Tuberculosis is a global public health threat, especially in developing countries. UDP-N-acetylmuramic-alanine ligase (MurC) is an enzyme that catalyzes an important step in the biosynthesis of peptidoglycans in the cell wall of Mycobacterium tuberculosis. This study aims to identify novel inhibitors of MurC using an in silico approach. The three-dimensional structure of MurC was determined using comparative modeling based on a template. Over 10,000 compounds were screened virtually for their ability to bind MurC. Six compounds showed desirable binding energies and properties. Molecular dynamics simulation revealed that four ligands formed a stable conformation when bound to MurC, making them potential novel inhibitors requiring further

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Life Sciences
journal homepage: www.elsevier.com/locate/lifescie
Comparative modeling and dynamic simulation of UDP-N-acetylmuramoyl-
alanine ligase (MurC) from Mycobacterium tuberculosis through virtual
screening and toxicity analysis
Mustafa Alhaji Isa
Department of Microbiology, Faculty of Sciences, University of Maiduguri, P.M.B. 1069, Nigeria
A R T I C L E I N F O
Keywords:
M. Tuberculosis
MurC
Homology modeling
MD simulation and ADMET
A B S T R A C T
Introduction: UDP-N-acetylmuramic-alanine ligase (MurC) is an enzyme catalyzing the addition of L-alanine to
UDP-acetylmuramoyl nucleotide precursor in Mycobacterium tuberculosis (M. tuberculosis). This enzyme is a
prerequisite for the biosynthesis of the peptidoglycans in M. tuberculosis.
Aim: This study aimed to identify the novel inhibitors of MurC using in silico approach.
Materials and Methods: The three dimensional (3D) structure of MurC was determined using comparative
modeling and based on the template obtained from Haemophilus influenza (1P31). The structural analysis of the
model structure shown that three residues (Lys126, Glu170, and Glu358) are critical for in the catalytic activity
of the enzyme, and their inhibition will block the function of the enzyme. Ten thousand and ninety-five (10095)
compounds obtained through virtual screening against Zinc and PubChem databases based on their ability to
bind to MurC with minimum binding energies. These ligands screened for the physicochemical properties,
molecular docking, and pharmacokinetic analyses.
Finding: Six compounds had desirable physicochemical and pharmacokinetic properties with excellent binding
energy ranged between −12.27 and −10.09 kcal/mol. These compounds subjected to Molecular Dynamic (MD)
Simulation and Molecular Mechanics Generalized Born Surface Area (MM-GBSA) analyses. The outcome of the
analysis revealed that four ligands (PubChem1548994, ZINC11882115, ZINC22241774, and ZINC12330603)
formed a stable conformation in the substrate-binding site of the protein during the 50 ns MD simulation.
Conclusion: Therefore, the ligands mentioned above might regard as novel inhibitors of M. tuberculosis which
requires further in vitro and in vivo validation.
1. Introduction
“Tuberculosis (TB) is regarded as a global public health threat,
especially in developing countries. Asia and Africa have the highest
prevalence of the disease [1]. Based on World Health Organization's
(WHO's) report, around 10 million individuals were infected with tu­
berculosis globally in 2018, of which 5.7 million were (57.0%) were
males, 3.2 million (32%) were females, and 1.1 million (11.0%) were
kids. The disease accounted for about 1.5 million deaths in 2018; India
had a high prevalence rate and death [2]. The problem of TB infection
has become further complicated by HIV co-infection. The issues of
multi-drug resistance (MDR) and extensively drug resistance (EDR) in
the treatment of TB have also aggravated the situation [3]. The MDR-TB
is the capacity of the organisms to exhibits resistance to at least two of
the first-line drugs (Isoniazid and Rifampicin). This situation led the
WHO to declare TB as a global emergency [4]. This global emergency
led to the implementation of the various surveillance project to assess
the MDR-TB around the world. The results of this surveillance revealed
that almost all the countries had MDR-TB cases and that more than fifty
nations had extensively drug-resistant tuberculosis cases. Also, there is
some population of M. tuberculosis that has developed total resistance,
and no available TB drugs can prevent their growth [5]. Therefore,
there is need to develop antituberculosis drugs to overcome the re­
sistant strain of the pathogen.”
“UDP-N-acetylmuramic-alanine ligase (MurC) is an enzyme cata­
lyzing the biosynthesis of peptidoglycans, which is a vital component of
the bacterial cell wall. MurC is responsible for catalyzing the addition of
L-alanine to UDP-acetylmuramoyl nucleotide precursor, and it is also
known as non-ribosomal peptide ligase. During the process, ATP is a
prerequisite for this reaction which produces ADP, inorganic phosphate
(Pi) and UDP-N-acetylmuramoyl-L-alanine (UMA) [6]. In M. tubercu­
losis, MurC has specificity towards L-Ala and Gly in vitro, although
https://doi.org/10.1016/j.lfs.2020.118466
Received 7 June 2020; Received in revised form 4 September 2020; Accepted 14 September 2020
E-mail address: mustafaisa@unimaid.edu.ng.
Life Sciences 262 (2020) 118466
Available online 19 September 2020
0024-3205/ © 2020 Elsevier Inc. All rights reserved.
T

2. Munshi et al. [7] reported that the enzyme has activity on L-Ala, Gly,
and L-Ser in M. tuberculosis. However, low specificity towards L-Ser and
Gly leads to the selection of L-Ala as a preferred substrate [7]. Also,
UDP-MurNAc-Gly, UDP-MurNAc-L-Ser or their products are unable to
use by Mur synthetases as a substrate for further reaction. That was why
UDP-MurNAc-L-Ala considered as preferred substrate. MurC consists of
three domains; domain I or UDP binding domain, domain II or ATP
binding domain and domain III or ligand-binding domain. Based on
biochemical kinetic assay it is revealed that ATP appeared to be the
primary substrate to react, followed by UDP-MurNAc and L-Ala [8]. The
interface between domain I and II serves as the ATP-binding site, with
domain II providing the residues involved in the interaction with ade­
nine ring and α and β-phosphates. Two conserved residues (Glu170 and
Thr130) contain the binding site for the essential Mg2+
ion. The in­
teraction between γ-phosphate and the free carboxylate is stabilized by
a second Mg2+
ion in the course of phosphoryl transfer. In M. tu­
berculosis, this second Mg2+
is bound by the side chain of His195 re­
sidue, surrounded by water molecules and stabilized by hydrogen
bonds formed by various conserved residues, such as Asp194, His354,
and Glu173 [9]. Deva et al. [9] reported that substrate fixes to hydro­
phobic residues (Ala42 and Ile84) of two loops (β2-α2 and β4-α4 loops)
and a hydrogen bond of the conserved His62 residue in a cleft formed,
with uracil sandwiched which further assists to strengthen the uracil
ring. The biochemical assay revealed that in E. coli the enzyme occurs in
equilibrium among the monomeric and dimeric forms, which remain
active in both types. The interactions between the top loops of two
different molecules of domains I and II formed the dimer interface [10],
with essential interactions with Phe223 and Tyr224. However, these
residues (Pro218 and Gly219) were not conserved in M. tuberculosis.
Site-directed mutagenesis revealed that three essential amino acids
(Lys126, Glu170, and Glu358) in MurC play a vital role in the catalytic
reaction, in the presence of the enzyme [11]. The inhibition of the
above-mentioned residues blocks the activity of the protein. This is a
critical step in drug development. Hence, the objective of this study was
to identify the inhibitors of MurC from M. tuberculosis.”
2. Methodology
2.1. Homology modeling
“The primary sequence of MurC was retrieved from the National
Centre for Biotechnological Information (NCBI) using its gene accession
number (NP_216668.1). The retrieved sequence was searched based on
the Basic Local Alignment Search Tool (BLASTP) [12] program using
Protein Data Bank (PDB) to identify templates similar to the MurC.
BLAST is a program used to analyze the similarity between multiple
protein sequences present in a database. Six protein templates (1GQQ,
4HV4, 1P31, 2F00, 5VVW and 1J6U) were obtained based on sequence
identity, query coverage and high statistical significant (E-value).
However, 1P31 was chosen due to its higher resolution of 1.85 Å.
Alignment between the MurC and the 1P31 sequence was performed
using ClustalW [13]. The modeled structure of MurC was built based on
the principle of homology modeling, using the Modeller9v16 [14].
Modeller is a computer-based technique used to predict the three-di­
mensional structure of a protein, using spatial restraints similar to
Nuclear Magnetic Resonance. The process started with the alignment
between the sequences of MurC and 1P31. Followed by model building
using the output of the sequence alignment via spatial restraints. The
conserved structural feature (1P31) of the template transferred to the
model. These features include the main chain, side chain, hydrogen
bonds, and dihedral angles to construct the 3D model of the MurC. After
model building, the modeled structure which possessed the minimum
Discrete Optimized Protein Energy (DOPE) value was chosen and fur­
ther subjected to energy minimization (Steric clashes and Van der
Waals repulsion energy) via molecular dynamic (MD) simulation, using
AMBERTOOLS10 package [15]”.
2.2. Evaluation of the model structure
“After energy minimization of the modeled structure of the MurC,
the structure was further evaluated to analyze the stability of the ste­
reochemistry quality. The stereochemistry features such as phi (Φ) and
psi (ψ) torsion angles, bonds angles, dihedral angles, and non-bonded
atom-atom were checked via Ramachandran map, using PROCHECK
[16]. The statistics of various atom types of non-bonded interactions of
the modeled structure was determined using ERRAT [17]. The com­
patibility of the modeled structure with its primary sequence residues
was determined using Verify_3D [18].”
2.3. Virtual screening (VS)
“Virtual screening of the modeled structure of MurC was carried out
to identify the ligands with desirable therapeutic properties such as
antimicrobial activity using Zinc and PubChem databases. A large
number of compounds screened from the two databases to identify
compounds capable of binding to MurC with minimum binding energy
via PyRx 8.0 tool and RASPD program [19]. A total of ten thousand and
ninety-five (10095) compounds that had affinities to bind to the MurC
with different binding conformation were collected. These compounds
were further screened for Lipinski rule of five (molecular weight, hy­
drogen bond donor (HBD), lipophilicity, and hydrogen bond acceptor
(HBA)) using the DataWarrior tool.”
2.4. Pan-assay interference structure (PAINS) analysis
“The compounds with desirable physicochemical properties were
screened for Pan-Assay Interference Strucrural (PAINS) alert to de­
termine their toxicity. This assay is also called toxicophores because of
the presence of some group elements that affects the biological process
by interference with DNA or proteins which lead to fatal condition such
as carcinogenicity and hepatoxicity [20]. All compounds with 0 PAINS
structural alert were selected for docking analysis.”
2.5. Docking analysis
“The docking analysis was performed to identify the binding con­
formation of the protein-ligand complex using AutoDock4.2 [21]. This
binding pattern would help to determine the binding energy of the
protein and the ligand. During the process, polar hydrogen with known
Kollman charges was used for the protonation of the protein. The model
of the MurC was obtained via parameterization of the AutoDock4.2.
The PDBQT was derived from protein data-bank using the graphical
interface of the AutoDock4.2. Autogrid was used to calculate electro­
static energy, desolvation potentials and grid affinity of each atom such
as carbon, hydrogen, nitrogen, and oxygen of the ligand. The x, y, and z
values of the grid center were set. The dimensions of the grid were kept
at 60 × 60 × 60 Å, with a spacing of 0.375 Å, large enough, to allow
the ligands to rotate freely within the active site of the protein. The
binding energy of the protein-ligand complex was calculated using a
Lamarckian genetic algorithm [22]. Lastly, the MurC-ligands complex
was analyzed using Pymol (1.7.4.5 Edu) [23] and Ligplot+ tool
[24,25].”
2.6. ADME and toxicity analysis
“The pharmacokinetic properties of a compound are essential since
several ligands cannot be used as drugs because of poor absorption,
distribution, metabolism, excretion, and toxicity properties. In this
study, all the compounds with good docking scores were selected and
their ADME and toxicity properties predicted using AdmetSAR tool
[26], DataWarrior program [27] and ADME/TOX tool [28,29]. The
predicted features comprised Human Intestinal Absorption (HIA),
Blood-Brain Barrier (BBB) penetration, Cytochrome P450 (CYP450
M.A. Isa Life Sciences 262 (2020) 118466
2

3. 2D6) Inhibitor, Plasma Protein Binding (PPB), Mutagenicity, Tumor­
igenicity, Irritation, and Reproduction. These properties are essential
due to their effects on the exposure of the inhibitor to the human body,
which affects the pharmacological activity and performance of the in­
hibitor.”
2.7. Molecular dynamic (MD) simulation
“The ligands which possessed acceptable pharmacokinetic features
were selected and subjected to MD simulation using AMBERTOOLS10
package [15]. A total of six protein-ligand complexes were analyzed for
their stability and rigidity. All the residues in the complexes were
prepared by the addition of explicit hydrogen to them, using protonate
3D, and the missing parameters of the ligands were added using ante­
chamber. TLeap contained molecular graphics component of the system
topology, and it was used to construct the topology and coordinate file
of the MurC and the ligand. The GAFF and ff12SB force fields were
assigned to protein and ligand respectively. The whole system was
placed in TIP3P water neutralized using sodium ions confined in a
buffer solution of 10 Å inside an octahedral box. The structural artefact
produced during construction of the model was removed by imperilling
the system to a minimization cycle of 5000 steps of conjugate gradient
and 5000 steps of steepest descent, with a restriction run at 544 kcal/
mol/Å on the complex. The system was heated from an initial to a final
temperature of 0.0 k and 300 k respectively in 100,000 incremental
steps, being regulated employing Langevin dynamics, temperature
regulator. The collision frequency fixed at 1 ps in the absence of pres­
sure control. The production of MD simulation was performed at a
steady temperature of 300 K and the pressure of 1 atm via Berendsen
barostat for constant pressure simulation using the time step of 2 fs. A
50 ns long MD simulation was produced and the stability of the com­
plexes was examined by determining the root means square deviation
(RMSD). The deviation of the residues around their initial position was
analyzed using root mean square fluctuation (RMSF). The entire ana­
lyses were carried out using Ptraj component of the AMBERTOOLS10
package.”
2.8. Free binding energy (MM-GBSA) analysis
“The Molecular Mechanics Generalized Born and Surface Area
technique is a popular method used to evaluate free binding energy of
the protein-ligand complex using the trajectories obtained in the MD
simulation. This technique has attained reasonable achievement over
the years regarding precision and computational strength [30]. In this
study, the free binding energy of the protein-ligand complex was de­
termined using MM-GBSA program implemented in Amber14 [31]. The
binding energy was examined based on an average of 500 snapshots
obtained at each 10 ps of the trajectory at the last 5 ns. The entire steps
are presented below:”
= +
G G (G G )
binding complex receptor ligand (1)
= +
G E G T S
bind MM solv (2)
= +
E E E
MM vdW ele (3)
= +
G G G
solv polar nonpolar (4)
= + + +
G G G G G
MM GBSA vdw elec polar nonpolar (5)
“The free binding energy was determined based on the variation
between the total binding energy of the complex (Gcomplex) and the
summation of the total free binding energy of the receptor Greceptor) and
the ligand (Gligand) (Eq. (1)). The molecular mechanical gas-phase free
binding energy (EMM), the sum of configurational entropy T∆S, and the
sum of solvation binding energy (Gsol) (Eq. (2)) were used to determine
the free binding energy of each component (Gbind). The molecular me­
chanical gas-phase free binding energy (EMM) further gave rise to gas-
phase electrostatic energy (Eele) and Van der Waals (EvdW) (Eq. (3)),
while solvation free energy (Gsolv) gave rise to polar (Gpolar) and non­
polar (Gnonpolar) energies (Eq. (4)). Finally, MM-GBSA value was ob­
tained based on the summation of the Van der Waals (EvdW), the polar
(Gpolar), the gas-phase electrostatic energy (Eele), and nonpolar
(Gnonpolar) component (Eq. (5)).
3. Results and discussions
3.1. Homology modeling of MurC
“Homology modeling was employed to predict the 3D structure of
MurC using reference sequence (NP_214996.1) obtained from NCBI and
based on the modeling program implemented in Modeller9.16 [32–34].
The reference sequence (query sequence), searched against PDB and six
proteins (1GQQ, 4HV4, 1P31, 2F00, 5VVW and 1J6U) were obtained
based on sequence identity, query coverage and high statistical sig­
nificance value (E-value). However, 1P31 was further selected based on
its higher resolution of 1.85 Å, making it a vital template for the
homology modeling. The pair sequence alignment between the MurC
and 1P31 showed that they shared 38.3% (184/480aa) sequence
identity, 53.5% (257/480aa) sequence similarity and with a 10% (48/
480aa) gaps (see the supplementary material). During the modeling
process, all conserved regions of the template, side chain, main chain
and structural variable region transferred to the MurC based on spatial
restraints. The process generated ten (10) models and the one with least
DOPE energy value and folded assessment score GA341 of 1 was se­
lected. The selected model was used for the energy minimization to
reduce Van der Waals repulsion energy and steric clashes via MD si­
mulation using AMBERTOOLS10 package. The final minimized model
structure of MurC has of three domains (Domain I, II and III). The first
domain(UDP Binding Domain) is situated in the N-termini part and
comprises of residues in the range of 1–103 amino acids. The domain
Fig. 1. The three-dimensional structure of MurC and the structural superposition of the 3D structure of MurC and 1P31. (a) The 3D modeled of MurC showing all the
three domains, and three highly conserved residues (Lys126, Glu170, and Glu358) which are critical for catalysis and glycine rich loop (Gly17, Gly19, and Gly20). (b)
Structural superimposition of the Cα traces MurC from Haemophilus influenzae (green) and the modeled structure (blue) (RMSD = 0.387 Å). (For interpretation of the
references to colour in this figure legend, the reader is referred to the web version of this article.)
M.A. Isa Life Sciences 262 (2020) 118466
3

4. contained a glycine rich-loop (Gly17, 18, and 20) which the phosphate
groups in UDP-N-acetylmuramoyl-L-alanine bind [6]. The second, and
the biggest among the domains comprises of residues ranged between
104–330 amino acids. The third domain (Ligand Binding Domain)
consisted of amino acids between 331–494 amino acids and located in
the C-terminal parts (Fig. 1a). The minimized model was superimposed
on the UDP-N-Acetylmuramic Acid: 1-Alanine Ligase (template) from
Haemophilus influenzae and had an RMSD of 0.387 Å (Fig. 1b). The
validation of the structural model, the minimized model and the tem­
plate was performed using the Ramachandran plot, ERRAT Quality
Factor, and Verify_3D. Analysis of the Ramachandran map revealed that
the general stereochemistry feature of the modeled structure had 89.3%
of all its residues in the most favorable region, 7.8% in the additional
allowed region, and 1.2% in the generously allowed region while 0.2%
were in the disallowed region. Conversely, the minimized model of
MurC and the template had 90.7%, and 94.6% of their entire amino
acids in the most favorable regions, 7.8% and 5.2% of their total re­
sidues were in the additional allowed regions, and 0.7% and 0.2% were
in the disallowed regions respectively. Based on the analyses of the
Ramachandran plot, the final minimized model structure had out­
standing and satisfactory feature, because above (more than) 90% of its
entire amino acids were in the most favorable region(Table 1). The G-
factor of both the model of MurC, minimized model structure and the
template were determined to ascertain the unusual properties of their
structures. If the overall threshold value was below −0.5, it was con­
sidered unusual, and if it was below −1 and above −0.5, it was con­
sidered as extremely unusual. The overall G-factor of the 3D model of
MurC was −0.49, very close to the threshold value of −0.5. However,
the minimized, model and the template had overall G-factors of −0.10
and 0.17 respectively. These values were more significant than the
satisfactory threshold limit of −0.5 (Table 1).”
“The quality factor of the model structures was checked using
protein structure verification algorithm implemented in the ERRAT.
ERRAT analysis indicates the proportion of non-bonded contacts among
several atoms, and determines whether the analyzed error scores is less
than the rejection limit of 95%. The ERRAT value of the 3D model, prior
to minimization, was 84.615%, whereas after minimization the quality
improved and had a score of 89.076%. Similarly, Verify_3D analysis
was employed to determine the suitability of the three-dimensional
atomic model with a primary sequence (1D), based on primary class on
its location and environment. The verify_3D score of the MurC modeled
and the minimized modeled were 95.14% and 95.75% respectively,
with an improvement in the 3D structure after the minimization. The
total amount of steric clashes and Van der Waals repulsion energy be­
fore minimization of the MurC model were 633 and 696.907 kcal/mol
respectively, while after minimization they were 220 and 124.421 kcal/
mol respectively. Decline in the values of both steric clashes and Van
der Waals repulsion energy in the minimized MurC modeled was evi­
dence of gaining stability after the minimization (Table 1).”
3.2. Docking studies
“As earlier mentioned the structural analysis of the modeled struc­
ture of MurC showed that it has three domains: domain I or UDP
binding domain (1–103 amino acids), domain II or ATP binding domain
(104–330) and domain III or ligand binding domain (331–494). Site-
directed mutagenesis of M. tuberculosis revealed that three residues
played an essential role in the catalytic activity of the protein. These
include Lys126, Glu170, and Glu358. Therefore, inhibition of these
critical residues would block the catalytic function of the protein.
Virtual screening of small compound libraries may assist in identifying
novel inhibitors which would serve as drugs in future. In this study, the
MurC was screened against two ligands libraries (Zinc database and
PubChem) to identify the compounds that have the potential to bind to
the MurC with minimum binding free energies. Ten thousand and
ninety-five (10095) compounds were obtained through virtual
Table
1
Validation
of
the
modeled
structure
of
MurC,
minimized
modeled
structure
and
the
1P31
(template).
Proteins
Procheck
G-factor
2
Errat
Quality
Factor
Verify_3D
(%)
Total
Number
of
steric
clashes
VDW
repulsion
energy
(kcal/mol)
The
most
favorable
region
(%)
The
additional
allowed
region
(%)
Generously
allowed
region
(%)
Disallowed
region
(%)
Torsional
angle
Covalent
geometry
The
overall
geometry
MurC
89.3
9.3
1.2
0.2
−0.83
−0.01
−0.49
84.615
95.14
633
696.907
Minimized
MurC
90.7
7.8
0.7
0.7
0.00
−0.26
−0.10
89.076
95.75
220
124.421
1P31
94.6
5.2
0.0
0.2
−0.09
0.56
0.17
97.952
95.70
240
137.407
M.A. Isa Life Sciences 262 (2020) 118466
4

5. screening against Zinc and PubChem databases based on their ability to
bind to MurC with minimum binding energies. These ligands were fil­
tered for Lipinski rule of five and PAINS analysis to eliminate those with
unwanted properties (Table 2). The ligands with desirable properties
were used for molecular docking analysis to determine different
binding interactions between MurC and the ligands. The analysis of the
molecular docking studies showed that the ligands had undergone
different non-covalent interactions with MurC. These interactions in­
clude hydrophobic, electrostatics and hydrogen bonds interactions.
Twelve (12) ligands were selected based on the binding affinities. The
binding affinities of the ligands alternated between −12.27 and
−10.09 kcal/mol (Table 3) (Fig. 2). From the docking results, Pub­
Chem1548994 had the high binding affinity of −12.27 kcal/mol. This
compound interacted with MurC by forming five hydrogen bonds
(Table 3) which include, the hydroxyl group of Thr127 (dis­
tance = 2.94 Å) and Tyr181 (distance = 3.02 Å), an amino group of
Arg104 (distance = 2.85 Å), and carboxyl groups of Glu151 (dis­
tance = 2.70 Å) and Asp175 (distance = 2.29 Å). All these residues
formed the ATP binding domain, and their inhibition plays an im­
portant role in disrupting the function of the enzyme. Also, the ligand
exhibited hydrophobic interaction with Glu170 and many other
residues (Fig. 3a). Glu170 was involved in the catalytic activity of the
MurC, and its inhibition would impede the function of the enzyme.”
Similarly, ZINC11882115 exhibited two hydrogen bonds with car­
boxylic groups of Glu170 (distance = 2.65 Å and 2.75 Å) (Fig. 3b). This
residue, as earlier mentioned, plays a vital role in the enzyme catalysis.
Therefore, the interaction of this ligand with the residue serves as a
stumbling block to the normal functioning of the MurC. Also,
ZINC14541597 possessed the binding affinity of −10.82 kcal/mol and
interacted with two essential residues (Glu170 and Lys126) involved in
the catalytic activity of the MurC. The ligand formed a hydrogen bond
with Glu170 and also underwent hydrophobic interaction with Lys126.
These two interactions of the ligand with the key residues hindered the
normal function of the MurC (Fig. 3c). Of the remaining ligands, four
compounds (ZINC22910025, ZINC22241774, ZINC14539483, and
ZINC20025924) formed hydrogen bonds with Glu170. Another five
compounds (PubChem72341, ZINC19841049, ZINC20598530,
ZINC12330603, and ZINC12041109) presented hydrophobic interac­
tions with the Glu170 of MurC(Fig. 3a-l). Therefore, all the identified
ligand have the potential to inhibit the MurC, as described above.
3.3. ADME and toxicity studies
“Molecular docking studies showed that 12 ligands possessed good
binding affinities and are potential lead molecules against MurC. These
ligands were further filtered for ADME and toxicity features. These
features include Human Intestinal Absorption (HIA), Blood-Brain
Barrier (BBB) penetration, Cytochrome P450 (CYP450 2D6) Inhibitor,
Table 2
Physicochemical properties (Lipinski rule of five) of selected ligands.
S/No. Zinc/PubChem code Molecular weight cLogP H-bond acceptors H-bond donors PAINS
1 PumChem1548994 482.440 2.1266 10 5 0 Alert
2 ZINC11882115 493.718 −0.0871 6 2 0 Alert
3 ZINC14541597 496.673 −0.0207 8 2 0 Alert
4 PubChem72341 475.631 4.7972 6 3 0 Alert
5 ZINC19841049 492.646 0.4235 9 2 0 Alert
6 ZINC22910025 493.694 2.3556 6 1 0 Alert
7 ZINC20025924 497.593 3.9179 7 0 0 Alert
8 ZINC20598530 477.694 0.3206 6 2 0 Alert
9 ZINC22241774 487.686 0.6187 5 2 0 Alert
10 ZINC14539483 492.685 0.5838 7 1 0 Alert
11 ZINC12330603 490.650 1.7593 7 1 0 Alert
12 ZINC12041109 480.634 2.7476 6 2 0 Alert
Table 3
Free binding energies and hydrogen bonds interactions of the selected ligands.
S/No. Zinc Code Docking Score
(kcal/mol)
Residues formed
Hydrogen bonds
Distance (Å)
1. PumChem1548994 −12.27 Arg104
Thr127
Glu151
Asp175
Tyr181
2.85
2.94
2.70
2.29
3.02
2 ZINC11882115 −11.30 Glu170
Glu170
Asp175
2.65
2.75
2.71
3 ZINC14541597 −10.82 Arg104
Glu151
Asp175
2.93
2.83
2.73
4 PubChem72341 −10.81 Arg104
Ala147
Glu154
2.77
2.95
3.13
5 ZINC19841049 −10.72 Asp175 2.50
6 ZINC22910025 −10.46 Glu170
Tyr181
3.02
3.20
7 ZINC20025924 −10.42 Glu170 3.09
8 ZINC20598530 −10.20 Arg104
Asp175
Tyr181
3.11
2.62
2.90
9 ZINC22241774 −10.14 Arg104
Glu151
Glu170
3.11
2.85
2.81
10 ZINC14539483 −10.12 Glu170 3.10
11 ZINC12330603 −10.10 Arg104 2.81
12 ZINC12041109 −10.09 0 0
-14
-12
-10
-8
-6
-4
-2
0
-12.27
-11.3
-10.82 -10.81 -10.72 -10.46 -10.42 -10.2 -10.14 -10.12 -10.1 -10.09
)
l
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Ligands
PumChem1548994 ZINC11882115 ZINC14541597
PubChem72341 ZINC19841049 ZINC22910025
ZINC20025924 ZINC20598530 ZINC22241774
ZINC14539483 ZINC12330603 ZINC12041109
Fig. 2. Docking scores of the selected ligands interacted with MurC.
M.A. Isa Life Sciences 262 (2020) 118466
5

6. Plasma Protein Binding (PPB), Mutagenicity, Tumorigenicity, Irritation,
and Reproduction (Table 4). All the compounds predicted in this study
had positive HIA and the BBB. Three ligands (PubChem72341, ZIN­
C22910025,and ZINC20598530) were inhibitors of Cytochrome P450
(CYP450 2D6). ZINC22910025 and ZINC20598530 had AC50 greater
than 57 μM, based on the model calculation of Cheng et al. [25],
whereas PubChem72341 had a PubChem activity value higher than
forty (40). The toxicity properties (Ames toxicity test, mutagenicity,
tumorigenicity, reproducibility, irritability, and carcinogens) of the
compounds were also checked. All the ligands were non-toxic except
PubChem72341, ZINC19841049, ZINC20025924, and ZINC14539483.
PubChem72341 was predicted to be tumorigenic and caused irritability
at a high rate. Similarly, ZINC19841049 was found to be Ames toxic
and could cause mutation and tumor at a high rate. ZINC20025924 and
ZINC14539483 caused irritability and Ames toxic respectively
(Table 4). Therefore, six (PubChem1548994, ZINC11882115,
ZINC14541597, ZINC22241774, ZINC12330603, and ZINC12041109)
compounds out of the 12 had desirable pharmacokinetics properties
and were selected for further studies.”
3.4. Molecular dynamic simulation studies
“Analysis of the ADME and toxicity properties of the selected li­
gands showed that six(6) compounds (PubChem1548994,
ZINC11882115, ZINC14541597, ZINC22241774, ZINC12330603, and
ZINC12041109) had all the desirable features. These compounds were
further subjected to molecular dynamics simulation analysis. The mo­
lecular dynamic simulation was carried out for 50 ns to analyzed the
stability and rigidity of the protein-ligand complex. The stability of the
entire complex (MurC-PubChem1548994, MurC-ZINC11882115, MurC-
ZINC14541597, MurC-ZINC22241774, MurC-ZINC12330603, and
MurC-ZINC12041109) was analyzed based on the RMSD (Fig. 4). RMSF
Fig. 3. Different bonds interaction between MurC and the selected ligands (a) PumChem1548994 (b) ZINC11882115 (c) ZINC14541597 (d) PubChem72341 (e)
ZINC19841049 (f) ZINC22910025 (g) ZINC20025924 (h) ZINC20598530 (i) ZINC22241774 (j) ZINC14539483 (k) ZINC12330603 (l) ZINC12041109.
M.A. Isa Life Sciences 262 (2020) 118466
6

7. was analyzed to determine the deviation of the ligands concerning their
binding affinity. It also determined the movement of each residue
within their protein-ligand complex (Fig. 5). The firmness of the system
complex was determined using the radius of gyration, by carefully
observing the degree of how folded or unfolded the complexes were
(Fig. 6). The RMSD values of atoms in all the complexes plotted from 0
to 50 ns. The results of the RMSD values showed that the MurC-Pub­
Chem1548994 complex equilibrated around 5 ns MD simulation. It
possessed the average mean value of 4.7274 ± 0.02443 Å, maximum
value of 6.20 Å, and minimum value of 1.26 Å. The equilibration of this
complex around the mean value of 4.7274 Å suggested high stability
and lower fluctuation of the complex. This stability occurred probably
due to the interaction of the proposed inhibitor and the flexible loop
regions of the MurC; this improved the stability of the complex.”
“Similarly, MurC-ZINC14541597 and MurC-ZINC22241774 com­
plexes equilibration and oscillated around the average values of 10 ns
and 4 ns simulation respectively. MurC-ZINC14541597 complex at­
tained equilibration with an average mean value of
5.7715 ± 0.03147 Å, a minimum value of 1.43 Å and a maximum
value of 6.98 Å. ZINC22241774 had an average mean value of
4.7670 ± 0.02444 Å, minimum value of 1.37 Å and a maximum value
of 6.35 Å. MurC-ZINC14541597 complex achieved less stability and
rigidity when compared with both MurC-PubChem1548994 and MurC-
ZINC22241774 complexes. The equilibration of MurC-ZINC22241774
complex indicated high stability and low flexibility. This stability was
perhaps due to the interaction of the ligand with the flexible loop re­
gions of the MurC. However, MurC-ZINC11882115 complex equili­
brated at 5 ns and later fluctuated between 20 and 30 ns. After 30 ns
MD simulation it equilibrated throughout the remaining time, with the
mean value of 6.7041 ± 0.02876 Å, a maximum value of 8.33 Å and a
minimum of 2.09 Å. Similarly, MurC-ZINC12330603 and MurC-
ZINC12041109 complexes equilibrated around 25 ns and 20 ns with the
mean RMSD values of 6.8921 ± 0.05358 Å and 5.4204 ± 0.03350 Å
respectively. The high RMSD values of (MurC-ZINC11882115, MurC-
ZINC12330603, and MurC-ZINC12041109) complexes occurred prob­
ably due to the inability of the proposed inhibitor to interact with the
flexible loop regions of the MurC, leading to greater oscillation in most
of their regions (Fig. 4). The RMSFs corresponding to the values of the
selected complexes (MurC-PubChem1548994, MurC-ZINC11882115,
MurC-ZINC14541597, MurC-ZINC22241774, MurC-ZINC12330603,
Fig. 3. (continued)
M.A. Isa Life Sciences 262 (2020) 118466
7

8. and MurC-ZINC12041109) is shown in Fig. 5, These values signify the
amount of motion of every amino acid residues from their original
position in the protein-ligand complex. In case of MurC-Pub­
Chem1548994 complex, all the amino acids positioned in domain I had
RMSF of ≤6 Å, except the residues between Met1-Leu6. Also, domains
II and III residues had RMSF values ≤6 Å, except Gly491, Val492,
Leu493 and Gly494 in domain II which had the values of > 6 Å. Si­
milarly, the residues in the domains I, II and III of the MurC-
ZINC22241774 complex had RMSF values ≤6 Å, except residues be­
tween Met1-Asp9 (in Domain I), Leu137-Leu142, Gly156-Asp165,
Leu280-Ala283 (in Domain II), Gly342-Ala344 and Ala482-Gly494 (in
Domain III). Lower RMSF values in all the domains of MurC-Pub­
Chem1548994 and MurC-ZINC22241774 complexes occurred probably
due to hydrophobic and hydrogen bonds interactions of the proposed
inhibitors with the flexible loop region as well as residue (Glu170)
involved in the catalytic activity of the MurC. These possibly led to the
high stability of the two complexes. Also, it confirmed the results of the
RMSD plot, where the two complexes had low values in all their do­
mains. Similarly, the entire residues of the MurC-ZINC12330603 com­
plex had RMSF values of ≤6 Å, except residues between Ser278-Pro286
(in Domain II), Gln372-Gly376, and Ile474-Gly494 (in Domain III). In
contrast to MurC-ZINC11882115 complex, where all the residues in the
entire domains had values greater than 6 Å, except the residues be­
tween the Thr117-Gly149, Val288-Val310, Ala314-Pro315, Leu320-
Arg330 (Domain II), Ala415-Gly422 and Ser485-Gly494 (Domain III).
Similarly, the MurC-ZINC14541597 complex had RMSF values of
greater than 6 Å in the entire domains. These high RMSF values of both
complexes (MurC-ZINC11882115 and MurC-ZINC14541597) agreed
with the RMSD plot, where the two complexes had high values of
RMSD. These were not unconnected with the inabilities of the ligands to
Fig. 3. (continued)
M.A. Isa Life Sciences 262 (2020) 118466
8

9. undergo hydrogen and hydrophobic interactions with the flexible loop
region of the MurC, which lead to high flexibility and low stability in
both complexes (Fig. 5). However, the four ligands (PubChem1548994,
ZINC11882115, ZINC22241774, and ZINC12330603) showed low
flexibility in the substrate binding site (Lys126, Glu170, and Glu358) of
the MurC. It is shown in Fig. 6, where the PubChem1548994 and
ZINC11882115 had residues fluctuation between 3–4 Å and 4–6 Å re­
spectively in the substrate binding site of the MurC, while
ZINC12330603 andZINC22241774 had residues fluctuation of 5 Å and
4 Å respectively (Fig. 6). It is evident that these four ligands remained
close to their original binding pockets after the molecular dynamic si­
mulation of the 50 ns.”
3.5. MM-GBSA analysis
The free binding energies of the MurC-ligands complex was de­
termined using trajectory obtained from MD simulation of 50 ns and based
on the principle of MM-GBSA implemented in Amber14. The energy
component used to calculate the strength of the complex includes Van der
Waals (EvdW), gas-phase electrostatic energy (Eele), polar (Gpolar) and
nonpolar (Gnonpolar) energy of the complexes. The lesser (negative) va­
lues of the free binding energies showed stronger binding of the ligands at
the receptor. The PubChem1548994 had least free binding energy of
−32.53 ± 0.4516 kcal/mol. The ligand presents tight binding energy
when compared with the other compounds, this is due to the less residues
fluctuation (Lys126, Glu170, and Glu358) in the substrate-binding site of
the MurC after the 50 ns MD simulation as shown in Fig. 6. Besides, the
ligand had better binding energy of −12.27 kcal/mol in the docking
studies with high numbers of hydrogen bonds interactions. Conversely,
ZINC14541597 (−12.74 ± 0.4158 kcal/mol) and ZINC12041109
(−15.85 ± 0.8394 kcal/mol) possessed weak binding energies due to
high residues fluctuation in the substrate-binding site (Fig. 6), the ligands
also possessed high RMSD during the 50 ns MD simulation, when com­
pared with other ligands (PubChem1548994, ZINC11882115,
ZINC22241774, and ZINC12330603) (Fig. 4). Also, ZINC12041109 had
high free binding energy (−10.09 kcal/mol) in the docking studies with
no hydrogen bonds interactions. Therefore, the analysis of the free binding
energy using the MMGBSA supported the result of the docking studies
(Table 5).
4. Conclusion
A total of ten thousand and ninety-five (10095) compounds that
have potential to binds to the MurC were selected from Zinc and
Table 4
ADME and toxicity analyses.
S/no. Compound code Human
Intestinal
Absorption
Blood-
Brain
Barrier
CYP450
2D6
Inhibitor
Plasma
Protein
Binding
(%)
Aqueous
Solubility
AMES
Toxicity
Test
Carcinogenic Mutagenic Tumorigenic Reproducibility Irritant
1 PubChem1548994 + + No 87.75 −3.406 Non-toxic No No No No No
2 ZINC11882115 + + No 22.55 −2.389 Non-toxic No No No No No
3 ZINC14541597 + + No 12.01 −2.167 Non-toxic No No No No No
4 PubChem72341 + + Inhibitor 74.47 −4.283 Non-toxic No No High No High
5 ZINC19841049 + + No 41.30 −3.055 toxic No High High No No
6 ZINC22910025 + + Inhibitor 48.84 −4.651 Non-toxic No No No No No
7 ZINC20025924 + + No 86.80 −4.126 Non-toxic No No No No High
8 ZINC20598530 + + Inhibitor 30.24 −3.6 Non-toxic No No No No No
9 ZINC22241774 + + No 55.26 −3.438 Non-toxic No No No No No
10 ZINC14539483 + + No 12.24 −3.079 toxic No No No No No
11 ZINC12330603 + + No 59.67 −4.705 Non-toxic No No No No No
12 ZINC12041109 + + No 88.05 −3.317 Non-toxic No No No No No
The bold in the table represent the ligands with desirable pharmacokinetics properties and are consider for MD simulation.
Fig. 4. The MD simulation (RMSD analysis) of MurC-PubChem1548994, MurC-ZINC11882115, MurC-ZINC14541597, MurC-ZINC22241774, MurC-ZINC12330603,
and MurC-ZINC12041109complexes for 50 ns.
M.A. Isa Life Sciences 262 (2020) 118466
9

10. PubChem databases. These compounds were screened for Lipinski rule
of five, PAINS and docking analysis. Twelve compounds with improved
binding energies ranged ranging between −12.27 and −10.09 kcal/
mol was obtained. These ligands further filtered for the pharmacoki­
netic analysis (ADME and Toxicity) which revealed that six compounds
(PubChem1548994, ZINC11882115, ZINC14541597, ZINC22241774,
ZINC12330603, and ZINC12041109) had suitable ADME and toxicity
properties. These compound subjected to molecular dynamic simula­
tion and MM-GBSA analyses. The analyses revealed that the four li­
gands (PubChem1548994, ZINC11882115, ZINC22241774, and
ZINC12330603) formed a stable complex throughout the 50 ns simu­
lation. The ligands mentioned above proposed as novel inhibitors of M.
tuberculosis after experimental validations.
Fig. 5. The MD simulation (RMSF analysis) of MurC-PubChem1548994, MurC-ZINC11882115, MurC-ZINC14541597, MurC-ZINC22241774, MurC-ZINC12330603,
and MurC-ZINC12041109 complexes for 50 ns.
Fig. 6. MurC residues fluctuation in the substrate binding site after 50 ns MD simulation.
Table 5
Free binding energy (MM-GBSA) of the selected ligands complex with MurC.
Compounds ∆Gvdw ∆Gele ∆Gpolar ∆Gnonpolar ∆GMM-GBSA
PubChem1548994 −54.77 ± 0.4070 −10.95 ± 0.4744 37.57 ± 0.5775 −4.38 ± 0.0156 −32.53 ± 0.4516
ZINC11882115 −44.85 ± 0.2041 −12.63 ± 0.3453 35.96 ± 0.3462 −6.73 ± 0.0078 −28.25 ± 0.3217
ZINC14541597 −238.80 ± 0.3815 −21.69 ± 0.4519 250.81 ± 0.7420 −3.06 ± 0.0439 −12.74 ± 0.4158
ZINC22241774 −114.72 ± 0.5528 −36.93 ± 0.5940 135.11 ± 0.6266 −4.25 ± 0.0311 −20.79 ± 0.5374
ZINC12330603 −231.82 ± 0.9811 −32.06 ± 0.5062 247.87 ± 0.6871 −3.47 ± 0.1619 −19.48 ± 1783
ZINC12041109 −236.11 ± 0.1698 −36.43 ± 0.9811 260.51 ± 0.7588 −3.82 ± 0.1008 −15.85 ± 0.8394
The bold represent the final free binding energies of the ligands after the 50ns MD simulation.
M.A. Isa Life Sciences 262 (2020) 118466
10

11. Declaration of competing interest
I declare that I have no conflict of interest.
Acknowledgment
I am grateful to Prof. B. Jayaram, (Supercomputing Facility for
Bioinformatics & Computational Biology, IIT Delhi), Prof. Pawan Dhar
(Jawaharlal Nehru University), Prof. N. B. Singh (Sharda University),
Dr. Kalaiarasan P. (Jawaharlal Nehru University), Prof. Rita Singh
Majumdar (Sharda University), and Mr. Shashank Shekhar, (IIT Delhi)
for their tremendous support during the research.
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://
doi.org/10.1016/j.lfs.2020.118466.
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