1. Introduction
The ongoing COVID-19 worldwide pandemic (declared by the WHO) has caused more than 6.58 million deaths and 632.9 million cases worldwide as of 24 October 2022 [1]. Even though the symptoms are relatively mild, the high transmission rate has caused a crisis that has resulted in a variety of supply chain and economic issues as workplaces and ports globally have shut down [2,3]. Treatments to fight the virus are often thwarted by the virus’s ability to mutate and evolve. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has already mutated into several different variants such as Alpha, Beta, Gamma, Delta, Epsilon, Zeta, Iota, Theta, and Kappa [4]. The newer variants and subvariants are not only more transmissible but also become more difficult for host antibodies to recognize. This deadly combination combats the long-term immunity that vaccines are trying to achieve.
Therefore, the innovations of less time-consuming antiviral drug development methods such as drug repurposing are needed to keep up with the rates of infection to eventually solve the problem [5]. Drug repurposing is one of the quickest ways to develop effective therapeutics against COVID-19 [6]. For example, in one study, researchers conducted experiments to test broad-spectrum antivirals (ribavirin, interferons, and cyclophilin inhibitors) against coronaviruses [7]. However, since the drug repurposing technique utilizes a “broad spectrum” treatment strategy, it may be too broad and likewise not very effective. This study also had a limitation in that it only screened a small range of drug varieties. Thus, we collected 225,667 ligand compounds from the traditional Chinese medicine (TCM) database [8] to offer a more comprehensive study with a higher scope of drug variety. One of the common processes of drug repurposing is to use both ligand-based virtual screening and structure-based virtual screening, which is an approach that utilizes the Schrödinger Maestro screening process to view the docking scores of the ligand binding process. However, focusing only on the docking scores without taking key residues into account may increase the false positive rate [9]. Therefore, all these points need to be considered and overcome when creating a new treatment process for SARS-CoV-2.
SARS-CoV-2 is an enveloped virus that belongs to the beta coronavirus family with a complex, single-stranded RNA genome (~30 kb) similar to the other known human coronaviruses, CoVs (SARS-CoV, MERS-CoV, HKU1, NL63, OC43, and 229E) [10]. SARS-CoV-2 contains spike proteins, membrane proteins, envelope proteins, and nucleocapsid proteins. SARS-CoV-2 enters the host’s cells through receptor binding with its viral spike proteins and membrane fusion, which allows the virus to release its RNA that causes the cell to replicate and produce several subgenomic RNAs [11]. The SARS-CoV genome consists of two open reading frames that can encode two polyproteins, pp1a and pp1ab [12]. Pp1a and pp1ab can moderate the mechanism of viral replication and transcription [13]. Genome analysis has shown that SARS-CoV-2 is similar to bat CoV, which has evolved to bind to human ACE2 receptor proteins with higher affinity than other coronaviruses [14]. Using several antiviral strategies, researchers are creating new ways to target and inhibit the binding of the spike protein with the receptor proteins. Therefore, the solutions based on targeting coronavirus rely on blocking the virus binding to receptors, inhibiting virus replication, preventing the synthesis of viral RNA, and inhibiting the virus self-assembly process [15,16,17]. The 3CL main protease protein is an effective target because of its crucial role in viral maturation and post-translational processing of replicase polyproteins [18,19].
Recently, the use of computational methods has become increasingly popular because of their high efficiency and effectiveness [20]. Applying computational methods in drug discovery and development can also save time, costs, and manpower [13]. Since 3CLpro is the most suitable target for new drug therapeutics, the most efficient way to discover a treatment plan is to utilize structural bioinformatics strategies to determine the best ligand structures to bind to it (3CLpro). In the present work, we conducted both ligand-based virtual screening and structure-based virtual screening to screen the Chinese medicine small molecule that has the potential to target the 3CLpro of SARS-CoV-2. MD simulations were then used to confirm these results for future in vitro testing. Furthermore, we used MCCS screening methods [9] to search for the ligands that had the most and strongest bonds to the key residues in 3CLpro. The ligands with the strongest bonds with key residues would most likely be used in effective antiviral therapeutic treatments to combat SARS-CoV-2. All these components (docking score, MCCS normalized total free energy, and MD binding free energy) were used to determine the most suitable ligands to combat SARS-CoV-2.
2. Results
2.1. Virtual Screening of TCM
We performed ligand-based virtual screening of the traditional Chinese medicines database (TCM) to exclude ligands that do not have the potential to bind with 3CLpro within the binding pocket of the five inhibitors listed above. We narrowed down the 177,764 TCM compounds to 17,939 compounds that have similar pharmacophore features to the five inhibitors listed above. Then we conducted structure-based virtual screening for these 17,939 compounds and listed the top 10 ligands with the best docking scores. The suitable ligands found in this study are ZINC15676170, ZINC15675325, ZINC12529667, ZINC13550544, etc. (Table 1). The best docking score, −9.178 kcal/mol, was reported for ZINC15676170. The candidates shown in Table 2 have high potential against the 3CLpro of SARS-CoV-2.
2.2. Molecular Dynamics Simulation of the Top Ligands
To better understand the conformational and dynamics features of the top ligands against the 3CLpro of SARS-CoV-2, molecular dynamics simulations were performed for the top 10 ligands. Dynamics features of the top 10 ligands were calculated after a 100 ns MD simulation.
As we can see from Table 2, the binding free energies predicted by the molecular dynamics simulation of ZINC09033700, ZINC15676170, and ZINC12530139 are −6.8953 kcal/mol, −4.3351 kcal/mol, and −3.0082 kcal/mol, respectively, which indicates that the binding of 3CLpro with ZINC09033700, ZINC15676170, and ZINC12530139 is stable.
2.3. The Time Course of Root-Mean-Square Deviations (RMSD) Analysis
The stabilities of all the top 10 systems were evaluated by using the time course of root-mean-square deviations (RMSD) analysis. As shown in Figures S1 and S2, the results indicated that the stabilities of all ten systems were high with acceptable deviation. The average RMSD for all systems ranged between 1 and 5Å.
2.4. The Root-Mean-Square Fluctuation (RMSF) Analysis
The RMSF delineated the average deviation of a particle over time from a reference location. As shown in Figure 1, the deviation pattern of these three systems is very similar. Significant fluctuations were observed within the regions 0–10, 40–50, 85–95, 145–160, 240–250, and 290–307, which represent the loop regions. The regions 10–20, 50–90, 100–110, and 250–275 are very stable, which represent helix strands or β turns. These findings are consistent with the structure of the 3CLpro of SARS-CoV-2. The region 200–220 fluctuates greatly, which may represent a connecting loop. Fluctuations in other regions were relatively flat. These findings indicate that the 3CLpro of SARS-CoV-2 is stabilized by the binding of ZINC15676170, ZINC09033700, and ZINC12530139.
2.5. Energy Decomposition of Three Potential Inhibitors and Their Complex by MM/GBSA Calculation and MCCS
Residue energy contribution is an important factor to be considered during the screening process. By acknowledging the importance of the bonds in key residues, the results from the screening process are more accurate and effective. The residue energy contributions shown in Table 3, Table 4 and Table 5 were predicted by MM/GBSA calculation. We only listed the top 10 residues. MCCS utilized the jdock function to calculate and display the energy contributions of each residue. To identify residues classified as “key residues”, we only chose residues that had an inter-ligand free energy contribution of <−0.3 kcal/mol (the lower values represent the stronger binding between the ligand and the receptor). The values differed from the docking scores because MCCS calculates total free energy with the jdock function, while Glide, the ligand docking program we used on Maestro, calculates docking scores with the SP and XP GlideScore functions.
The residue energy contribution between ZINC15676170 and the 3CLpro of SARS-CoV-2 was analyzed by both MD simulation and MCCS. As shown in Figure 2, the binding pose between ZINC15676170 and the 3CLpro of SARS-CoV-2 predicted by MD simulation indicated that the nitrogen atom of ZINC15676170 can form a hydrogen bond with the oxygen atom of HIS164, with the distance of 3.5Å, and the oxygen atom of ZINC15676170 can form a hydrogen bond with the nitrogen atom of GLY143, with the distance of 3.1Å. The MCCS prediction results indicated that the HIS164 and GLY143 have a very high chance of forming a hydrogen bond with ZINC15676170, which is consistent with the MD simulation prediction.
The residue energy contribution between ZINC09033700 and the 3CLpro of SARS-CoV-2 was analyzed by both MD simulation and MCCS. As shown in Figure 3, the binding pose between ZINC09033700 and the 3CLpro of SARS-CoV-2 predicted by MD simulation indicated that the oxygen atom of ZINC15676170 can form a hydrogen bond with the nitrogen atom of GLU166, with the distance of 3.1Å. The MCCS prediction results indicated that the GLU166 has a very high chance of forming a hydrogen bond with ZINC09033700, which is consistent with the MD simulation prediction.
The residue energy contribution between ZINC12530139 and the 3CLpro of SARS-CoV-2 was analyzed by both MD simulation and MCCS. As shown in Figure 4, the binding pose between ZINC12530139 and the 3CLpro of SARS-CoV-2 predicted by MD simulation indicated that the nitrogen atom of ZINC15676170 can form a hydrogen bond with the oxygen atom of SER144, with the distance of 3.3Å. The MCCS prediction results indicated that the SER144 has a very high chance of forming a hydrogen bond with ZINC12530139, which is consistent with the MD simulation prediction.
The purpose of MD simulation is to simulate a dynamic process, while MCCS uses the jdock function to predict a static binding pose. The representative conformation is the conformation found in 1000 snapshots of the MD simulation that is closest to the average conformation. The energy decomposition of MM/GBSA predicted by the MD simulation represents the energy decomposition of the representative conformation. Therefore, there may be some difference between the energy decomposition of MM/GBSA and the energy decomposition of MCCS. Although the rankings of residues were not identical due to different scoring mechanisms for residues, we found that at least 8 out of the top 10 residues (80%) were consistent. The accuracy of MCCS prediction has been approved by another manuscript [21].
From the energy contribution between the three potential inhibitors and 3CLpro calculated by MM/GBSA and MCCS, we can predict that HIS41, ASN142, GLY143, SER144, HIS163, HIS164, MET165, GLU166, and GLN189 are the key residues that play an important role within the binding between the potential inhibitors and 3CLpro. Among the key residues, GLY143, SER144, HIS164, and GLU166 have a very high potential to form hydrogen bonds with potential inhibitors. Steric hindrance and hydrogen bonding are the main forms of energy contribution. As shown in Figure S3, we also analyzed the hydrogen-bonding occupancy of ZINC15676170, ZINC09033700, and ZINC12530139. In ZINC15676170, the hydrogen bonds of GLY143 and GLU166 accounted for 25.98% and 15.6% of the trajectory, respectively. In ZINC09033700, the hydrogen bonds of GLU166 accounted for 21.5% of the trajectory. In ZINC12530139, the hydrogen bonds of GLY143 and SER144 accounted for 16.5% and 0.4% of the trajectory, respectively.
3. Methods
3.1. Protein and Ligand Structure Organization
In this study, we retrieved the three-dimensional coordinates of 3CLpro from RCSB protein data bank (PDB id: 6LU7) [22]. This structure consists of N3 co-crystal inhibitor and 3CLpro. Protein preparation wizard implemented in Schrödinger software (Schrödinger, LLC, New York, NY, USA) was used to prepare and optimize the structure of 6LU7, which included filling in missing side chains, using Prime and adding hydrogens. The OPLS3 force field was used for protein-energy minimization. LigPrep was used to prepare the ligands.
3.2. TCM Database
The chemical compounds used in this study were collected from the TCM database. We chose to use the TCM database because the TCM database is currently the largest and most comprehensive database on traditional Chinese medicine [8]. We collected 225,667 natural compounds, which were then filtered through using Lipinski’s rule of five (we excluded any natural compounds that violated this rule). This narrowed 225,667 natural compounds down to 177,764 ligands, which were more likely to be a hit. These ligands were then uploaded to Maestro for scoring function calculations and virtual screening.
3.3. Inhibitor Filtering by Ligand-Based Pharmacophore Model
In this study, we collected 5 inhibitors that bonded well to 3CLpro in SARS-CoV-2. These include the N3, GRL-2420, ML188, CALPAIN1, and nirmatrelvir inhibitors retrieved from Protein databank 6LU7, 7JKV, 7L0D, 7LBN, and 7U29 respectively. Among these inhibitors, we conducted the ligand-based virtual screening process, which uses structural commonalities between the 5 inhibitors to accurately identify the key pharmacophore model that plays an important role in the binding process. The pharmacophores that were inserted into the crystal structure of 3CLpro let us identify the key residues and the interaction type that play a major role in the binding between the inhibitor and 3CLpro of SARS-CoV-2. Each inhibitor contains 4 or 5 pharmacophore features that can form interactions with 3CLpro, and we collected the ligands that contain at least 2/4 or 3/5 pharmacophore features as the inhibitors listed above. This allowed us to narrow down the 177,764 TCM compounds to 17,939 compounds that have similar pharmacophore features as the 5 inhibitors listed above.
3.4. Virtual Screening and Selection Method
The Maestro software allows us to screen and select ligands from the TCM database. First, we used the Maestro virtual screening software to display the preprocessed and minimized structures of the 6LU7 protein. Then, we used several visual tools to find the various bonds (hydrogen bonds, salt bridges, and π-π stacks) between the ligand and the crystal structure of 3CLpro. The bonds between the key residues of 3CLpro and the ligand are considered very important because of the lower binding free energy between the key residues of 3CLpro and the ligands, thus the more stable the complex formed by ligand and 3CLpro is. The set of 17,939 ligands were docked into the active site of 3CLpro and then displayed in the order of highest (most negative) docking score. The docking score was calculated using the Glide program that utilized the SP and XP GlideScore scoring functions. We selected the top 10 ligands (according to docking score). The Schrödinger Maestro tools calculate various bond types (hydrogen bonds, salt bridges, and π-π stacks), which help us screen the number of bonds that each ligand has with the protein.
3.5. Molecular Dynamics (MD) Simulation
We performed molecular dynamics simulations on the top 10 ligands and 3CLpro complexes to further validate our virtual screening results. The MD simulations were carried out in a cubic box (~90Å × 90Å × 90Å) with periodic boundary conditions to model a representative part of the interface devoid of any arbitrary boundary effects [23]. The system was solvated in a 0.15 mol/L NaCl solution, including 15,021 water molecules, 40 Cl− and 44 Na+ ions. The systems were protonated prior to the MD simulations. Special caution was applied to histidine residues, which can be ionized at pH 7.40. AMBER ff14SB force field [24] and GAFF [25] were applied to the protein and ligands, respectively. The TIP3P water model was used to treat the water molecules [26]. Subsequently, the PMEMD.mpi and PMEMD.cuda modules in the AMBER16 software package were used to perform the MD simulations [27,28,29].
Before the MD simulations, we first relaxed the complex by minimizing 5000 cycles to avoid the possible spatial conflicts—500 cycles of steepest descent and 4500 cycles of conjugated gradient minimization [30]. After minimization, it entered the heating phase. In this step, each system was gradually heated from 0 K to 303.15 K, followed by equilibrium and production stages (303.15 K) with a time step of 2 fs. Periodic boundary conditions were used to maintain constant temperature and pressure (NPT) ensembles. The pressure was set to 1 atm and was controlled by an anisotropic (x-, y-, z-) pressure scaling protocol, and the pressure relaxation time was 1 ps. Langevin dynamics were used to control the temperature to keep the temperature at the collision frequency of 2 ps−1 [31,32]. The particle grid Ewald (PME) method [33,34] was used to deal with long-distance static electricity, and the critical value of actual spatial interaction was set to 10 Å. The SHAKE algorithm was used to constrain all covalent bonds related to hydrogen atoms [35]. Both systems were subjected to 100 ns MD simulations, and a trajectory of the simulated system was saved every 100 ps.
3.6. Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) Calculation
In MM/GBSA, their binding free energy (ΔGbind) of 3CL/molecule was calculated by the following equation:
ΔGbind = ΔH − TΔS ≈ ΔEMM + ΔGsol − TΔS
Among them, ΔEMM was the gas phase MM energy during binding, ΔGsol and −TΔS were the changes in solvation free energy and the conformational entropy on ligand binding, respectively [30]. T was the absolute temperature and ΔS was the molecule’s entropy [36]. ΔEinternal specifically refers to bond, angle, and dihedral energy, which together with electrostatic ΔEelectrostatic energy and van der Waals force ΔEvdw form ΔEMM [30]. The specific calculation formula is as follows:
ΔEMM = ΔEinternal + ΔEelectrostatic + ΔEvdw
The energy contribution of ΔGsol includes electrostatic solvation energy (polar contribution) ΔGGB and non-electrostatic solvation component ΔGSA (non-polar contribution). The non-polar contribution obtained by fitting the solvent accessible surface area (SASA) [37] and linear combination of pairwise overlaps (LCPO) model [38,39]. The specific calculation formula is as follows:
ΔGsol = ΔGGB + ΔGSA
In addition, the energy of each residue could also be calculated. Energy analysis could analyze the energy contribution of key residues in the decomposition of the binding process [40].
3.7. Molecular Complex Characterizing System
As stated in the introduction before, looking only at the docking scores produces false positive results [9]. Considering the key residues in the docking process can make the results of virtual screening more convincing and make it easier for biological experiments to verify the inhibitory efficacy of ligands against 3CLpro. The energy contribution of key residues was calculated by Molecular Complex Characterizing System (MCCS). Firstly, Chimera (version 1.15) [41] was applied to fix the incomplete side chain of protein PDBs. To be more specific, the full protein structures were scanned by Chimera and then the incomplete residues were revealed. The truncated side chains were replaced with entire side chains of the same residue type with the aid of the Dunbrack rotamer library [42]. Secondly, the polar hydrogens, Vina force field, and Gasteiger charges were added by VEGA [43]. Thirdly, the PDB format was converted to the PDBQT format. For ligand files, VEGA was first used to prepare the ligand PDBs as the protein preparation. Secondly, PROPKA (version 3.1) [44] was used to predict the pKa values of ligands [45]. MCCS would protonate the tertiary (3°) amide in the compounds when the computed pKa value of the ligands was greater than or equal to the supplied pH (7.4 by default). Thirdly, the torsions of ligands were determined by VEGA, and the ligand PDB format was converted to the PDBQT format. Finally, the PDBQT files of the ligand and the protein and the pKa file of the ligand were used as inputs into MCCS, which includes scoring and docking with jdock to calculate the residue energy contribution.
Jdock is the core implementation of the MCCS algorithm, which can generate nine different energy contribution vectors in total, including Gauss, Gauss1, Gauss2, hydrogen-bonding, hydrophobic, non-steric (hydrogen-bonding and hydrophobic), repulsion, steric (Gauss1, Gauss2, and repulsion), and total energy contribution [9]. The docking mechanism in jdock can generate up to 999 bound poses. The top 10-15 key residues were then recorded and used in the virtual screening process to filter out false positive results.
4. Conclusions
The SARS-CoV-2 virus relies on receptor binding using spike proteins to release its genetic material and spread the infection of the virus to other cells. Therefore, techniques that utilize drugs to inhibit the spike glycoprotein or 3CLpro are effective treatments against SARS-CoV-2 [11]. In this study, the molecular docking and molecular dynamics simulations proved that ZINC15676170, ZINC09033700, and ZINC12530139 are very likely candidates for an antiviral therapeutic treatment process against SARS-CoV-2. MCCS displayed the energy contributions and key residues that greatly helped the virtual screening process, and MD simulations validated these results. In order to evaluate our virtual screening results, we again used Glide docking in Schrödinger to dock N3, GRL-2420, CALPAIN1, nirmatrelvir, and ML188 inhibitors retrieved from the protein databases 6LU7, 7JKV, 7LBN, 7U29, and 7L0D, respectively, into 3CLpro. The docking scores of N3, GRL-2420, CALPAIN1, nirmatrelvir, and ML188 are −10.073, −9.655, −7.28, −6.552, and −6.002 kcal/mol, respectively. N3 can form a covalent bond with 3CLpro, and GRL-2420 can also form a covalent bond with the Cys-145 of 3CLpro, which account for the high docking scores of N3 and GRL-2420 [46,47]. The docking scores of ZINC15676170, ZINC0903370, and ZINC12530139 are −9.178, −8.799, and −8.774, respectively, which are significantly higher than the docking scores of CALPAIN1, nirmatrelvir, and ML188. We also performed MD simulations of ZINC15676170, ZINC0903370, and ZINC12530139 with the 3CLpro of SARS-CoV-2, respectively, and the system was subjected to 500 ns MD simulations. The time course of the root-mean-square deviations (RMSD) plot (Figure S4) of the 500 ns MD simulations indicated the bindings of ZINC15676170, ZINC0903370, and ZINC12530139 with the 3CLpro of SARS-CoV-2, respectively, are stable. Obviously, we still need to conduct in vitro tests and human clinical studies to determine the efficacy of these drugs and to further validate our results. However, in silico computational prediction has proven to be a large asset for drug discovery and repurposing. The speed and efficiency of such studies have been essential to the development of effective antiviral therapeutic treatments [20].
J.C., Y.H. and Q.S. contributed equally to this work. J.C., Y.W. (Yuanqiang Wang), Y.C. and G.Y. conceived and designed the study. J.C., Y.H. and Q.S. performed the experiments and wrote the manuscript, G.H., Y.W. (Yanan Wang), Y.W. (Yong Wang), W.X., J.O. and J.Q. reviewed and edited the manuscript. All authors have read and agreed to the published version of the manuscript.
Not applicable.
Not applicable.
Data is contained within the article.
The authors are grateful for the financial support of this work provided by the Natural Science Foundation. The authors would like to thank Zhiwei Feng for their guidance for virtual screening and Xibing He for their guidance on generating figures by gnuplot.
The authors declare no conflict of interest, financial or otherwise.
Samples of the compounds are not available from the authors.
Footnotes
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Enlarge this image.Figure 1. RMSFs of all systems including ZINC15676170 (red), ZINC09033700 (green), and ZINC12530139 (blue). The abscissa represents the residue number. The ordinate represents the value of Root-Mean-Square Fluctuation, and the unit is angstrom.
Enlarge this image.Figure 2. Binding poses between ZINC15676170 and 3CLpro of SARS-CoV-2 predicted by MD simulation and residue energy contribution between ZINC15676170 and 3CLpro of SARS-CoV-2 predicted by MCCS.
Enlarge this image.Figure 3. Binding poses between ZINC09033700 and 3CLpro of SARS-CoV-2 predicted by MD simulation and residue energy contribution between ZINC09033700 and 3CLpro of SARS-CoV-2 predicted by MCCS.
Enlarge this image.Figure 4. Binding poses between ZINC12530139 and the 3CLpro of SARS-CoV-2 predicted by MD simulation and residue energy contribution between ZINC12530139 and 3CLpro of SARS-CoV-2 predicted by MCCS.
Top 10 ligands with the best docking scores predicted by Glide.
| Rank | ZINC ID | Structure | Docking Score (kcal/mol) | Molecular Weight (amu) | LogP Value |
|---|---|---|---|---|---|
| 1 | ZINC15676170 | [Image omitted. Please see PDF.] | −9.178 | 418.432 | 3.228 |
| 2 | ZINC15675325 | [Image omitted. Please see PDF.] | −9.171 | 417.486 | −2.19 |
| 3 | ZINC12529667 | [Image omitted. Please see PDF.] | −9.124 | 378.449 | −1.228 |
| 4 | ZINC13550544 | [Image omitted. Please see PDF.] | −9.031 | 317.325 | −0.354 |
| 5 | ZINC03838803 | [Image omitted. Please see PDF.] | −8.947 | 286.352 | −2.335 |
| 6 | ZINC12664661 | [Image omitted. Please see PDF.] | −8.845 | 442.499 | 2.476 |
| 7 | ZINC09033700 | [Image omitted. Please see PDF.] | −8.799 | 483.589 | 3.683 |
| 8 | ZINC12530139 | [Image omitted. Please see PDF.] | −8.774 | 407.409 | 2.525 |
| 9 | ZINC00198624 | [Image omitted. Please see PDF.] | −8.704 | 329.488 | 1.703 |
| 10 | ZINC08299537 | [Image omitted. Please see PDF.] | −8.602 | 442.54 | −1.292 |
The table above shows the structure, docking score, molecular weight, and logP value of the ligands with the highest therapeutic potential against 3CLpro of SARS-CoV-2.
MD binding free energy ranking of top 10 ligands with the highest docking score.
| Rank | ZINC ID | MD Binding Free Energy (kcal/mol) |
|---|---|---|
| 1 | ZINC09033700 | −6.8953 |
| 2 | ZINC15676170 | −4.3351 |
| 3 | ZINC12530139 | −3.0082 |
| 4 | ZINC15675325 | −1.8083 |
| 5 | ZINC12529667 | −1.3637 |
| 6 | ZINC00198624 | −0.1468 |
| 7 | ZINC12664661 | 0.0053 |
| 8 | ZINC03838803 | 0.4986 |
| 9 | ZINC08299537 | 0.6072 |
| 10 | ZINC13550544 | 8.7553 |
Energy decomposition of MM-GBSA free energy for ZINC15676170. All energies are in kcal/mol.
| Residue | EVDW | EEEL | EINT | GPOL | ESAS | EMM | EEELPOL | EVDWEELPOL | GGBSA | GMM/GBSA |
|---|---|---|---|---|---|---|---|---|---|---|
| CYS145 | −1.8152 | −1.767 | 0 | 0.0997 | −1.2208 | −3.5822 | −1.6673 | −3.4824 | −1.121 | −4.7032 |
| ASN142 | −2.2802 | −0.8786 | 0 | 0.0751 | −1.3824 | −3.1588 | −0.8035 | −3.0838 | −1.3073 | −4.4662 |
| MET165 | −2.4885 | 0.1252 | 0 | −0.396 | −1.5208 | −2.3633 | −0.2709 | −2.7594 | −1.9169 | −4.2802 |
| HIS164 | −2.066 | −1.1163 | 0 | 0.3195 | −0.9614 | −3.1823 | −0.7968 | −2.8628 | −0.6419 | −3.8242 |
| GLY143 | −0.7966 | −1.5772 | 0 | −0.053 | −0.6921 | −2.3738 | −1.63 | −2.4266 | −0.7448 | −3.1187 |
| HIS163 | −0.9209 | −1.5059 | 0 | −0.055 | −0.576 | −2.4268 | −1.5613 | −2.4822 | −0.6314 | −3.0582 |
| MET49 | −1.3443 | −0.2809 | 0 | 0.1433 | −1.1549 | −1.6252 | −0.1376 | −1.4819 | −1.0116 | −2.6368 |
| GLU166 | −0.8813 | −2.6098 | 0 | 1.815 | −0.877 | −3.4911 | −0.7948 | −1.6761 | 0.938 | −2.553 |
| PHE140 | −0.8967 | −0.9905 | 0 | 0.4139 | −0.4562 | −1.8872 | −0.5766 | −1.4733 | −0.0423 | −1.9295 |
| HIS41 | −1.1417 | 0.2094 | 0 | −0.212 | −0.7811 | −0.9322 | −0.0026 | −1.1442 | −0.993 | −1.9253 |
EVDW: van der Waals energy; EEEL: electrostatic energy; EINT: internal energy; GPOL: the polar part of solvation free energy; ESAS: non-polar solvation component; EMM: gas phase Molecular Mechanics energy during binding; EEELPOL: electrostatic energy and the polar part of solvation free energy; EVDWEELPOL: van der Waals energy and electrostatic energy and the polar part of solvation free energy; GGBSA: Generalized Born Surface Area free energy; GMM/GBSA: Molecular Mechanics/Generalized Born Surface Area free energy.
Energy decomposition of MM/GBSA free energy for the ZINC09033700. All energies are in kcal/mol.
| Residue | EVDW | EEEL | EINT | GPOL | ESAS | EMM | EEELPOL | EVDWEELPOL | GGBSA | GMM/GBSA |
|---|---|---|---|---|---|---|---|---|---|---|
| MET165 | −3.6331 | −2.335 | 0 | 0.827 | −2.2815 | −5.9681 | −1.508 | −5.1411 | −1.4545 | −7.4226 |
| ASN142 | −2.2616 | −3.8489 | 0 | 1.0485 | −1.8162 | −6.1106 | −2.8004 | −5.062 | −0.7676 | −6.8782 |
| HIS164 | −2.005 | −3.4553 | 0 | 0.29 | −1.2107 | −5.4603 | −3.1653 | −5.1703 | −0.9207 | −6.381 |
| MET49 | −2.363 | −1.6125 | 0 | 0.8082 | −1.6008 | −3.9755 | −0.8043 | −3.1673 | −0.7926 | −4.7681 |
| GLU166 | −1.7504 | 2.5706 | 0 | −3.6206 | −1.093 | 0.8203 | −1.0499 | −2.8003 | −4.7136 | −3.8933 |
| CYS145 | −1.5458 | −0.2229 | 0 | −0.1515 | −1.2428 | −1.7687 | −0.3743 | −1.9201 | −1.3943 | −3.1629 |
| HIS41 | −1.3246 | −0.288 | 0 | −0.015 | −1.01 | −1.6126 | −0.303 | −1.6276 | −1.025 | −2.6376 |
| LEU167 | −1.2952 | 0.2722 | 0 | −0.3641 | −0.8727 | −1.023 | −0.0919 | −1.3871 | −1.2368 | −2.2598 |
| ASP187 | −1.1167 | −1.877 | 0 | 1.5806 | −0.7113 | −2.9937 | −0.2963 | −1.4131 | 0.8693 | −2.1244 |
| PRO168 | −0.9398 | −0.0493 | 0 | −0.0512 | −0.9239 | −0.9892 | −0.1006 | −1.0404 | −0.9751 | −1.9643 |
EVDW: van der Waals energy; EEEL: electrostatic energy; EINT: internal energy; GPOL: the polar part of solvation free energy; ESAS: non-polar solvation component; EMM: gas phase Molecular Mechanics energy during binding; EEELPOL: electrostatic energy and the polar part of solvation free energy; EVDWEELPOL: van der Waals energy and electrostatic energy and the polar part of solvation free energy; GGBSA: Generalized Born Surface Area free energy; GMM/GBSA: Molecular Mechanics/Generalized Born Surface Area free energy.
Energy decomposition of MM/GBSA free energy for the ZINC12530139. All energies are in kcal/mol.
| Residue | EVDW | EEEL | EINT | GPOL | ESAS | EMM | EEELPOL | EVDWEELPOL | GGBSA | GMM/GBSA |
|---|---|---|---|---|---|---|---|---|---|---|
| ASN142 | −2.8483 | −0.7788 | 0 | 0.1051 | −1.774 | −3.627 | −0.6737 | −3.522 | −1.669 | −5.296 |
| GLN189 | −1.2062 | −2.8307 | 0 | 0.3067 | −1.3396 | −4.0368 | −2.524 | −3.7301 | −1.0329 | −5.0697 |
| HIS164 | −1.8092 | −2.6969 | 0 | 0.5538 | −0.9579 | −4.5061 | −2.1431 | −3.9523 | −0.4041 | −4.9101 |
| MET165 | −2.4619 | −0.3918 | 0 | 0.0996 | −1.6995 | −2.8537 | −0.2922 | −2.7541 | −1.5999 | −4.4536 |
| GLY143 | −1.6115 | −1.0566 | 0 | 0.2761 | −0.9806 | −2.6681 | −0.7805 | −2.392 | −0.7045 | −3.3726 |
| CYS145 | −1.2837 | −0.0759 | 0 | −0.2514 | −0.873 | −1.3596 | −0.3273 | −1.611 | −1.1244 | −2.484 |
| GLU166 | −1.9215 | 2.6406 | 0 | −1.892 | −1.2823 | 0.7191 | 0.7486 | −1.173 | −3.1743 | −2.4553 |
| SER144 | −1.0346 | −0.6017 | 0 | 0.1506 | −0.5429 | −1.6363 | −0.4511 | −1.4857 | −0.3923 | −2.0286 |
| LEU141 | −1.0447 | −0.5713 | 0 | 0.0816 | −0.4866 | −1.616 | −0.4897 | −1.5344 | −0.405 | −2.021 |
| HIS41 | −0.7741 | −0.919 | 0 | 0.4292 | −0.6046 | −1.6931 | −0.4898 | −1.2639 | −0.1755 | −1.8685 |
EVDW: van der Waals energy; EEEL: electrostatic energy; EINT: internal energy; GPOL: the polar part of solvation free energy; ESAS: non-polar solvation component; EMM: gas phase Molecular Mechanics energy during binding; EEELPOL: electrostatic energy and the polar part of solvation free energy; EVDWEELPOL: van der Waals energy and electrostatic energy and the polar part of solvation free energy; GGBSA: Generalized Born Surface Area free energy; GMM/GBSA: Molecular Mechanics/Generalized Born Surface Area free energy.
Supplementary Materials
The following supporting information can be downloaded at:
References
1. COVID-19 Coronavirus Pandemic. Available online: https://www.worldometers.info/coronavirus/ (accessed on 24 October 2022).
2. DiMaio, D.; Enquist, L.W.; Dermody, T.S. A New Coronavirus Emerges, This Time Causing a Pandemic. Annu. Rev. Virol.; 2020; 7, pp. iii-v. [DOI: https://dx.doi.org/10.1146/annurev-vi-07-042020-100001]
3. Sarkis, J. Supply chain sustainability: Learning from the COVID-19 pandemic. Int. J. Oper. Prod. Manag.; 2021; 41, pp. 63-73. [DOI: https://dx.doi.org/10.1108/IJOPM-08-2020-0568]
4. Tracking SARS-CoV-2 Variants. Available online: https://www.who.int/en/activities/tracking-SARS-CoV-2-variants/ (accessed on 13 July 2022).
5. Nguyen, K.V. Containing the spread of COVID-19 virus facing to its high mutation rate: Approach to intervention using a nonspecific way of blocking its entry into the cells. Nucl. Nucl. Nucleic Acids; 2022; 41, pp. 778-814. [DOI: https://dx.doi.org/10.1080/15257770.2022.2071937]
6. Zhu, W.; Xu, M.; Chen, C.Z.; Guo, H.; Shen, M.; Hu, X.; Shinn, P.; Klumpp-Thomas, C.; Michael, S.G.; Zheng, W. Identification of SARS-CoV-2 3CL Protease Inhibitors by a Quantitative High-Throughput Screening. ACS Pharmacol. Transl. Sci.; 2020; 3, pp. 1008-1016. [DOI: https://dx.doi.org/10.1021/acsptsci.0c00108] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33062953]
7. Zumla, A.; Chan, J.F.W.; Azhar, E.I.; Hui, D.S.C.; Yuen, K.-Y. Coronaviruses—drug discovery and therapeutic options. Nat. Rev. Drug Discov.; 2016; 15, pp. 327-347. [DOI: https://dx.doi.org/10.1038/nrd.2015.37]
8. Chen, C.Y.-C. TCM Database@Taiwan: The World’s Largest Traditional Chinese Medicine Database for Drug Screening In Silico. PLoS ONE; 2011; 6, e15939. [DOI: https://dx.doi.org/10.1371/journal.pone.0015939] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/21253603]
9. Feng, Z.; Chen, M.; Xue, Y.; Liang, T.; Chen, H.; Zhou, Y.; Nolin, T.D.; Smith, R.B.; Xie, X.Q. MCCS: A novel recognition pattern-based method for fast track discovery of anti-SARS-CoV-2 drugs. Brief. Bioinform.; 2021; 22, pp. 946-962. [DOI: https://dx.doi.org/10.1093/bib/bbaa260]
10. Fung, T.S.; Liu, D.X. Human Coronavirus: Host-Pathogen Interaction. Annu. Rev. Microbiol.; 2019; 73, pp. 529-557. [DOI: https://dx.doi.org/10.1146/annurev-micro-020518-115759]
11. Wang, Q.; Zhang, Y.; Wu, L.; Niu, S.; Song, C.; Zhang, Z.; Lu, G.; Qiao, C.; Hu, Y.; Yuen, K.-Y. et al. Structural and Functional Basis of SARS-CoV-2 Entry by Using Human ACE2. Cell; 2020; 181, pp. 894-904.e899. [DOI: https://dx.doi.org/10.1016/j.cell.2020.03.045]
12. Firth, A.E. A putative new SARS-CoV protein, 3c, encoded in an ORF overlapping ORF3a. J. Gen. Virol.; 2020; 101, pp. 1085-1089. [DOI: https://dx.doi.org/10.1099/jgv.0.001469]
13. Paul, A.; Sarkar, A.; Saha, S.; Maji, A.; Janah, P.; Kumar Maity, T. Synthetic and computational efforts towards the development of peptidomimetics and small-molecule SARS-CoV 3CLpro inhibitors. Bioorg. Med. Chem.; 2021; 46, 116301. [DOI: https://dx.doi.org/10.1016/j.bmc.2021.116301] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34332853]
14. Andersen, K.G.; Rambaut, A.; Lipkin, W.I.; Holmes, E.C.; Garry, R.F. The proximal origin of SARS-CoV-2. Nat. Med.; 2020; 26, pp. 450-452. [DOI: https://dx.doi.org/10.1038/s41591-020-0820-9] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32284615]
15. Bosch, B.J.; Zee, R.v.d.; Haan, C.A.M.d.; Rottier, P.J.M. The Coronavirus Spike Protein Is a Class I Virus Fusion Protein: Structural and Functional Characterization of the Fusion Core Complex. J. Virol.; 2003; 77, pp. 8801-8811. [DOI: https://dx.doi.org/10.1128/JVI.77.16.8801-8811.2003] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/12885899]
16. Khan, A.; Saleem, S.; Idrees, M.; Ali, S.S.; Junaid, M.; Chandra Kaushik, A.; Wei, D.-Q. Allosteric ligands for the pharmacologically important Flavivirus target (NS5) from ZINC database based on pharmacophoric points, free energy calculations and dynamics correlation. J. Mol. Graph. Model.; 2018; 82, pp. 37-47. [DOI: https://dx.doi.org/10.1016/j.jmgm.2018.03.004] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/29677482]
17. Omrani, A.S.; Saad, M.M.; Baig, K.; Bahloul, A.; Abdul-Matin, M.; Alaidaroos, A.Y.; Almakhlafi, G.A.; Albarrak, M.M.; Memish, Z.A.; Albarrak, A.M. Ribavirin and interferon alfa-2a for severe Middle East respiratory syndrome coronavirus infection: A retrospective cohort study. Lancet Infect. Dis.; 2014; 14, pp. 1090-1095. [DOI: https://dx.doi.org/10.1016/S1473-3099(14)70920-X] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/25278221]
18. Chauhan, S.; Jain, N.; Nagaich, U. Nanodiamonds with powerful ability for drug delivery and biomedical applications: Recent updates on in vivo study and patents. J. Pharm. Anal.; 2020; 10, pp. 1-12. [DOI: https://dx.doi.org/10.1016/j.jpha.2019.09.003]
19. Boopathi, S.; Poma, A.B.; Kolandaivel, P. Novel 2019 coronavirus structure, mechanism of action, antiviral drug promises and rule out against its treatment. J. Biomol. Struct. Dyn.; 2021; 39, pp. 3409-3418. [DOI: https://dx.doi.org/10.1080/07391102.2020.1758788]
20. Park, K. A review of computational drug repurposing. Transl. Clin. Pharmacol.; 2019; 27, pp. 59-63. [DOI: https://dx.doi.org/10.12793/tcp.2019.27.2.59]
21. Chen, M.; Feng, Z.; Wang, S.; Lin, W.; Xie, X.-Q. MCCS, a novel characterization method for protein–ligand complex. Brief. Bioinform.; 2020; 22, bbaa239. [DOI: https://dx.doi.org/10.1093/bib/bbaa239]
22. Rose, P.W.; Prlić, A.; Altunkaya, A.; Bi, C.; Bradley, A.R.; Christie, C.H.; Costanzo, L.D.; Duarte, J.M.; Dutta, S.; Feng, Z. et al. The RCSB protein data bank: Integrative view of protein, gene and 3D structural information. Nucleic Acids Res.; 2016; 45, pp. D271-D281. [DOI: https://dx.doi.org/10.1093/nar/gkw1000%J]
23. Salarvand, Z.; Amirnasr, M.; Talebian, M.; Raeissi, K.; Meghdadi, S. Enhanced corrosion resistance of mild steel in 1 M HCl solution by trace amount of 2-phenyl-benzothiazole derivatives: Experimental, quantum chemical calculations and molecular dynamics (MD) simulation studies. Corros. Sci.; 2017; 114, pp. 133-145. [DOI: https://dx.doi.org/10.1016/j.corsci.2016.11.002]
24. Maier, J.A.; Martinez, C.; Kasavajhala, K.; Wickstrom, L.; Hauser, K.E.; Simmerling, C. ff14SB: Improving the accuracy of protein side chain and backbone parameters from ff99SB. J. Chem. Theory Comput.; 2015; 11, pp. 3696-3713. [DOI: https://dx.doi.org/10.1021/acs.jctc.5b00255] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/26574453]
25. Wang, J.; Wolf, R.M.; Caldwell, J.W.; Kollman, P.A.; Case, D.A. Development and testing of a general amber force field. J. Comput. Chem.; 2004; 25, pp. 1157-1174. [DOI: https://dx.doi.org/10.1002/jcc.20035]
26. Jorgensen, W.L.; Chandrasekhar, J.; Madura, J.D.; Impey, R.W.; Klein, M.L. Comparison of simple potential functions for simulating liquid water. J. Chem. Phys.; 1983; 79, pp. 926-935. [DOI: https://dx.doi.org/10.1063/1.445869]
27. Gotz, A.W.; Williamson, M.J.; Xu, D.; Poole, D.; Le Grand, S.; Walker, R.C. Routine microsecond molecular dynamics simulations with AMBER on GPUs. 1. Generalized born. J. Chem. Theory Comput.; 2012; 8, pp. 1542-1555. [DOI: https://dx.doi.org/10.1021/ct200909j] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/22582031]
28. Salomon-Ferrer, R.; Götz, A.W.; Poole, D.; Le Grand, S.; Walker, R.C. computation. Routine microsecond molecular dynamics simulations with AMBER on GPUs. 2. Explicit solvent particle mesh Ewald. J. Chem. Theory Comput.; 2013; 9, pp. 3878-3888. [DOI: https://dx.doi.org/10.1021/ct400314y]
29. Cerutti, D.; Cheatham, T., III; Darden, T.; Duke, R.; Giese, T.; Gohlke, H.; Goetz, A.; Homeyer, N.; Izadi, S.; Janowski, P.J. AMBER 16; University of California: San Francisco, CA, USA, 2016.
30. Hou, T.; Wang, J.; Li, Y.; Wang, W.J. Assessing the Performance of the MM/PBSA and MM/GBSA Methods. 1. The Accuracy of Binding Free Energy Calculations Based on Molecular Dynamics Simulations. J. Chem. Inf. Model.; 2011; 51, pp. 69-82. [DOI: https://dx.doi.org/10.1021/ci100275a] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/21117705]
31. Loncharich, R.J.; Brooks, B.R.; Pastor, R.W. Langevin dynamics of peptides: The frictional dependence of isomerization rates of N-acetylalanyl-N′-methylamide. Biopolym. Orig. Res. Biomol.; 1992; 32, pp. 523-535. [DOI: https://dx.doi.org/10.1002/bip.360320508]
32. Izaguirre, J.A.; Catarello, D.P.; Wozniak, J.M.; Skeel, R.D. Langevin stabilization of molecular dynamics. J. Chem. Phys.; 2001; 114, pp. 2090-2098. [DOI: https://dx.doi.org/10.1063/1.1332996]
33. Darden, T.; York, D.; Pedersen, L. Particle mesh Ewald: An N log (N) method for Ewald sums in large systems. J. Chem. Phys.; 1993; 98, pp. 10089-10092. [DOI: https://dx.doi.org/10.1063/1.464397]
34. Essmann, U.; Perera, L.; Berkowitz, M.L.; Darden, T.; Lee, H.; Pedersen, L.G. A smooth particle mesh Ewald method. J. Chem. Phys.; 1995; 103, pp. 8577-8593. [DOI: https://dx.doi.org/10.1063/1.470117]
35. Ryckaert, J.-P.; Ciccotti, G.; Berendsen, H.J. Numerical integration of the cartesian equations of motion of a system with constraints: Molecular dynamics of n-alkanes. J. Comput. Phys.; 1977; 23, pp. 327-341. [DOI: https://dx.doi.org/10.1016/0021-9991(77)90098-5]
36. Genheden, S.; Ryde, U. The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert Opin. Drug Discov.; 2015; 10, pp. 449-461. [DOI: https://dx.doi.org/10.1517/17460441.2015.1032936] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/25835573]
37. Sitkoff, D.; Sharp, K.A.; Honig, B. Accurate calculation of hydration free energies using macroscopic solvent models. J. Phys. Chem.; 1994; 98, pp. 1978-1988. [DOI: https://dx.doi.org/10.1021/j100058a043]
38. Still, W.C.; Tempczyk, A.; Hawley, R.C.; Hendrickson, T. Semianalytical treatment of solvation for molecular mechanics and dynamics. J. Am. Chem. Soc.; 1990; 112, pp. 6127-6129. [DOI: https://dx.doi.org/10.1021/ja00172a038]
39. Weiser, J.; Shenkin, P.S.; Still, W.C. Approximate atomic surfaces from linear combinations of pairwise overlaps (LCPO). J. Comput. Chem.; 1999; 20, pp. 217-230. [DOI: https://dx.doi.org/10.1002/(SICI)1096-987X(19990130)20:2<217::AID-JCC4>3.0.CO;2-A]
40. Hu, J.; Feng, Z.; Ma, S.; Zhang, Y.; Tong, Q.; Alqarni, M.H.; Gou, X.; Xie, X.-Q. Difference and influence of inactive and active states of cannabinoid receptor subtype CB2: From conformation to drug discovery. J. Chem. Inf. Model.; 2016; 56, pp. 1152-1163. [DOI: https://dx.doi.org/10.1021/acs.jcim.5b00739]
41. Pettersen, E.F.; Goddard, T.D.; Huang, C.C.; Couch, G.S.; Greenblatt, D.M.; Meng, E.C.; Ferrin, T.E. UCSF Chimera—A visualization system for exploratory research and analysis. J. Comput. Chem.; 2004; 25, pp. 1605-1612. [DOI: https://dx.doi.org/10.1002/jcc.20084]
42. Shapovalov, M.V.; Dunbrack, R.L., Jr. A smoothed backbone-dependent rotamer library for proteins derived from adaptive kernel density estimates and regressions. Structure; 2011; 19, pp. 844-858. [DOI: https://dx.doi.org/10.1016/j.str.2011.03.019]
43. Pedretti, A.; Villa, L.; Vistoli, G. VEGA—An open platform to develop chemo-bio-informatics applications, using plug-in architecture and script programming. J. Comput. Aided Mol. Des.; 2004; 18, pp. 167-173. [DOI: https://dx.doi.org/10.1023/B:JCAM.0000035186.90683.f2]
44. Olsson, M.H.; Søndergaard, C.R.; Rostkowski, M.; Jensen, J.H. PROPKA3: Consistent Treatment of Internal and Surface Residues in Empirical pKa Predictions. J. Chem. Theory Comput.; 2011; 7, pp. 525-537. [DOI: https://dx.doi.org/10.1021/ct100578z] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/26596171]
45. Søndergaard, C.R.; Olsson, M.H.; Rostkowski, M.; Jensen, J.H. Improved Treatment of Ligands and Coupling Effects in Empirical Calculation and Rationalization of pKa Values. J. Chem. Theory Comput.; 2011; 7, pp. 2284-2295. [DOI: https://dx.doi.org/10.1021/ct200133y] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/26606496]
46. Jin, Z.; Du, X.; Xu, Y.; Deng, Y.; Liu, M.; Zhao, Y.; Zhang, B.; Li, X.; Zhang, L.; Peng, C. et al. Structure of Mpro from SARS-CoV-2 and discovery of its inhibitors. Nature; 2020; 582, pp. 289-293. [DOI: https://dx.doi.org/10.1038/s41586-020-2223-y] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32272481]
47. Hattori, S.-i.; Higashi-Kuwata, N.; Hayashi, H.; Allu, S.R.; Raghavaiah, J.; Bulut, H.; Das, D.; Anson, B.J.; Lendy, E.K.; Takamatsu, Y. et al. A small molecule compound with an indole moiety inhibits the main protease of SARS-CoV-2 and blocks virus replication. Nat. Commun.; 2021; 12, 668. [DOI: https://dx.doi.org/10.1038/s41467-021-20900-6] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33510133]
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Details
; Hou, Guanyu 2 ; Wang, Yanan 1 ; Wang, Yong 1 ; Xiao, Wen 1 ; Othman, Joseph 2 ; Qi, Junnan 2 ; Wang, Yuanqiang 3 ; Chen, Yan 4 ; Yu, Guanghua 1 1 School of Pharmacy, Jiangsu Vocational College of Medicine, Yancheng 224005, China
2 Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, National Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh, Pittsburgh, PA 15261, USA
3 School of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing 400054, China
4 College of Pharmacology Sciences, Zhejiang University of Technology, Hangzhou 310014, China