Computational design, synthesis and evaluation of new sulphonamide derivatives targeting HIV-1 gp120

Article abstract of DOI:10.1007/s10822-019-00258-0

Attachment of envelope glycoprotein gp120 to the host cell receptor CD4 is the first step during the human immunodeficiency virus-1 (HIV-1) entry into the host cells that makes it a promising target for drug design. To elucidate the crucial three dimensional (3D) structural features of reported HIV-1 gp120 CD4 binding inhibitors, 3D pharmacophores were generated and receptor based approach was employed to quantify these structural features. A four-partial least square factor model with good statistics and predictive ability was generated for the dataset of 100 molecules. To further ascertain the structural requirement for gp120-CD4 binding inhibition, molecular interaction studies of inhibitors with gp120 was carried out by performing molecular docking using Glide 5.6. Based on these studies, structural requirements were drawn and new molecules were designed accordingly to yield new sulphonamides derivatives. A water based green synthetic approach was adopted to obtain these compounds which were evaluated for their HIV-1 gp120 CD4 binding inhibition. The newly synthesized compounds exhibited remarkable activity (10-fold increase) when compared with the standard BMS 806. Further the stability of newly synthesized derivatives with HIV-1 gp120 was also investigated through molecular dynamics simulation studies. This provides a proof of concept for molecular modeling based design of new inhibitors for inhibition of HIV-1 gp120 CD4 interaction.

Full text of DOI:10.1007/s10822-019-00258-0

Journal of Computer-Aided Molecular Design
https://doi.org/10.1007/s10822-019-00258-0
Computational design, synthesis and evaluation of new sulphonamide
derivatives targeting HIV‑1 gp120
1
2
1
1
Radhika Vangala · Sree Kanth Sivan · Saikiran Reddy Peddi · Vijjulatha Manga
Received: 29 August 2019 / Accepted: 19 November 2019
©
Springer Nature Switzerland AG 2019
Abstract
Attachment of envelope glycoprotein gp120 to the host cell receptor CD4 is the ꢀrst step during the human immunodeꢀ-
ciency virus-1 (HIV-1) entry into the host cells that makes it a promising target for drug design. To elucidate the crucial
three dimensional (3D) structural features of reported HIV-1 gp120 CD4 binding inhibitors, 3D pharmacophores were
generated and receptor based approach was employed to quantify these structural features. A four-partial least square factor
model with good statistics and predictive ability was generated for the dataset of 100 molecules. To further ascertain the
structural requirement for gp120-CD4 binding inhibition, molecular interaction studies of inhibitors with gp120 was carried
out by performing molecular docking using Glide 5.6. Based on these studies, structural requirements were drawn and new
molecules were designed accordingly to yield new sulphonamides derivatives. A water based green synthetic approach was
adopted to obtain these compounds which were evaluated for their HIV-1 gp120 CD4 binding inhibition. The newly synthe-
sized compounds exhibited remarkable activity (10-fold increase) when compared with the standard BMS 806. Further the
stability of newly synthesized derivatives with HIV-1 gp120 was also investigated through molecular dynamics simulation
studies. This provides a proof of concept for molecular modeling based design of new inhibitors for inhibition of HIV-1
gp120 CD4 interaction.
Keywords HIV-1 gp120 · Pharmacophore · Molecular docking · Molecular dynamics · Sulphonamide derivatives
Introduction
that is larger than that obstructed by a typical antibody-pro-
tein interaction. The interface is reported to display several
unusual features, that includes a shallow, water-ꢀlled cavity
that is considered to function in immune evasion. A second
interfacial cavity penetrates into the hydrophobic interior
of HIV-1 gp120 that is bounded by Phe 43 residue of host
CD4. Mutagenesis, conservation and structural analysis all
indicate that this hydrophobic interior “Phe 43 cavity” and
its surrounding structures are critically important for CD4
binding. Accordingly, the CD4-gp120 cavity has been sug-
gested to be a potential target for drug design [2–4]. Publi-
cations and patent disclosures indicate that there has been
an increased eꢁort to identify small-molecule HIV-1 gp120
inhibitors which are able to block HIV-1 gp120-CD4 inter-
actions [5–10]. BMS-378806 (BMS 806) and BMS-488043
discovered through a cell-based screening assay, are potent
Human immunodeꢀciency virus-1 (HIV-1) has changed
to a chronic disease from an acute one but, unfortunately,
a cure is not on the perspective and there is an mounting
population of HIV-1 patients relying on existing therapies
[
1]. Attachment of the viral envelope glycoprotein gp120 to
host cell receptor CD4 is an essential step in the process of
HIV-1 entry into host cells, gp120 protein is an important
drug target. CD4 binds in a recessed cavity of HIV-1 gp120
Electronic supplementary material The online version of this
article (https://doi.org/10.1007/s10822-019-00258-0) contains
supplementary material, which is available to authorized users.
*
Vijjulatha Manga
vijjulathamanga@gmail.com
(
nano molar range) small-molecule inhibitors that prevent
1
Molecular Modeling and Medicinal Chemistry Group,
Department of Chemistry, University College of Science,
Osmania University, Hyderabad, Telangana 500 007, India
the binding of HIV-1 gp120 to CD4 receptors [11–13].
The consideration that Phe 43 cavity is highly con-
served and less prone to resistance-conferring mutations
2
Department of Chemistry, Nizam College, Osmania
University, Hyderabad 500 001, India
[
14], Phe 43 cavity was chosen as target for computational
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1
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Journal of Computer-Aided Molecular Design
simulations, a computational protocol based on pharmaco-
phoric model generation, and molecular docking, to identify
ligands targeting Phe 43 cavity that resulted in new chemical
scaꢁolds, able to inhibit HIV-1 replication [15, 16].
and the active ligands were chosen to derive a set of suit-
able pharmacophores. The prepared ligands were used
for generating pharmacophores that deꢀne the chemical
features of ligands. The features include H-bond accep-
tor (A), H bond donor (D), hydrophobic group (H), nega-
tively charged group (N), positively charged group (P),
and aromatic ring (R). Common pharmacophore hypoth-
esis was culled from all the conformation of active set
ligands. It uses a tree based partitioning technique that
groups together similar pharmacophores according to
their inter-site distance [30]. The resulting pharmaco-
phore hypotheses were scored and ranked, by algorithm
that considers the contributions from the alignment of site
points and vectors, volume overlap, selectivity, relative
conformational energy, number of ligands matched, and
activity [29]. After the careful analysis of the scores and
alignment of the active ligands to the generated hypoth-
esis, a best pharmacophore hypothesis AADRR (Fig. 1a)
was selected for further studies. The selected 3D phar-
macophore hypothesis included the following features:
two hydrogen bond acceptors (A), one donor (D) and two
aromatic rings (R). QSAR modeling was carried out using
the selected hypothesis by dividing the molecular dataset
into training (60) and test set (40) in a random manner. To
Materials and methods
A series of 100 reported gp120 inhibitors [17–25] were
used for the present study. The structures of the inhibi-
tors are provided in Supplementary Fig. S1. LigPrep [26]
was used to add hydrogens, convert 2D structures to three
dimensional (3D), generate stereo isomers and optionally
neutralize charged structures or determine the most proba-
ble ionization state at pH 7± 2. Conformers for each ligand
were generated using ConfGen by applying OPLS-2005
force ꢀeld method [27]. The three dimensional quantitative
structure–activity relationship (3D QSAR) studies were
carried out using PHASE [28, 29] module of Schrodinger
Suite 2010. The ligands were assigned as active and inac-
tive by providing activity threshold value were in activity
above 10 were consider active ligands and below 6.5 as
inactive ligands. The activity threshold value was selected
on the basis of dataset activity distribution (4.862–10.699)
Fig. 1 Common pharmacophore features: a common pharmacoph-
ore superimposed with highly active molecule [aromatic rings (dark
orange circle)], acceptor (pink sphere with two arrows), and donor
(distance are in Å unit), c alignment of all active ligands to the phar-
macophore, and d alignment of all inactive ligands to the pharmaco-
phore
(
blue sphere with arrow), b distance between pharmacophoric sites
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Journal of Computer-Aided Molecular Design
develop the 3D QSAR model that is useful in explaining
the structure–activity relationship, atom based alignment
option in PHASE was used [30, 31]. The models were
generated for the selected hypothesis using the training set
having a grid spacing of 1.0Å. The best QSAR model was
validated by predicting activities of test set molecules. A
four component [partial least square (PLS) factor] model
was obtained by incremental method where in PLS factor
was increased till there is no further improvement in the
model statistics or predictive ability [32].
diagrams while energetic contribution of protein–ligand
binding aꢂnities were estimated from binding free energy
calculations.
Results and discussion
The aim of this study was to elucidate the vital 3D structural
features of reported HIV-1 gp120 CD4 binding inhibitors, by
generating 3D pharmacophore and to enumerate the struc-
tural features by generating atom-based 3D QSAR model.
For the generation of pharmacophore model, 8 compounds
having activity greater than 10 were considered. To obtain
best possible combination of sites or features common to the
most active compounds four minimum sites and ꢀve maxi-
mum sites were used. Thirty common pharmacophore mod-
els were generated with diverse combination of variants in
which all models were considered for further QSAR model
development. Among these pharmacophores, the models
that showed better alignment with active compounds were
identiꢀed by mapping them and calculating the survival
score. The algorithm calculating survival score includes con-
tributions from the alignment of site points and vectors, vol-
ume overlap, selectivity, number of ligands matched, relative
conformational energy, and activity. Based on survival score
overall ranking of all the hypotheses is carried out and best
candidate hypothesis from the generated models is selected.
Developed hypothesis is also subjected to inactive sur-
vival scoring to discriminate between the active and inactive
molecules and survival score was calculated by subtract-
ing the inactive score from survival score of these phar-
macophores. Finally the models with maximum adjusted
survival score and lowest relative conformational energy
were selected for generating pharmacophore, atom based
alignment of gp120 inhibitors and model AADRR (Fig. 1a)
has been chosen because it produced good predictive power
above other models.
Molecular docking studies were carried out using
Glide 5.6 [33]. The crystal structure of the gp120-CD4
complex was obtained from the RCSB protein data bank
(
PDB: 1RZJ). The compounds have been evaluated using
Glide extra precision (Glide XP) docking mode. Prime/
MM-GBSA was used to predict the binding free energy of
ligand molecules to receptor. The docked protein–ligand
complexes were minimized by local optimization features
from prime and the energies of complex were calculated
using OPLS 2005 force ꢀeld and GB/SA continuum sol-
vent model. The binding free energy (ΔG ) was esti-
bind
mated using following equation:
ΔGbind = ER∶L−(E + E ) = ΔG + ΔGSA,
R
L
solv
where E is energy of the protein–ligand complex, E +E
L
R:L
R
is sum of the energies of the receptor and ligand separately.
ΔG
and ΔG are solvation energy and surface area
SA
solv
energy of the complex [34].
Molecular dynamics (MD) simulations were also per-
formed for BMS 806 (standard) and newly synthesized
molecules (V4 and V10) using Desmond 3.8 [35] as
reported by Peddi et al. [36]. The co-ordinates of protein
complexes were obtained from docking, OPLS-2005 force
ꢀ
eld was used for MD simulations. Simple point charge
water molecules were used for solvating the complexes
in an orthorhombic box (15 Å × 15 Å × 15 Å). To balance
the net charge counter ions were added and minimized
twice, initially with restraints on the solute and without
restraints later. The steepest descent and limited memory
Broyden–Fletcher–Goldfarb–Shanno (LBFGS) algorithms
were used for the minimization. Nose–Hoover thermo-
stat was used for controlling the temperature and Mar-
tyana–Tobaisklein barostat for monitoring the pressure.
Non-bonded interactions were pruned at 9 Å, particle
mesh Ewald (PME) method was used for including long
range electrostatics and hydrogen bonds were constrained
using SHAKE algorithm. MD simulations were carried
out for 10 ns at NPT canonical ensemble (temperature
The spatial arrangement of features along with their dis-
tance present in ꢀve-featured pharmacophore, AADRR, is
shown in Fig. 1b. As depicted in the Fig. 1a the two aromatic
ring features are mapped to the indole ring of the active
inhibitors, both hydrogen bond acceptor features are mapped
to the two carbonyls of oxalyl moiety and donor feature is
mapped onto the NH of indole ring.
For generating an atom-based 3D QSAR hypothesis, a
dataset of 60 molecules having inhibitory activity against
HIV-1 gp120 were taken. The model was validated using
4
0 molecules, which cover wide range of inhibitory activity.
The alignment generated by the best pharmacophore model
AADRR was used for QSAR model generation. Figure 1c, d
represents good alignment of the active ligands and scattered
alignment of inactive ligands to the developed pharmacoph-
ore model.
3
10 K) with a time step of 2 fs. The trajectories were
recorded at 4.8 ps with recording interval of 1.2 ps. The
stability of hydrogen bonds and RMSD’s were examined
during MD study with the help of simulation interaction
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Journal of Computer-Aided Molecular Design
Table 1 PLS statistical
r2
Stability RMSE q2
Hypothesis
AADRR.21
Factors SD
F
P
Pearson-R
parameters of 3D QSAR models
1
2
3
4
1
2
3
4
1
2
3
4
1.086 0.275
0.799 0.614
0.532 0.832
22
1.68E−05 0.925
0.794
0.583
0.654
0.685
0.826
0.652
0.712
0.721
0.893
0.878
0.809
0.833
0.489 0.725
45.4 1.61E−12 0.611
92.7 1.07E−21 0.265
0.724 0.852
0.653 0.809
0.620 0.794
0.447 0.698
0.655 0.816
0.590 0.772
0.579 0.765
0.354 0.619
0.375 0.616
0.470 0.687
0.438 0.664
0.384 0.914 146.5 1.24E−28 0.251
AADRR.262
AADRR.164
1.082 0.282
0.782 0.631
0.570 0.807
0.374 0.919 155.2 2.89E−29 0.123
1.093 0.266
0.714 0.692
22.7 1.30E−05 0.936
48.7 4.61E−13 0.560
78.2 5.16E−20 0.208
21.1 2.43E−05 0.904
64.1 2.63E−15 0.293
0.465 0.872 126.7 6.24E−25 0.125
0.328 0.937 205.9 2.15E−32 0.060
A QSAR model with good statistical reliability and pre-
dictive ability was generated for the dataset (Table 1). The
number of PLS factors included in the model development
is four as incremental increase in prediction accuracy and
statistical signiꢀcance was observed for each incremen-
tal increase in the incorporated PLS factors up to four.
The model exhibited 91% variance signifying a close
agreement of ꢀtting points on the regression line for the
observed and PHASE-predicted activity (Supplementary
Fig. S2; Table S1). The large value of F (146.5, Table 1)
indicates a statistically signiꢀcant regression model, which
is also supported by the small value of the variance ratio
(P), an indication of a high degree of conꢀdence. Further,
small values of standard deviation (0.383) of the regres-
sion and RMSE (RMSE=0.685) makes an apparent impli-
cation that the data used for model generation are amicable
for the QSAR analysis. Validity of the model deꢀned by
2
cross-validated correlation coeꢂcient (q = 0.619) that
2
was obtained by leave one out method. The q is a more
Fig. 2 QSAR visualization of various substituent’s eꢁect: a hydrogen-bond donor, b hydrophobic features, and c combined eꢁect (blue cubes
showing positive potential while red cubes showing negative potential of particular substitution)
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Journal of Computer-Aided Molecular Design
2
reliable and robust statistical parameter than r because it
is obtained by external validation method.
Analyses of the protein–ligand interaction for the mol-
ecules from docked complex display a similar binding mode
as that of the Phe 43 of CD4. The benzoyl group linked to
the piperazine ring occupied the hydrophobic cavity of Phe
43. Dock pose of indole derivatives into the Phe 43 cavity
showed a match between the compound moieties and CD4
residues Ser 42, Phe 43, and Leu 44. The benzoyl moiety
linked to piperazine is docked deep into the cavity than the
phenyl ring of the CD4 Phe 43. Binding pose of high active
indole derivative is shown in Fig. 3, it clearly indicates that
the hydrophobic interaction at this cavity plays a key role
in the binding of the inhibitors to the gp120 receptor. A
hydrogen bond interaction is seen between NH of the indole
ring and residue Lys 429 and NH of triazole ring with Trp
427. The triazole ring also showed π hydrophobic interac-
tions with His 105 and hydrogen bond interaction with Arg
476. Based on these vital information of molecular interac-
tion it can be summarized that molecules with hydropho-
bic groups can accommodate within this cavity and having
some hydrogen bond donor groups can interact with the
hydrophilic region at the opening of the cavity comprising
of residues Trp 427, Arg 476 would act as potential inhibi-
tors. Docking studies were also carried out on the native
unliganded structure (PDB ID 3DNN) and a trimer bound to
a small molecule in complex with human antibodies (PDB
ID 5U7M) by taking BMS-806, best active compound and
newly synthesized compounds. A similar trend in binding
was observed in all the three structures but the dock scores
were relatively less when compare to docking studies in the
native unliganded structure i.e. in 3DNN and dock scores
were increased for docking studies on 5U7M compared to
docking scores in 1RZJ. The dock scores, binding energies
and interaction pictures of the compounds with 3DNN and
3
D characteristics of the QSAR model is displayed (Fig. 2)
as cubes that represent the model and colour according to the
sign of their coeꢂcient values. Blue colour cubes represent
positive and red colour cubes represent negative coeꢂcients
respectively. Positive coeꢂcients indicate an increase in activ-
ity, negative coeꢂcients a decrease. The visualization of the
coeꢂcients is useful to identify characteristics of molecular
structures that tend to increase or decrease the activity. This
gives information as to which functional groups are advanta-
geous or disadvantageous at certain positions in a molecule.
The blue cubes in 3D plots of the 3D pharmacophore regions
refer to ligand regions in which the speciꢀc feature is vital for
better activity, whereas the red cubes indicate that particular
structural feature or functional group, which is not important
for the activity or probable reason for decreased binding aꢂn-
ity. Figure 2a illustrates that H-bond donor characteristic is
necessary at NH of indole ring. The eꢁect of hydrophobic
groups on gp120-CD4 binding inhibition is visible in Fig. 2b.
It can be deduced from the Fig. 2b that hydrophobic groups
are well tolerated near the benzoyl group on piperazine ring
(
green cubes). So, presence of hydrophobic groups at this
position is favourable for activity. Further, Fig. 2c showed
combined eꢁect of all the features. The presence of hydropho-
bic groups will have positive eꢁect (blue cubes). To further
establish the structural requirement for gp120 CD4 binding
inhibition, molecular interaction studies of inhibitors with
gp120 is carried out by performing molecular docking using
Glide 5.6. Prime MM/GBSA was also carried out post molec-
ular docking to obtain the binding aꢂnity (ΔG ) of ligands
bind
towards gp120. The Glide XP score and ΔG values of the
bind
molecules are provided in Supplementary TableS2.
Fig. 3 Binding pose of most potent molecule i.e. molecule 20 (pink lines represent hydrogen bond interactions whereas green lines represent π–π
interactions)
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Journal of Computer-Aided Molecular Design
5
U7M are provided in Supplementary Fig.S3 and TableS3.
hydrophobic cavity and PHASE studies showed two rings
were required for the activity (rings from indole: RR). We
presumed that benzimidazole moiety can encroach into the
hydrophobic cavity and satisꢀes the pharmacophoric feature
of two ring system. This assumption was validated by us
in our previous study [37] where we have synthesized N,
N-disubstituted ureas which showed good activity. To further
increase the potency of these molecules one of the benzimi-
dazole rings is substituted with other hydrophobic phenyl
rings. The urea moiety or the oxalyl moiety is substituted
This encouraged us to design new molecules with the above
said structural requirements.
Design of new gp120 inhibitors
The molecules which were reported as gp120 inhibitors
used in the present study were having hydrophobic groups
at both the ends (phenyl as well as indole rings). Docking
studies showed that the phenyl rings were going into the
Fig. 4 Strategy implied for
design of new molecules
Table 2 Structure, dock score
Molecule
Core
R
Dock score
kcal/mol)
ΔGbind (kcal/mol)
and binding free energy values
of new molecules
(
N
V1
V2
V3
V4
4-t-butyl
4-Methyl
4-Nitro
−5.863
−6.394
−6.408
−6.083
−44.982
−50.386
−60.362
−64.641
N
H
O
S
NH
R
2-Nitro
O
V5
V6
V7
4-t-butyl
4-Nitro
2-Nitro
−7.152
−7.574
−7.897
−64.321
−64.375
−62.056
N
N
H
R
O
S
NH
O
N
V8
V9
V10
BMS 806
4-t-butyl
4-Nitro
2-Nitro
−6.193
−7.170
−8.350
−5.607
−65.029
−66.727
−67.471
−62.137
N
H
O
NH
S
R
O
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Journal of Computer-Aided Molecular Design
with sulphonamide group, as some previous gp120 inhibi-
tors are reported to posses this group [38]. Thus incorporat-
ing the benzimidazole ring, the sulphonamide group and
the hydrophobic group new gp120 inhibitors were designed
The yield for amines A1, A2, A3 ranged from 68% to
82%, an appreciable result. The amines obtained were col-
umn puriꢀed and further reacted with diꢁerent sulphonyl
chlorides to give the ꢀnal compounds (Scheme2). In a typi-
cal procedure to synthesise sulphonamides, a suspension of
amine in water was treated with an addition of substituted
sulphonyl chlorides at room temperature and the reaction
was monitored through TLC (8–10 h) with yields ranging
between 74% and 95%. Bis-sulfonylated products were not
formed during course of reaction. All the obtained products
(
Fig. 4). Based on these assumptions 10 new molecules (V1
to V10) were designed. Structures of these molecules are
provided in Table 2. These were screened through PHASE
and care was taken while designing the new molecules
that at least four sites were matching out of the ꢀve sites
(
AADRR) (Fig. 5) obtained from PHASE.
1
13
The new molecules were docked into the protein active
were characterized by H NMR, C NMR, IR and mass
analysis.
site and MM/GBSA values were calculated and provided in
Table 2. All of these molecules show a good Glide XP scores
and similar binding energy values as that of BMS 806. Fig-
ure 6 shows binding interactions of BMS 806 and most
active molecules V4 and V10 among the newly designed
molecules.
HIV‑1 gp120 CD4 binding inhibition
Synthesized molecules were screened for their HIV-1 gp120
CD4 binding inhibition ability by HIV-1 gp120 CD4 cap-
ture enzyme-linked immunosorbent assay (ELISA). All the
screened compounds were found to inhibit HIV-1 gp120
CD4 binding in nano molar concentrations. Inhibitory activ-
ity (IC ) values are listed in Table 3 and Fig. 8 represents
Analysis of docking interaction of new molecules showed
a similar binding mode, as expected the benzimidazole
group was deeply seated in the hydrophobic cavity showing
π–π interactions with Trp 427 and Trp 112, the sulphona-
mide group showed hydrogen bond interaction with Gly 473
and Asp 368. The change in mode of interaction of newly
designed molecules is depicted in Fig. 7. To verify our
assumptions, these molecules were synthesized and gp120
CD4 binding inhibition studies were carried out.
5
0
the bar graph showing comparison between V1–10 and
BMS-806. All the molecules showed 10-folds increase in
the activity than the standard BMS 806 amongst which V4
showed highest potency. When XP scores are considered
V10 possess highest score but the potency though lowered
by ꢀve units than V4 that has 10-folds increase in the activ-
ity than the standard.
Synthesis of designed molecules
With the help of these activity studies it is ascertained
that our assumptions are correct in designing new molecules
by taking a two ring system, a sulphonamide group and a
hydrophobic phenyl group. All the molecules designed on
the basis of these criteria showed more or less similar inhibi-
tory values. Also they showed 10-folds increase in the activ-
ity compared with that of BMS 806.
Sulfonamides are a diverse group of pharmacophores [39,
4
0] widely used as anticancer, anticonvulsant, anti-inꢃam-
matory, antibacterial and antiviral agents [41–44]. To syn-
thesize our designed molecules in environmentally friendly
manner, we applied the base-free monosulfonylation of
amines in water which is reported by Kamal et al. [45–52]
Initially to begin with all the three amines A1, A2 and A3
are prepared by the literature procedure (Scheme1) [37, 53].
To further evaluate and compare the structural interac-
tions MD simulations were run for these molecules. In
order to assess the stability and molecular interactions of
Fig. 5 Alignment of new molecules on pharmacophore model obtained from PHASE a V4 and b V10
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Journal of Computer-Aided Molecular Design
Fig. 6 Dock pose, interactions of ligands in the binding cavity and ligand interaction diagram of BMS 806, new molecules V4 and V10 (pink
lines represent hydrogen bond interactions whereas green lines represent π–π interactions)
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Journal of Computer-Aided Molecular Design
of BMS 806 protein complex was found to be around
.59 Å whereas for V4 and V10 protein complex it was
2
around 2.43 Å and 2.41 Å respectively. The average stand-
ard deviations of these compounds (BMS 806, V4 and
V10) were found to be 0.41 Å, 0.27 Å and 0.33 Å respec-
tively. The values clearly indicate that newly designed
molecules have better binding and stability compared to
BMS 806.
Analysis of protein–ligand interactions is the most
important part in MD simulations. It describes the mode
of binding and changes occurring in binding during simu-
lations. Protein–ligand interactions or contacts are mainly
of four types: hydrogen bonding, hydrophilic, ionic, and
water bridged hydrogen bonding. The CD4 binding site
of gp120 includes Trp 112, Val 255, Asp 368, Glu 370,
Phe 382, Tyr 384, Ile 424, Asn 425, Trp 427, Gly 473,
Arg 474, Met 475, Arg 476 residues. Figure 10 shows
protein–ligand contact interaction over trajectory. From
Fig. 10 it is evident that the newly designed molecule V4
and V10 have shown more interactions compared to stand-
ard BMS 806.
Fig. 7 Comparison of docking interaction mode of newly designed
molecules and known indole based inhibitors
BMS 806 majorly showed only water mediated hydro-
gen bonds with Glu 370 and Met 475 for about 52% and
newly designed molecules V4, V10 and standard BMS
2
5% of simulation time respectively. The molecule was
8
06, MD simulations were performed for 10 ns. Figure 9
showing only hydrophobic interaction through its benzoyl
group. In case of molecule V4 it showed two hydrogen
bond interactions with Asp 368 and Asn 425 for 31% and
shows the RMSD of protein backbone as a function of
simulation time of each complex with respect to initial
structure. Initially, RMSD reached a value of 1.91 Å from
3
4% of simulation time, five water mediated hydrogen
1
3
Å during 0–500 ps and then retained between 1.95 and
.46 Å throughout the simulation up to 10 ns for pro-
bonds with Trp 112, Asp 368, Glu 370, Ser 375 and Gly
73 (Fig. 10). The benzimidazole ring of the molecules
showed specific π–π interactions with Phe 382 and Tyr
4
tein complex of compound BMS 806. On the other hand
RMSD of molecules V4 and V10 reached a value of 1.66Å
and 1.82 Å initially, and then retained between 1.98 and
3
84 for 45% and 31% of simulation time respectively.
Interaction studies of V10 showed hydrogen bond inter-
action with Asn 425 for 99% of simulation time and 91%
2
.72 Å in case of V4 and 1.80 and 3.18 Å in case of V10
during entire 10 ns simulation time. The averaged RMSD
Scheme 1 Synthesis of amines
N
NH2
A1, A2 and A3
5.5M HCl
NH2
NH
+ O
Reflux 3 days
NH2
NH2
OH
2
A1
1
NH2
NH2
O
5
.5M HCl
N
NH2
NH
2
+
Reflux 12 hrs
HO
N
H
3
1
A2
NH2
N
5
.5M HCl
O
NH2
+
NH2
Reflux 12 hrs
N
H
OH
NH2
4
1
A3
1
3

Journal of Computer-Aided Molecular Design
O
S
O
Cl
N
N
RT, water
+
NH
NH
NH
NH2
R
R
O
S
A1
5
O
R = p-t-butyl (V1), p-CH (V2), p-nitro (V3), o-nitro (V4)
3
O
O
S
Cl
N
N
RT, water
NH
S
NH2
+
N
H
N
H
R
O
R
A2
5
O
R = p-t-butyl (V5), p-nitro (V6), o-nitro (V7)
O
N
N
O
S
Cl
N
RT, water
H
+
NH
N
H
NH2
O
S
R
5
A3
O
R = p-t-butyl (V8), p-nitro (V9), o-nitro (V10)
Scheme 2 Synthesis of target compounds V1–V10
with Gly 473. The benzimidazole ring showed π–π inter-
action with Trp 427. The molecule also showed water
mediated hydrogen bonds with Asp 368 and Asp 474
BMS 806 and hence they have better binding with gp120.
Thus validating our design protocol, that was based on
CADD for design of HIV-1 gp120 inhibitor that can act
as antiviral agents.
(
Fig. 10). The analysis of MD simulation study of the
complexes sufficiently explains the binding modes of
inhibitors and variation in their binding affinity. It is evi-
dent from these results that the newly designed molecules
V4 and V10 are showing better interaction compared to
Conclusions
The PHASE and dock pose analysis of the inhibitors of
HIV-1 gp120 revealed important pharmacophoric features
that are required for better binding. The requirements
include the need of a hydrophobic group that can interact
with the hydrophobic cavity in gp120 and also have donor
atoms that can form hydrogen bond with residues Trp 427
and Asp 368. The systematic substitution of a sulphona-
mide group and benzimidazole ring was carried out for
design of new molecules that have potential to act as CD4
mimic and inhibit the entry of HIV-1 viral components
into the cell by hindering the binding of HIV-1 gp120 to
CD4. Proof of concept for enhanced HIV-1 gp120 inhibi-
tory activity of these new molecules were experimentally
validated by HIV-1 gp120 CD4 capture ELISA. All the
designed compounds were found to inhibit HIV-1 gp120
Table 3 HIV-1 gp120 CD4
Molecule
IC50 (nM)± SD
binding inhibition (IC values)
5
0
for synthesized molecules
V1
21.74± 0.21
23.34± 0.15
25.16± 0.34
21.50± 0.62
27.45± 0.62
28.15± 0.99
28.77± 0.30
25.35± 0.35
23.63± 0.29
26.76± 0.65
274.53± 0.64
V2
V3
V4
V5
V6
V7
V8
V9
V10
BMS 806
1
3

Journal of Computer-Aided Molecular Design
Fig. 8 Bar graph showing
inhibitory activities of synthe-
sized molecules and standard
BMS 806
Fig. 9 Red color shows RMSD
of protein back bone of mol-
ecule BMS 806 gp120 complex
whereas green and violet color
shows RMSD of protein back
bone of molecules V4 and V10
gp120 complex
CD4 binding in nano molar concentrations. A simple,
convenient, effective and environment friendly method
was used for the preparation of sulphonamides in a vola-
tile organic solvent free reaction.
Typical procedure for preparation of amines (A1–A3)
To a mixture of o-phenylenediamine (0.01 mol) and ami-
nocarboxylic acid (0.015 mol), 20 ml of 5.5 M hydrochlo-
ric acid was added and the reaction mixture was reꢃuxed
for 12 h to 3 days at 150 °C. The reaction mixture was
cooled over night in a refrigerator and then neutralised
with ammonia solution to obtain the corresponding amines
which is ꢀltered, washed and recrystallized from ethanol
[37, 53].
Experimental
Chemicals and solvents were purchased from Aldrich/
Fluka or from local manufacturers. Further purifica-
1
tion was done according to standard procedures [54]. H
NMR spectra were recorded in DMSO-D and CDCl
6
3
on Bruker (Bio-spin) Ultrashield Arance-III Nano Bay
General procedure of preparation (V1–V10)
4
00 MHz NMR spectrometer using TMS as internal
standard. IR spectra were recorded on Tensor-27 (Bruker-
optics) FTIR spectrophotometer. LC–MS were obtained
on SHIMADZU 2010A. Melting points were measured
and uncorrected.
A suspension of amine A1–A3 (1 mmol) in water is taken
in a round bottomed flask; to this sulphonyl chloride
(1 mmol) was added. This was stirred at room temperature
1
3

Journal of Computer-Aided Molecular Design
Fig. 10 Average conformation of the binding pocket of protein complexed with BMS 806, V4 and V10 throughout the simulation
while monitoring the reaction. The product was ꢀltered,
recrystallised using methanol–water and column puriꢀed.
N‑(4‑((1H‑benzo[d]imidazol‑2‑yl methyl) phenyl‑4‑methyl
benzenesulphonamide (V2)
1
13
The structures were conꢀrmed by H NMR, C NMR, IR
and mass spectral analysis.
White amorphous solid Yield 74%; Melting point
−
1
2
16–218 °C; IR (KBr) cm : 1152 and 1320 (S=O), 1512
1
N‑(4‑((1H‑benzo[d]imidazol‑2‑yl methyl)
phenyl‑4‑(tert‑butyl)benzenesulphonamide (V1)
(C=N), 2912 (CH, aliphatic), 3266 (NHSO ); H NMR
2
[DMSO-D , 400 MHz]: δ 2.32 (s, 3H, CH ), 4.40 (s, 2H,
6
3
CH ), 7.09–7.12 (m, 3H, Ar–H), 7.33–7.35 (d, J=8 Hz, 2H,
2
White amorphous solid Yield 87%; Melting point
Ar–H), 7.47–7.52 (m, 3H, Ar–H), 7.65–7.67 (d, J=8 Hz,
−
1
1
72–174 °C; IR (KBr) cm : 1160 and 1338 (S=O), 1507
2H, Ar–H), 7.73–7.75(q, J =J =4 Hz, 2H, Ar–H), 10.40 (s,
1
2
1
13
(
C=), 2925 (CH, aliphatic), 3240 (NHSO ), 3536 (NH); H
1H, NH), 14.71 (bs, 1H, NH); C NMR (CDCl , 100 MHz)
2
3
NMR [DMSO-D , 400 MHz]: δ 1.24 (s, 9H, CH ), 4.04 (s,
δ 20.9, 34.1, 110.8, 118.2, 120.1, 121.5, 126.6, 129.4, 129.6,
133.2, 136.2, 136.8, 143.1, 153.3; MASS (ESI): 378 [M+1];
HRMS (ESI), m/z calcd for C H N O S (MH+) 378.1276,
6
3
2
H, CH ), 7.04–7.06 (d, J = 8 Hz, 2H, Ar–H), 7.08–7.11
2
(
m, 2H, Ar–H), 7.17–7.19 (d, J = 8 Hz, 2H, Ar–H),
.37–7.39 (m, 2H, Ar–H), 7.54–7.57 (d, J = 12 Hz, 2H,
Ar–H), 7.68–7.70 (d, J = 8 Hz, 2H, Ar–H), 10.27 (s, 1H,
21
19
3
2
7
found 378.1273.
1
3
NH), 12.20 (s, 1H, NH); C NMR (CDCl , 100 MHz) δ
N‑(4‑((1H‑benzo[d]imidazol‑2‑yl methyl) phenyl‑4‑nitro
benzenesulphonamide (V3)
3
2
8.9, 31.1, 34.1, 114.5, 120.9, 121.3, 124.6, 128.2, 129.7,
1
34.1, 135.3, 144.9, 149.8, 153.3; MASS (ESI): 420 [M+1];
HRMS (ESI), m/z calcd for C H N O S (MH+) 420.1746,
Yellow amorphous solid Yield 83%; Melting point
24
25
3
2
−
1
found 420.1741.
202–204 °C; IR (KBr) cm : 1160 and 1347 (S=O), 1524
(
N=O), 2923 (CH, aliphatic), 3375 (NHSO ), 3619 (NH);
2
1
3

Journal of Computer-Aided Molecular Design
1
H NMR [DMSO-D , 400 MHz]: δ 4.08 (s, 2H, CH ),
(d, J=8 Hz, 2H, Ar–H), 7.97–7.99 (d, J=8 Hz, 2H, Ar–H),
6
2
1
3
7
.06–7.08 (m, 4H, Ar–H), 7.22–7.24 (d, J = 8 Hz, 2H,
8.25–8.28 (d, J = 12 Hz, 2H, Ar–H); C NMR (CDCl ,
3
Ar–H), 7.44–7.55 (d, J= 4 Hz, 2H, Ar–H), 7.99–8.01 (d,
J = 8 Hz, 2H, Ar–H), 8.35–8.37 (d, J = 8 Hz, 2H, Ar–H),
100 MHz) δ 120.4, 121.3, 123.8, 126.9, 127.5; MASS
(ESI): 394 [M+1]; HRMS (ESI), m/z calcd for C H N O S
19
14
4
4
1
3
1
1
1
0.59 (bs, 1H, NH), 12.20 (bs, 1H, NH); C NMR (CDCl ,
(MH+) 395.0814, found 395.0816.
3
00 MHz) δ 34.1, 120.9, 121.3, 124.6, 128.2, 129.6, 134.1,
35.3, 144.9, 149.8, 152.6; MASS (ESI): 409 [M+1];
N‑(4‑(1H‑benzo[d]imidazol‑2‑yl)phenyl)‑2‑nitro
benzenesulphonamide (V7)
HRMS (ESI), m/z calcd for C H N O S (MH+) 409.0971,
20
16
4
4
found 409.0969.
Yellow crystalline solid Yield 82%; Melting point
−
1
1
50–152 °C; IR (KBr) cm : 1163 and 1364 (S=O), 1534
N‑(4‑((1H‑benzo[d]imidazol‑2‑yl methyl) phenyl‑2‑nitro
benzenesulphonamide (V4)
(N=O), 3019 (CH, aliphatic), 3324 (NHSO ), 3468 (NH);
2
1
H NMR [DMSO-D , 400 MHz]: δ 7.11–7.19 (m, 2H,
6
Ar–H), 7.29–7.31 (d, J = 8 Hz, 2H, Ar–H), 7.56 (s, 1H,
Ar–H), 7.82–7.88 (m, 2H, Ar–H), 8.00–8.02 (d, J=8 Hz,
1H, Ar–H), 8.05–8.08 (m, 3H, Ar–H), 11.08 (s, 1H, NH),
Yellow amorphous solid Yield 95%; Melting point
−
1
1
32–135 °C; IR (KBr) cm : 1149 and 1361 (S=O), 1532
1
3
(
N=O), 2924 (CH, aliphatic), 3197 (NHSO ), 3530 (NH);
12.79 (s, 1H, NH); C NMR (CDCl , 100 MHz) δ 120.1,
2
3
1
H NMR [DMSO-D , 400 MHz]: δ 4.09 (s, 2H, CH ),
121.9, 124.7, 126.3, 127.6, 129.9, 131.3, 132.6, 134.8,
138.2, 147.9, 150.5; MASS (ESI): 394 [M+1]; HRMS
(ESI), m/z calcd for C H N O S (MH+) 395.0814, found
6
2
7
.08–7.12 (m, 4H, Ar–H), 7.23–7.25 (d, J = 8 Hz, 2H,
Ar–H), 7.44–7.47 (m, 2H, Ar–H), 7.77–7.84 (m, 2H, Ar–H),
1
9
14
4
4
7
.96–7.98 (d, J=8 Hz, 2H, Ar–H), 10.76 (s, 1H, NH), 12.17
395.0816.
1
3
(
bs, 1H, NH); C NMR (CDCl , 100 MHz) δ 34.1, 120.6,
3
1
1
21.3, 124.6, 128.2, 129.6, 129.9, 131.5, 132.5, 134.5,
35.1, 147.9, 153.3; MASS (ESI): 409 [M+1]; HRMS
N‑(3‑(1H‑benzo[d]imidazol‑2‑yl)phenyl)‑4‑(tert butyl)
benzenesulphonamide (V8)
(
ESI), m/z calcd for C H N O S (MH+) 409.0971, found
2
0
16
4
4
4
09.0969.
White amorphous solid Yield 75%; Melting point
−
1
2
38–240 °C; IR (KBr) cm : 1159 and 1331 (S=O), 2922
1
(
CH, aliphatic), 3238 (NHSO ); H NMR [DMSO-D ,
2
6
N‑(4‑(1H‑benzo[d]imidazol‑2‑yl)phenyl)‑4‑(tert butyl)
benzenesulphonamide (V5)
400 MHz]: δ 1.23 (s, 9H, CH ), 7.22–7.28 (m, 3H, Ar–H),
3
7.42–7.64 (t, J =J =8 Hz, 1H, Ar–H), 7.57–7.61 (m, 4H,
1
2
Ar–H), 7.77–7.83 (m, 4H, Ar–H), 8.08 (s, 1H, Ar–H),
1
3
White amorphous solid Yield 78%; Melting point
10.53 (s, 1H, NH), 12.98 (s, 1H, NH); C NMR (CDCl ,
3
−
1
3
00–302 °C; IR (KBr) cm : 1162 and 1335 (S=O), 2924
100 MHz) δ 30.6, 34.8, 117.9, 121.0, 121.8, 122.2, 126.1,
126.5, 129.8, 131.1, 136.8, 138.6, 150.6, 155.9; MASS
(ESI): 406 [M+1]; HRMS (ESI), m/z calcd for C H N O S
1
(
CH, aliphatic), 3259 (NHSO ); H NMR [DMSO-D ,
2
6
4
00 MHz]: δ 1.25 (s, 9H, CH ), 7.17–7.19 (d, J=8 Hz, 2H,
3
23 23
3
2
Ar–H), 7.27–7.29 (d, J= 8 Hz, 2H, Ar–H), 7.48–7.49 (d,
J=4 Hz, 1H, Ar–H), 7.76–7.79 (d, J=12 Hz, 2H, Ar–H),
(MH+) 406.1589, found 406.1588.
7
2
.58–7.60 (d, J=8 Hz, 3H, Ar–H), 8.02–8.04 (d, J=8 Hz,
N‑(3‑(1H‑benzo[d]imidazol‑2‑yl)phenyl)‑4‑nitro
benzenesulphonamide (V9)
13
H, Ar–H), 10.62 (s, 1H, NH), 12.75 (s, 1H, NH); C NMR
(
CDCl , 100 MHz) δ 30.7, 34.8, 111.1, 118.6, 119.1, 121.5,
3
1
1
22.3, 125.4, 126.2, 126.5, 127.5, 138.8, 139.3, 150.7,
56.1; MASS (ESI): 406 [M+1]; HRMS (ESI), m/z calcd
Pale green amorphous solid, Yield 90%; Melting point
−
1
132–134 °C; IR (KBr) cm : 1162 and 1347 (S=O), 1528
1
for C H N O S (MH+) 406.1589, found 406.1585.
(N=O), 2920 (CH, aliphatic), 3384 (NHSO ); H NMR
2
3
23
3
2
2
[
DMSO-D , 400 MHz]: δ 7.20–7.24 (m, 3H, Ar–H),
6
7
.44–7.48 (t, J =J =8 Hz, 1H, Ar–H), 7.60 (s, 2H, Ar–H),
1 2
N‑(4‑(1H‑benzo[d]imidazol‑2‑yl)phenyl)‑4‑nitro
benzenesulphonamide (V6)
7.86–7.88 (d, J=8 Hz, 1H, Ar–H), 8.04–8.06 (d, J=8 Hz,
3H, Ar–H), 8.38–8.40 (d, J = 8 Hz, 2H, Ar–H), 10.83 (s,
1
3
1
H, NH), 12.97 (s, 1H, NH); C NMR (CDCl , 100 MHz)
3
Orange amorphous solid Yield 81%; Melting point
δ 118.9, 122.0, 122.7, 124.7, 128.3, 129.9, 131.3, 137.6,
144.7, 149.8, 150.4; MASS (ESI): 394 [M+1]; HRMS
(ESI), m/z calcd for C H N O S (MH+) 395.0814, found
−
1
1
28–130 °C; IR (KBr) cm : 1122 and 1349 (S=O), 1565
1
(
[
N=O), 2922 (CH, aliphatic), 3414 (NHSO ); H NMR
2
19 14
4
4
DMSO-D , 400 MHz]: δ 7.00–7.02 (d, J = 8 Hz, 2H,
395.0815.
6
Ar–H), 7.11 (s, 1H, Ar–H), 7.49 (s, 1H, Ar–H), 7.82–7.84
1
3

Journal of Computer-Aided Molecular Design
N‑(3‑(1H‑benzo[d]imidazol‑2‑yl)phenyl)‑2‑nitro
benzenesulphonamide (V10)
Supplementary
Structures, Glide XP scores, activity values, ΔG
val-
bind
Light yellow amorphous solid Yield 90%; Melting point
ues, ꢀtness scores and predicted activity values along
with scatter plot of experimental versus predicted activity
values of HIV-1 gp120 inhibitors used in this study were
−
1
1
80–182 °C; IR (KBr) cm : 1164 and 1350 (S=O), 1538
1
(
N=O), 2921 (CH, aliphatic), 3357 (NHSO ); H NMR
2
1
13
[
DMSO-D , 400 MHz]: δ 7.20–7.24 (m, 3H, Ar–H), 7.45–7.48
provided in supplementary. H NMR, C NMR, IR, Mass
and HRMS spectra of synthesized compounds (V1–V10)
were also provided in supplementary.
6
(
t, J =J =8 Hz, 1H, Ar–H), 7.60 (s, 2H, Ar–H), 7.82–7.88
1
2
(
m, 3H, Ar–H), 7.98–8.05 (m, 3H, Ar–H), 10.91 (s, 1H, NH),
1
3
1
1
1
2.95 (s, 1H, NH); C NMR (CDCl , 100 MHz) δ 119.1,
3
22.1, 122.7, 124.7, 129.9, 131.1, 132.6, 134.7, 137.3, 147.8,
Acknowledgements We greatly acknowledge Schrӧdinger LLC, New
York for providing the software. This research was made possible
through grants from DST-SERB (EEQ/2018/000117).
50.4; MASS (ESI): 394 [M+1]; HRMS (ESI), m/z calcd for
C H N O S (MH+) 395.0814, found 395.0815.
19
14
4
4
Compliance with ethical standards
HIV‑1 gp120 CD4 capture enzyme‑linked
immunosorbent assay (ELISA)
Conflict of interest Authors do not have any conꢃict of interest.
Synthesized molecules were screened for their HIV-1 gp120
CD4 binding inhibition ability by HIV-1 gp120 CD4 capture
ELISA as reported by Sivan et al. [37]. Several dilutions of
positive reference CD4 (1000 ng/ml) in diluents buꢁer were
prepared and labeled accordingly: 1000 ng/ml to 0.5 ng/ml in
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