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Article

Discovery of Potential SARS-CoV-2 Papain-like Protease Natural Inhibitors Employing a Multi-Phase In Silico Approach

by
Eslam B. Elkaeed
1,*,
Ahmed M. Metwaly
2,3,
Mohamed S. Alesawy
4,
Abdulrahman M. Saleh
4,
Aisha A. Alsfouk
5 and
Ibrahim H. Eissa
4,*
1
Department of Pharmaceutical Sciences, College of Pharmacy, AlMaarefa University, Riyadh 13713, Saudi Arabia
2
Pharmacognosy and Medicinal Plants Department, Faculty of Pharmacy (Boys), Al-Azhar University, Cairo 11884, Egypt
3
Biopharmaceutical Products Research Department, Genetic Engineering and Biotechnology Research Institute, City of Scientific Research and Technological Applications (SRTA-City), Alexandria 21934, Egypt
4
Pharmaceutical Medicinal Chemistry & Drug Design Department, Faculty of Pharmacy (Boys), Al-Azhar University, Cairo 11884, Egypt
5
Department of Pharmaceutical Sciences, College of Pharmacy, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
*
Authors to whom correspondence should be addressed.
Life 2022, 12(9), 1407; https://doi.org/10.3390/life12091407
Submission received: 15 August 2022 / Revised: 1 September 2022 / Accepted: 5 September 2022 / Published: 9 September 2022
(This article belongs to the Special Issue Pharmaceutical Biotechnology)
Browse Figures
Versions Notes

Abstract

:
As an extension of our research against COVID-19, a multiphase in silico approach was applied in the selection of the three most common inhibitors (Glycyrrhizoflavone (76), Arctigenin (94), and Thiangazole (298)) against papain-like protease, PLpro (PDB ID: 4OW0), among 310 metabolites of natural origin. All compounds of the exam set were reported as antivirals. The structural similarity between the examined compound set and S88, the co-crystallized ligand of PLpro, was examined through structural similarity and fingerprint studies. The two experiments pointed to Brevicollin (28), Cryptopleurine (41), Columbamine (46), Palmatine (47), Glycyrrhizoflavone (76), Licochalcone A (87), Arctigenin (94), Termilignan (98), Anolignan B (99), 4,5-dihydroxy-6″-deoxybromotopsentin (192), Dercitin (193), Tryptanthrin (200), 6-Cyano-5-methoxy-12-methylindolo [2, 3A] carbazole (211), Thiangazole (298), and Phenoxan (300). The binding ability against PLpro was screened through molecular docking, disclosing the favorable binding modes of six metabolites. ADMET studies expected molecules 28, 76, 94, 200, and 298 as the most favorable metabolites. Then, molecules 76, 94, and 298 were chosen through in silico toxicity studies. Finally, DFT studies were carried out on glycyrrhizoflavone (76) and indicated a high level of similarity in the molecular orbital analysis. The obtained data can be used in further in vitro and in vivo studies to examine and confirm the inhibitory effect of the filtered metabolites against PLpro and SARS-CoV-2.

Graphical Abstract

1. Introduction

As of 26 July 2022, the WHO stated the confirmation of the incidence of 57,223,945 COVID-19 infections and 6,390,401 deaths [1]. Accordingly, a constant search in the field of drug discovery should be sustained to discover a cure.
Cheminformatics (computational- in silico) labels the connection between informatics and chemistry [2]. This approach applies the software in the field of chemistry [3] and has been used effectively to predict a cure against COVID-19 [4,5,6]. The chemoinformatic approach was also employed efficiently in drug discovery [7], drug molecular design [8,9], computational chemistry [10,11], toxicity prediction [12], ADMET assessment [13], and DFT calculation [14].
Human interest in the use of natural products has been back-traced for thousands of years [15,16]. The power of natural products as antiviral medicines has been confirmed in several scientific reports [17,18,19,20].
PLpro is a crucial protein in the coronavirus that has an essential role in the processing mechanism of viral polyproteins. This step results in the generation of an efficient replicase complex [21]. PLpro has another essential role against human immunity through post-translational modifications on human proteins [22].
Against COVID-19, we employed in silico methods to disclose the potential inhibition of several types of natural compounds. For example, four isoflavonoids [23] and three alkaloids [24] were proposed to exert promising anti-SARS-CoV-2 activities. We designed and applied in silico experiments to recommend the most fitting inhibitor against certain essential enzymes of SARS-CoV-2 such as SARS-CoV-2 nsp10 [25], SARS-CoV-2 PLpro [26], SARS-CoV-2 nsp16-nsp10 2′-o-Methyltransferase Complex [27], SARS-CoV-2 MPro [28,29], and SARS-CoV-2 RdRp [30].
In the current study, we report the use of several computational filtration methods on 310 metabolites of natural origin that belong to diverse chemical classes and are reported as antivirals (Figure S1 and Table S1). Our experiments revealed the most expected inhibitors of human coronavirus PLpro among them. We depended on the reported similarities between the PLpro of SARS-CoV-1 and SARS-CoV-2 (Figure 1).

2. Results and Discussion

2.1. Molecular Similarity

It is worth mentioning that S88 was used as a positive control (lead molecule) in this work as S88 is the co-crystallized ligand of our target protein and has a reported binding mode. Additionally, currently, there are no FDA-approved drugs for the treatment of coronavirus targeting PLpro. Accordingly, it was found that S88 may serve as a good candidate to check the similarity of our compounds against it.
The following descriptors (H-bond donor (HBA) [31], H-bond acceptor (HBD) [32], partition coefficient (ALog p) [33], molecular weight (M. Wt) [34], rotatable bonds [35], rings, and aromatic rings [36] besides molecular fractional polar surface area (MFPSA) [37]) were examined between the 310 metabolites (Figure S1, Supplementary data) and S88 using Discovery Studio software (Vélizy-Villacoublay, France). The degree of likeness was calculated through the computation of minimum distances. The minimum distances were computed based on the variations in the aforementioned parameters and represent the computed quantitative difference in the structure between S88 and the examined compounds and are inversely proportional to the similarity degree.
The 310 molecules were spit into five equal groups of 50 molecules each, and one (last group) that contained 60 molecules. The study determined the 30 most suitable metabolites (Figure 2 and Figure 3, and Table 1).

2.2. Filter Using Fingerprints

Various computational methods that describe the similarities between different molecules have gained more interest in drug discovery [38]. One of the most helpful techniques in this approach is fingerprints [39]. The fingerprint study includes binary strings that compute the existence or absence of vital sub-structural fragments to calculate the structural similarity between molecules. This technique is currently utilized in virtual screening and detection of similarities between hit compounds and the lead one. The main difference between the fingerprints and molecular similarity studies is that the first individually calculates the presence and or absence of certain descriptors in S88 and the examined compounds, while molecular similarity calculates the degree of similarity between them as a whole structure.
The fingerprints technique was carried out using Discovery Studio software and examined the following parameters: HBA, HBD [40], charge [41], hybridization [42], positive and negative ionizable [43], halogen, aromatic, or none of them besides the ALogP category of atoms. All the mentioned parameters were converted to pits by the computer. Then, the computer calculated the bits in both S88 and the target compounds (SA), in the target compounds only (SB), or S88 only (SA). The identification of the most similar (that have the most identical molecular fingerprints) compounds to S88 is important to pick compounds with a higher degree of similarities. The most similar compounds are expected to exert greater protein binding and activity.
The study (Table 2) favored 28, 41, 46, 47, 76, 87, 94, 98, 99, 192, 193, 200, 211, 298, and 300 due to their similarity with S88.

2.3. Docking Studies

The docking analysis of 28, 41, 46, 47, 76, 87, 94, 98, 99, 192, 193, 200, 211, 298, and 300 was carried out against the coronavirus PLpro enzyme’s binding site (PDB ID: 4OW0). The crystallized ligand (S88) was used as a reference compound. For each compound, 30 run poses were carried out. The applied procedure of molecular docking was verified through the there-docking of S88 against the PLpro active site for another time. The small value of the RMSD (0.98 Å) between the two poses indicated the applicability of the applied protocol (Figure 4).
Differentiation between the tested compounds for their binding affinity was dependent on certain factors. (i) The first factor is the correct binding mode of a tested compound. The compound that exerted a binding mode very close to S88 was expected to have a good affinity against PLpro. The correct binding modes were determined according to the nature of the interactions (hydrogen or hydrophobic bonds) with the specific amino acid residues in the active pocket of PLpro. This factor is critical as a compound with the correct binding mode is expected to have a higher affinity than a compound with high binding energy having an incorrect binding mode. Therefore, the incorrect binding mode, resulting in incorrect affinity predictions, decreases the compound’s rate of virtual screening [44,45]. (ii) Gibbs free energy (ΔG binding) indicates the stability of the obtained conformation between the tested compound and PLpro (Table 3). According to the thermodynamic balance law, the value of ΔG is inversely proportional to the stability of the examined molecule and indicates that binding with PLpro will occur spontaneously. In other words, the increase in the negative free energy of a compound (reactant) will increase the reaction spontaneously and result in more stable conformations [46,47].
The molecular docking energy for compounds 76, 94, and 98 exhibited final values of −51.63, −50.82, and 52.21 kcal/mol, respectively. These values of free energies are the highest score indicating the spontaneity of the interactions and the stability of these compounds in the active site. Moreover, compounds 76, 94, and 98 have correct binding modes as these compounds formed many HBs with the crucial amino acid residues in the active sites. On the other hand, compounds 193 (ΔG = −44.02), 200 (ΔG = −41.31), and 298 (ΔG = −48.46) showed less free energies than some of the other tested compounds but had correct binding modes. For this reason, such compounds were selected for further investigation.
The proposed binding mode of S88 expressed a ΔG of −59.13 kcal/mol. S88 made one HB between its amide moiety and Tyr269. Additionally, the naphthyl moiety made eight hydrophobic interactions (HI) withAsp165, Met209, Arg167, Ala247, Thr302, Pro248, and Pro249. The ethyl bridge was included in two hydrophobic interactions with Pro249 and Tyr265. The piperidine moiety formed two hydrophobic bonds with Tyr265 and Tyr269. (Figure 5).
Life 12 01407 g005a 550Life 12 01407 g005b 550
Figure 5. (A) Three-dimensional and (B) two-dimensional binding modes of S88 in the active site of PLpro. As shown in Figure 6, compound 76 expressed a ΔG of −51.63 into the PLpro active site. Compound 76 made four HBs with Tyr265, Thr302, Tyr274, and Gln270. Moreover, the aromatic systems were included in many HIs with Asp165, Pro249, Tyr265, Gly164, Leu163, and Tyr269.
Figure 5. (A) Three-dimensional and (B) two-dimensional binding modes of S88 in the active site of PLpro. As shown in Figure 6, compound 76 expressed a ΔG of −51.63 into the PLpro active site. Compound 76 made four HBs with Tyr265, Thr302, Tyr274, and Gln270. Moreover, the aromatic systems were included in many HIs with Asp165, Pro249, Tyr265, Gly164, Leu163, and Tyr269.
Life 12 01407 g005aLife 12 01407 g005b
Compound 94 showed good binding energy (ΔG = −50.82) against the PLpro active site. It formed four HBs with Lys158, Tyr274, and Arg167. Additionally, the phenyl rings were involved in five HIs with Leu163, Tyr269, Tyr265, and Asp165 (Figure 7).
Compound 98 revealed good fitting with a docking score of −52.21 kcal/mol. The OH group formed one HB with Asp303, and the methoxy group formed another HB with Lys158. Many HIs were observed between the tested compound and Asp165, Arg167, Pro249, Tyr269, Tyr265, Leu163, and Tyr274 (Figure 8).
The top docking poses of compounds 193 and 200 (affinity values of −44.02 and −41.31 kcal/mol), respectively, were investigated. Compound 193 demonstrated eight HIs with Leu163, Tyr269, and Asp165 (Figure 9). The compound demonstrated two HBs with Tyr274. In addition, it formed 12 HIs, as shown in Figure 10.
Compound 298 showed a binding mode against the PLpro active site with a binding affinity of −48.46 kcal/mol. It was incorporated in eight HIs with Pro248, Tyr265, Leu163, Tyr269, and Pro249 (Figure 11).

2.4. ADMET

ADMET studies were achieved using Discovery Studio 4.0, with remdesivir as a reference. The following descriptors were examined. (i) The ability to penetrate the blood–brain barrier [48] (BBB), intestinal absorption [49] (HIA), aqueous solubility [50] (S), CYP2D6 binding [51], hepatotoxicity, and plasma protein binding [52] (PPB). The calculated properties are listed in (Table 4). All compounds showed high levels of BBB penetration except molecules 28, 76, 94, 200, and 298, which displayed medium to very low BBB levels. All the tested molecules showed good absorption characteristics comparable to remdesivir, which exhibited a very low level of absorption. Moreover, the solubility of the tested molecules was projected to be between low and good levels except for molecule 211, which showed a very low level. All molecules in addition to remdesivir were calculated to be inhibitors against CYP2D6 except molecules 28, 87, 94, 98, 99, 192, 200, 298 and 300. All the tested molecules were expected to have unfavorable hepatotoxic effects except molecules 28, 41, and 192, which were predicted to be non-toxic. All tested molecules and remdesivir were expected to bind to the plasma protein with a percentage of >90%, except molecule 46, which demonstrated plasma protein binding <90%. (Figure 12).

2.5. Toxicity Studies

Toxicity predictions were made using Discovery Studio 4.0 software, which was based on validated and assembled models for the following parameters: the FDA rat carcinogenicity test [53,54], carcinogenic potentiality TD50 [55], maximum tolerated dose (MTD) in rats [56,57], oral LD50 in rats [58], chronic LOAEL in rats [59,60], ocular [61], and skin irritancies [61,62].
In silico testing revealed that the majority of molecules had expected low levels of toxicity (Table 5).
All compounds were expected to be non-carcinogens except molecules 28, 99, 193, 200, 211, and 300, which were predicted to be carcinogens in the FDA rat carcinogenicity model.
Molecules 41, 46, 47, 192, and 211 showed TD50 values within range of (0.16 to 0.730 mg·kg−1/day), which were less than remdesivir (1.012 mg·kg−1/day), while molecules 28, 76, 87, 94, 98, 99, 193, 200, 298, and 300 showed TD50 values within the range of (1.58 to 69.07 mg·kg−1/day), which were higher than remdesivir.
All molecules revealed an MTD within the range of 0.012 to 0.113 g·kg−1, less than remdesivir (0.235 g·kg−1), except molecules 99 and 192, which demonstrated MTD of 0.240 and 1.099 g·kg−1, respectively, which are higher than remdesivir.
All molecules showed oral LD50 values higher than remdesivir (0.309 mg·kg−1/day) except compounds 41, 211, and 298, which exhibited oral LD50 values less than remdesivir ranging from 0.118 to 0.245 mg·kg−1/day.
Excluding compound 211, all the tested molecules showed LOAEL higher than that of remdesivir (0.003 g·kg−1), ranging from 0.008 to 0.398 g·kg−1.
Additionally, all molecules and remdesivir were expected to be mild ocular irritants, except molecules 87, 98, and 300, which were non-irritant. On the other hand, the examined molecules were expected to be skin non-irritant except for molecules 28, 94, 193, 211, 300, and remdesivir, which were mild irritants.

2.6. DFT Studies

DFT parameters including binding energy [63], HOMO [64], LUMO [64], gap energy [65], and dipole moment [66,67] were studied for the most promising molecules, 76, 94, and 298, using Discovery Studio software. S88 was used as a reference. The results of the DFT studies are summarized in Table 6 and Figure 13 and Figure 14.
Molecules 76 and 94 showed higher values of dipole moment (1.700 and 3.582, respectively) than molecule 298 (1.094).

2.6.1. Frontier Molecular Orbitals Analysis

Frontier molecular orbitals analysis can efficiently demonstrate active sites in addition to determining the kinetic stability and the chemical reactivity of a molecule [68]. The EHOMO and ELUMO of the tested molecules were computed using DMol3 implemented in Discovery Studio software [69]. The LUMO may be engaged in a nucleophilic attack, while the HOMO refers to the most probable site of an electrophilic attack. The HOMO energy represents the ionization potential of a drug, while that of the LUMO describes the electron affinity.
For gap energy, it was reported that a molecule is thought to be softer and more chemically reactive when its energy gap is small. In addition, a molecule was assumed to have greater chemical hardness and to be more stable when it had a large energy gap [70]. In this study, molecule 76 was found to have a low level of gap energy of 0.096 Ha, while molecules 94 and 298 were found to have high gap energy of 0.141 and 0.131, respectively. These findings indicate that compound 76 has higher reactivity than compounds 94 and 298. On the contrary, compounds 94 and 298 may possess higher stability than compound 76.
For the dipole moment values, compound 94 had a dipole moment value of 3.582. This value is nearly equal to that of S88 (3.621). The elevated dipole moment was expected to increase HBing, and non-bonded interactions in the compound–protein complexes were predicted to increase the binding affinity during SARS-CoV-2 inhibition. Compounds 76 and 298 had fewer values of the dipole moment of 1.700 and 1.094, respectively. From these findings, it can be concluded that compounds 76 and 94 have a higher chance of interacting with the target protein than compound 298 (Table 6 and Figure 13).
As shown in Figure 13B, the HOMO spatial distributions of molecule 76 were mainly distributed on the 3-(3,4-dihydroxyphenyl) -7-hydroxy-5-methoxy-4H-chromen-4-one moiety, while those of LUMO were located on the 7-hydroxy-5-methoxy-4H-chromen-4-one moiety (the electron acceptor zones).
The specific role of the HOMO center (3-(3,4-dihydroxyphenyl) -7-hydroxy-5-methoxy-4H-chromen-4-one moiety) in the binding of the receptor was previously confirmed by our docking experiments. As we noticed in Figure 13, the carbonyl group at position-4 of 4H-chromen-4-one (HOMO center) formed an H-bond acceptor with the phenolic OH group (LUMO center) of Tyr229. Furthermore, the LUMO of the accepting species (the two phenolic OH groups of catechol moiety) formed two H-bond donors with the HOMO of the donating species (OH group of Thr302 and OH group of Tyr274).

2.6.2. Molecular Electrostatic Potential Maps (MEP)

MEP is a very helpful technique for understanding the 3D charge distributions over a molecule.
In MEP, the electronegative atoms are highlighted with red and can be acceptors in H-bonding interactions. On the other hand, the electron-poor atoms are highlighted in blue and are incorporated into H-bonds as donors. Finally, the neutral atoms are highlighted from green to yellow and incorporated in HIs [71].
The MEP map of molecule 76 shows that the negative potential sites are on oxygen atoms (seven red patches) and the positive potential sites are around the hydrogen atoms (six blue patches). This indicates that molecule 76 has seven positions available for H-bonding acceptors and six positions suitable for H-bond donors. This map defines the region in which the molecule can have non-covalent interactions (Figure 14).
The presented study preferred glycyrrhizoflavone (76) as the most relevant inhibitor of human coronavirus PLpro. Glycyrrhizoflavone is a flavonoid that has been isolated from licorice and Glycyrrhiza glabra roots [72]. Glycyrrhisoflavone exhibited potent antiviral activity against the human immunodeficiency virus by inhibiting giant cell formation in the infected cells and inhibiting viral transcription [73,74].

3. Conclusions

Several computational filtration methods (similarity assessment, fingerprints check, docking, ADMET, toxicity, and DFT) were carried out on 310 metabolites of natural origin that were reported as antivirals against PLpro, (PDB ID: 4OW0) and its co-crystallized ligand S88. The experiments predicted a high degree of binding between glycyrrhizoflavone (76) and PLpro. Accordingly, the potential of glycyrrhizoflavone to be an inhibitor against human coronavirus PLpro inhibitor is highly expected. More studies must be carried out on such a promising drug to affirm its inhibitory potential against PLpro.

4. Method

4.1. Molecular Similarity Detection

Was applied using Discovery Studio 4.0 software. Details have been discussed in detail in the Supplementary data.

4.2. Fingerprint Studies

Were applied using Discovery Studio 4.0 software. Details have been discussed in detail in the Supplementary data.

4.3. Docking Studies

Were applied using Discovery Studio 4.0 software. Details have been discussed in detail in the Supplementary data.

4.4. ADMET Analysis

Was applied using Discovery Studio 4.0 software. Details have been discussed in detail in the Supplementary data.

4.5. Toxicity Studies

Were applied using Discovery Studio 4.0 software. Details have been discussed in detail in the Supplementary data.

4.6. DFT Studies

Were applied using Discovery Studio 4.0 software. Details have been discussed in detail the Supplementary data.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/life12091407/s1. Chemical structures, names, molecular formulas of the examined compounds, detailed methodology and toxicity reports.

Author Contributions

Conceptualization, I.H.E. and A.M.M.; Funding acquisition, A.A.A. and E.B.E.: Software, I.H.E., M.S.A., and A.M.S.; Writing—review & editing, all authors revised the published version of the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2022R116), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

All data are enclosed in the manuscript and supplementary data.

Acknowledgments

The authors extend their appreciation to the Research Center at AlMaarefa University for funding this work.

Conflicts of Interest

No conflict of interest to be declared.

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Life 12 01407 g001 550
Figure 1. In silico protocol to select the most promising candidate against PLpro.
Figure 1. In silico protocol to select the most promising candidate against PLpro.
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Life 12 01407 g002 550
Figure 2. Thirty molecules with good molecular similarity with S88.
Figure 2. Thirty molecules with good molecular similarity with S88.
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Life 12 01407 g003 550
Figure 3. The similarity outputs of the tested compounds and S88. Green balls = S88, red balls = similar molecules, blue balls = not similar molecules. (A) First 50 molecules, (B) second 50 molecules, (C) third 50 molecules, (D) fourth 50 molecules, (E) fifth 50 molecules, and (F) last 60 molecules.
Figure 3. The similarity outputs of the tested compounds and S88. Green balls = S88, red balls = similar molecules, blue balls = not similar molecules. (A) First 50 molecules, (B) second 50 molecules, (C) third 50 molecules, (D) fourth 50 molecules, (E) fifth 50 molecules, and (F) last 60 molecules.
Life 12 01407 g003
Life 12 01407 g004 550
Figure 4. Superimposition of the co-crystallized pose (magenta) and the re-docking pose (turquoise) of the same ligand (S88) in the active site of the PLpro enzyme.
Figure 4. Superimposition of the co-crystallized pose (magenta) and the re-docking pose (turquoise) of the same ligand (S88) in the active site of the PLpro enzyme.
Life 12 01407 g004
Life 12 01407 g006a 550Life 12 01407 g006b 550
Figure 6. (A) Three-dimensional and (B) two-dimensional binding modes of compound 76 in the PLpro active site.
Figure 6. (A) Three-dimensional and (B) two-dimensional binding modes of compound 76 in the PLpro active site.
Life 12 01407 g006aLife 12 01407 g006b
Life 12 01407 g007a 550Life 12 01407 g007b 550
Figure 7. (A) Three-dimensional and (B) two-dimensional binding modes of compound 94 in the PLpro active site.
Figure 7. (A) Three-dimensional and (B) two-dimensional binding modes of compound 94 in the PLpro active site.
Life 12 01407 g007aLife 12 01407 g007b
Life 12 01407 g008a 550Life 12 01407 g008b 550
Figure 8. (A) Three-dimensional and (B) two-dimensional binding modes of compound 98 in the PLpro active site.
Figure 8. (A) Three-dimensional and (B) two-dimensional binding modes of compound 98 in the PLpro active site.
Life 12 01407 g008aLife 12 01407 g008b
Life 12 01407 g009a 550Life 12 01407 g009b 550
Figure 9. (A) Three-dimensional and (B) two-dimensional binding modes of compound 193 in the PLpro active site.
Figure 9. (A) Three-dimensional and (B) two-dimensional binding modes of compound 193 in the PLpro active site.
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Life 12 01407 g010a 550Life 12 01407 g010b 550
Figure 10. (A) Three-dimensional and (B) two-dimensional binding modes of compound 200 in the PLpro active site.
Figure 10. (A) Three-dimensional and (B) two-dimensional binding modes of compound 200 in the PLpro active site.
Life 12 01407 g010aLife 12 01407 g010b
Life 12 01407 g011a 550Life 12 01407 g011b 550
Figure 11. (A) Three-dimensional and (B) two-dimensional binding modes of compound 298 in the PLpro active site.
Figure 11. (A) Three-dimensional and (B) two-dimensional binding modes of compound 298 in the PLpro active site.
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Figure 12. The expected ADMET characters.
Figure 12. The expected ADMET characters.
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Figure 13. Molecular orbitals spatial distribution for (A) S88, (B) 76, (C) 94, and (D) 298.
Figure 13. Molecular orbitals spatial distribution for (A) S88, (B) 76, (C) 94, and (D) 298.
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Figure 14. Molecular electrostatic potential map of (A) S88, (B) 76, (C) 94, and (D) 298.
Figure 14. Molecular electrostatic potential map of (A) S88, (B) 76, (C) 94, and (D) 298.
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Table
Table 1. Structural properties of the most similar molecules to S88.
Table 1. Structural properties of the most similar molecules to S88.
Comp.Molecular FormulaALog pM. WtHBAHBDRotatable BondsRingsAromatic RingsMFPSAMinimum Distance
4C24H27NO42.658394.483414530.1020.654
28C17H19N31.457266.361121430.1190.693
41C24H27NO33.131378.484313530.0830.546
46C20H20NO43.936338.377413430.1490.709
47C21H22NO44.161352.404404430.110.714
76C21H20O63.98368.38634320.2571.101
87C21H22O44.667338.397426220.1781.102
94C21H24O63.743372.412617320.1921.057
98C19H20O34.784296.36326220.1531.100
99C18H18O24.8266.334225220.141.108
101C22H18O73.584394.374704530.1920.356
111C23H30O54.65386.481524510.2090.486
127C14H12O42.466244.243411320.2350.539
146C21H30O26.109314.462214310.0840.493
147C27H34O53.325437.548504510.1820.412
188C14H16BrN3OS1.287355.273320420.2820.789
189C14H16BrN3OS1.287355.273320420.2820.789
192C21H18BrN3O3.919408.291323530.1680.418
193C21H20N4S2.122361.483313540.1670.509
200C15H8N2O22.331248.236300420.2220.670
211C21H17N3O4.078327.379211530.1760.529
215C12H9ClN23.043216.666100330.0840.582
216C12H8Cl2N23.707251.111100330.0760.558
217C12H8Cl2N2O2.846267.111110320.1420.578
227C22H32O35.507344.488311410.1010.798
287C29H37NO54.1479.608532410.1960.679
291C15H19NO22.932245.317225220.1980.524
298C18H21N3O2S2.716343.443415320.2520.600
300C23H25NO45.22379.449407320.1490.473
303C19H26ClNO33.006350.86416210.1580.650
S88C25H27FN2O3.098391.501125430.083
Table
Table 2. Fingerprint similarity between the tested molecules and S88.
Table 2. Fingerprint similarity between the tested molecules and S88.
Comp.SimilaritySASBSC
S881.00056500
Brevicollin (28)0.614304−70261
Cryptopleurine (41)0.64240160164
Columbamine (46)0.60535318212
Palmatine (47)0.58436357202
Glycyrrhizoflavone (76)0.56132921236
Licochalcone A (87)0.645354−16211
Arctigenin (94)0.59135536210
Termilignan (98)0.635343−25222
Anolignan B (99)0.615346−2219
4,5-dihydroxy-6″-deoxybromotopsentin (192)0.720394−18171
Dercitin (193)0.62135710208
Tryptanthrin (200)0.633337−33228
6-Cyano-5-methoxy-12-methylindolo [2, 3A] carbazole (211)0.594329−11236
Thiangazole (298)0.580307−36258
Phenoxan (300)0.57435452211
SA: The number of bits in S88 and target compound, SB: The number of bits in target compound but not S88, SC: The number bits in S88 but not the target.
Table
Table 3. Binding free energies (calculated ΔG in Kcal/mol) of the examined compounds and S88 as a reference compound against PLpro.
Table 3. Binding free energies (calculated ΔG in Kcal/mol) of the examined compounds and S88 as a reference compound against PLpro.
Comp.ΔG
[Kcal/mol]		Comp.ΔG
[Kcal/mol]
28−40.4499−39.43
41−47.34192−30.85
46−44.13193−44.02
47−46.06200−41.31
76−51.63211−37.33
87−35.48298−48.46
94−50.82300−33.61
98−52.21S88−59.13
Table
Table 4. Predicted ADMET descriptors for the examined molecules and remdesivir.
Table 4. Predicted ADMET descriptors for the examined molecules and remdesivir.
Comp.BBB aHIA bAq cCYP2D6 dHepatotoxicity Probability ePPB f
28cadn0.298c
41baci0.39b
46baci0.907a
47baci0.966c
76eaci0.894b
87bacn0.735b
94cacn0.774c
98bacn0.834c
99bacn0.847c
192bacn0.152c
193baci0.814c
200cacn0.98c
211babi0.874c
298cacn0.549c
300bacn0.622c
Remdesivireddn1.777b
a BBB level, b is high, c is medium, d is low, e is very low. b HIA, a is good, b is moderate, c is poor, d is very poor. c Aq. solubility level, a is extremely low, b is very low, c is low, d is good, e is optimal. d CYP2D6, n is a non-inhibitor, i is an inhibitor. e Hepatotoxicity, if >0.5 is toxic, if <0.5 is non-toxic. f PPBb is >90%, c is >95%.
Table
Table 5. Toxicity properties of tested molecules and remdesivir.
Table 5. Toxicity properties of tested molecules and remdesivir.
Comp.FDA * Rat CarcinogenicityTD50
(Rat) mg/kg Body Weight/Day		MTD *LD50 *LOAEL *Ocular Irritancy ***Skin Irritancy ***
28s9.5710.0500.9390.077mm
41n0.2190.0420.2020.018mn
46n0.7300.0811.2480.009mn
47n0.1690.0351.4460.008mn
76n19.2160.1530.3620.150mn
87n48.1730.1130.3640.030nn
94n8.9070.0919.2090.107mm
98n35.3700.1031.1330.398nn
99m69.0770.2402.0400.301mn
192n0.8571.0990.3480.016mn
193s1.5870.0120.3520.048mm
200s7.5680.0550.6890.277mn
211s0.6040.0130.2450.001mm
298n65.5420.0180.1180.019mn
300s13.5020.0290.4050.029nm
Remdesivirn1.0120.2350.3090.003mm
* s is single-carcinogen, m is multi-carcinogen n is non-carcinogen. *** n is nonirritant, m is mild irritant.
Table
Table 6. Frontier molecular orbital of 76, 94, 298, and S88.
Table 6. Frontier molecular orbital of 76, 94, 298, and S88.
Comp.Total Energy (Ha)Binding Energy (Ha)HOMO Energy (Ha)LUMO Energy (Ha)Dipole MagBand Gap Energy (Ha)
76−1252.956−9.601−0.166−0.0701.7000.096
94−1255.298−10.037−0.177−0.0363.5820.141
298−1401.286−8.702−0.195−0.0641.0940.131
S88−1242.952−11.181−0.292−0.1923.6210.101
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Elkaeed, E.B.; Metwaly, A.M.; Alesawy, M.S.; Saleh, A.M.; Alsfouk, A.A.; Eissa, I.H. Discovery of Potential SARS-CoV-2 Papain-like Protease Natural Inhibitors Employing a Multi-Phase In Silico Approach. Life 2022, 12, 1407. https://doi.org/10.3390/life12091407

AMA Style

Elkaeed EB, Metwaly AM, Alesawy MS, Saleh AM, Alsfouk AA, Eissa IH. Discovery of Potential SARS-CoV-2 Papain-like Protease Natural Inhibitors Employing a Multi-Phase In Silico Approach. Life. 2022; 12(9):1407. https://doi.org/10.3390/life12091407

Chicago/Turabian Style

Elkaeed, Eslam B., Ahmed M. Metwaly, Mohamed S. Alesawy, Abdulrahman M. Saleh, Aisha A. Alsfouk, and Ibrahim H. Eissa. 2022. "Discovery of Potential SARS-CoV-2 Papain-like Protease Natural Inhibitors Employing a Multi-Phase In Silico Approach" Life 12, no. 9: 1407. https://doi.org/10.3390/life12091407

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