Ala328 amino acid resi-
due showed hydrophobic interaction with the anionic subsite
similarly to the rivastigmine (Table 1). The benzylidine ring
was also involved in hydrophobic interactions with Val288
and Phe329 at an acyl binding pocket (Fig. 5c, d).
Binding mode analysis of JFD03947 The binding mode
of JFD03947 on AChE showed very promising results at
the CAS, His447, and Ser203 showed charged interaction
with o-hydroxy benzylidine similar to the benzyl pipera-
zine moiety of donepezil, which showed charged interac-
tion with CAS residues. 1,4,Di-bis methylamine moiety of
JFD03947 was found to be actively involved in hydropho-
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Journal of Computer-Aided Molecular Design
Fig. 3 In silico molecular docking simulations analysis of AW00308 on AChE (4EY7) and BChE (6EYF) a 3D docked pose of AW00308 with
AChE; b 2D docked pose of AW00308 with AChE; c 3D docked pose of AW00308 with BChE; d 2D docked pose of AW00308 with BChE
Binding free energy calculation using Prime/MM-GBSA
In silico estimation of drug likeliness properties
The binding energies of the docked protein-ligand com-
plexes for all three hits along with standards (Donepezil
and Rivastigmine) were evaluated using the prime MM/
GBSA module of Schrödinger 2018-1. The JFD03947
showed minimum ∆G binding energy score for both the
enzyme complexes (AChE, ∆G = − 89.23 kcal/mol and
BChE, ∆G = − 79.19 kcal/mole). The details of ∆G bind
score of all the compounds are shown in Table 1. The
docking score of JFD03947 was found to be relatively
similar to the donepezil and rivastigmine on AChE and
BChE, respectively.
Drug-likeliness characteristics for identifed hits were pre-
dicted using the QikProp module of Schrödinger Maestro
2018-1. The predicted outcomes of some important parame-
ters are reported in Table 2. From the calculated parameters,
Qplog Po/w, which predicts the partition coefcient revealed
that all compounds (AW00308 = 4.09, HTS04089 = 4.78,
and JFD03947 = 4.65) showed higher lipophilicity com-
pared to standard donepezil (4.25). QPPCaco provided the
prediction of gut-blood barrier transportation abilities using
Caco-2 cells. The results exhibited excellent permeability
for all the compounds. The predicted QPlogKhsa values
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Journal of Computer-Aided Molecular Design
Fig. 4 In silico molecular docking simulations analysis of HTS04089 on AChE (4EY7) and BChE (6EYF) a 3D dock pose of HTS04089 with
AChE; b 2D dock pose of HTS04089 with AChE; c 3D dock pose of HTS04089 with BChE; d 2D dock pose of HTS04089 with BChE
afrmed their strong binding with plasma protein. The out-
come of Lipinski’s rule of fve (MW<500, QPlog Po/w<5,
donorHB 0–6.0, acceptHB 2.0–20), along with the other pre-
dicted parameters indicated that the investigated compounds
elicited “drug-like” characteristics, which were comparable
with standard drug.
hits (AW00308, HTS04089, JFD03947) complex with
AChE (PDB Code: 4EY7) [47] and BChE (PDB Code:
6EYF) [48]. We also performed the 100 ns simulation to
the docked complexes of donepezil with AChE and riv-
astigmine with BChE to compare the results with identi-
fed hits. The 100 ns time scale was enough for the side-
chain rearrangement in the native as well as the docked
complexes to gain the most stable binding conformation.
The residual fuctuations in the target proteins were deter-
mined by the root mean square deviations (RMSD) and
root mean square fuctuations (RMSF) protocols. The anal-
ysis of interacting residues was evaluated to predict the
binding behavior in a docked pose during MD simulation.
Molecular dynamics simulation of selected hits identifed
from VS
The molecular dynamics simulation runs of 100 ns were
performed to evaluate the stability and fexibility of the
docked complexes on AChE and BChE enzymes. Six sim-
ulation systems were run independently for three identifed
1 3

Journal of Computer-Aided Molecular Design
Table 1 Results of molecular docking, interacting residues and binding free energies
S. No.
Enzyme
Compound ID
Glide Score#
Interacting residues*
ΔG binding energy†
− 48.29
CAS
PAS
Other active sites
1
AChE (4EY7)
AW00308
− 14.35
His447a
Tyr341c
Trp286c,e
Tyr124bc
Tyr72c
Trp86c,d,e
Phe295c
Phe297c
Phe 338c,d
Ser203a
Asp74f
HTS04089
− 15.01
His447a
Ser203a
Tyr341c,e
Trp286c
Tyr124c
Tyr72c
Trp86c,e
Phe295c
Phe297c
Phe 338c
− 53.64
Asp74f
JFD03947
Donepezil
− 16.74
− 14.6
His447a
Ser203a
Tyr341c
Trp286c
Tyr124c
Trp86c,d,e
Phe295b,c
Phe297c
Phe 338c
− 89.23
− 59.06
His447a
Ser203a
Tyr341c
Trp286c,e
Tyr124c
Tyr72c
Trp86c,d,e
Phe295b,c
Phe297c
Phe338c,d
Asp74f
2
BChE (6EYF)
AW00308
− 7.15
His438b,f
Asp70f,g
Trp82c
− 69.28
Tyr332b,c
Leu286c
Phe329c
Ala328c
Glu197f
HTS04089
JFD03947
− 8.37
− 5.58
His438b,f
His438f
Asp70b,f
Tyr332c
Trp82c,e
Phe329c
Ala328c
Glu197f
− 65.09
− 79.19
Asp70f,g
Tyr332c
Trp82c,d,e
Val288c
Phe329c
Glu197b,f
Ala328c
Rivastigmine
− 4.9
His438b,f
Asp70f
Trp82c
− 39.32
Tyr332b,c
Leu286c
Val288c
Trp231c
Phe329c,d
Ala199a
Ala328c
Glu197f
†
#Glide scores were calculated in kcal/mol. Glide XP visualizer module was used to analyze the docked poses; binding free energy (kcal/mol)
was calculated using the molecular mechanics-generalized born surface area (MM/GBSA); *all interactions were observed in the range of 4 Å
radius between the ligands and interacting residues
Types of interactions: apolar; bH-bonding; chydrophobic; dπ–π cation; eπ–π stacking; and felectrostatic interaction; gsalt bridge
The structural stability of the docked complexes was
assessed by the RMSD pattern of all the docked complexes.
The results revealed that all the docked complexes have sta-
ble trajectories throughout the simulation time scale with
average fuctuations in the range of 1–3 Å (Fig. 6a, b). In
AChE docked complexes, RMSD value showed a sharp
increase up to 15 ns after with fuctuations to be maintained
within the acceptable range. These observations suggested
that AChE and its docked complexes reached the equilibrium
states along with structural stability during the simulation.
The RMSD backbone of the AChE protein was found to
be stable during the entire simulation time. Moreover, The
RMSD value for the AChE–JFD03947 complex was found
to have lesser fuctuations as compared to other simulation
systems after 15 ns. Thus, small fuctuations in the entire
simulation time scale of AChE–JFD03947 complex indi-
cated that fexibility of AChE was declined and the com-
plex was found to be relatively stable. The comparative
1 3

Journal of Computer-Aided Molecular Design
Fig. 5 In silico molecular docking simulations analysis of JFD03947 on AChE (4EY7) and BChE (6EYF) a 3D dock pose of JFD03947 with
AChE; b 2D dock pose of JFD03947 with AChE; c 3D dock pose of JFD03947 with BChE; d 2D dock pose of JFD03947 with BChE
simulation results JFD03947 and Donepezil with docked
complexes showed similar RMSD behavior and fuctuation
patterns. The RMSD patterns of other docked complexes
(AW00308, HTS04089) showed the larger fluctuations
behavior during the entire simulation time than donepezil,
which inferred that docked complexes were not stable during
the simulation time scale. In the BChE simulation system,
trajectories of the other two systems showed larger fuctua-
tions except for the BChE–JFD03947 complex (Fig. 5b). The
BChE-JFD03947 docked complex trajectory was found to
be stable and showed fewer variations within the acceptable
range of RMSD values. The stability and fuctuation behav-
ior of JFD03947 was also found to be comparable to the riv-
astigmine docked complex in BChE. Further, a comparison
of trajectories between docked complexes of AW00308,
HTS04089 with rivastigmine inferred larger fuctuation
during the entire simulation time and their trajectories were
also not found to be in the range of the acceptable region.
The RMSF values were calculated using Cα atoms of the
enzymes to get insights into the structural fuctuations of
active amino acid residues due to the presence of ligand.
The RMSFs were evaluated for the CAS and PAS active site
residues and found to be stable for all the enzyme-ligand
complex systems (Fig. 7a, b).
The results of interaction analysis were performed using
the Simulation Interaction Diagram module of Desmond in
Schrodinger Maestro 2018-1. The interaction analysis was
performed using stacked bar charts as interaction fractions.
1 3

Journal of Computer-Aided Molecular Design
The graphical representation showed as interaction show-
ing % interactions, and timeline representation as interacting
residues.
The interaction analysis of AW00308 ligand on AChE
revealed it interacted for the maximum period of the simu-
lation run with the active site of PAS (Tyr124, Tyr341) and
anionic subsite (Trp86, Phe338) residues. It was observed
that hydrophobic interaction, which was formed with Phe295
and Phe297 at an acyl binding pocket, was lost during the
entire simulation. In the CAS region, water bridge interac-
tion was observed with Ser203 and His447 amino acid resi-
dues during simulation interaction (Supplementary Figs. S2,
S3). Similarly, in BChE, AW00308 showed interactions at
PAS (Asp70, Tyr332) and anionic subsite (Trp82) only and
not with the CAS and other active site residues (Supplemen-
tary Figs. S4, S5). The RMSD trajectories of AW00308 were
not found to be stable in the acceptable range and showed
greater fuctuations.
The interaction analysis of HTS04089 on AChE revealed
that it showed stable interactions with anionic subsite (Trp86
and Phe338), and acyl binding pocket residue (Phe295). In
the CAS region, water bridge interactions were observed
with His447 amino acid. The other active site interactions,
including PAS, were not seen with the HTS04089-AChE
complex (see Supplementary Figs. S6, S7). Further, the
interaction analysis of HTS04089 on BChE showed inter-
action at CAS with His438, which was found to be unstable.
At the PAS, it showed the interaction with Asp70, anionic
subsite with Trp82 and Ala328, and an acyl binding pocket
with Phe329 residue. Apart from these interactions, other
active site interactions were not observed or negligible,
which formed signifcant interactions in the docking pose
(see Supplementary Figs. S8, S9). The RMSD trajectory
of HTS04089 on BChE was also not found to be stable and
acceptable region like AW00308 hit.
The interaction analysis of JFD03947 on AChE revealed
that it showed hydrophobic interactions with anionic
subsite residues Trp86 and Phe338 and hydrogen bond-
ing interaction with Glu202 residue. At PAS, JFD03947
also formed hydrophobic interactions with active site
residues Tyr72, Tyr124, Trp286, Tyr341, and hydrogen
bonding interaction with Asp74 (Figs. 8, 9). In the CAS
region, His447 formed stable water bridge interactions
during simulation time. Further, interaction analysis of
JFD03947 on BChE showed that His438 residue of CAS
region formed hydrogen bonding interaction. The interac-
tion analysis also revealed that in the PAS region, Asp70
and Tyr332, both amino acid residues showed hydrogen
bonding and hydrophobic interactions. In the anionic sub-
site, Trp82 also formed hydrophobic interaction (Figs. 10,
11). All the interactions of docked pose binding patterns
in JFD03947 with AChE and BChE enzymes were suc-
cessfully observed for respective interaction during the
1 3

Journal of Computer-Aided Molecular Design
Fig. 6 RMSD fuctuations of protein backbone (black), AW00308 (orange), HTS04089 (blue), JFD03947 (brown), donepezil (red), and rivastig-
mine (red) during 100 ns simulation runs on a AChE, b BChE
molecular dynamics simulation study. The RMSD trajec-
tories of JFD03947 was also found to be stable in both the
enzyme system as compare to the other hits like AW00308
and HTS04089.
The interaction analysis of donepezil on AChE revealed
stable interaction with PAS (Asp74, Tyr341, Trp286),
anionic subsite (Trp86, Phe338), and acyl binding pocket
(Phe295, Phe297) (see Supplementary Figs. S10, S11).
1 3

Journal of Computer-Aided Molecular Design
Fig. 7 Comparative RMSF of
a AChE and b BChE (protein
backbone) for top 3 hits in the
protein-ligand complex through-
out the simulations period of
100 ns.
(A)
(B)
Fig. 8 Stacked bar chart rep-
resentation of JFD03947 with
active site amino acid residues
of AChE
The interaction pattern of rivastigmine during a simulation
on BChE revealed hydrogen bonding interaction with CAS
residue His438. It also showed stable interaction with PAS
(Asp70, Tyr332), acyl binding pocket (Val288, Trp231,
Phe329) and anionic subsite (Trp82) residues (see Supple-
mentary Figs. S12, S13). The formed docking interactions
were found to be stable in the donepezil and rivastigmine
simulations study.
Along with the MM/GBSA calculation, in molecular
dynamics simulation studies, RMSD and RMSF proto-
col were found to be in favor of JFD03947 against both
the targets (AChE and BChE). The other hits (AW00308,
1 3

Journal of Computer-Aided Molecular Design
Fig. 9 A timeline representation for 100 ns simulation run analysis of JFD03947 and AChE docked complex
Fig. 10 Stacked bar chart rep-
resentation of JFD03947 with
active site amino acid residues
of BChE
HTS040889) obtained after virtual screening showed
lesser MM/GBSA values than JFD03947. In the MD sim-
ulation study, their trajectories and binding stability in
the CAS, PAS, and Anionic sub-site of AChE and BChE
targets were also found to be unstable during the whole
simulation time. Trajectories of other hits (AW00308,
HTS040889) show more signifcant fuctuations in AChE
and not found to be acceptable regions in the BChE. These
preceding fndings have prompted us to select JFD03947
as a potential hit.
1 3

Journal of Computer-Aided Molecular Design
Fig. 11 A timeline representation for 100 ns simulation run analysis of JFD03947 and BChE docked complex.
(2) to form an unstable amino methanol intermediate
Chemistry: synthesis and spectral characterization
that generated an imine (JFD03947) after dehydration
(Scheme 1). The preliminary identifcation of imine was
confrmed by a positive dragendorf test on TLC (Thin layer
chromatography). The FT-IR of the synthesized compound
showed a diagnostic stretching of (>C=N) imine group at
The compound (JFD03947) was synthesized by the
nucleophilic addition of amino group (nucleophile) of
1,4-bis(aminomethyl)cyclohexane (cis- and trans- mixture)
(1) on carbonyl group (>C=O) of 2-hydroxybenzaldehyde
Scheme 1 Synthesis of JFD03947
1 3

Journal of Computer-Aided Molecular Design
1606 cm−1, and the phenolic hydroxyl (OH) group exhib-
ited a broad stretching at 3460 cm−1. 1H NMR spectrum of
compound JFD03947 showed the appearance of a charac-
teristic peak of imine proton (N=CH) at 8.53 ppm and also
observed the disappearance of aldehydic (–CHO) proton.
The phenolic hydroxyl group (Ph-OH) found at 13.75 ppm.
13C NMR spectrum showed a distinctive peak of 166.36 ppm
(>C=N–) and, 161.42 ppm (Ph–1C–OH). These spectral
data confrmed the hypothesis of the reaction and formation
of the compound (JFD03947). The result of the elemental
analysis was also found within the range of 0.4% of theo-
retical values.
inhibition for hAChE and hBChE (Figs. 12a and 13a). The
Dixon plots were constructed between Lineweaver-Burk
slope and inhibitor concentration (Figs. 12b and 13b). The
intersection point at the x-axis of the plot considered as
dissociation constant Ki for the inhibitor, which was esti-
mated to be 0.036 µM and 1.2 µM for hAChE and hBChE,
respectively.
Aβ aggregation inhibition by thiofavin T assay
Accumulation and aggregation of Aβ peptide in the CNS
regions of the brain is a signifcant detrimental cause of AD.
The thiofavin-T assay was performed to evaluate the dis-
aggregation potential of compound JFD03947. The experi-
ment was conducted at three diferent concentration ratios
of Aβ and inhibitor (10:5 µM, 10:10 µM, and 10:20 µM,
respectively). The results were reported as NFI and % Aβ
aggregation inhibition. The inhibitory potential of the com-
pound was dependent on inhibitor concentration, and higher
concentration inhibits maximum (Fig. 14a, b). The results
revealed that the inhibitory potential of compound JFD0394
(NFI: 0.55–0.81), (% Aβ aggregation inhibition: 36–73%),
was signifcantly higher than donepezil (NFI: 0.25–0.64) (%
Aβ aggregation inhibition: 30–52%). The results of the Aβ
inhibition assay were corroborated with the in-silico study.
In vitro biological evaluation
Cholinesterase (hAChE and hBChE) inhibition assay
The IC50 values and inhibitory potential of synthesized
compound (JFD03947) and standard (donepezil and riv-
astigmine) against hAChE and hBChE were determined
by the Ellman method. In vitro Ellman assay showed that
JFD03947 signifcantly inhibited hAChE (IC50 =0.062 µM)
as well as hBChE, (IC50 =1.482 µM). The obtained hAChE
inhibitory potential was comparable to standard donepezil
(hAChE, IC50 =0.040 µM), a selective AChE inhibitor, while
BChE inhibitory potential was observed to slightly lower
than selective BChE inhibitor, i.e., rivastigmine (hBChE,
IC50 =0.864 µM) (Table 3). The obtained results of inhibi-
tion assay also validated the outcome of in silico study with
JFD03947 being the potent compound interacting signif-
cantly with PAS and CAS residues of both cholinesterases.
To gain further insights into the mechanism of inhibition
by JFD03947, enzyme kinetics study was performed against
hAChE and hBChE. The type of inhibition was elucidated by
the Lineweaver-Burk double reciprocal plot between the ini-
tial velocity of the substrate (y-axis) at increasing concentra-
tions (x-axis; acetylthiocholine iodide for hAChE; butyrylth-
iocholine iodide for hBChE). The plots revealed decreased
Vmax, while Km increased with increasing concentration of
JFD03947, the trend being attributed to the mixed-type of
Neuroprotection studies on SH-SY5Y cell line
The neuroprotective activity was determined against human
neuroblastoma SH-SY5Y cell lines using 3-(4,5-dimethylth-
iazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT) assay.
The activity was performed using four diferent concentra-
tions of test compounds in the range of 10–80 µM to deter-
mine their potential to prevent cell death against Aβ-induced
oxidative stress. In this assay, 20 µM of Aβ was incubated
with SH-SY5Y cells to attenuate % cell viability to 58%
compared to control (Fig. 15). The results demonstrated
augmented cell viability from 70 to 89% by the compound
in a dose-dependent manner. The obtained result suggested
the neuroprotective activity of compound JFD03947 towards
SH-SY5Y neuroblastoma cell lines against the Aβ-induced
oxidative stress.
Table 3 In vitro inhibition assay results of screened compound
JFD03947 and standard (Donepezil and Rivastigmine)
S. no. Compound
IC50 (µM) SEM
Selectivity
Index
Discussion
hAChE
hBChE
The multifactorial nature of Alzheimer’s disease and contin-
ued failure of the clinical candidate creates a need to explore
the drugs which could act as multi-target therapeutics against
Alzheimer’s disease [49]. The present work is focused on
the computational identifcation of a potent lead molecule
against AD. Donepezil is a second-generation cholinesterase
inhibitor widely used for the treatment of AD disease [50].
1.
2.
3.
JFD03947
Donepezil
0.062 0.003 1.482 0.071
0.040 0.002 4.820 0.124 120.5
0.513
23.90
Rivastigmine 1.682 0.082 0.864 0.046
All the results are reported as IC50 SEM of at least three separate
experiments
aSI (Selectivity Index)=IC50 of BChE/IC50 of AChE
1 3

Journal of Computer-Aided Molecular Design
(A)
250
200
150
100
50
JFD03947 (0.090 µM)
JFD03947 (0.060 µM)
JFD03947(0.030 µM)
Control
-10
-5
5
10
15
(ACTI)mM-1
-50
(B) Dixon Plot
0.0020
0.0010
R² = 0.9851
-0.060
-0.030
0.030
0.060
0.090
[I] (µM)
-0.01
Fig. 12 Enzyme kinetics study of JFD03947 against hAChE showing mixed type of inhibition; a Lineweaver–Burk plot. b Dixon plot
Donepezil acts as a reversible inhibitor and is used for the
mild to severe cases of AD and readily cross the blood–brain
barrier permeability to inhibit the AChE in the central nerv-
ous system [51, 52]. Donepezil occupies and binds equally
well in the anionic binding site PAS, the acyl pocket, and the
catalytic site CAS by adopting their outward–inward–inward
orientations regardless of the substrate occupancy [53]. Riv-
astigmine, a reversible cholinesterase inhibitor, has higher
selectivity toward the BChE and is used for the treatment of
mild to moderate Alzheimer’s disease [54].
Further, molecular dynamics simulation studies of the
JFD03947-AChE docked complex revealed the similar
RMSD trajectories and stability behavior like the donepezil-
AChE docked complex during the simulation time. The
docked pose of JFD03947 at BChE revealed that it showed
the charged interaction with the CAS site similar to the riv-
astigmine. At the PAS site, it was observed that JFD03947
showed the hydrophobic interaction and salt bridge interac-
tion identical to the rivastigmine. Anionic subsite interaction
was also found similarities with the rivastigmine.
The docking study of JFD03947 on AChE revealed
similar CAS binding site interactions similar to donepezil.
Whereas, at acyl binding pocket, o-hydroxy benzylidine
moiety formed hydrophobic interaction identical to the
1H-inden-1-one moiety of donepezil. The anionic subsite
and PAS region were also found to show the same interac-
tion pattern.
The dynamics simulations of the JFD03947-BChE
docked complex showed the same stability and fuctuating
behavior comparable to the rivastigmine, while the other
docked complexes were found to show their unstable behav-
ior during the simulation studies. Thus, results suggested
JFD03947 as a potential inhibitor acting on AChE and BChE
with a similar mode of interaction pattern and comparable
1 3

Journal of Computer-Aided Molecular Design
(A)
JFD03947 (2.0 µM)
JFD03947 (1.5 µM)
JFD03947 (1.0 µM)
Control
350
250
150
50
-10
-5
5
10
15
[BTCI] mM-1
-50
0.0050
0.0030
0.0010
-0.01
(B) hBChE-Dixon Plot
R² = 0.9824
-2.000
-1.000
1.000
2.000
[I] (µM)
Fig. 13 Enzyme kinetics study of JFD03947 against hBChE showing mixed type of inhibition; a Lineweaver–Burk plot. b Dixon plot
stability and fuctuation behaviors like donepezil and riv-
astigmine, respectively. Donepezil is more selective towards
AChE, while rivastigmine has higher selectivity in inhibiting
BChE [55]. Further, in vitro results showed that JFD03947
inhibited hAChE with an IC50 value of 0.062 µM and hBChE
with an IC50 value of 1.482 µM. The substantial inhibition of
AChE and BChE with the treatment of JFD03947, which has
dimethenamine moiety, supports the fact of previous studies
about choline esterase inhibitor.
which have benzamide and picolinamide moiety contain-
ing dimethylamine side chain [57]. Thus, JFD03947 has
shown potential activity and inhibition as compared to pre-
viously designed compounds containing dimethenamine
moiety. To identify the mechanism of inhibition by the
JFD03947, an enzyme kinetics study was performed, and
the results elicited its mixed-type of inhibition against the
hAChE and hBChE.
Further, thiofavin T assay also established its potential
to prevent the Aβ aggregation apart from its cholinergic
activity. JFD03947 was also found to be devoid of neuro-
toxic liabilities against SH-SY5Y neuroblastoma cell lines
study. Based on the study, we conclude that the present study
encompasses the identifcation of preliminary scafold tar-
geting on both cholinesterases, which will be further used
The IC50 value of the identifed compound JFD03947
showed better results in the micromolar ranges as com-
pared to the dimethylamine derivatives, which showed
an IC50 value of 1.83 µM for AChE [56]. The identifed
compound JFD03947 has also shown better activity in
the micromolar range as compared to the compounds
1 3

Journal of Computer-Aided Molecular Design
(A)
100
(B)
1.00
75
50
25
0
0.75
0.50
0.25
0.00
5
0
0
5
:
0
0
2
5
0
0
5
:
0
0
2
:
1
2
1
:
1
2
1
0
:
:
0
:
:
0
0
:
:
0
:
:
0
1
0
0
1
0
1
0
0
1
0
1
1
1
1
1
1
1
1
Aβ : JFD0394
(µM)
Aβ : DONE
(µM)
Aβ : JFD03947
(µM)
Aβ : DONE
(µM)
Fig. 14 Result of thiofavin T assay by JFD03947 a % Aβ aggregation inhibition, b NFI. All the data are represented as mean SEM of three
independent experiments (n=3); DONEdonepezil
potential hits were processed for MM-GBSA, and molecular
100
dynamics simulations to identify the best hit (JFD03947).
The preceding fndings of in silico computational studies
have encouraged us to synthesize, characterize, and evalu-
50
ate the best hit JFD03947 against cholinesterases (hAChE
and hBChE) by in vitro Ellman assay. The molecule was
obtained by single-step synthesis and resulted in good yield
and also suggested the low production cost of the synthe-
0
sized compound. The experimental results showed that
JFD03947 inhibits hAChE with an IC50 value of 0.062 µM
and hBChE with an IC50 value of 1.482 µM. Further, bio-
JFD03947
logical evaluations indicated that JFD03947 showed poten-
Aβ
tial activity against Aβ aggregation as well as signifcant
neuroprotective activity towards SH-SY5Y neuroblastoma
Fig. 15 Neuroprotective activity of JFD03947 against Aβ-induced
cell lines. The fndings of in vitro assays showed that identi-
oxidative stress in SH-SY5Y cell lines by MTT assay. Each bar dis-
fed hit JFD03947 elicited dual inhibitory potential against
both cholinesterases and Aβ aggregation, which were also
concurrent with the in silico fndings. The overall results
advocated that JFD03947 could be considered as a promis-
ing lead and further utilized to design a series of derivatives
that could be explored to target multiple pathways of the AD.
plays the values of % cell viability as the mean SEM of three sepa-
rate experiments (n=3).
to optimized chemically using molecular hybridization or
bioisosterism approaches to get more signifcant results.
Acknowledgments The authors gratefully acknowledge to Indian Insti-
tute of Technology (BHU) for providing fnancial assistance through
postdoctoral fellowship (Grant No. PDF/007/15-16/16-17). The authors
are also thankful to the Department of Health Research, Ministry of
Health and Family Welfare for providing the Young Scientist Project in
newer areas of Drugs Chemistry (DHR/HRD/YS-15-2015-16).
Conclusion
Computational exploration of the database yielded signif-
cantly potential hit JFD03947 with stable and active binding
site interactions against AChE and BChE. Virtual screen-
ing protocols were employed to screen the ligands initially
from the Maybridge database of 14,400 compounds (18,930
generated conformers). Docking-post processing and pose
fltration protocols were initiated to identify three poten-
tial hits (AW00308, HTS04089, and JFD03947) interact-
ing efectively with both targets (AChE and BChE). These
References
1. Albert MS, DeKosky ST, Dickson D, Dubois B, Feldman HH,
Fox NC, Gamst A, Holtzman DM, Jagust WJ, Petersen RC (2011)
Alzheimer’s Dementia 7(3):270
1 3

Journal of Computer-Aided Molecular Design
30. Inestrosa NC, Dinamarca MC, Alvarez A (2008) FEBS J
275(4):625
2. Darras FH, Pockes S, Huang G, Wehle S, Strasser A, Wittmann
H-J, Nimczick M, Sotrifer CA, Decker M (2014) ACS Chem
Neurosci 5(3):225
31. Inestrosa NC, Alvarez A, Perez CA, Moreno RD, Vicente M,
Linker C, Casanueva OI, Soto C, Garrido J (1996) Neuron
16(4):881
3. Sharma P, Tripathi A, Tripathi PN, Prajapati SK, Seth A, Tripathi
MK, Srivastava P, Tiwari V, Krishnamurthy S, Shrivastava SK
(2019) Eur J Med Chem 167:510
32. Sastry GM, Adzhigirey M, Day T, Annabhimoju R, Sherman W
(2013) J Comput Aided Mol Des 27(3):221
4. Wilson RS, Leurgans SE, Boyle PA, Bennett DA (2011) Arch
Neurol 68(3):351
33. Shivakumar D, Williams J, Wu Y, Damm W, Shelley J, Sherman
W (2010) J Chem Theory Comput 6(5):1509
5. Tripathi PN, Srivastava P, Sharma P, Tripathi MK, Seth A, Tri-
pathi A, Rai SN, Singh SP, Shrivastava SK (2019) Bioorg Chem
85:82
34. Friesner RA, Murphy RB, Repasky MP, Frye LL, Greenwood
JR, Halgren TA, Sanschagrin PC, Mainz DT (2006) J Med Chem
49(21):6177
6. Klimova B, Maresova P, Valis M, Hort J, Kuca K (2015) Clin
Interv Aging 10:1401
35. Jacobson MP, Pincus DL, Rapp CS, Day TJ, Honig B, Shaw DE,
Friesner RA (2004) Proteins 55(2):351
7. Shrivastava SK, Sinha SK, Srivastava P, Tripathi PN, Sharma P,
Tripathi MK, Tripathi A, Choubey PK, Waiker DK, Aggarwal LM
(2019) Bioorg Chem 82:211
36. MacKerell AD Jr, Bashford D, Bellott M, Dunbrack RL Jr, Evan-
seck JD, Field MJ, Fischer S, Gao J, Guo H, Ha S (1998) J Phys
Chem B 102(18):3586
8. Kalaria RN, Maestre GE, Arizaga R, Friedland RP, Galasko D,
Hall K, Luchsinger JA, Ogunniyi A, Perry EK, Potocnik F (2008)
Lancet Neurol 7(9):812
37. Heindel ND, Lemke SM, Fish VB (1967) J Chem Eng Data
12(4):596
38. Ellman GL, Courtney KD, Andres V Jr, Featherstone RM (1961)
Biochem Pharmacol 7(2):88
9. Iqbal K, Grundke-Iqbal I (2010) Alzheimer’s disease, a multifac-
torial disorder seeking multitherapies. Elsevier, New York
10. Sharma P, Tripathi A, Tripathi PN, Singh SS, Singh SP, Shrivas-
tava SK (2019) ACS Chem Neurosci 10(10):4361
11. Ferreira Neto DC, Alencar Lima J, Sobreiro Francisco Diz de
Almeida J, Costa França TC, Jorge do Nascimento C, Figueroa
Villar JD (2018) J Biomol Struct Dyn 36(15):4099
12. Mishra P, Sharma P, Tripathi PN, Gupta SK, Srivastava P, Seth
A, Tripathi A, Krishnamurthy S, Shrivastava SK (2019) Bioorg
Chem 89:103025
39. Peauger L, Azzouz R, Gembus V, Tintas M-L, Sopková-de
Oliveira Santos J, Bohn P, Papamicaël C, Levacher V (2017) J
Med Chem 60(13):5909
40. Lineweaver H, Burk D (1934) J Am Chem Soc 56(3):658
41. Dixon M (1972) Biochem J 129(1):197
42. Levine IIIH (1993) Protein Sci 2(3):404
43. More SS, Vince R (2012) ACS Chem Neurosci 3(3):204
44. Kontoyianni M, McClellan LM, Sokol GS (2004) J Med Chem
47(3):558
13. Tripathi PN, Srivastava P, Sharma P, Seth A, Shrivastava SK
(2019) Biorg Med Chem 27(7):1327
45. Wang R, Lu Y, Wang S (2003) J Med Chem 46(12):2287
46. Kryger G, Silman I, Sussman JL (1999) Structure 7(3):297
47. Cheung J, Rudolph MJ, Burshteyn F, Cassidy MS, Gary EN, Love
J, Franklin MC, Height JJ (2012) J Med Chem 55(22):10282
48. Dighe SN, De la Mora E, Chan S, Kantham S, McColl G, Miles
JA, Veliyath SK, Sreenivas BY, Nassar ZD, Silman I (2019) Com-
mun Chem 2(1):1
14. Xu Y, Cheng S, Sussman JL, Silman I, Jiang H (2017) Molecules
22(8):1324
15. Srivastava P, Tripathi PN, Sharma P, Rai SN, Singh SP, Srivastava
RK, Shankar S, Shrivastava SK (2019) Eur J Med Chem 163:116
16. Srivastava P, Tripathi PN, Sharma P, Shrivastava SK (2019) Biorg
Med Chem 27(16):3650
49. Carmo Carreiras M, Mendes E, Jesus Perry M, Paula Francisco
A, Marco-Contelles J (2013) Curr Top Med Chem 13(15):1745
50. Cacabelos R (2007) Neuropsychiatr Dis Treat 3(3):303
51. Agatonovic-Kustrin S, Kettle C, Morton DW (2018) Biomed Phar-
macother 106:553
17. Tripathi A, Choubey PK, Sharma P, Seth A, Saraf P, Shrivastava
SK (2020) Bioorg Chem 95:103506
18. Sharma P, Tripathi MK, Shrivastava SK (2020) Cholinesterase
as a target for drug development in Alzheimer’s disease. Methods
Mol Biol 2089:257–286
52. Goodman LS (1996) Goodman and Gilman’s the pharmacological
basis of therapeutics. McGraw-Hill, New York
19. Schelterns P, Feldman H (2003) Lancet Neurol 2(9):539
20. Eskander MF, Nagykery NG, Leung EY, Khelghati B, Geula C
(2005) Brain Res 1060(1–2):144
53. Silva MA, Kiametis AS, Treptow W (2020). J Chem Inf Model.
https://doi.org/10.1021/acs.jcim.9b01073
21. Santarpia L, Grandone I, Contaldo F, Pasanisi F (2013) J Cachexia
Sarcopenia Muscle 4(1):31
54. Schneider LS (2000) Dialogues Clin Neurosci 2(2):111
55. Kandiah N, Pai M-C, Senanarong V, Looi I, Ampil E, Park KW,
Karanam AK, Christopher S (2017) Clin Interv Aging 12:697
56. Luo W, Su Y-B, Hong C, Tian R-G, Su L-P, Wang Y-Q, Li Y, Yue
J-J, Wang C-J (2013) Bioorg Med Chem 21(23):7275
57. Gao X-h, Liu L-b, Liu H-r, Tang J-j, Kang L, Wu H, Cui P, Yan J
(2018) J Enzyme Inhib Med Chem 33(1):110
22. Hartmann J, Kiewert C, Duysen EG, Lockridge O, Greig NH,
Klein J (2007) J Neurochem 100(5):1421
23. Manoharan I, Boopathy R, Darvesh S, Lockridge O (2007) Clin
Chim Acta 378(1–2):128
24. Greig NH, Utsuki T, Yu Q-s, Zhu X, Holloway HW, Perry T, Lee
B, Ingram DK, Lahiri DK (2001) Curr Med Res Opin 17(3):159
25. Lane RM, Potkin SG, Enz A (2006) Int J Neuropsychopharmacol
9(1):101
Publisher’s Note Springer Nature remains neutral with regard to
jurisdictional claims in published maps and institutional afliations.
26. Perry EK, Perry R, Blessed G, Tomlinson B (1978) Neuropathol
Appl Neurobiol 4(4):273
27. Bullock R, Lane R (2007) Curr Alzheimer Res 4(3):277
28. Sharma P, Srivastava P, Seth A, Tripathi PN, Banerjee AG, Shriv-
astava SK (2019) Prog Neurobiol 174:53
29. Smith MA, Rottkamp CA, Nunomura A, Raina AK, Perry
G (2000) Biochim Biophys Acta (BBA). Basis of Disease
1502(1):139
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