Anti-HIV potential of diarylpyrimidine derivatives as non-nucleoside reverse transcriptase inhibitors: design, synthesis, docking, TOPKAT analysis and molecular dynamics simulations

Article abstract of DOI:10.1080/07391102.2020.1748111

In view of the low toxicity of NNRTIs in comparison to NRTIs, a new series of diarylpyrimidine derivatives has been designed as NNRTIs against HIV-1. In silico studies using DS 3.0 software have shown that these compounds behaved as NNRTIs while interacting at the allosteric site of HIV-RT. The designed compounds have shown promising docking results, which revealed that all compounds formed hydrogen bonds with Lys101, Lys103, Tyr181, Tyr318 and π- interactions with Tyr181, Tyr188, Phe227 and Trp229 amino acid residues located in the non-nucleoside inhibitor binding pocket (NNIBP) of HIV-RT protein. The intended molecules have shown high binding affinity with HIV-1 RT, analogous to standard drug molecule—etravirine. TOPKAT results confirmed that the designed compounds were found to be less toxic than the reference drug. Further, employing molecular dynamics simulations, the complexes of the best screened compound 6 and etravirine with the HIV-1 RT protein were analyzed by calculating the RMSD, RMSF, R _g, number of hydrogen bonds, principal components of the coordinates, molecular mechanics-Poisson-Boltzmann surface area-based binding free energy and their decomposition for different interactions. The analysis demonstrated the higher stability of compound 6 than the standard drug etravirine with HIV-1 RT. The interactions like hydrogen-bonding, van-der-Waals, electrostatic and the solvent accessible surface energy have favorable contributions to the complex stability. Thus, the shortlisted designed compound has great promise as a potential inhibitor against HIV-1 RT.

Full text of DOI:10.1080/07391102.2020.1748111

Journal of Biomolecular Structure and Dynamics
ISSN: 0739-1102 (Print) 1538-0254 (Online) Journal homepage: https://www.tandfonline.com/loi/tbsd20
Anti-HIV potential of diarylpyrimidine derivatives
as non-nucleoside reverse transcriptase inhibitors:
Design, synthesis, docking, TOPKAT analysis and
molecular dynamics simulations
Vishal K. Singh, Ritika Srivastava, Parth Sarthi Sen Gupta, Farha Naaz,
Himani Chaurasia, Richa Mishra, Malay Kumar Rana & Ramendra K. Singh
To cite this article: Vishal K. Singh, Ritika Srivastava, Parth Sarthi Sen Gupta, Farha Naaz,
Himani Chaurasia, Richa Mishra, Malay Kumar Rana & Ramendra K. Singh (2020): Anti-HIV
potential of diarylpyrimidine derivatives as non-nucleoside reverse transcriptase inhibitors: Design,
synthesis, docking, TOPKAT analysis and molecular dynamics simulations, Journal of Biomolecular
Structure and Dynamics, DOI: 10.1080/07391102.2020.1748111
To link to this article: https://doi.org/10.1080/07391102.2020.1748111
View supplementary material
Accepted author version posted online: 27
Mar 2020.
Submit your article to this journal
View related articles
View Crossmark data
Full Terms & Conditions of access and use can be found at
https://www.tandfonline.com/action/journalInformation?journalCode=tbsd20

Anti-HIV potential of diarylpyrimidine derivatives as non-nucleoside reverse transcriptase
inhibitors: Design, synthesis, docking, TOPKAT analysis and molecular dynamics
simulations
1
2
2
1
1
Vishal K. Singh , Ritika Srivastava , Parth Sarthi Sen Gupta , Farha Naaz , Himani Chaurasia , Richa
1
2
1
Mishra , Malay Kumar Rana and Ramendra K. Singh *
1
Bioorganic Research Laboratory, Department of Chemistry, University of Allahabad, Prayagraj- 211002, India
2
Department of Chemical Sciences, Indian Institute of Science Education and Research Berhampur, Odisha-760010,
India
*Corresponding author: Ramendra K. Singh, rksinghsrk@gmail.com
Abstract
In view of the low toxicity of NNRTIs in comparison to NRTIs, a new series of
diarylpyrimidine derivatives has been designed as NNRTIs against HIV-1. In silico studies using
DS 3.0 software have shown that these compounds behaved as NNRTIs while interacting at the
allosteric site of HIV-RT. The designed compounds have shown promising docking results,
which revealed that all compounds formed hydrogen bonds with Lys101, Lys103, Tyr181,
Tyr318 and π- interactions with Tyr181, Tyr188, Phe227 and Trp229 amino acid residues
located in the non-nucleoside inhibitor binding pocket (NNIBP) of HIV-RT protein. The
intended molecules have shown high binding affinity with HIV-1 RT, analogous to standard drug
molecule - etravirine. TOPKAT results confirmed that the designed compounds were found to be
less toxic than the reference drug. Further, employing molecular dynamics simulations, the
complexes of the best screened compound 6 and etravirine with the HIV-1 RT protein were
analyzed by calculating the RMSD, RMSF, R , number of hydrogen bonds, principal
g
components of the coordinates, molecular mechanics-Poisson-Boltzmann surface area-based
binding free energy and their decomposition for different interactions. The analysis demonstrated
the higher stability of compound 6 than the standard drug etravirine with HIV-1 RT. The
interactions like hydrogen-bonding, van-der-Waals, electrostatic and the solvent accessible
surface energy have favorable contributions to the complex stability. Thus, the shortlisted
designed compound has great promise as a potential inhibitor against HIV-1 RT.
Highlights

New diarylpyrimidine derivatives have been designed as potential anti-HIV agents.
1





The compounds were docked at the allosteric site of HIV-RT protein (PDB ID: 3MEC) to
study the stability of protein-ligand complex.
Docking studies indicated the stable ligand-protein complexes of all designed
compounds.
The TOPKAT protocol in DS 3.0 software was used to evaluate the toxicity of the
designed diarylpyrimidine derivatives.
Molecular Dynamics studies were performed on best screened compound.
Graphical Abstract
SASA of Etravirine: HIV RT (black) &
Compound 6: HIVRT (red) Complexes
Compound 6: HIV RT Complex
Keywords: HIV-RT, Docking, QSTR analysis, Molecular dynamics, MM/PBSA, PCA
Abbreviations: ABNR - Adopted Basis Set Newton Raphson, ADME - Absorption Distribution
Metabolism Excretion, ART - Antiretroviral therapy, ED - Essential Dynamics, EV - Eigen
Vector, DS - Dock Score, DS 3.0 - Discovery Studio 3.0, DTP - Developmental Toxicity
Potential, EC - 50% Effective Concentration, FDA - Food and Drug Administration, GPCR -
50
G-protein coupled receptor, HBA - Hydrogen Bond Acceptor, HBD - Hydrogen Bond Donor,
LC - 50% Lethal Concentration, LD - 50% Lethal Dose, LIG - Ligand Internal energy, MDS
5
0
50
2

-
Molecular Dynamics Simulation, MM - Molecular Mechanism, MW - Molecular Weight,
NNIBP - Non-Nucleoside Inhibitor Binding Pocket, NNRTIs - Non-Nucleoside Reverse
Transcriptase Inhibitors, NRTIs - Nucleoside Reverse Transcriptase Inhibitors, PBSA - Poisson-
Boltzmann Surface Area, PCA - Principal Component Analysis, PLP - Piecewise Linear
Potential, QSTR - Quantitative Structure Toxicity Relationship, R - Radius of gyration, RMSD -
g
Root-Mean Square Deviation, RMSF - Root-Mean Square Fluctuation, RT - Reverse
Transcriptase, SASA - Solvent-Accessible Surface Area, TPSA - Total Polar Surface Area
1
. Introduction
The reverse transcriptase (RT) enzyme of HIV plays a vital role in viral replication and,
thus becomes a dedicated target for the discovery of new HIV-RT inhibitors [Jonckheere et al.,
000, Yisma et al., 2014, Bhole et al., 2020]. Till date, several RT inhibitors belonging to the
2
categories either nucleoside reverse transcriptase inhibitors (NRTIs) or non-nucleoside reverse
transcriptase inhibitors (NNRTIs) have been discovered [Boone et al., 2006]. Currently, NNRTIs
have been used as an important ingredient of drug regimen for HIV-1 treatment in antiretroviral
therapy (ART) [Kaufmann et al., 2000, Vella et al., 2000, Wang et al., 2019] due to their high
potency and low toxicity. Six NNRTIs, i-vi (Fig. 1) have been approved by the US Food and
Drug Administration (FDA) and are currently being used in the treatment of HIV/AIDS [Shaung
et al., 2019, Gu et al., 2018].
Diarylpyrimidine derivatives, i.e., rilpivirine (iv) and etravirine (v) represent a new
generation of NNRTIs having high potency against wild and mutant type HIV strains [
Sarafianos et al., 2009, Chen et al., 2011, Zhan et al., 2016, Zuo et al., 2018]. Therefore, these
two NNRTIs have a better chance to reduce the problem of drug resistance, which was observed
in earlier NNRTIs. However, resistance mutation in the new generation NNRTIs, i.e., rilpivirine
(iv) and etravirine (v) have also been observed in patients [Anta et al., 2013, Wainberg et al.,
2
016]. So, the scientific community is still putting serious efforts to discover additional NNRTI
drugs, with different heterocyclic scaffolds, belonging to the new generation of molecules having
high specificity and low toxicity [Yang et al., 2016, Huang et al., 2015, Chen et al., 2015, Wu et
al., 2014].
In continuation of our previous work on first-generation NNRTIs of oxathiadiazole
derivatives against HIV-1 [ Yadav et al., 2019, Kumari et al., 2016, Singh et al., 2015], we,
3

hereinreport the design and anti-HIV potential of some new diarylpyrimidine derivatives, 1-8, as
second-generation NNRTIs (Fig. 2) having different substituents on aromatic rings A, B and C.
All these molecules have been designed through docking simulations and essential drug-
like characteristics using computational methods. All molecules have been docked in non-
nucleoside inhibitor binding pocket (NNIBP), the allosteric site of HIV-RT protein to prove their
behaviour as potential NNRTIs.
These substituted diarylpyrimidine derivatives, 1-8, have been synthesized from 2-4-
dichloro-pyrimidine. In compounds 1-8, modifications were introduced in rings A, B and C of
diarylpyrimidine. Introduction of different substituents at 4/5/6 positions of rings B and C of
designed compounds established that these groups (hydrophobic/hydrophilic) affect the drug like
properties and in silico anti-HIV potency of all molecules.
The analysis and comparison of molecular dynamics (MD) simulations and molecular
mechanics-Poisson-Boltzmann surface area (MM/PBSA) based binding free energies between
the target-ligand and target-etravirine complex and demonstrated a better stability of the former
than the latter, which was characterized by a larger contribution of hydrogen bonding, van der
Waals, electrostatic interactions and lower RMSD values, resulting in a larger negative binding
free energy between the target-ligand complex [Cong et al., 2020, Triki et al., 2019, Sk et al.,
2
2
2
020]
. Materials and methods
.1. Design of diarylpyrimidine derivatives as NNRTIs
Pyrimidine moiety plays a significant role in biological system and also works as a
building block of nucleic acids. So, considering the importance of pyrimidine nucleobases, 20
pyrimidine derivatives have been designed as NNRTIs against HIV-1 on the basis of Lipinski’s
rule of five using Moleinspiration and Chemdraw software. After performing the in silico
structure based assessment, eight (1-8) promising molecules have been selected for further
studies.
Some physicochemical descriptors, like molecular weight (MW), hydrogen bond donor
(HBD), hydrogen bond acceptor (HBA), and the logarithmic partition coefficient in
octanol/water, and logP (lipophilicity) have been used to investigate or evaluate the drug like
properties of the molecules under study [Lipinski et al., 2015].
2
.2. Docking analysis
4

Molecular docking simulation is a computational process used to evaluate the binding
efficiency of derived ligands to the target receptor protein by keeping the target receptor protein
rigid and the varying conformations and poses of ligands. Docking simulation was performed by
Discovery Studio (DS) 3.0 software (Accelrys Ltd, USA). The x-ray crystal structure of wild
type HIV-1 RT protein bound to the etravirine was retrieved from the protein data Bank
(www.rcsb.org) (PDB ID:3MEC) and used as a docking receptor protein [ Yadav et al., 2019].
During the docking process from the receptor target protein 3MEC, ligand was extracted,
missing hydrogens added and the position of atoms in the protein was optimized using CHARM
force field and the Adopted Basis Set Newton Raphson (ABNR) method available in the protocol
of DS 3.0 software using default parameters. The ligand preparation and docking process have
been according to the previously published procedure using the DS, 3.0 protocol default
parameters [Yadav et al., 2019, Muegge et al., 1999, Lagos et al., 2008, Singh et al., 2016].
Finally, the hydrogen bonding and non-covalent interactions like π-π, π-cation interactions and
all scoring functions, such as PLP1, PLP2, LIG, binding energy, EC , Dock scores, etc.,
5
0
generated during docking simulation, were evaluated that helped predict the exact pose of the
designed ligand to the target protein receptor.
2
.3. QSTR analysis
All diarylpyrimidine derivatives were also subjected to QSTR (Quantitative Structure
Toxicity Relationship) studies to predict their pharmaceutical potential. TOPKAT protocol
embedded in the DS 3.0 software was used for QSTR analysis.
2
.4. MD simulations
MD simulations were carried out for the target-ligand complex and target-etravirine
complex for a period of 50 ns using GROMACS (GROningenMAchine for Chemical
Simulations) v5.1 molecular dynamics package [Pronk et al., 2013].The unit cell defined as a
cubical box, with a minimal distance of 10 Å from the protein surface to the edges of the box,
was solvated using the Simple Point Charge (SPC) water model GROMOS96 53a6 force field
[
Oostenbrink et al., 2004]. Counter-ions were added to make every system electrically neutral at
a salt concentration of 0.15 mol/L. Before the MD run, each system was subjected to energy
minimization by employing the steepest descent integrator for 5000 steps with force convergence
-1
-1
of <1000 kcal mol nm .
5

Thereafter, each protein-ligand complex was equilibrated for 1 ns using canonical (NVT)
and isothermal-isobaric (NPT) ensembles. During equilibration, each system was coupled with
the Berendsen temperature and Parrinello-Rahman pressure controllers, respectively, to maintain
temperature 300 K and pressure 1 bar. The Particle Mesh Ewald (PME) algorithm [Essmann et
al., 1995] was employed to deal with the long-range Coulomb interactions with a Fourier grid
spacing of 0.12 nm. The short-range van der Waals interactions were given by the Lennard-Jones
potential with a cut-off distance of 1 nm. All bond lengths were constrained by the linear
constraint solver (LINCS) method [Hess et al., 1997].
Subsequently, 50 ns were considered for production under micro-canonical ensemble by
relaxing the couplings with the thermostats. In principle, the same protocol was applied to both
systems. A time step of 2 fs was used and the coordinates were saved every 10 ps during the
production run. For the structural analyses of every system, the resultant MD trajectories saved at
the interval of 100 ps were analyzed using the built-in modules of GROMACS and visual
molecular dynamics (VMD 1.9.1) [ Schuler et al., 2001]. The 2-D plots depicting the intrinsic
dynamical stabilities captured by the root mean square deviation (RMSD), root mean square
fluctuation (RMSF), hydrogen bond distribution and radius of gyration (R ) of the complexes
g
were generated by Grace 5.1.23 program.
2
.5. Principal Component Analysis (PCA)
Essential dynamics (ED) or principal component analysis (PCA) is an efficient statistical
method applied to reduce the number of dimensions needed to describe protein molecular
dynamics through a decomposition process [Humphrey et al., 1996]. The Gromacs module tool
called g_covar was used to yield the eigenvalues and eigenvectors by calculating and
diagonalizing the covariance matrix, whereas the g_anaeig tool was used to analyze and plot the
eigenvectors [Swain et al., 2018].
2
.6. Binding Free Energy Calculation
Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) widely used for
free energy calculation from MD trajectory [ Aalten et al., 1995, Wang et., 2018]. The binding
free energy (ΔGbind) in a solvent medium was calculated as follows:
ΔGbind = Gcomplex - (Gprotein + Gligand
)
Where Gcomplexis the total free energy of the substrate-protein complex, Gprotein and Gligand
are the total energies of protein and substrate alone in a solvent, respectively.
6

The free energies for each individual Gcomplex, Gprotein and Gligand were estimated by:
G = E
p
+ Gsolv
MM
Where p can be protein, ligand, or complex. EMM is the average molecular mechanics
potential energy in vacuum and Gsolv is the solvation free energy.
The molecular mechanics potential energy was calculated in the vacuum as follows:
EMM = Ebonded + E non-bonded = Ebonded(E ) + E + Eelec
int
vdw
Where Ebonded or (E ) is the total bonded interaction, which includes all bonded
int
interactions like bond, angle, dihedral and improper interactions; Enon-bonded is the total non-
bonded interaction consisting of both van der Waals (Evdw) and electrostatic (E ) interactions.
elec
Ebonded is always taken as zero.
The solvation free energy (Gsolv) was estimated as the sum of electrostatic solvation free
energy (Gpolar) and nonpolar solvation free energy (Gnon-polar) as given below:
Gsolv = Gpolar + Gnon-polar
Where Gpolar, the polar solvation energy, was determined using the Poisson-Boltzmann
(PB) linear equation and the non-polar contribution, Gnon-polar was estimated from the solvent-
accessible surface area (SASA) as per the following equation:
Gnon-polar = γSASA + b
2
Where γ (coefficient related to surface tension of the solvent) = 0.02267 kJ/mol/Å or
2
0
.0054 kcal/mol/Å and b = 3.849 kJ/mol or 0.916 kcal/mol
The binding free energies for all four complexes were calculated based on 5000
snapshots taken at an equal interval of time from 50 ns MD simulations. The per-residue energy
contribution was also computed to understand the contribution of individual amino acids to the
total binding energy.
3
. Results and discussion
3
.1. Physicochemical and drug-like characteristics of diarylpyrimidine derivatives
All diarylpyrimidines (1-8) have been designed as probable NNRTIs using computational
methods. The physicochemical properties of diarylpyrimidine derivatives 1-8 and the reference
drug etravirine were evaluated using Moleinspiration and Swiss ADME online software [Daina
et al., 2017].
All physicochemical data and pharmaceutical properties of compounds 1-8 were
calculated using Lipinski’s rule of five. To evaluate the drug-likeness of any compound,
7

Lipinski’s rule is a thumb rule, which helps to predict the pharmacological and biological
activity. All compounds 1-8 can ionize at definite pH due to the presence of H-bond donor and
H-bond acceptor sites, which helps to increase the solubility of compounds. The compounds
having more than one violation are rejected for further studies because of difficulty in solubility
and bioavailability.
Lower TPSA values of compounds were expected to exhibit the better cellular
internalization in comparison to the reference drug etravirine. Suitable modification in the active
moiety was introduced in order to find out the higher drug-likeness scores as explained by the
thumb rule of Lipinski. All physicochemical data of diarylpyrimidine derivatives 1-8 are
summarized in Table 1 and represented as radar graph in Fig.3.
All compounds 1-8 were found within the range of Lipinski’s rule and expected to
behave as probable NNRTIs against wild type HIV-1, like etravirine. The drug-likeness scores of
all compounds 1-8 and the reference drug etravirine are given in Table 2.
Lower value of lipophilicity indicated that all compounds can easily pass through cell
membrane like the reference drug etravirine. Lower TPSA values of all compounds confirmed
their better cellular internalization in comparison to reference drug except compounds 6 and 8.
The drug-likeness scores for proper bioactivity were also evaluated for G-protein coupled
receptor (GPCR) ligand, ion channel modulator, kinase inhibitor and nuclear receptor ligand
(Table 2). The compounds having drug-likeness bioactivity scores more than zero are found to
be pharmaceutically active, whereas the bioactivity scores of compounds between 0.00-0.50,
show moderate activity and the scores less than -0.50, show inactivity.
All diarylpyrimidine derivatives 1-8 showed better drug-likeness scores than the
reference drug etravirine except compounds 2 and 3 in the case of GPCR, compounds 7 and 8 in
the case of kinase inhibitor, compounds 4 and 5 in the case of nuclear receptor ligand and
compounds 7 and 8 in the case of enzyme inhibitor for bioactivity. These compounds showed
moderate in silico bioactivity in comparison to other compounds and the reference drug
etravirine as shown in Table 2.
3
.2. Synthesis of diarylpyrimidine derivative
8

Substituted diarylpyrimidine derivatives, 1-8, have been synthesized from 2-4-dichloro-
pyrimidine. The further modifications of the replacement of both chloro group of 2-4-dichloro-
pyrimidine via ring B and C with different substituents at 4/5/6 positions are clearly outlined in
Scheme 1. Purification and characterization data of these compounds have been provided in
supporting information.
3
.3. Docking analysis of diarylpyrimidine derivatives
To better understand the binding mode of diarylpyrimidine derivatives 1-8 and etravirine,
molecular docking was performed using DS 3.0 software. The compounds formed H-bonds with
Lys101 and Lys103 amino acid residues, π-bonds with Tyr181, Tyr188, Phe227 and Trp229 at
the allosteric site in the hydrophobic NNIBP of HIV-RT, with the exception of compounds 7 and
8
.
H-bonds, π-π (hydrophobic), and non-polar π-cation (non covalent) interactions were
used to explain the docking results. The existence of these non-covalent interactions implies that
the interatomic distances are within the range of 6 Å. The docking results of diarylpyrimidine
derivatives 1-8 with HIV-RT protein are represented in Table 3.
th
Docking studies revealed that compound 1 having chloro group at 4 position of aryl
th
rings B and C, and compound 2 having fluoro group at 6 position of aryl rings B and C
interacted in similar fashion like the reference drug with HIV-RT and formed a H-bond with
Lys103 at the distance of 2.8 Å.
th
Similarly, compound 3 with bromo group at 6 position and compound 4 with bromo
th
group at 5 position of both aryl rings B and C formed a H-bond (2.9Å) with His235 residue of
HIV-RT protein. Additionally, compound 4 also interacted with Tyr188 residue to form a π-bond
at a distance of 6Å.
th
Compound 5 having chloro group at 5 position of both rings B and C formed two H-
bonds, i.e., one with Lys101 and another with Lys103 at a distance of 2.6 Å and 2.8 Å,
respectively.
th
Introduction of the nitro group at 5 position of both aryl rings B and C in place of
halogen atom stabilized the complex of compound 6 with HIV-RT as the compound 6 interacted
with amino acids His235 and Trp229 through the H-bonds. Furthermore, compound 6 also
showed four π-cation (non-covalent) interactions with Trp229 (two π-cation bonds), Tyr181 (one
9

π-cation bond) and Tyr188 (one π-cation bond) amino acid residues at the allosteric site of HIV-
RT protein.
Docking interactions of diarylpyrimidine derivatives are shown in Fig. 4. The binding of
all these compounds with HIV-RT protein revealed their behaviour as NNRTIs, just like the
reference drug.
During docking simulation some scoring functions were generated, which helped in
predicting the suitable orientation of ligands within the target protein. The stability of ligand-
protein complexes was evaluated on the basis of scoring functions, like PLP1, PLP2, DS, LIG,
Ludi values, binding energy and predicted EC . Scoring functions of all diarylpyrimidine
50
derivatives 1-8 are presented in Table 4.
Piecewise Linear Potential (PLP) functions of a ligand are used to correlate the binding
affinity of the ligand to a target receptor protein. The compound having higher PLP scores
showed stronger binding with receptor protein. All compounds 1-8 exhibited considerable
binding affinity to the receptor protein (HIV-RT) via PLP1 and PLP2 scores ranging from 96.08
(compound 7) to 109.74 (compound 6) and 83.07 (compound 7) to 95.68 (compound 6).
The Ligand Internal energy (LIG) consisting of van der Waals and electrostatic
interactions was used to evaluate the binding efficiency of ligand to the target protein. All
diarylpyrimidine derivatives had much better LIG values than the reference drug etravirine, as
shown in Table 4, and thus exhibited higher binding efficiency than the reference drug.
Dock Score (DS), another factor generated from docking simulation again supported the
interaction of the designed ligands with the HIV-RT protein in a better way. All compounds 1-8
showed a significant value of dock score in comparison to the reference drug etravirine (Table
4
).
Ludi2 and Ludi3 are empirical scoring functions derived from the Ludi algorithm and
used to select the exact conformations of protein-ligand complexes. From Ludi2 value, the
binding energy of the target compound and from Ludi3 value, predicted EC50 values of
compounds were calculated [Lagos et al., 2008, Kumar et al., 2010]. The binding energy and
predicted EC values of all compounds 1-8 were found to be comparable with known drug
5
0
etravirine as shown in Table 4.
1
0

From all scoring values, it was confirmed that compounds 1-8 had a similar mode of
binding to the RT allosteric site and also had an excellent binding affinity with HIV-RT protein,
just like the reference drug used in the docking studies.
3
.4. QSTR analysis of diarylpyrimidine derivatives
All compounds 1-8 were also subjected to their in silico toxicity risk assessment using
TOPKAT protocol in DS 3.0 software and data are summarized in Table 5. All the designed
molecules and the reference drug were evaluated for carcinogenicity, which helped to recognize
the common structural features between the molecules and reference drug. Carcinogenicity of all
designed compounds and reference drug etravirine was calculated using two models - NTP
Carcinogenicity Call (Female mouse) and NTP Carcinogenicity Call (Male rat) in TOPKAT
module. During TOPKAT analysis, neither diarylpyrimidine derivatives; 1-8 nor the reference
drug showed carcinogenicity become of their negative discriminant scores (Table 5).
TOPKAT mutagenicity predictor (Ames Heteroaromatics Module) was used to predict
the mutagenicity of the compounds under study. The compounds with the negative value of
discriminant scores were found to be non-mutagenic during TOPKAT mutagenicity prediction
process.
TOPKAT analysis revealed that the in silico toxicity values, like DTP (Developmental
Toxicity Potential), LD (Lethal Dose causing 50% death of test animals or cells), LC (50%
5
0
50
Lethal Concentration) and EC (50% Effective Concentration) of all compounds, were found to
50
be less than the reference drug etravirine. It unambiguously indicated that all diarylpyrimidine
derivatives 1-8 should be less toxic as compared to the reference drug and, in turn, the designed
molecules should have a higher safety index.
TOPKAT results also revealed that all compounds exhibited a lower value of LogP
(lipophilicity) than the reference etravirine as shown in Table 5, which showed the hydrophobic
character of the designed compounds with non-polar atoms, a necessary condition for NNRTIs.
3
.5. Molecular dynamics trajectory analysis
The proteins suffer a significant conformational change during the course of interaction
with a drug molecule [Amadei et al., 1993, Chou et al., 2006], thus Molecular Dynamics
Simulations (MDS) play a significant role to understand the internal motions, conformational
changes, stability, etc. of protein-ligand complexes. Using the resulted MDS trajectories, the
root-mean square deviation (RMSD), root-mean square fluctuation (RMSF), radius of gyration
1
1

(
R ), number of hydrogen bonds, principal component analysis (PCA) and binding free-energy of
g
the complexes were computed to study their structural stabilities, binding modes and binding
strengths.
The MDS were performed on HIV-1 RT protein with compound 6 and reference drug etravirine.
3
.5.1. Conformational stability
The RMSD plots against simulation time are shown in Figure 5 where the small fluctuations
indicate attainment of a stable conformation and vice-versa. While the RMSD plots of the
etravirine complex has a relatively larger deviation and strong oscillation (till 28 ns) than that for
compound 6, a stronger binding of the latter with the protein HIV-1 RT than the reference drug is
suggested.
3
.5.2. Residue flexibility analysis
To evaluate the flexibility of the complexes, fluctuations of each amino acid residue
presented by the RMSF plots as a function of residue number are shown in Fig. 6. A large RMSF
value indicates the flexibility of the protein structure, loose bonding or the presence of loops;
contrarily a small value indicates stability and also the presence of secondary structures such as
sheets and helices. While comparing between the two complexes, the mean values of RMSF
were found to be in the same, which demonstrates a balance between stability and flexibility,
necessary for their activities.
3
.5.3. Compactness analysis
The radius of gyration (R ) accounts for the conformational variation, compactness and
g
tertiary structural volume of the protein-ligand complexes. In general, an expanded structural
form of protein shows a higher R value than the corresponding globular or compact form. A
g
larger value of R describes more elongation, whereas a lower value indicates more compact
g
structure. The plots of R versus time (Fig. 7) are quite smooth and almost constant for both
g
complexes, having the mean values at 2.98 nm and 3.34 nm, for compound 6 (red) and etravirine
(
black), respectively, with HIV-1 RT. A smaller mean R value of compound 6 reiterates to the
g
previous finding that it forms a more compact and stable complex with HIV-1 RT, arising from a
stronger interaction between them than that for etravirine.
3
.5.4. Analysis of hydrogen bonding
1
2

Towards the stability of a target-inhibitor complex, the presence of a large number of
inter-molecular hydrogen bonds is very crucial. Fig. 8 gives the number of hydrogen bonds
formed during the simulations, which is more in case of compound 6 (red) than etravirine (black)
with HIV-1 RT. Intactness of hydrogen bonds during dynamics is an indication of gaining more
stable conformation and stronger binding between compound 6 and HIV-1 RT. On an average,
compound 6 formed approximately three hydrogen bonds, whereas etravirine exhibited only two
hydrogen bonds with HIV-1 RT.
3
.5.5. Solvent accessible surface area
Solvent Accessible Surface Area (SASA) accounts for the surface area of a protein-ligand
complex which directly interacts with the solvent molecules. The increase in SASA denotes
relative expansion.
The SASA of the HIV-1 RT complexes of compound 6 and etravirine is ranging between
2
2
2
80 to 303 nm and 285 to 302 nm , respectively (Fig. 9).
Within initial few ps, the SASA value drops suddenly and then gradually decreases for
both complexes. A similar pattern of fluctuation is noted in both complexes, which is very small.
Comparatively, to the end od 45 ns, the compound 6:HIV-1 RT complex has lower SASA values
implying more stability than the etravirine:HIV1 RT complex.
3
.6. Essential dynamics
For principal component analysis (PCA), essential dynamics (ED) was employed to
represent the directions of principal motions by a set of eigenvectors (EV) called principal or
essential modes. PCA is a linear transformation that extracts the most important special data
using a covariance matrix using atomic coordinates that describe the accessible degrees of
freedom (DOF) of a protein, e.g., the cartesian coordinates that define atomic displacements in
each conformation in MD trajectory. This covariance matrix is diagonalized to extract a set of
eigenvectors and eigenvalues that reflect the correlated concert motion of the molecule [ Khan et
al., 2016]. Each MD trajectory was projected onto the phase space to yield a spectrum of EVs,
which depicted the vectoral representation of each single component in motion. Each EV holds
an eigenvalue that describes the energetic contribution of each component to the motion. The
PCA results are represented in Fig.10 and 11.
1
3

It was observed that 90-95% of the backbone motion was covered by the first 20 EVs
where an exponentially decaying curve of eigenvalues was obtained against the EVs (Fig. 11).
The differential scattering of atoms in contour-like plots specifies the occurrence of
conformational changes in the complexes in agreement with the other MD analysis.
Taken the first two PCs into consideration, simulation results revealed a higher subspace
dimension for the etravirine:HIV-1 RT complex (Fig 10). On the contrary, the compound 6:HIV-
1
RT complex covered the least subspace and showed the least variations. In the 2-D projection
plots of trajectories, the etravirine:HIV-1 RT complex showed higher trace values of the
covariance matrix than the compound 6:HIV-1 RT complex.
The overall analysis manifests again that the compound 6:HIV-1 RT complex had higher
stability as reflected from its least conformational changes due to decreasing collective motions
than the etravirine:HIV-1 RT complex. Emphatically, the PCA and RMSD analyses elicited the
same characteristics about structural fluctuations of the protein-inhibitor complexes.
3
.7. Binding free energy and residue interaction energy
MD trajectories of both the complexes were used to calculate binding free energy using
MM/PBSA method. Currently, this method has been widely applied in various studies including
stability, protein-ligand interactions, drug design, and re-scoring [ Genheden et al., 2015, Wang
et al., 2018]. Among existing different classical methods, the MM/PBSA approach has been
considered as a very efficient and reliable method to study crucial molecular recognition
processes [, Kollman et al., 2000, Wang et al., 2019, Gupta et al., 2020].
According to the constituents of the inhibitors, different interactions, e.g., hydrophobic,
hydrogen bond, electrostatics and pi-pi interactions prevailed in the corresponding HIV-1
complexes. In this study, 5000 snapshots were extracted at equal intervals from the 50 ns MD
trajectories to compute binding free energy for both complexes. The contributions of different
interactions were either positive or negative to the overall binding free energy and summarized in
Table 6 for both complexes.
The compound 6:HIV-1RT complex possessed the higher negative binding energy (-
-1
-1
1
12.81 kJ mol ) than etravirine:HIV-1 RT (-111.4 kJ mol ). Decomposition into separate energy
terms revealed immediately that the polar solvation energy opposes the binding of inhibitors to
HIV-1 significantly, and thereby reducing the total binding energy in both complexes due to the
positive energy contributions (Table 6).
1
4

Among the various interactions, van der Waal energy (ΔEvdW) was the most and solvent-
accessible surface area (SASA) energy was the least favourable contributions towards the
negative binding free energy of both complexes (Table 6). The greater contribution of
electrostatic energy (ΔE ) and lesser penalty of polar solvation energy in the compound 6:
elec
HIV-1 RT complex gave rise to slightly more negative binding free energy than the etravirine:
HIV-1 RT complex, making the former relatively more stable than the latter.
In addition to the information presented in Table 6, the contributions of individual amino
acids to the binding free energy (∆Gbind) were also computed using the MM/PBSA method and
presented in Figure 12. For clarity, only actively contributing residues towards the positive and
negative binding energies are highlighted.
The per-residue interactional energy profiles revealed that Pro97, Lys103, Glu169,
Asn175, Asp177, Lys233, Met230 and Glu308 actively participated in interaction to give rise to
a stronger binding and stability in both complexes. Except Thr165, Leu234 and Asn343, most of
the amino acid residues showed a negative binding energy contribution to the stability of the
complexes.
Interestingly, amino acid residues contributing favorably or unfavorably in both
complexes were the same; the only difference was that a common amino acid residue Lys103
contributed more favorably to the compound 6: HIV-1 RT complex than to the reference drug
etravirine: HIV-1 RT complex. The most favourably contributing residue for both the complexes
was Lys223 having a negative binding energy of -13.23 kJ/mol and -12.98 kJ/mol, for the
compound 6: HIV-1RT and etravirine: HIV-1RT complexes, respectively.
In order to know the conformation of the compound 6 after MD simulations, we have captured
snapshots of the protein-ligand complex every 10 ns, starting from 0 ns to 50 ns. From the Fig.
1
3, it was observed that the compound 6 (pink color) remained stable in the binding site of the
target protein throughout 50 ns MD simulation process.
HIV RT consists of two domains; polymerase and Rnase H, in which the catalytic site is located
on the former site. While the NNRTIs bind in the binding site (polymerase domain), etravirine
complexes are mostly seen in the Rnase H domain, Fig. 13 and 14, respectively, Any flexibility
indicated in the RMSF for the Rnase H domain does not affect the stability of the compound 6-
HIV RT complex, marked by lower RMSD values, as the binding site is located far from the
1
5

RNase H domain, which starts from residue 425 and is connected with a loop. (Kohlstaedt et al.,
1
992)
4
. Conclusion
A new series of diarylpyrimidine derivatives 1-8 has been developed as potent and
selective inhibitors of HIV-1 RT by using in silico structure-based approach and is synthesized in
the laboratory. Docking results revealed that the designed compounds showed high potency and
some of the compounds were predicted be more potent than the known inhibitor etravirine. A
good correlation had been observed between the predicted EC , various energy terms and
5
0
descriptors for all newly reported compounds against HIV-1 RT. Investigation of these
relationships shall definitely be useful for the prediction of the activity of novel derivatives.
Based on structural fluctuations, dominating interactions and binding free energies of the
complexes of HIV-1 RT, the molecular dynamics simulation results demonstrated higher stability
of the shortlisted compound 6 and a better inhibitor capability in comparison to the reference
drug. The present study, thus, provided a valuable advance in the search for novel second
generation NNRTIs and can be considered as a starting point for lead optimization.
Acknowledgement
Financial assistance to Vishal K Singh in the form of Junior ResearchFellowship (Ref
No: 349/CSIR-UGC NET DEC. 2017) by University Grants Commission is sincerely
acknowledged. Ritika Srivastava and Parth Sarthi Sen Gupta sincerely thank IISER Berhampur,
Odisha for postdoctoral fellowship as well as computational support.
Conflict of interest
The authors report no conflicts of interest.
References
A. Amadei, A. B. M. Linssen, H. J. C. Berendsen, 1993. Essential dynamics of proteins. Pro.:
Str., Fun., and Gen., 17, 412-425. doi:org/10.1002/prot.340170408
A. Daina, O. Michielin, V. Zoete, 2017. Swiss ADME: a free web tool to evaluate
pharmacokinetics, drug likeness and medicinal chemistry friendliness of small molecules.
Sci. Rep., 7, 42717. doi: org/10.1038/srep42717
A. Singh, D. Yadav, M. Yadav, A. Dhamanage, S. Kulkarni, Ramendra K. Singh, 2015.
Molecular Modeling, Synthesis and Biological Evaluation of N-Heteroaryl Compounds
as Reverse Transcriptase Inhibitors Against HIV-1. Chem. Biol. Drug Des., 85, 336-347.
doi: org/10.1111/cbdd.12397
1
6

A. Singh, M. Yadav, R. Srivastava, 2016. Design and anti-HIV activity of aryl sulphonamides as
non-nucleoside reverse transcriptase inhibitors. Med. Chem. Res., 25, 2842-2859. doi:
org/10.1007/s00044-016-1707-7
B. Huang, X. Liang, C. Li, W. Chen, T. Liu, X. Li, Y. Sun, L. Fu, H. Liu, E. D. Clercq, C.
Pannecouque, P. Zhan, X. Liu, 2015. Fused heterocycles bearing bridgehead nitrogen as
potent HIV-1 NNRTIs. Part 4: design, synthesis and biological evaluation of novel
imidazo[1,2-a]
pyrazines.
Eur.
J.
Med.
Chem.,
93,
330-337.
doi:
org/10.1016/j.ejmech.2015.02.022
C. A. Lipinski, F. Lambardo, B.W. Dominy, P.J. Fenney, 2012. Experimental and computational
approaches to estimate solubility and permeability in drug discovery and development
settings. Adv. Drug Deliv. Rev., 64, 4-17. doi: org/10.1016/j.addr.2012.09.019
C.F. Lagos, J. Caballero, F.D. Gonzalez-Nilo, D.P.C. Mahana,T.Perez-Acle, 2008 Docking and
quantative structure-activity relationship studies for the bisphenyl benzimidazole family
of non-nucleoside inhibitors of HIV-1 reverse transcriptase. Chem. Biol. Drug Des., 72,
3
60-369. doi: org/10.1111/j.1747-0285.2008.00716.x
C. Oostenbrink, A. Villa, A. E. Mark, W. F. Van Gunsteren, 2004. A biomolecular force field
based on the free enthalpy of hydration and solvation: The GROMOS force-field
parameter sets 53A5 and 53A6. J. of Com. Che., 25, 1656-1676. doi:
org/10.1002/jcc.20090
C. Wang, D. Greene, L. Xiao, R. Qi, R. Luo, 2018. Recent Developments and Applications of
the MMPBSA Method. Fro. in Mol. Bio., 4, 1-18. doi:org/10.3389/fmolb.2017.00087
C. Wang, F. Schmich, S. Srivatsa, J. Weidner, N. Beerenwinkel, A. Spang, 2018 .Context-
dependent deposition and regulation of mRNAs in P-bodies. Cel. Bio. Chr. and Gen.
Exp., doi: 10.7554/eLife.29815
D. M. F. va.Aalten, J. B. C. Findlay, A. Amadei, H. J. C. Berendsen, 1995. Essential dynamics of
the cellular retinol-binding protein evidence for ligand-induced conformational changes.
Pro. Eng. Des. and Sel., 8, 1129-1135. doi: org/10.1093/protein/8.11.1129
D. Triki, M. Kermarrec, B. Visseaux, D. Descamps, D. Flatters, A.-C. Camproux, & L. Regad,
2
019. Exploration of the effects of sequence variations between HIV-1 and HIV-2
proteases on their three-dimensional structures. J. of Bio. Str. and Dyn., 1–
0. doi:10.1080/07391102.2019.1704877
2
E.Yisma, B. Dessalegn, A. Astatkie,N. Fesseha, G. Benagiano, C. Bastianelli, S. Brusamento, H.
Elmoniry, M. Velthoven, U. van, Pape, 2014. Global report: UNAIDS report on the
global AIDS epidemic 2013, Rep. Hea., 10, 23. doi: org/10.1186/1742-4755-10-23
G. Kumai, Ramendra K. Singh, 2015. Molecular Modeling, Synthesis, and Anti-HIV Activity of
Novel Isoindolinedione Analogues as Potent Non-nucleoside Reverse Transcriptase
Inhibitors. Chem. Biol. Drug Des., 87, 200-212. doi: org/10.1111/cbdd.12620
G.R. Kaufmann, D.A. Cooper, 2000. Antiretroviral therapy of HIV-1 infection: established
treatment strategies and new therapeutic options. Curr. Opin. Microbiol., 3, 508 -514.
doi: org/10.1016/s1369-5274(00)00131-4
1
7

G.-X. Shuang, T. Xiao, Y.-Y. Zhu, G.-Y. Liu, F.-E. Chen, 2019. Recent progress in HIV-1
inhibitors targeting the entrance channel of HIV-1 non-nucleoside reverse transcriptase
inhibitor binding pocket. Eur. J. Med. Cem., 174, 277-291. doi:
org/10.1016/j.ejmech.2019.04.054
H. Jonckheere, J. Ann, E.De Clercq, 2000. The HIV-1 Reverse Transcription (RT) Process as
Target for RT Inhibitors. Med. Res. Rev., 20 (2), 129-154. doi: org/10.1002/(sici)1098-
1
128(200003)20:2%3C129::aid-med2%3E3.0.co;2-a
I. Muegge, Y.C. Martin, 1999. A general and fast scoring function for protein-ligand
interactions:a simplified potential approach. J. Med. Chem., 42 (5), 791-804. doi:
org/10.1021/jm980536j
J.Yang, W. Chen, D. Kang, X. Lu, X. Li, Z. Liu, B. Huang, D. Daelemans, C. Pannecouque, E.
D. Clercq, P. Zhan, X. Liu, 2016. Design, synthesis and anti-HIV evaluation of novel
diarylpyridine derivatives targeting the entrance channel of NNRTI binding pocket. Euro.
J. Med. Chem., 109, 294-304. doi: org/10.1016/j.ejmech.2015.11.039
L. A. Kohlstaedt, J. Wang, J. M. Friedman, P. A. Rice and T. A. Steitz, 1992. Science, 256,
1
783-1790. doi: org/10.1126/science.1377403
L. Anta, J.M. Llibre, E. Poveda, J.L. Blanco, M. Alvarez, M.J. Perez-Elias, A. Aguilera, E.
Caballero, V. Soriano, C. deMendoza, 2013. Rilpivirine resistance mutations in HIV
patients failing non-nucleoside reverse transcriptase inhibitor-based therapies, AIDS. 27
(1), 81-85. doi: org/10.1097/QAD.0b013e3283584500
L. D. Schuler, X. Daura, W. F. Van Gunsteren, 2001. An Improved GROMOS96 Force Field for
Aliphatic Hydrocarbons in the Condensed Phase. J. of Com. Che., 22, 1205-1218. doi:
org/10.1002/jcc.1078
L.R.Boone, 2006. Next-generation HIV-1 non-nucleoside reverse transcriptase inhibitors, Curr.
Opin. Investig. Drugs (London, Engl. 2000). 7 (2), 128-135.
M.A.Wainberg, 2013. Combination therapies, effectiveness, and adherence in patients
HIV infection: clinical utility of a single tablet of emtricitabine, rilpivirine, and tenofovir
HIV/AIDS. 5, 41-49. doi.org/10.2147/HIV.S32377
with
.
M. Kumar, R.Vijayakrishnan, Gita S.Rao, 2010. In silico structure-based design of a novel class
of potent and selective small peptide inhibitor of Mycobacterium tuberculosis
Dihydrofolate reductase, a potential targetfor anti-TB drug discovery. Mol. Divers. 14,
5
95-604. doi: org/10.1007/s11030-009-9172-6
M. F. Sk, R. Roy & P. Kar, (2020). Exploring the potency of currently used drugs against HIV-1
protease of subtype D variant by using multiscale simulations. J. of Bio. Str. and Dyn., 1–
3
0. doi:10.1080/07391102.2020.1724196
M. Yadav, R. Srivastava, F. Naaz, A. Singh, R. Verma, Ramendra K.Singh, 2019. In silico
studies on new oxathiadiazoles as potential anti-HIV agents. Gen. Rep. 14, 87-93. doi:
org/10.1016/j.genrep.2018.12.004
P. A. Kollman, I. Massova, C. Reyes, B. Kuhn, S. Huo, L. Chong, T. E. Cheatham,
2
000. Calculating Structures and Free Energies of Complex Molecules: Combining
1
8

Molecular Mechanics and Continuum Models. Acc. of Che. Res., 33, 889-897. doi:
org/10.1021/ar000033j
P. S. S. Gupta, R. N. U. Islam, S. Banerjee, A. Nayek, M. K. Rana, A. K. Bandyopadhyay, 2020.
Screening and Molecular Characterization of Lethal Mutations of Human Homogentisate
1
, 2 Dioxigenase. J. of Bio. Str. and Dyn., 1–16. doi:10.1080/07391102.2020.1736158
P. Zhan, C. Pannecouque, E. D. Clercq, X. Liu, 2016. Anti-HIV drug discovery and
development: Current innovations. J. Med. Chem., 59 (7), 2849-2878. doi:
org/10.1021/acs.jmedchem.5b00497
R. P. Bhole, C. G. Bonde, S. G. Bonde, R. V. Chikhale, & R. D. Wavhale, 2020. Pharmacophore
Model and Atom-Based 3D Quantitative Structure activity Relationship (QSAR) of
Human Immunodeficiency Virus-1 (HIV-1) Capsid Assembly Inhibitors. J. of Bio. Str.
and Dyn., 1–14. doi:10.1080/07391102.2020.1715258
S. Genheden, U. Ryde, 2015. The MM/PBSA and MM/GBSA methods to estimate ligand-
binding affinities. Exp. Opi. on Dru. Dis., 10 (5), 449-461. doi:
org/10.1517%2F17460441.2015.1032936
S.G. Sarafianos, B. Marchand, K. Das, D. M. Himmel, M. A. Parniak, S. H. Hughes, E. Arnold,
2
009. Structure and function of HIV-1 reverse transcriptase: molecular mechanisms of
polymerization and inhibition. J. Mol. Biol. 385 (3), 693-713. doi:
org/10.1016/j.jmb.2008.10.071
S. Pronk, S. Páll, R. Schulz, P. Larsson, P. Bjelkmar, R. Apostolov, E. Lindahl,
2
013. GROMACS 4.5: a high-throughput and highly parallel open source molecular
simulation toolkit. Bioinformatics, 29 (7), 845-854. doi:
org/10.1093/bioinformatics/btt055
S. S. Swain, S. K. Paidesetty, B. Dehury, J. Sahoo, S. C. Vedithi, N. Mahapatra, R. N. Padhy,
018. Molecular docking and simulation study for synthesis of alternative dapsone
derivative as a newer antileprosy drug in multidrug therapy. J. of Cel. Bio.,119 (12),
838-9852. doi: org/10.1002/jcb.27304
S.Vella, L.Palmisanom, 2000. Antiretroviral therapy: state of the HAART. Antiviral Res., 45 (1),
-7. doi: org/10.1016/s0166-3542(99)00068-6
2
9
1
S.-X. Gu, H.-H. Lu, G.-Y. Liu, X.-L. Ju, Y.-Y. Zhu, 2018. Advances in diarylpyrimidines and
related analogues as HIV-1 nonnucleoside reverse transcriptase inhibitors. Eur. J. Med.
Cem., 158, 371-392. doi: org/10.1016/j.ejmech.2018.09.013
T.-C. Chou, 2006. Theoretical Basis, Experimental Design, and Computerized Simulation of
Synergism and Antagonism in Drug Combination Studies. Pha. Rev., 58 (3), 621-681.
doi: org/10.1124/pr.58.3.10
U. Essmann, L. Perera, M. L. Berkowitz, T. Darden, H. Lee, L. G. Pedersen, 1995. A smooth
particle mesh Ewald method. The J. of Che. Phy., 103, 8577-8593. doi:
org/10.1063/1.470117
U. Hess, S. Blairy, R. E. Kleck, 1997. The Intensity of Emotional Facial Expressions and
Decoding Accuracy. J. of Nver. Beh., 21, 241-257. doi: org/10.1023/A:1024952730333
1
9

W. Humphrey, A. Dalke, K. Schulten, 1996. VMD: Visual molecular dynamics. J. of Mol. Gra.,
4, 33-38. doi: org/10.1016/0263-7855(96)00018-5
1
X. Chen, P. Zhan, D. Li, E. D. Clercq, X. Liu, 2011. Recent Advances in DAPYs and Related
Analogues as HIV-1 NNRTIs. Curr. Med. Chem., 18 (3), 359-376. doi:
org/10.2174/092986711794839142
X. Chen, Q. Meng, L. Qiu, P. Zhan, H. Liu, E. D. Clercq, C. Pannecouque, X. Liu, 2015.
Design, synthesis, and anti-HIV evaluation of novel triazine derivatives targeting the
entrance channel of the NNRTI binding pocket, Chem. Biol. Drug Des., 86, 122-128. doi:
org/10.1111/cbdd.12471
X. Zuo, Z. Huo, D. Kang, G. Wu, Z. Zhou, X. Liu, P. Zhun, 2018. Current insights into anti-
HIV drug discovery and development: A review of recent patent literature (2014-2017).
Exp. Opi. on Thera. Pat. 28 (4), 299-316. doi.org/10.1080/13543776.2018.1438410Y.
Cong, L. Duan, K. Huang, J. Bao & J. Z. H. Zhang, 2020. Alanine Scanning Combined
with Interaction Entropy Studying the Differences of Binding Mechanism on HIV-1 and
HIV-2 Proteases with Inhibitor.
0. doi:10.1080/07391102.2020.1734488
J.
of Bio. Str. and Dyn., 1–
3
Y. Khan, M. Garg, Q. Gui, M. Schadt, A. Gaikwad, D. Han,A. C. Arias, 2016. Flexible Hybrid
Electronics: Direct Interfacing of Soft and Hard Electronics for Wearable Health
Monitoring. Adv. Fun. Mat., 26, 8764-8775. doi: 10.1002/adfm.201603763
Y. Wang, X. Wang, Y. Xiong, A. C. Kaushik, J. Muhammad, A. Khan, H. Dai, D. Q. Wei, 2019.
New strategy for identifying potential natural HIV-1 non-nucleoside reverse transcriptase
inhibitors against drug-resistance: an in silico study, J. of Bio. Str. and Dyn. doi:
1
0.1080/07391102.2019.1656673.
Z.-Y. Wu, N. Liu, B. Qin, L. Huang, F. Yu, K. Qian, S. L. Morris- Natschke, S.Jiang, C. H.
Chen, K.-H. Lee, L. Xie, 2014. Optimization of the Antiviral Potency and Lipophilicity
of Halogenated 2,6-Diarylpyridinamines as a Novel Class of HIV-1 NNRTIS. Chem.
Med. Chem. 9 (7), 1546-1555. doi: org/10.1002/cmdc.201400075
2
0

Fig. 1. FDA approved NNRTI drugs
2
1

Fig. 2. Design consideration of substituted diarylpyrimidine derivatives, 1-8, as NNRTIs
2
2

1
2
3
4
5
6
2
3

Fig. 3. Radar graph of diarylpyrimidine derivatives; 1, 2, 3, 4, 5, 6, 7, 8 and reference drug showing drug
likeness characteristics (pink area focuses the permissible values of drug likeness characteristics of
molecule), (LIPO – Lipophilicity ( ≤5), SIZE - Molecular weight ( ≤500) , FLEX - Rotatable bonds ( ≤9),
*
*
INSOLU – Insolubility (≤6) , **INSATU – Insaturation (≤1 ))
INSOLU: Insolubility of a compound is one of the physicochemical descriptors used to define the
physicochemical properties of a drug to evaluate their drug likeness.
*INSATU: Insaturation in the designed molecule can be defined as “the fraction of carbons in the sp3
*
hybridization is increased from 0.25 to a higher value”. This parameter is also considered as a
physiochemical descriptor in drug likeness evaluation.
2
4

Compound 1
Compound 2
Compound 3
Compound 4
2
5

Compound 5
Compound 6
Fig. 4. Docking interactions of diarylpyrimidine derivatives; 1-6 within NNIBP of RT allosteric site of
wild type HIV-1.
2
6

Fig. 5. RMSD plots of the HIV-RT complexes of Etravirine (ETV) and compound 6. (shown in black and
red, respectively)
2
7

Fig. 6. RMSF plots of the protein backbone of HIV-RT:Etravirine (ETV) (black) and HIV-RT:Compound
(red) complexes. Ribbon representation of HIV RT (PDB id: 3MEC) with Rnase H domain shown in
golden color
6
2
8

Fig. 7. R (radius of gyration) plots of HIV-RT:Etravirine (ETV) (black) and HIV-RT:Compound 6 (red)
g
complexes
2
9