pharmaceuticals
Article
Exploring Toxins for Hunting SARS-CoV-2 Main Protease
Inhibitors: Molecular Docking, Molecular Dynamics,
Pharmacokinetic Properties, and Reactome Study
Mahmoud A. A. Ibrahim 1, * , Alaa H. M. Abdelrahman 1 , Laila A. Jaragh-Alhadad 2,3, *, Mohamed A. M. Atia 4 ,
Othman R. Alzahrani 5 , Muhammad Naeem Ahmed 6 , Moustafa Sherief Moustafa 2 , Mahmoud E. S. Soliman 7 ,
Ahmed M. Shawky 8 , Paul W. Paré 9 , Mohamed-Elamir F. Hegazy 10 and Peter A. Sidhom 11
1
2
3
4
Citation: Ibrahim, M.A.A.;
Abdelrahman, A.H.M.;
Jaragh-Alhadad, L.A.; Atia, M.A.M.;
Alzahrani, O.R.; Ahmed, M.N.;
Moustafa, M.S.; Soliman, M.E.S.;
Shawky, A.M.; Paré, P.W.; et al.
5
6
7
8
9
Exploring Toxins for Hunting
SARS-CoV-2 Main Protease
10
Inhibitors: Molecular Docking,
Molecular Dynamics,
11
Pharmacokinetic Properties, and
Reactome Study. Pharmaceuticals 2022,
15, 153. https://doi.org/10.3390/
ph15020153
Academic Editor: Paweł Kafarski
Received: 4 January 2022
Accepted: 23 January 2022
Published: 27 January 2022
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Copyright: © 2022 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
*
Computational Chemistry Laboratory, Chemistry Department, Faculty of Science, Minia University,
Minia 61519, Egypt; a.abdelrahman@compchem.net
Department of Chemistry, Faculty of Science, Kuwait University, Kuwait City 13060, Kuwait;
mostafa_msm@hotmail.com
Cardiovascular and Metabolic Sciences Department, Lerner Research Institute, Cleveland Clinic,
Cleveland, OH 44195, USA
Molecular Genetics and Genome Mapping Laboratory, Genome Mapping Department,
Agricultural Genetic Engineering Research Institute (AGERI), Agricultural Research Center (ARC),
Giza 12619, Egypt; matia@ageri.sci.eg
Department of Biology, Faculty of Science, University of Tabuk, Tabuk 71491, Saudi Arabia;
o-alzahrani@ut.edu.sa
Department of Chemistry, The University of Azad Jammu and Kashmir, Muzaffarabad 13100, Pakistan;
drnaeem@ajku.edu.pk
Molecular Modelling and Drug Design Research Group, School of Health Sciences,
University of KwaZulu-Natal, Westville, Durban 4000, South Africa; soliman@ukzn.ac.za
Science and Technology Unit (STU), Umm Al-Qura University, Makkah 21955, Saudi Arabia;
amesmail@uqu.edu.sa
Department of Chemistry & Biochemistry, Texas Tech University, Lubbock, TX 79409, USA;
paul.pare@ttu.edu
Chemistry of Medicinal Plants Department, National Research Centre, 33 El-Bohouth St., Dokki,
Giza 12622, Egypt; elamir77@live.com
Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Tanta University, Tanta 31527, Egypt;
peter.ayoub@pharm.tanta.edu.eg
Correspondence: m.ibrahim@compchem.net (M.A.A.I.); laila.alhadad@ku.edu.kw (L.A.J.-A.)
Abstract: The main protease (Mpro ) is a potential druggable target in SARS-CoV-2 replication. Herein,
an in silico study was conducted to mine for Mpro inhibitors from toxin sources. A toxin and
toxin-target database (T3DB) was virtually screened for inhibitor activity towards the Mpro enzyme
utilizing molecular docking calculations. Promising toxins were subsequently characterized using a
combination of molecular dynamics (MD) simulations and molecular mechanics-generalized Born
surface area (MM-GBSA) binding energy estimations. According to the MM-GBSA binding energies
over 200 ns MD simulations, three toxins—namely philanthotoxin (T3D2489), azaspiracid (T3D2672),
and taziprinone (T3D2378)—demonstrated higher binding affinities against SARS-CoV-2 Mpro than
the co-crystalized inhibitor XF7 with MM-GBSA binding energies of −58.9, −55.9, −50.1, and
−43.7 kcal/mol, respectively. The molecular network analyses showed that philanthotoxin provides
a ligand lead using the STRING database, which includes the biochemical top 20 signaling genes
CTSB, CTSL, and CTSK. Ultimately, pathway enrichment analysis (PEA) and Reactome mining
results revealed that philanthotoxin could prevent severe lung injury in COVID-19 patients through
the remodeling of interleukins (IL-4 and IL-13) and the matrix metalloproteinases (MMPs). These
findings have identified that philanthotoxin—a venom of the Egyptian solitary wasp—holds promise
as a potential Mpro inhibitor and warrants further in vitro/in vivo validation.
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Pharmaceuticals 2022, 15, 153. https://doi.org/10.3390/ph15020153
https://www.mdpi.com/journal/pharmaceuticals
Pharmaceuticals 2022, 15, 153
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Keywords: toxins; SARS-CoV-2 Mpro ; in silico screening; molecular docking calculations; molecular
dynamics (MD) simulations; reactome
1. Introduction
The causative factor in COVID-19 infection is Severe Acute Respiratory Syndrome
Coronavirus-2 (SARS-CoV-2), a novel β-coronavirus of the positive-stranded RNA virus
that results in gastrointestinal, respiratory, and neurological symptoms in humans [1,2].
From December 2019, a gigantic economic epidemic has been disseminated globally because
of COVID-19 disease [3,4]. As of 29 December 2021, more than 281 million confirmed cases
and over 5.4 million international deaths had been reported [5]. A small number of vaccines
have currently been approved under emergency use authorization [6]. Notwithstanding
the weak vaccination rate, the deficiency of specific therapies, and the development of
numerous viral variants, the pandemic goes on to distribute quickly and intricately. As
a consequence, more outstanding efforts are required to discover safe and potent drugs
against SARS-CoV-2.
SARS-CoV-2 main protease (Mpro ) is a crucial enzyme for viral gene replication, expression, and transcription [7–9]. Therefore, inhibition of the viral Mpro enzyme is a
putative strategy towards SARS-CoV-2 antiviral drug development. Several in silico and
experimental attempts have been made to repurpose approved drugs as prospective curative candidates for the remediation of COVID-19 [10–13]. Further, a combination of
virtual screening and molecular dynamics (MD) simulations of chemical libraries towards
SARS-CoV-2 targets has been executed [14–17]. Sundry small compounds have been identified during and after the first and second coronavirus prevalence waves. Among these,
many edible plant-derived natural products and their related synthetic derivatives have
attracted considerable interest as prospective COVID-19 drug candidates. Furthermore,
natural products from marine species have recently demonstrated substantial antiviral
characteristics [18–21]. Very recently, an emergency use authorization of PAXLOVID (PF07321332), a covalent Mpro inhibitor with submicromolar activity developed by Pfizer, has
been granted by the U.S. Food and Drug Administration (FDA) for treatment of patients
with mild-to-moderate COVID-19 [22].
Toxin or toxin subunits have been used as therapeutic agents to treat an enormous number of diseases when they are not capable of causing damage or death to humanity [10,23].
Among the approved drugs, eleven drugs were recognized as toxins, such as exanta, ziconotide, exenatide, and lixisenatide [24]. Toxin and Toxin-Target Database (T3DB) database
is an unparalleled bioinformatics resource that collects overall information about popular
or omnipresent toxins and their toxin-targets into a single electronic storehouse [25]. The
database includes more than 2900 small compounds and peptide toxins, over 33,000 toxintarget associations, and 1300 toxin-targets [25].
Exploring toxins to hunt potential inhibitors towards SARS-CoV-2 Mpro has not been
conducted. So, in the current study, the T3DB database was virtually screened as Mpro
specific drug candidates. Based on the predicted docking scores, the most potent toxins
were submitted to molecular dynamics (MD) simulations combined with binding energy
calculations using the molecular mechanics-generalized Born surface area (MM-GBSA)
approach. Pathway enrichment analysis (PEA) and Reactome mining was performed to
dissect biological aspects of the inhibitor hits on drug-target interactions with an interactive layout [16,26]. Such in silico screening can provide worthy insights with respect
to the appropriateness of the obtained hits as future development of prospective clinical
candidates.
2. Results and Discussion
In searching for small compounds to prohibit viral replication and transcription,
in silico techniques were utilized to explore a chemical library containing more than
Pharmaceuticals 2022, 15, 153
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3678 toxins as potential SARS-CoV-2 Mpro inhibitors. The employed protocol was first
validated based on available experimental data.
2.1. Validation of In Silico Protocol
The performance of the utilized in silico protocol to predict the binding mode of
SARS-CoV-2 Mpro inhibitor was evaluated. The co-crystallized (5S)-5-(3-{3-chloro-5-[(2chlorophenyl)methoxy]phenyl2-oxo[2H-[1,3′ -bipyridine]]-5-yl)pyrimidine-2,4(3H,5H)dione (XF7) inhibitor was redocked against the SARS-CoV-2 Mpro , as well as the anticipated
binding mode was compared to the experimental binding mode (PDB code: 7L13) [27]. As
shown in Figure 1, the predicted binding mode was very similar to the resolved experimental binding mode with an RMSD of 0.20 Å and a binding affinity of −9.5 kcal/mol.
This data comparison revealed the superior performance of AutoDock4.2.6 software in
anticipating the experimental binding mode of Mpro inhibitors. The robust binding of XF7
with Mpro is attributed to the NH and two CO groups of pyrimidine-2,4(1H,3H)-dione ring
to form hydrogen bonds with a backbone CO, NH group of THR26 and the backbone NH
of GLY143 with bond lengths of 2.49, 2.28, and 2.07 Å, respectively (Figure 1). Besides, a
nitrogen atom of pyridine rings interacts with the backbone CO group of SER144 and the
imidazole ring of HIS163 with bond lengths of 3.29 and 1.91 Å, respectively (Figure 1). At
the same time, the carbonyl group of pyridin-2(1H)-one ring exhibits a hydrogen bond
with the backbone NH group of GLU166 with a bond length of 1.81 Å (Figure 1). Therefore,
the docking protocol confirms the outperformance of this approach in identifying potent
inhibitors as prospective SARS-CoV-2 Mpro inhibitors.
2.2. T3DB Database Virtual Screening
To identify Mpro inhibitors from toxins, AutoDock4.2.6 software was employed to
virtually screen the T3DB database. At the outset, the T3DB database was filtered towards
Mpro with conventional docking parameters. According to the portended binding affinity,
200 toxins demonstrated docking scores less than −8.0 kcal/mol towards Mpro . Therefore,
those 200 toxins were submitted to more elaborate molecular docking calculations with
costly docking parameters. The evaluated docking scores for the top 200 hits are summarized in Table S1. Thirty-two toxins displayed docking scores less than the co-crystalized
ligand (XF7 = −9.5 kcal/mol). 2D docking poses showing key amino acids inside the
Mpro ’s binding site are illustrated in Figure S1. Most of the scrutinized toxins demonstrated
similar binding modes inside the Mpro ’s active site, forming a fundamental hydrogen bond
with GLN189 and GLU166 (Figure S1). 2D chemical structures and calculated docking
scores for those toxins are listed in Table 1.
Philanthotoxin (T3D2489), a component of the venom of the Egyptian solitary wasp,
demonstrated the highest binding affinity with a docking score of −11.7 kcal/mol, displaying a total of six hydrogen bonds with the key amino acid residues of Mpro (Table 1).
Inspecting its binding mode showed that the ammonium group (NH3 + ) participates in
a hydrogen bond with the backbone CO group of GLY170 with a bond length of 2.25 Å
(Figure 2). Furthermore, two dimethylaminium groups participate in two hydrogen bonds
with the backbone CO groups of PHE140 and ASN142 with bond lengths of 1.98 and
1.75 Å, respectively (Figure 2). The CO and NH of two N-methylacetamide groups display
two hydrogen bonds with the backbone NH of GLU166 and the backbone carbonyl of
GLN189 with bond lengths of 2.97 and 2.19 Å, respectively (Figure 2). The hydroxy group
of the phenol ring interacts with the backbone NH of THR26 with a bond length of 1.99 Å
(Figure 2). It is worth mentioning that gram-scale synthesis of philanthotoxin analogs has
been obtained [28].
Azaspiracid (T3D2672), an alkaloid from Mytilus edulis (blue mussel) [29], exhibited
the second-greatest binding affinity towards Mpro with a docking score of −11.6 kcal/mol
(Table 1). Inspecting the binding mode of T3D2672 within the Mpro ’s binding pocket
disclosed that the OH group of the tetrahydro-3,5-dimethyl-2H-pyran-2-ol ring and NH
of 3,5-dimethylpiperidine ring exhibit two hydrogen bonds with the backbone CO group
Pharmaceuticals 2022, 15, 153
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of GLN189 with bond lengths of 2.1 and 2.18 Å, respectively (Figure 2). Furthermore, CO
of the carboxylate group contributes a hydrogen bond with the backbone NH of ALA191
with a bond length of 2.23 Å (Figure 2).
Taziprinone (T3D2378), a β-amino acid derivative, also showed a strong binding
affinity against Mpro with an average value of −11.2 kcal/mol. The interaction was based
in part on two hydrogen bonds with GLN189 and GLU166 with bond lengths of 2.32 and
1.85 Å, respectively (Figure 2).
Figure 1. (a) 3D representation of the anticipated docking pose of XF7 (in pink) and experimental
structure (in mauve) of XF7, (b) 3D, in addition to (c) 2D representations of the predicted binding
mode of XF7 complexed with SARS-CoV-2 main protease (Mpro ).
Pharmaceuticals 2022, 15, 153
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Table 1. Estimated conventional and expansive docking scores (in kcal/mol), 2D chemical structures,
and origin/usage for most promising potent toxins towards SARS-CoV-2 Mpro a .
Compound Origin/
Name/Code Usage b
Insect in
toxin an
(Egyptian
solitary
wasp)
Marinetoxin
Marine
toxin
T3D2672
(Mytilus uedulis)
T3D2378
T3D2807
Industrial/
workplace
toxin
place toxin
Synthetic
Synthetic
compound nd
(anti- ety
anxiety
agent)
Synthetic
T3D2825
Conv.
c
Exp.
d
− 9.1 − − −9.5
− −
− − − −
XF7
XF7
T3D2489
Docking Score
(kcal/mol)
Chemical Structure
Synthetic
com- nd
pound
(antihyper(antihypertensive
agent)
T3D2874
Synthetic
Synthetic
compound nd
(antiic
allergic
agent)
T3D2938
Synthetic
Synthetic
compound nd
(antiic
allergic
agent)
− − − −
− 11.7− − −11.7
− −
−
−
−
−
−
− −11.6
− −
− −
− −
− −
− −
−
−
−
−
−
−
−
−
−
−
−
− −11.2
−
−
−
−
−
−
−
−
−
−
− −
− −
−−10.9−
− −
− −
− −
−
−
− −10.9
−
−
−
−
−
−
−
−
−
− −
− −
− −
−
− 10.9−
− −
− −
−
−
−
− −10.9
−
−
−
−
−
−
−
−
− −
− −
− −
− −
−
− 10.8−
− −
−
−
−
−
− −10.9
−
−
−
−
−
−
−
− −
− −
− −
− −
− −
−
− 10.5−
−
−
−
−
−
− −10.8
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
− 11.3−
−−11.2−
−
−
−
−
−
−
Compound Origin/
Name/Code Usage b
Conv.
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
c
Exp.
d
T3D2324
Industrial/
workplace
toxin
− − −9.2
− −
− − − −
−9.9
T3D2680
Synthetic
com-nd
pound
(anticholesteremic
agent)
− − − −
− − −9.8
− −
−9.9
Synthetic
compoundnd
T3D2884
(antineoplastic
plastic
agent)
agent)
T3D4082
Plant
Plant
toxin
toxin
(Veratrum licalifornicum) )
Food
toxin
Food toxin
(antihy(antihypoparT3D2694
poparathyathyroid
roid
agent)
T3D2871
Food toxin
(antipsychotic
chotic
agent)
Plant
toxin
Plant
T3D4050
T3D2536
toxin
(Solanum
chacoense)
Animal
Animal
toxin
toxin (B.
rubescens, s,
B.
marinus) )
O
OO
O
O
O
O
OO
O
OO
OO
O
O
N
N
O
OO
O
O
N
N
O
N
N
N
N
O
O
HO
HO
HO
HO
HO
HO
− −9.8
−
− −
− −
− −
− −
− −
−
−
−
−
−
−
−
−
−
−
−
−
− −
−−9.8
−
− −
− −
− −
− −
−
−
−
−
−
−
−
−
−
−
−
−
− −
− −
−−9.7
−
− −
− −
− −
−
−
−
−
−
−
−
−
−
−
−
−
− −
− −
− −
− −9.7
−
− −
− −
−
−
−
−
−
−
−
−
−
−
−
−
− −
− −
− −
− −
−
− −9.6
− −
−
−
−
−
−
−
−
−
−
−
−
−
− −
− −
− −
− −
− −
− −9.6
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
N
N
O
−
−
−
−
−
−
N
N
−
−
−
−
−
−
Docking Score
kcal/mol)
Chemical Structure
O
O
O
O
O
O
O
O
O
−9.9
−9.8
−9.8
−9.8
−9.8
−9.8
Pharmaceuticals 2022, 15, 153
−
− −−
−−
−
−
−
−
−
−
− −−
−
−
Table 1. Cont.
−
Compound Origin/
Name/Code Usage b
T3D2913
T3D4084
Chemical Structure
−
−
−−
−
Plant
Planttoxin
toxin
(genus
−
−−
c
−10.5
−−
− −
−
Exp.
−
−
−
−
−
−− −−10.2
−−
−
−
Veratrum)
−
−−− − −
−Docking
Score
−(kcal/mol)
−
Conv.
Synthetic
Synthetic
compound nd
(antipsychotic
chotic
agent)
agent)
−
d
−−10.8
T3D2933
−
−
− −10.6
−
Chemical Structure−
−
−
−− −
−
Synthetic
compound nd
(antipsychotic
chotic
agent)
O
toxinO
(Tiostrea
chilensis)
−
O
OO
O
O
O
N
O
− −
−−
− −Docking
− Score
−
kcal/mol)
−
HO
HO
O
HO
O
Exp.
−9.6
− − −− − − −−
−
−
−
− −−
−− −−
− 9.5
−
−
−
O
HO
c
d
−9.7
N
N
O
HO
6 of 22
N
N
N
O
−−
−
Conv.
O
Marine
toxin
Marine
T3D4051
−−
−
−
Compound Origin/
Name/Code Usage b
−− −
O
−9.7
O
O
HO
Animal
T3D2727
Synthetic
Synthetic
compound nd
(antineoplastic
plastic
agent)agent)
T3D2460
Synthetic
com- nd
pound
(vasoconstrictor
agent)
T3D2750
Synthetic
com- nd
pound
(vasoconstrictor
agent)
T3D2801
Synthetic
Synthetic
compound nd
(psychotropic
chotropic
drug)
− −
−
− −
−
− −10.0
−
− −
−
−− − −−
−
− −10.1
−
−
−
Planttoxin
Plant
toxin
(genus
−−
−
−−−−
−−
− −−9.9−
−− − −−
−
−−
− −10.1
−
−−
−−−
−−
−−9.8−
−
−
−
−−
−
− −10.0
−−
−−−
−−
−−
− −9.8−
−
−−
−
−
−
−− − −−
−
10.1
−
−
− −10.5
−
− −−10.1
−
− −
−
−−
− −−
− −−
−
− −10.2
−
−−
−
− −
−
− −−10.0
−
−−
− −−
− −−
−
−
− −10.2
−−
−
Animal
toxintoxin
−
T3D2527
−
−
−− − − − −
−
9.5
−
−
−
(genus
Dendroand
bates and
genus
PhylloPhyllobates)
−9.7
Synthetic
Synthetic
T3D4083
Veratrum)
Synthetic
T3D2939
T3D2143
−
−−
−
−
Synthetic
com- nd
pound
(vasocon(vasoconstrictor
agent)
−
−−
−
−
Industrial/
workplace
toxin
place toxin
T3D0233
Synthetic
compoundnd
(pesticide)
Synthetic
com- nd
pound
T3D2910
(cholinester(cholinesterase
aseinhibitor)
inhibitor)
T3D2863
Synthetic
com- nd
pound
(anti-HIV
agent)
Animal
toxin
Animal
T3D2535
T3D4232
toxin (B.
gargarizans)
Bacterial
toxin
yano(cyanobacteria)
−
−
− −
−−
−
−
−−
−
−
−
− −
−−
−
−
−−
−
−
−
−
−−
−
−
−
−
−
−
− −
−
−−
−
− −
−
−
−
−
−
− 9.5 −
−
−
− −−
−− −
−
− −−
−− −
−
−9.7
−
− 9.5 −
−9.6
−9.5 −
−
−
−
−
−9.6
−
−−
−
−
−
−
− −−
−
−9.4 −
−− −
−9.6
−
−−−
−
−
−
−
− −−
−
−
−
− 9.3 −
−
−
−
−
−−
−9. 6
−10.0
a
Data sorted according to the expensive docking scores. b Taken from T3DB website 25 . c Conv. stands for the
conventional docking computation. d Exp. stands for the expensive docking estimation.
−
− −
−
−
−
−−
−
−
−
Pharmaceuticals 2022, 15, 153
7 of 22
Figure 2. 3D and 2D molecular interactions of the predicted docking poses of toxins (i) T3D2489, (ii)
T3D2672, and (iii) T3D2378 towards SARS-CoV-2 Mpro .
2.3. Molecular Dynamics (MD) Simulations
Molecular dynamics (MD) simulations investigate the stabilization of the inhibitorreceptor complexes, conformational pliabilities, the reliability of inhibitor-receptor binding
energies, and structural details [30]. Therefore, the most potent toxins with docking scores
lower than −9.5 kcal/mol were subjected to MD simulations, followed by binding affinity
estimations. The simulations were executed for 5 ns to reduce time and computational
costs. The corresponding MM-GBSA binding affinities were computed and summarized in
Table S2. From these data, it is apparent that seven toxins showed lower binding energies
(∆Gbinding ) compared
to −the co-crystallized XF7 inhibitor (calc. −40.1 kcal/mol)−(Figure
−
− 3).
As a result, these toxins were selected and subjected to a 50 ns MD simulation to obtain more
meticulous Mpro binding affinities. In addition, MM-GBSA binding energies were evaluated
(Figure 3). Three out of these seven toxins—namely T3D2489, T3D2672, and T3D2378—
displayed lower binding energies (∆Gbinding ) than the co-crystallized XF7 inhibitor (calc.
−43.4 kcal/mol). The evaluated MM-GBSA binding affinities for T3D2489, T3D2672, and
−
−
−
−
T3D2378 towards Mpro were −58.2, −54.7, and −48.7 kcal/mol over 50 ns MD simulations,
respectively. To obtain more reliable binding affinities, longer MD simulations of 200 ns
were executed for those three potent toxins in complex with Mpro , and the corresponding
binding affinities were computed (Figure 3).
−
Pharmaceuticals 2022, 15, 153
Δ
−
−
−
8 of 22
Figure 3. Calculated MM-GBSA binding energies for the native XF7 inhibitor and the most seven
potent toxins complexed with SARS-CoV-2 Mpro throughout 5 ns, 50 ns, and 200 ns MD simulations.
There is no noteworthy disparity between the computed MM-GBSA binding affinities
throughout 50 ns and 200 ns MD simulations for T3D2489-, T3D2672-, and T3D2378-Mpro
complexes (Figure 3). Compared to the binding energy of the co-crystallized XF7 (calc.
−43.7 −kcal/mol), T3D2489, T3D2672, and T3D2378 revealed a greater binding affinity
against Mpro throughout the 200 ns MD simulations with an average ∆GΔbinding of −−58.9,
−−55.9, and −
− 50.1 kcal/mol, respectively.
To identify the principal driving forces in binding the identified toxins with Mpro ,
decomposition of the MM-GBSA binding affinities was carried out (Figure 4). Evdw
was a significant contributor in the XF7-Mpro binding
affinity with an average value
ffi
−
value of
of −54.5 kcal/mol; besides, Eele contribution was favorable with an average
−
−21.7 kcal/mol (Figure 4). For compounds T3D2489 and T3D2378, a predominance
with a −value of −442.9 and −115.8 kcal/mol, respectively
of Eele forces was observed
−
(Figure 4). Evdw was also favorable, with
− an average
− value of −53.3 and −51.2 kcal/mol
for the T3D2489- and T3D2378-Mpro complexes, respectively (Figure 4). On the other hand,
Evdw and Eele had approximately the same contribution in T3D2672-Mpro binding affinity
with− average values
of −68.9 and −65.4 kcal/mol (Figure 4).
−
To more fully scrutinize enzyme-inhibitor interactions and the participation of proximal amino acids in the inhibitor-enzyme complexes, total ∆Gbinding values were separated to individual residues with the assistance of an MM-GBSA approach (Figure 5);
only residues with ∆Gbinding values lower than −0.50 kcal/mol were considered. GLY143,
HIS164, GLU166, and GLN189 interact with T3D2489, T3D2672, T3D2378, and XF7. GLN189
contributed to the total binding affinity significantly with values of −3.7, −4.7, −4.7, and
−3.2 kcal/mol for T3D2489-, T3D2672-, T3D2378-, and XF7-Mpro complexes, respectively
(Figure 5). GLU166 was the second-highest contributor to total binding free with values of
−0.7, −3.1, −1.2, and −2.3 kcal/mol for T3D2489-, T3D2672-, T3D2378-, and XF7-Mpro complexes, respectively (Figure 5). It is worth mentioning that all investigated complexes have
similar interaction patterns with key amino acid residues, which signifies a resemblance in
the binding mode in these complexes.
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Δ
Δ
−
ffi
−
−
−
−
−
−
−
−
Figure 4. Components of the MM-GBSA binding energies for (a) XF7, (b) T3D2489, (c) T3D2672, and
(d) T3D2378 complexed with SARS-CoV-2 Mpro throughout the simulation time of 200 ns.
Δ
Δ
−
ffi
−
−
−
−
−
−
−
−
Figure 5. Energy participation of the proximal residues to the total binding free energy (kcal/mol) of
T3D2489, T3D2672, T3D2378, and XF7 complexed with SARS-CoV-2 main protease (Mpro ).
2.4. Post-MD Analyses
To further examine the constancy of T3D2489, T3D2672, and T3D2378 complexed
with Mpro , structural and energetical analyses were executed over 200 ns MD simulations
and compared to those of the co-crystalized XF7 inhibitor. Monitoring the structural
steadiness of the scrutinized complexes was effectuated via inspecting hydrogen bond
length, root-mean-square deviation (RMSD), binding energy per frame, and center-of-mass
(CoM) distance.
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2.4.1. Binding Energy per Frame
The structural steadiness of T3D2489, T3D2672, T3D2378, and XF7 complexed with
Mpro was comprehensively evaluated over the 200 ns MD simulations via mensuration
correlations between binding affinity and time (Figure 6). An interesting aspect of binding
energy per frame was the overall stabilities for T3D2489, T3D2672, T3D2378, and XF7 with
average ∆G
Δ binding of−−58.9,− −55.9,
− −50.1, and
− −43.7 kcal/mol, respectively. Based on this
analysis, all investigated systems preserved constancy over the 200 ns MD simulations.
Figure 6. Computed MM-GBSA binding energy per frame for XF7 (in black), T3D2489 (in red),
T3D2672 (in blue), in addition to T3D2378 (in cyan) in complex with SARS-CoV-2 main protease
(Mpro ) throughout 200 ns MD simulations.
2.4.2. Intermolecular Hydrogen Bonds
Hydrogen bond analysis was used to estimate the constancy of hydrogen bonding
between identified toxins and Mpro throughout a 200 ns MD simulation. The number
of hydrogen bonds per frame was evaluated and depicted in Figure 7. The number
of hydrogen bonds oscillated throughout the 200 ns MD simulations, and the average
number of hydrogen bonds was four, two, and two for T3D2489-, T3D2672-, and T3D2378Mpro complexes. It is worth noting that XF7 showed the lowest number of hydrogen
bonds with the proximal amino acids within Mpro ’s binding pocket, while the outstanding
Δ
−
binding affinity of XF7 with average
∆Gbinding
of −43.7 kcal/mol may be ascribed to other
interactions like van der Waals and hydrophobic interactions. The dominance of van der
Waals feature of interactions of XF7 with Mpro is in agreement with the MM-GBSA binding
energies decomposition (Figure 4). The results obtained from the intermolecular bonds
assured the presentence of a great stationary for the T3D2489, T3D2672, and T3D2378
complexed with Mpro compared to the XF7-Mpro complex.
2.4.3. Center-of-Mass Distance
To obtain further in-depth insight into the steadiness of toxin-Mpro complexes throughout the 200 ns MD simulations, the center-of-mass (CoM) distance was estimated between
the toxin and GLN189 (Figure 8). From the CoM graph, it is apparent that the measured
CoM distance was more stable for T3D2672 and T3D2378 in complex with Mpro than
T3D2489 and XF7 with average values of 5.1, 3.5, 10.2, and 5.6 Å, respectively. These findings demonstrate that the most identified toxins bind more tightly with the SARS-CoV-2
Mpro than XF7.
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Figure 7. Number of hydrogen bonds formed between XF7 (in black), T3D2489 (in red), T3D2672 (in
blue), and T3D2378 (in cyan) and SARS-CoV-2 Mpro throughout 200 ns MD simulations.
Figure 8. Distance between the center-of-mass (CoM) (in Å) of XF7 (in black), T3D2489 (in red),
T3D2672 (in blue), and T3D2378 (in cyan) and GLN189 of SARS-CoV-2 Mpro over the 200 ns MD
simulations.
2.4.4. Root-Mean-Square Deviation
The principal objective of the MD simulations is to inspect the positional and conformational changes of ligands upon binding to the binding pocket, which supplies insight into
the binding steadiness. For ease of comparison, the root-mean-square deviation (RMSD) of
the entire complex backbone atoms were evaluated to examine the structural stability of the
T3D2489, T3D2672, T3D2378, and XF7 in complex with Mpro (Figure 9). Unambiguously,
the evaluated RMSD values for the identified toxins complexed with Mpro stayed below
0.25 nm throughout the MD simulations (Figure 9). It is worth noting that RMSD analysis
shows that the complexes stabilized after 10 ns and conserved their stabilities up to the end
of the simulations. In general, the current findings confirmed that T3D2489, T3D2672, and
T3D2378 are tightly bonded and do not leverage the structural constancy of the Mpro , in
addition to keeping structural integrity.
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constancy of the M , in addition to keeping structural integrity.
Figure 9. Root-mean-square deviation (RMSD) of the backbone atoms from the initial structure of
XF7 (in black), T3D2489 (in red), T3D2672 (in blue), and T3D2378 (in cyan) with SARS-CoV-2 Mpro
during the simulation time of 200 ns.
2.5. Drug-like Properties
The efficacy of therapeutic medicaments is substantially based on the molecular
characteristics and bioactivity of the chemical compounds [31]. To predict the drug-like
and bioactivity of the identified toxins as SARS-CoV-2 inhibitors, a Molinspiration tool was
employed to estimate the in silico molecular characteristics. The anticipated physiochemical
properties are summarized in Table 2. Promising drug-likeness properties were observed,
except for T3D2672, which showed violations in some parameters such as mi logP, TPSA,
number of hydrogen bonds acceptors (nON), and molecular weight. The mi logP values of
T3D2489, T3D2378, and XF7 were promising, with values less than 5 [32]. The TPSA values
of the T3D2489, T3D2378, and XF7 were less than 140 Å, revealing that the compounds
have eminent oral absorption or membrane permeability [33]. In addition, the number
of hydrogen bond acceptors (nON) was lower than 10, while the number of hydrogen
bond donors (nOHNH) was lower than 5, except for T3D2489. The molecular weights for
T3D2489, T3D2378, and XF7 were 435.6, 385.5, and 492.5 daltons, respectively, proposing
that most investigated toxins have good absorption and/or permeation across the cell
membrane.
Table 2. Portended physiochemical parameters and structural descriptors of T3D2489, T3D2672,
T3D2378, and XF7 as SARS-CoV-2 main protease (Mpro ) inhibitors.
Compound
Name/Code
mi logP
TPSA
nON
nOHNH
Nrotb
MWt
%ABS
T3D2489
T3D2672
T3D2378
XF7
0.03
6.7
1.7
4.3
128.5
163.7
61.9
109.9
8
13
6
8
7
4
1
2
18
9
4
6
435.6
842.1
385.5
492.5
64.7%
52.5%
87.6%
71.1%
2.6. In Silico ADMET Analysis
ADMET analysis is especially helpful in simplifying clinical trials, especially in the
early stage of drug design [34]. GI absorption, skin, Caco2 permeability, and aqueous solubility are absorption properties needed to be considered in any drug discovery process [35].
It is signified that a GI absorption value greater than 30% indicates perfect absorbance.
T3D2489 (47.6%), T3D2672 (56.7%), T3D2378 (95.0%), and XF7 (93.5%) manifested good
absorbance rates (Table 3). The identified toxins revealed appropriate skin permeability,
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with a skin permeability value higher than −2.5 cm/h. The identified toxins also showed
low Caco2 permeability (<0.9 cm/s). Another substantial agent through ADMET analysis
was to anticipate the P-glycoprotein non-substrate. All toxins were identified as a substrate
for P-glycoprotein (Table 3).
Table 3. Anticipated ADMET characteristics of the top potent inhibitors.
ADME Parameters
XF7
T3D2489
Water solubility
Caco2 permeability
Intestinal absorption (human)
Skin Permeability
P-glycoprotein substrate
P-glycoprotein I inhibitor
P-glycoprotein II inhibitor
−3.5
0.5
93.5
−2.7
Yes
Yes
Yes
T3D2672
T3D2378
−3.0
−0.2
47.6
−2.7
Yes
No
No
−3.4
−0.1
56.7
−2.7
Yes
Yes
Yes
−2.5
0.3
95.0
−3.2
Yes
No
No
1.6
−0.7
−4.1
0.6
−1.7
−3.4
1.3
0.2
−2.6
No
No
No
No
No
No
No
No
Yes
No
No
No
No
No
No
Yes
No
No
No
No
No
1.4
−0.1
0.8
No
0.3
No
Yes
2.7
3.1
Yes
No
0.3
2.2
No
−0.3
No
No
2.8
2.7
Yes
No
0.3
0.9
No
−0.4
No
Yes
2.4
0.7
Yes
No
0.6
4.7
Absorption
Distribution
VDss (human)
BBB permeability
CNS permeability
0.3
−1.1
−2.5
Metabolism
CYP2D6 substrate
CYP3A4 substrate
CYP1A2 inhibitior
CYP2C19 inhibitior
CYP2C9 inhibitior
CYP2D6 inhibitior
CYP3A4 inhibitior
No
Yes
No
Yes
Yes
No
Yes
Excretion
Total Clearance
0.8
AMES toxicity
Max. tolerated dose (human)
hERG I inhibitor
hERG II inhibitor
Oral Rat Acute Toxicity (LD50)
Oral Rat Chronic Toxicity (LOAEL)
Hepatotoxicity
Skin Sensitisation
T. Pyriformis toxicity
Minnow toxicity
No
0.4
No
Yes
3.3
1.1
Yes
No
0.3
2.4
Toxicity
In order to examine the drug distribution, the VDss, BBB membrane permeability,
and CNS were assessed [32]. Higher distribution volumes were observed for T3D2489,
T3D2672, and T3D2378 with log VDss values of 1.6, 0.6, and 1.3, respectively (Table 3). For
BBB membrane permeability, log BB values in the range of −1.0 to 0.3 implied that the drug
candidates passed the BBB membrane. For CNS permeability, log PS values ranged from
−3 to −2, pointing out impenetrability. Most of the investigated inhibitors were forecasted
to be neither able to permeate the CNS nor pass the BBB membrane (Table 3).
CYP450 has a substantial role in drug metabolism in the liver system [36]. The
metabolism scores revealed that all the inspected inhibitors did not prohibit CYP2D6
enzymes and did not perform as inhibitors for CYP3A4, CYP2C9, and CYP2C19 enzymes,
except for the reference compound. The total drug clearance was inspected via a collection
of hepatic and renal clearance. Total clearance construes the drug concentration in the body
utilizing its removal rate. The data indicated that compound excretion rates range from
−0.1 to 1.4 mL/min/kg (Table 3).
In drug design, toxicity is a significant criterion and plays a remarkable role in selecting
of sufficient drug candidates [32]. All the drug candidates in this analysis have not passed
any skin allergic action and hepatotoxic influence (Table 3). hERG inhibition (I and II) is a
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fundamental agent for toxicity analysis in addition to it also including cardiotoxicity. None
of the inhibitors displayed inhibitory behaviors for hERG I. T3D2489, T3D2378, and XF7
were foretold to be hERG II inhibitors. All the investigated inhibitors had not crossed any
Tetrahymena pyriformis as well as AMES toxicities. The maximum tolerated dosage range,
lowest-observed-adverse-effect level (LOAEL), and LD50 were expected via the toxicity
analysis server and the anticipated scores as summarized in Table 3. Consequently, this
study concluded that these bioactive identified toxins could be utilized as possible Mpro
inhibitor candidates.
2.7. Molecular Target Prediction and Network Analysis
The lysosomal cathepsins, primarily cathepsin L (CTSL) and cathepsin B (CTSB) cleave
and activate S proteins, which then merge with host cells [37,38]. SARS-CoV-2 upregulates
CTSL expression both in vivo and in vitro [39]. This increases pseudo-virus infection in
human cells. CTSL may be a therapeutic target to remedy COVID-19 disease [40,41]. CatL
is a secreted lysosomal protease that is also known as a lysosomal protease. Excessive
production of cysteine cathepsins has been related to various clinical conditions, including
inflammation. As a result, high quantities of CatL might be activated in inflammatory
cells under inflammatory circumstances. Moreover, because this enzyme is involved in
the genesis, progression, and metastasis of cancer, its inhibition might be beneficial in
treatment [42]. The plasma CatL has been recommended as a marker for pancreatic cancer.
Philanthotoxin (T3D2489) was identified as an inhibitor against Mpro and provided a ligand
lead using the STRING database, including the biochemical top 20 signaling genes CTSB,
CTSL, and CTSK (Figure 10 and Table S3).
Figure 10. Bioinformatic analysis: (A) The PPI network of top genes; (B) the top 20 gene comparison in
lung adenocarcinoma and lung squamous cell carcinoma; (C,D) Gene Expression Profiling Interactive
Analysis (GEPIA), the expression level of CTSB was increased in lung adenocarcinoma and squamous
cell lung carcinoma.
2.8. Pathway Enrichment Analysis (PEA) and Reactome Mining
For extensive and biological-wide mining of philanthotoxin (T3D2489) target-function
interactions, a Reacfoam map was constructed based on PEA analysis and Reactome mining/modeling. The Reacfoam map tree was constructed to illustrate the top enriched
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pathways affected by 20 top gene targets in response to philanthotoxin (Figure 11). Moreover, an illustrative graphical map was built to show the top enriched pathway as a response
to T3D2489 treatment (Figure 12). Prominently, the top enriched pathways involved: (1)
signaling by the interleukin pathway, (2) interleukin-4 and interleukin-13 signaling pathway, and (3) immune system pathway. These pathways were determined to be the most
significantly enriched pathways targeted by T3D2489 (Table S3).
Figure 11. The Reacfoam map shows the top enriched pathway (Interleukin-4 and Interleukin-13
signaling) influenced by the top 20 gene targets in response to philanthotoxin (T3D2489).
The SARS-CoV-2 infection causes an induced inflammation and generates subsequent
higher levels of immune cell infiltration and cytokines that trigger matrix metalloproteinases (MMPs) activation. Interestingly, the PEA-Reactome mining coupled approach
revealed higher enrichment of signaling by Interleukins (particularly: Interleukin-4 and
Interleukin-13 signaling), activation of matrix metalloproteinases signaling pathway, and
immune system as a result of philanthotoxin induction among all other human biological
pathways. Interestingly, remodeling of interleukins (IL-4 and IL-13) can transform growth
factor-beta (TGF-β) levels, and consequently, the number of M2 macrophages in SARSCoV-2 patients, which ultimately can prevent severe lung injury [43]. Furthermore, a recent
report emphasized that gathering and remodeling the immune cells, matrix metalloproteinases, secreted cytokines (especially interleukin-4 and Interleukin-13), and several other
mediators is proposed as a feasible option for treating COVID-patients [44].
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Figure 12. Graphic representation of the Interleukin-4 and Interleukin-13 signaling Reactome pathway
influenced as a response to philanthotoxin (T3D2489) in the human genome.
3. Computational Methodology
3.1. Target Preparation
The X-ray resolved three-dimensional structure of SARS-CoV-2 main protease (Mpro )
(PDB ID: 7L13, resolution: 2.17 Å) in complex with a noncovalent inhibitor (XF7) was
retrieved and utilized as a template for all in silico calculations [27]. The viral target was
prepared by eradicating all heteroatoms involving ligands, water molecules, and ions. The
protonation states of the titratable amino acids were investigated and assigned using the
H++ web server [45]. In addition, all missing hydrogen atoms are topologically added.
In H++ estimations, physiologic conditions of 10, 0.15, 80, and 6.5 for internal dielectric
constant, salinity, external dielectric constant, and PH, respectively.
3.2. Database Preparation
The Toxin and Toxin Target Database (T3DB) was downloaded and prepared for
virtual screening [25]. All the molecules were retrieved in a 2D structural data format
(SDF). Omega2 software was then utilized to construct the 3D chemical structures [46,47].
A conformational search was executed to generate all conformers with an energy window
of 10 kcal/mol. The lowest energy conformer was then minimized using an MMFF94S force
field available within SZYBKI software [48,49]. Tautomer and fixpka applications implemented inside QUACPAC software were applied to investigate the tautomer enumeration
and the protonation state of the toxins [50]. The partial atomic charges of each compound
within the T3DB database were determined using a Gasteiger-Marsili method [51]. Duplicated molecules with congruent international chemical identifier keys (InChIKey) were
removed [52]. Prepared T3DB data files are available through www.compchem.net/ccdb.
An illustrative diagram of the employed computational approaches for the screening
process of the T3DB database is illustrated in Figure S2.
3.3. Molecular Docking
AutoDock4.2.6 software was utilized to conduct all molecular docking calculations [53].
On the basis of the AutoDock protocol [54], the MGL tools (version 1.5.7) were used to
generate the pdbqt file for the SARS-CoV-2 Mpro . For molecular docking calculations,
the number of generations and population size were set to 27,000 and 300, respectively.
The maximum number of energy evaluations (eval) and the number of genetic algorithms
(GA) run variables were adjusted to 5,000,000, and 25,000,000, and 50, and 250 for con-
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ventional and expensive molecular docking calculations, respectively. The rest of the
docking parameters were maintained at the default settings. The grid box size was set
at 60 Å × 60 Å × 60 Å, which is capable of accommodating the entire Mpro ’s binding site.
Grid map files with a spacing value of 0.375 Å were established utilizing AutoGrid 4.2.6.
The grid was centered at the coordinates X = −13.069, Y = 9.740, and Z = 68.490. A genetic
algorithm inside the AutoDock software was employed to evaluate the various inhibitor
conformers. Conformations were clustered via the root-mean-square deviation (RMSD)
tolerance of 1.0 Å and were sorted based on the docking score [55]. Besides, the lowest
docking score within the largest cluster was considered for opting as the representative
pose.
3.4. Molecular Dynamics Simulations
AMBER16 software was used to conduct all molecular dynamics (MD) simulations
for the most potent toxins in complex with SARS-CoV-2 Mpro [56]. A general AMBER
force field (GAFF2) [57] was employed to characterize the investigated toxins, whereas the
AMBER force field of 14SB [58] was adopted for the characterization of the viral enzyme.
The restrained electrostatic potential (RESP) fitting approach was used to estimate the
atomic partial charge of the studied toxins at the HF/6-31G* level with the assistance of
Gaussian09 software [59,60]. A solvated octahedron box of TIP3P water model with 12 Å
distances between the box edge and atoms of the toxin-Mpro complexes was constructed.
The sodium (Na+ ) and chloride (Cl− ) counter-ions were inserted to neutralize all solvated
systems, as well as to preserve the isosmotic condition (0.15 M NaCl). The prepared systems
were initially minimized through combined steepest and conjugate gradient algorithms
for 5000 steps to remove any steric clashes or inappropriate geometries. The minimized
systems were thereafter gradually heated to 300 K over a brief interval of 50 ps with a weak
constraint of 10 kcal mol−1 Å−1 on the amino acid residues. To guarantee a reasonable
initial structure, an equilibration stage was executed over a total duration of 1000 ps under
a constant number of particles, pressure (1 atm), and temperature (NPT) ensemble. Eventually, the production runs were conducted over simulation times of 5 ns, 50 ns, and 200 ns.
Snapshots were recorded and saved each 10 ps for post-MD analyses. A cutoff distance
of 12 Å was used for the non-bonded interactions. The Particle-Mesh Ewald (PME) algorithm was utilized to estimate the long-range electrostatic interactions [61]. The collision
frequency (gamma_ln) 1.0 ps−1 was applied to maintain the temperature at 298 K [62]. The
Berendsen barostat with a pressure relaxation time of 2 ps was utilized to keep pressure
constant [63]. The SHAKE algorithm with 2 fs integration step was employed to restrict
all bonds including hydrogen atoms [64]. All MD simulations were carried out utilizing
the CUDA version of pmemd in AMBER 16 software. All molecular docking calculations,
MD simulations, as well as quantum mechanics (QM) evaluations were performed on the
CompChem GPU/CPU hybrid cluster (hpc.compchem.net). All molecular interactions
were visualized via the Discovery Studio module of Biovia software [65].
3.5. MM-GBSA Binding Energy
A molecular mechanic generalized Born surface area (MM-GBSA) approach was
adopted to calculate binding free energies of the investigated toxins in complex with
SARS-CoV-2 Mpro . The MM-GBSA (∆Gbinding ) binding energies were computed based on
uncorrelated snapshots collected over the MD simulations as follows:
∆Gbinding = Gcomplex − (Gtoxin + GMpro )
(1)
where the energy term (G) is calculated as:
G = Evdw + Eele + GGB + GSA
(2)
Eele and Evdw stand for electrostatic and van der Waals energies, respectively. GSA
implies the nonpolar solvation-free energy, generally computed with a linear relation to
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the solvent-accessible surface area (SASA). GGB indicates the electrostatic solvation free
energy estimated from the generalized Born equation. In this study, the modified GB model
developed via Onufriev et al. (igb = 2) was used [66]. A single-trajectory method was
utilized, in which the coordinates of every toxin-Mpro , toxin, and Mpro were calculated
from a single trajectory. Due to the expensive computational demand, entropy estimations
were neglected [67,68].
3.6. Drug-Likeness Properties
For the most promising toxins, the physicochemical properties were estimated utilizing an online Molinspiration cheminformatics package (http://www.molinspiration.com)
according to Lipinski Rules. Molinspiration supports for prediction of significant physicochemical properties such as molecular weight (MW), topological polar surface area (TPSA),
octanol/water partition coefficient (mi logP), hydrogen bond donor (HBD), hydrogen bond
acceptor (HBA), rotatable bond count (RB), and percent absorption (%ABS). %ABS was
computed as follows [69]:
%ABS = 109 − [0.345 × TPSA]
(3)
3.7. In Silico ADMET Analysis
A freely accessible web server, pkCSM (http://biosig.unimelb.edu.au/pkcsm/prediction)
is an in silico tool for anticipating substantial pharmacokinetic properties [34]. ADMET
properties involve absorption (A): human intestinal absorption (HIA), water-solubility,
P-glycoprotein I and II inhibitors, Caco-2 permeability, P-glycoprotein substrate, skin
permeability. Distribution (D) is anticipated according to blood-brain barrier (BBB) permeability, steady-state volume of distribution (VDss), fraction unbound, and central nervous
system (CNS) permeability. CYP2D6/CYP3A4 substrate is used to detect the metabolism
(M). Excretion (E) is determined via drug total clearance. Subsequently, the toxicity (T) of
the drugs is predicated on the Human Ether-a-go-go-related gene inhibition, carcinogenic
status, mutagenic status, as well as acute oral toxicity [31].
3.8. Protein Interaction Network Analyses
To identify the probable targets for each ligand, the target molecules were tested using
SwissTargetPrediction (http://www.swisstargetprediction.ch), an internet website-based
program. We obtained the top one hundred genes for the possible metabolite. After that,
a functional STRING database for the most likely targets was utilized to build proteinprotein interactions (PPI). According to network architecture, Cytoscape 3.8.2 was utilized
to analyze all possible receptor-function relationships. Pathway enrichment analysis was
also performed utilizing Cytoscape 3.8.2 to scrutinize all prospective receptor-function
connections for the top 20 targeted genes. The top 20 genes were investigated further using
a newly developed interactive webserver (Gene Expression Profiling Interactive Analysis,
GEPIA, http://gepia.cancer-pku.cn/index.html). Besides, to investigate all potential targetfunction relationships for the top most 20 targeted genes and their biological influencedpathways/networks, the pathway enrichment analysis (PEA) approach was conducted
with the assistance of Cytoscape 3.8.2 software [70]. Subsequently, the Reactome mining
analysis and visualization were achieved using the ReactomeFIViz tool towards modeling
and annotating all the philanthotoxin (T3D2489)-target interactions [71].
4. Conclusions
Herein, a toxin and toxin-target database (T3DB) was mined to identify potential SARSCoV-2 Mpro inhibitors utilizing combined molecular docking and molecular dynamics
simulations. On the basis of molecular docking calculations and MD simulations combined
with molecular mechanics-generalized born surface area binding energy calculations, three
compounds, T3D2489, T3D2672, and T3D2378, demonstrated promising binding affinity
with ∆Gbinding < −50.0 kcal/mol towards Mpro . The energetic and structural analyses
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during 200 ns MD simulations pointed to great constancy for these compounds in complex
with Mpro . Additionally, the identified toxins demonstrated favorable pharmacokinetic
and pharmacodynamic properties. The results obtained from PEA analysis combined
Reactome-mining presented an interesting primary role of philanthotoxin in remodeling
the interleukins (especially IL-4, IL -13) and matrix metalloproteinases (MMPs), which
could alleviate lung injury in COVID-19 patients. In vitro and in vivo evaluations are
planned to elucidate further the role of these compounds as prospective drug candidates
and validate the computational findings.
Supplementary Materials: The following supporting information can be downloaded at: https:
//www.mdpi.com/article/10.3390/ph15020153/s1, Figure S1: 2D representations of the binding
modes of the thirty-two potent toxins complexed with SARS-CoV-2 main protease (Mpro ); Figure S2:
Schematic representation of the utilized in silico techniques and the filtration process; Table S1:
Estimated conventional and expansive docking scores (in kcal/mol), for top 200 toxins towards
SARS-CoV-2 main protease (Mpro ); Table S2: Estimated conventional and expansive docking scores
(in kcal/mol), and MM-GBSA binding energies (in kcal/mol) over 5 ns MD simulations for XF7
and the top 32 potent toxins towards SARS-CoV-2 main protease (Mpro ); Table S3: Top 20 enriched
pathways influenced by philanthotoxin (T3D2489) targets resulted from PEA analysis.
Author Contributions: Conceptualization, M.A.A.I. and L.A.J.-A.; Data curation, A.H.M.A.; Formal analysis, A.H.M.A. and M.A.M.A.; Investigation, A.H.M.A., M.A.M.A. and P.A.S.; Methodology, M.A.A.I., O.R.A., M.-E.F.H. and P.A.S.; Project administration, M.A.A.I. and L.A.J.-A.; Resources, M.A.A.I.; Software, M.A.A.I.; Supervision, M.A.A.I.; Visualization, A.H.M.A., M.A.M.A. and
P.A.S.; Writing—original draft, A.H.M.A. and P.A.S.; Writing—review & editing, M.A.A.I., L.A.J.-A.,
M.A.M.A., O.R.A., M.N.A., M.S.M., M.E.S.S., A.M.S., P.W.P. and M.-E.F.H. All authors have read and
agreed to the published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: The data presented in this study are available in the supplementary
material.
Acknowledgments: Ahmed M. Shawky would like to thank the Deanship of Scientific Research at
Umm Al-Qura University for supporting this work by Grant: 22UQU433174DSR01. Laila A. JaraghAlhadad acknowledges and appreciates both Research Sector at Kuwait University and Learner
Research Institute at Cleveland Clinic for their support. The computational work was completed
with resources supported by the Science and Technology Development Fund, STDF, Egypt, Grants
No. 5480 & 7972 (Granted to Mahmoud A. A. Ibrahim).
Conflicts of Interest: The authors declare that they have no conflict of interest.
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