INTRODUCTION:
A viral pneumonia called COVID-19, or Severe Acute
Respiratory Syndrome coronavirus 2 (SARS-CoV-2), was first discovered in
December 2019 in Wuhan, China. It is a newly discovered coronavirus that is
extremely contagious and represents a major risk to global public health 1.
Investigations into the structure, mechanism of action, epidemiology, and
genome sequencing of SARS-CoV-2 have delivered valuable knowledge about the new
virus2,3. Various efforts are in progress to identify and develop
new therapeutic agents as well as an effective antiviral vaccine4.
However, the appropriate drug for COVID-19 therapy has not yet been found and
most treatments for COVID-19 currently in use, are based on drug repurposing or
analogous therapies for COVID-19 patients5. Thus, there is
significant potential for the exploration and discovery of new active compounds
as anti-COVID-19 agents.
SARS-CoV-2 is an enveloped, positive-sense,
single-stranded RNA virus. Once inside a cell, it translates its genetic
material into two polyproteins, pp1a and pp1ab, which are essential for viral
replication and transcription. These polyproteins are then processed by viral
proteases to produce 16 nonstructural proteins (nsp1–nsp16). This translation
step is crucial for the virus to replicate6,7. The two crucial
proteases involved in this process are, the Main protease or 3C-Like Protease
(Mpro) and the Papain-like protease (PLpro). PLpro is responsible for cleaving
nsp1–3, while Mpro cleaves nsp4–nsp168. SARS-CoV-2 is vulnerable to
induced mutations, however, the Mpro and PLpro proteases remain highly
conserved because mutations in these essential proteins are usually fatal to
the virus9. As a result, drugs that target the conserved regions of
Mpro and PLpro can effectively prevent viral replication and spread, offering
broad-spectrum antiviral effects, and they also help prevent drug resistance
associated with viral mutations10. SARS-CoV-2 PLpro plays a crucial
role by not only inhibiting the host's interferon-related antiviral responses
but also help the virus in escaping immune detection11,12.This dual
function is particularly important because SARS-CoV-2 is known to have a higher
mortality rate in elderly individuals and those with compromised immune systems13,14.
Targeting SARS-CoV-2 PLpro with therapeutic interventions can thus not only
suppress viral infection directly but also enhance the body's immune response
against the virus.
Previous studies indicate that peptide inhibitors of
Mpro and PLpro have significantly different substrate specificities 15,16.
As a result, creating a peptide inhibitor that effectively targets both
proteases is challenging. In this context, the development of small molecule
inhibitors that can inhibit both SARS-CoV-2 Mpro and PLpro represents a
significant intention. The main difference between PLpro and Mpro lies in their
active site composition. Mpro features a catalytic dyad (His41, Cys145) whereas
PLpro possesses a catalytic triad (Cys111, His272, and Asp286) 17,18.
The crucial function of cysteine in the active sites of both enzymes for viral
replication allows the possibility of designing inhibitors that target both
simultaneously.
Dihydropyrimidinones (DHPMs) and Chromene are a group
of heterocyclic compounds that are of significant interest for exploration in
drug discovery research due to their simple synthesis methods and widely
recognized studies as antivirals, antibacterials, antiparasitics, antifungals,
antioxidants, analgesics, anti-inflammatories, anticoagulants, antitumor, and
anticancer activities 19,20. These compounds are
known to be easily varied and modified based on their constituent components 21,22,
yet their activity against SARS-CoV-2 remains largely unexplored.
Based on a preliminary experiments, four candidate
compounds have already successfully passed cell-based assays for cytotoxicity
and antiviral activity against SARS-CoV-2. These include two DHPM compounds and
two chromene compounds. The specific compounds are: (S-4),
(S)-1-(6-methyl-4-(thiophen-2-yl)-2-thioxo-1,2,3,4-tetrahydropyrimidin-5-yl)ethan-1-one
(IC50 value 25.2±5.38 µM); (S-12) ethyl
(R)-6-methyl-2-oxo-4-phenyl-1,2,3,4-tetrahydropyrimidine-5-carboxylate (IC50
value 6.187±0.41µM); (S-11) (R)-2,7-diamino-4-phenyl-4H-chromene-3-carbonitrile
(IC50 value 19.48 ± 0.91 µM); and (S-10)
(R)-2-amino-7-hydroxy-4-phenyl-4H-chromene-3-carbonitrile (IC50
value 8.52±0.28µM). The four candidate compounds, as scaffolds were modified by
adding substituents from 14 different functional groups, based on their
electron-donating and electron-withdrawing characteristics, to the aromatic
groups23. These modifications and variations were compiled into a
chemical database of candidate compounds, which was then used to explore
potential inhibitors targeting both Mpro and PLpro proteases24. The
development of this database was intended to search for compounds that indicate
increased protease inhibition activity and lower toxicity. Subsequently, the
candidate compounds were analyzed using in silico screening.
Castro-Gonzalez et al. (2020)25 developed a
selection score (SS) model for assessing drug candidates by comparing
Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET)
parameters to evaluate drug-likeness. This model also employs docking
parameters such as docking score and pharmacophore score (docking pose),
analyzed the interactions between receptor targets and the candidate compounds,
as well as reference drugs. Drug candidates and reference drugs are then ranked
by ordering the highest SS values relative to the reference drugs. This study
aims to: (1) Design, describe, and select candidate compounds derived from the
four groups of DHPM and Chromene scaffolds after the addition of substituents
and their variations, followed by screening using an in silico approach ; (2)
Analyze the visualization of molecular docking results of the interactions
between candidate compounds and the active site of the target receptors, and
compare them with reference drug compounds.
MATERIALS AND METHODS:
Materials:
The SARS-CoV-2 Main Protease (Mpro) is crystallized in
complex with the non-covalent ligand X77
(N-(4-tert-butylphenyl)-N-[(1R)-2-(cyclohexylamino)-2-oxo-1-(pyridin-3-yl)ethyl]-1H-imidazole-4-carboxamide)
(PDB ID: 6w63, resolution 2.10Å). The Papain-Like Protease (PLPro) enzyme model
used is complexed with the non-covalent ligand 3k or
S88(N-[(3-fluorophenyl)methyl]-1-[(1R)-1-naphthalen-1-ylethyl]piperidine-4-carboxamide)
(PDB ID: 7TZJ, resolution 2.66 Å). All structural data were retrieved from the
RCSB Protein Data Bank website http://www.rcsb.org/pdb. The eight repurposed drugs chosen were
Lopinavir, Remdesivir, Indinavir, Ritonavir, Molnupiravir, Favipiravir,
Ensitrelvir, and Nirmatrelvir, selected as reference drugs for their
well-established safety profiles. Reference drug structures taken from the website
https://pubchem.ncbi.nlm.nih.gov. A total of 838 DHPM and Chromene derivatives
were developed as chemical database candidates derived from four different
small molecule scaffolds.
Instrumentation:
Molecular docking analysis was conducted using a
combination of software tools on both Windows 10 and Linux operating systems.
The software used on the Windows system includes ChemDraw Professional 15.1,
Notepad++, Chimera 1.16, PuTTY (64-bit), WinSCP, and Discovery Studio Visualizer (BIOVIA,
San Diego) 2021 v21.1.0.2098. On the LINUX system, Gaussian 16 was used for
optimizations of the 3D structures and DOCK6 Program was utilized for molecular
docking. The 3D structures were drawn using the tool available at
https://cactus.nci.nih.gov/translate/ while Prediction of ADMET (Absorption,
Distribution, Metabolism, Excretion and Toxicity) of candidate ligands was
conducted using the SwissADME web service and software T.E.S.T (Toxicity
Estimation Software Tool) for assessing toxicity and mutagenicity.
Preparation of Small Molecular Structure:
The initial step focused on designing derivatives of
four DHPM and Chromene compounds, including their stereoisomers, which have
passed the cell-based assays. Subsequently, a database of candidate compounds
was created by adding substituents from 14 functional groups based on their
electron-donating or electron-withdrawing properties. The functional groups
are: -OH, -OCH3, -OCH2CH3, -F, -Cl, -Br, -I,
-NO2, -NH2, -CH3, -CH2CH3,
-CN, -N(CH3)2, and -N(CH2CH3)2.
A total of 838 derivative compounds were generated by
adding 14 substituents to each configuration on the aromatic rings. These
compounds were drawn in ChemDraw, organized into a library, and saved in SMILES
format. The reference drug compounds are obtained by downloading the SMILES
format from the website https://pubchem.ncbi.nlm.nih.gov. Their 3D structures
were constructed using the tool which is available online on the website
https://cactus.nci.nih.gov. Geometric optimizations of these structures were performed
using the semi-empirical PM6 method with the Gaussian16 software. Subsequently,
charges were assigned to the optimized candidate ligands using the AM1-BCC
method via the Antechamber program. Finally, the ligands, were stored in MOL2
format files and merged into a single input file in order to improve the
efficiency of Molecular Docking calculations26.
Analysis of ADMET Predictions:
The selection of drug candidates begins with the
prediction analysis of drug-likeness. Absorption, distribution, metabolism, and
excretion (ADME) were evaluated using the SwissADME program, based on
Lipinski's Rule of Five, along with Ghose, Veber, Egan, and Muegge's rules27.
Toxicity was assessed using the Toxicity Estimation Software Tool (T.E.S.T)
program, which included tests for Ames mutagenicity (M) and LD50
(mg/kg)28. This analysis was conducted for 8 reference drugs and 838
candidates.
Molecular Docking:
The docking experiment was initiated by validating
parameters, beginning with the preparation of both the co-crystallized ligand
and enzyme using DockPrep tools in Chimera. This preparation involved removing
water molecules, non-complex ions, selecting occupancy, and adding hydrogen
atoms and charges, with the ff14SB method for the receptor and AM1-BCC for
non-protein parts29. The receptor surface and spheres were generated
using DOCK6 30 with Chimera's support, both programs
requiring server communication via tools like WinSCP and PuTTY.
The receptor surface, without hydrogen atoms, was
created using Chimera’s Write DMS feature and saved in a DMS file. This file
was then used to generate spheres with SPHGEN, with input parameters from the
INSPH file. Spheres within an 8Å radius of the ligand were selected using the
sphere_selector program. Both SPHGEN and sphere_selector are CLI tools,
necessitating their use via PuTTY. The DOCK6 program’s flexible docking method was then used to redock the
original ligand, with validation achieved by an RMSD of ≤ 2 Å, as
referenced by Allen et al. (2015) 31.
All ligand candidates were subsequently docked onto Mpro and PLPro receptors,
with selection based on grid scores and generated poses.
Drug Candidate Selection:
Drug candidate selection is based on ADME, Toxicity
parameters, and molecular docking data. Castro-Gonzalez et al. (2020) developed
an assessment model called the selection score (SS) to sort drug candidates by
comparing ADMET parameters, drug-likeness combined with docking parameters
including grid score and pharmacophore score between candidate compounds and
reference drugs. Candidate compounds and reference compounds are selected by
ranking the highest SS values against reference SS. Each parameter given scored
of 1 if it fulfills the criteria and 0 if it does not.
RESULTS AND DISCUSSION:
Design Inhibitors:
Dihydropyrimidinone and chromene are heterocyclic
compounds. The modification of these compounds with functional groups such as
hydroxyl (-OH), amine (-NH₂), alkyl (-Me, -Et), nitrile (-CN), and all 14
functional groups will be involved in hydrogen bond formation with amino acid
residues at the active sites of protease enzymes. Hydrogen bonding plays a
crucial role in enhancing the strength and specificity of the interaction
between the inhibitor and the enzyme. Furthermore, hydrophobic groups,
including aromatic rings or non-polar aliphatic chains, are important for their
ability to interact with the hydrophobic pockets of protease enzymes. These
hydrophobic interactions can significantly improve the binding affinity of the
compound to the enzyme's active site 32,33.
The selection process was conducted in three stages.
In the first stage, a single substituent from 14 functional groups was added by
positioning it at different locations on an aromatic group, followed by the
addition of pairs of identical substituents. The second and third stages also
consist of placing a single substituent for each 14 subtituents variations in
different positions on an aromatic group. These stages resulted in the creation
of 838 derivative compounds, along with 8 reference drugs. The modification of
the scaffold compounds along with the locations for substituent addition can be
seen in Table 1.
Docking Analysis:
Validation was conducted by redocking ligand X77 with
the Mpro receptor, preceded by sphere generation and the creation of a
simulation box with dimensions x = -21.046 Å; y = 17.557 Å; z = -26.780 Å,
resulting in 3,516,399 grid points. Similarly, ligand 3k was redocked with the
PLPro receptor, with a simulation box measuring x = -35.967 Å; y = -40.195 Å; z
= -39.556 Å, yielding 2,560,701 grid points. The molecular docking parameters
were validated using RMSD values obtained from redocking, confirming the accuracy
of the docking method. The RMSD for ligand X77 with Mpro was 0.3305 Å and for
ligand 3k with PLPro was 1.2327 Å, indicating successful validation of the
docking results. Then, 838 designed candidates along with 8 reference drugs
were docked. The results provided information on grid scores, docking poses,
and the interactions of the candidates with both Mpro and PLPro.
The original ligand of Main protease, X77 (PDB ID:
6W63) forms three hydrogen bonds with the Mpro residues Gly143, His163, and
Glu166. Key active site residues involved in substrate binding include His41,
Met49, Leu141, Asn142, Cys145, Met165, Asp187, and Gln189, which establish
important contacts and binding hotspots within the active site. The residues
Gly143, His163, and Glu166 also play an important role as they contribute to
the stability and orientation of the active site34. However, Cys145
and His41 are the most critical for the enzymatic activity of Mpro. Inhibition
of these interactions, especially with ligands targeting Cys145, can
significantly reduce protease activity, making it a primary target in the
development of therapeutic strategies 35.
Meanwhile, Tyr268 in the BL2 (Binding loop-2), a key
residue in PLpro (PDB ID: 7TZJ), plays a crucial role in stabilizing the
original ligand, compound 3k, within the PLpro binding site. Tyr268 forms a hydrophobic interaction with
the piperidine ring and also participates in forming a hydrogen bond through
the carbonyl backbone in the BL2 loop. Therefore, the interaction of the
compound with flexible residues like the BL2 loop (Tyr268) can induce an
allosteric effect, where the compound locks PLpro in an inactive form. The naphthyl ring extends into a
hydrophobic pocket, interacting with Pro247 and Pro248, while the piperidine
group forms a hydrogen bond with Asp164 of the Thumb domain. The phenyl group
extends outward, rotating Leu162 and blocking access to the catalytic Cys111,
keeping 3k at a distance of 9.7 Å from Cys111 36,37.
This observations indicates that interactions with key
residues, such as Tyr268 and Pro247, are crucial for stabilizing ligands in the
binding pocket of PLpro. Hydrophobic interactions with Tyr268, Pro247 and
Pro248, along with hydrogen bonding with Asp164, are essential for maintaining
ligand stability and efficacy38. All visualizations of the docking
results, from the original ligand interactions with their individual Mpro and
PLpro receptor sites can be seen in Figure 1.
Figure 1. Interaction between X77 and Mpro (PDB ID:
6W63) based on molecular docking in 3D (IA) and 2D visualization (IB).
Interaction between 3k and PLpro (PDB ID:7TZJ) based on molecular docking in 3D
(IIA) and 2D visualization (IIB).
Selection Candidates:
The docking results for the original ligands, 838
compounds, along with 8 repurposed drugs as references, were followed by ADMET
and drug-likeness prediction analyses. All data were compiled into a database
for scoring and used to calculate the selection score (SS). Each parameter was
assigned a score of 1 if it met the criteria and 0 if it did not. The drug
candidates, along with commercial drugs for SARS-CoV-2 therapy, were then
ranked based on their highest SS. The calculation of SS for candidate compounds
and repurposing drugs against Mpro are fully displayed in Figure 2.
Figure 2. The Selection Score (SS) analysis results of
drug candidate ligands Against the SARS-CoV-2 Main Protease (Yellow = compounds
with the highest Selection Score (SS) value; Red = Reference drugs)
The compounds with the best interaction values based
on selection scores against the Main Protease (Mpro) receptor were subsequently
screened against the Papain-Like Protease (PLpro) receptor, along with 8
commercial drug compounds as references, and the selection results are shown in
Figure 3.
Figure 3. The Selection Score (SS) analysis results of
drug candidate ligands Against the SARS-CoV-2 Papain-Like Protease (Yellow =
compounds with the highest Selection Score (SS) value; Blue = Reference drugs)
From 838 derivatives compound, there are three
compounds ranked highest after the scoring process.
(R)-2-amino-4-(2,4-diethoxy-5-methylphenyl)-7-hydroxy-4H-chromene-3-carbonitrile
(S10-2-26-18); (R)-2-amino-4-(2,4-diethoxy-5-fluorophenyl)-7-hydroxy-4H-chromene-3-carbonitrile
(S10-2-26-23); and
(R)-2-amino-4-(5-(dimethylamino)-2,4-diethoxyphenyl)-7-hydroxy-4H-chromene-3-carbonitrile
(S10-2-26-27). These candidates have higher SS values than reference drugs. All
compounds are derivatives of chromene, varying substituents on phenyl ring,
such as methyl, fluorine and dimethylamine groups. Their structural
modifications contribute to better performance as potential inhibitor of
proteases both Mpro and PLpro. Their structure as displayed in Figure 4.
A
B
C
Figure 4. The molecules structure of selected compounds
from virtual screening A. S10-2-26-18, B. S10-2-26-23 and C. S10-2-26-27
Table 2 illustrates the interactions between each
candidate ligand, X77, and reference compounds with the Mpro receptor (PDB ID:
6W63), as well as the original ligand 3k with the PLpro receptor (PDB ID:
7TZJ). The interaction profiles of these original ligands provide a basis for
visualizing the interactions of all candidate ligands, including the three
selected compounds, along with the key residues involved in each receptor.
These interactions include hydrogen bonding and hydrophobic interactions between
the selected ligands and their respective receptors39.
Upon reviewing the ADMET analysis profile, the data in
Table 3 indicates that the selected compounds have a molecular weight (MW)
below 500, which is a desired property for drug likeness44. However,
some reference drugs have a higher MW, this condition can be tolerated. Several
reference drugs with molecular weights exceeding 500 daltons are still
considered viable candidates for drug development due to their unique
properties and mechanisms of action. While Lipinski's Rule of Five suggests
that lower molecular weights generally correlate with favorable drug likeness,
it is important to note that drugs with high molecular weight are often more
suitable for intravenous use. These drugs tend to have lower cell penetration
capabilities and may not be well absorbed when administered orally. In this
context, Lipinski's Rule of Five serves as a guideline for evaluating the
suitability of compounds as orally administered drugs45.
The selected compounds also have a Topological Polar
Surface Area (TPSA) value below 120 Ų, indicating good oral bioavailability.
The calculated WLOGP values for all compounds range from -0.4 to 5.6. The Log P
value is a well-established measure of a compound's hydrophilicity and lipophilicity; therefore, drug candidates should avoid compounds
with low solubility 46. Higher Log P values can result in
poor absorption or permeability. Additionally, none of the compounds are
predicted to penetrate the Blood-Brain Barrier (BBB), as indicated by
"No" in the BBB column. Overall, the selected compounds exhibit
promising properties for drug likeness and oral bioavailability47,48.
The T.E.S.T (Toxicity Estimation Software Tool) is
used to predict the toxicity and mutagenicity of a compound based on various
parameters, including LD50 and the results of the Ames test for
mutagenicity. LD50
provides an indication of the toxicity of a substance, with a lower LD50
value indicating a higher toxicity of the substance and a mutagenicity index of ≤ 1 indicates no
significant increase in mutagenicity, suggesting that the compounds are
relatively safe. All presented in Table 3.
CONCLUSION:
This study demonstrates the potential of
dihydropyrimidinone (DHPM) and chromene derivatives as dual inhibitors of the
SARS-CoV-2 proteases Mpro and PLpro. Utilizing virtual screening and molecular
docking analysis, 838 derivatives were designed and screened, resulting in the
identification of three chromene derivatives as promising candidates. These
compounds exhibited higher Selection Scores (SS) compared to reference drugs,
along with favorable ADMET properties, such as molecular weights below 500,
good oral bioavailability, and non-mutagenicity. The structural modifications
made, particularly on the aromatic substituents, enhanced their binding
interactions with key active site residues in both Mpro and PLpro, indicating
their potential efficacy as anti-SARS-CoV-2 agents. This comprehensive approach
successfully identified and optimized potential inhibitor, supporting the
development of effective therapeutic agents against SARS-CoV-2.
CONFLICT OF INTEREST:
The
authors have no conflicts of interest regarding this investigation.
ACKNOWLEDGMENTS:
This Research is financially supported by Universitas
Airlangga through Research Funds in platform Penelitian Riset Unggulan in 2024
based on No.122/UN3.LPT/PT.01.03/2024, also all the support provided from
Research Center for Bio-Molecule Engineering (BIOME)-LIHTR Universitas
Airlangga and doctoral research support program from RC-GERID Universitas
Airlangga.
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