Next Article in Journal
Identification of Serum Metabolites for Predicting Chronic Kidney Disease Progression according to Chronic Kidney Disease Cause
Next Article in Special Issue
Diagnostic, Prognostic and Mechanistic Biomarkers of COVID-19 Identified by Mass Spectrometric Metabolomics
Previous Article in Journal
Plasma Ceramide Concentrations in Full-Term Pregnancies Complicated with Gestational Diabetes Mellitus: A Case-Control Study
Previous Article in Special Issue
Signatures of Mitochondrial Dysfunction and Impaired Fatty Acid Metabolism in Plasma of Patients with Post-Acute Sequelae of COVID-19 (PASC)
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Metabolites
Volume 12
Issue 11
10.3390/metabo12111122
metabolites-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

The Discovery of Potential SARS-CoV-2 Natural Inhibitors among 4924 African Metabolites Targeting the Papain-like Protease: A Multi-Phase In Silico Approach

by
Eslam B. Elkaeed
1,*,
Mohamed M. Khalifa
2,
Bshra A. Alsfouk
3,
Aisha A. Alsfouk
3,
Abdul-Aziz M. M. El-Attar
4,
Ibrahim H. Eissa
2,* and
Ahmed M. Metwaly
5,6,*
1
Department of Pharmaceutical Sciences, College of Pharmacy, AlMaarefa University, Riyadh 13713, Saudi Arabia
2
Pharmaceutical Medicinal Chemistry & Drug Design Department, Faculty of Pharmacy (Boys), Al-Azhar University, Cairo 11884, Egypt
3
Department of Pharmaceutical Sciences, College of Pharmacy, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
4
Pharmaceutical Analytical Chemistry Department, Faculty of Pharmacy, Al-Azhar University, Nasr City, Cairo 11884, Egypt
5
Pharmacognosy and Medicinal Plants Department, Faculty of Pharmacy (Boys), Al-Azhar University, Cairo 11884, Egypt
6
Biopharmaceutical Products Research Department, Genetic Engineering and Biotechnology Research Institute, City of Scientific Research and Technological Applications (SRTA-City), Alexandria 21934, Egypt
*
Authors to whom correspondence should be addressed.
Metabolites 2022, 12(11), 1122; https://doi.org/10.3390/metabo12111122
Submission received: 3 November 2022 / Revised: 14 November 2022 / Accepted: 15 November 2022 / Published: 16 November 2022
(This article belongs to the Special Issue COVIDomics: Metabolomic Views on COVID-19 and Related Diseases)
Browse Figures
Versions Notes

Abstract

:
Four compounds, hippacine, 4,2′-dihydroxy-4′-methoxychalcone, 2′,5′-dihydroxy-4-methoxychalcone, and wighteone, were selected from 4924 African natural metabolites as potential inhibitors against SARS-CoV-2 papain-like protease (PLpro, PDB ID: 3E9S). A multi-phased in silico approach was employed to select the most similar metabolites to the co-crystallized ligand (TTT) of the PLpro through molecular fingerprints and structural similarity studies. Followingly, to examine the binding of the selected metabolites with the PLpro (molecular docking. Further, to confirm this binding through molecular dynamics simulations. Finally, in silico ADMET and toxicity studies were carried out to prefer the most convenient compounds and their drug-likeness. The obtained results could be a weapon in the battle against COVID-19 via more in vitro and in vivo studies.

Graphical Abstract

1. Introduction

On 11 November 2022, the WHO stated that the confirmed global infections of COVID-19 were 630,832,131, with 6,584,104 people dead [1]. Conforming to these gigantic numbers, massive work is demanded from scientists worldwide to find a cure.
The evolution of computational chemistry methods as a successful tool to conclude the physical and chemical properties of a molecule and the molecular reactions allowed deep identification of the molecular properties of compounds in addition to their interactions with different proteins [2]. Consequently, the in silico prediction of the activity of large libraries of compounds against a specific molecular target became available [3]. The computational (in silico) chemistry methods have been employed in drug discovery [4,5,6], molecular modeling [7], and design [8,9]. Additionally, it has been used to predict ADMET [10,11,12], toxicity [13,14,15] as well as DFT [16] properties.
The interest of humans in the use of natural products can be traced back hundreds of years and continues to the present [17,18].
The papain-like protease, PLpro, is a vital enzyme in the coronavirus. PLpro has an important role in the processing mechanism of viral polyproteins. This process leads to the formation of an active replicase complex [19]. Besides, PLpro has an additional vital role in deactivating human immunity. PLpro acts on human enzymes via cleaving proteinaceous post-translational modifications [20].
Our teamwork employed the in silico approaches to explore the potentialities of natural products against COVID-19 several times before. The determination of the most convenient inhibitors between fifty-nine isoflavonoids against hACE2 and main viral protease has been reported [21]. Likely, the activities of a set of fifteen alkaloids against COVID-19 five enzymes have been published [22].
In the presented work, a set of 4924 African natural products (compounds isolated from African natural sources) has been selected. The experiment set was obtained from the African Natural Products Database (ANPDB), a collection of several natural product databases in different African regions. The selected data set covered the period of 1962–2019 and was derived from international and local African journals in addition to MSc and Ph.D. theses in African university libraries [23].
The selected compounds were screened using multistage computational methods to detect the most potent SARS-CoV-2 PLpro inhibitors. The applied methods included molecular structures similarity study against the co-crystallized ligand (TTT) of PLpro (PDB ID: 3E9S) [24], fingerprint study against the same ligand, molecular docking against PLpro, ADMET, toxicity and molecular dynamics (MD) simulation experiments.

2. Method

2.1. Molecular Similarity Detection

Discovery Studio 4.0 software, 2016, Vélizy-Villacoublay, France, was used to investigate the similarities between 4924 African natural metabolites and TTT, the co-crystallized ligand of PLpro (Supplementary Data provides comprehensive details).

2.2. Fingerprint Studies

Discovery Studio 4.0 software was used to investigate the similarities between 100 African natural metabolites and TTT, the co-crystallized ligand of PLpro (Supplementary Data provides comprehensive details).

2.3. Docking Studies

Docking studies were done for the most similar 40 metabolites against the PLpro protease (PDB ID: 3E9S) using Discovery studio software [25] to investigate the binding energies as well as binding modes (Supplementary Data provides comprehensive details).

2.4. ADMET Analysis

Discovery Studio 4.0 was used [26] to examine 5 different ADMET parameters for 17 metabolites of correct binding scores (Supplementary Data provides comprehensive details).

2.5. Toxicity Studies

Discovery Studio 4.0 software was used [27,28,29] to examine 7 different toxicity parameters for 7 metabolites of good ADMET profile (Supplementary Data provides comprehensive details).

2.6. Molecular Dynamics Simulation

The PLpro-wighteone system was prepared using the web-based CHARMM-GUI [30,31,32] interface utilizing the CHARMM36 force field [33] and NAMD 2.13 [34] package. The TIP3P explicit solvation model was used (Supporting Data provides comprehensive details).

3. Results and Discussion

3.1. Structure Fingerprints Study

The basic assumption of structure-activity relationship studies is that “Chemical compounds with similar structures may have similar activities” [35]. This assumption was very useful in discovering several bioactive ligands [36]. The high affinity of the co-crystallized ligand to bind with the targeted protein was our main motive in this work. We utilized some ligand-based computational techniques such as structure fingerprints and similarity to select the natural compounds (through the examined library) that have high degrees of similarities and hence could bind with PLpro effectively. The fingerprint study is a molecular descriptor technique widely used to figure out the similarity or dissimilarity between the chemical structures of two molecules or more [37,38]. In fingerprint study, the software converts the basic chemical molecular descriptors into mathematical symbols. The resulting data is displayed as bit strings that identify the presence (1) or absence (0) of a specific 2D atomic or fragment descriptor in both test and reference compounds [39,40]. In this study, Discovery Studio software examined the molecular fingerprints of 4924 compounds against TTT. This study aims to extract the most similar natural compounds to the ligand. The employed descriptors are H-bond acceptor [41] and donor [42], charge [43], hybridization [44], positive [45] and negative ionizable atoms [46], halogens [47], aromatic [48], or none of the above besides the ALogP [49] category of fragments and atoms. The study was adjusted to choose the most structurally similar 200 compounds to TTT (Table 1).

3.2. Molecular Similarity

The molecular similarity study differs from the fingerprints study in that the first computes certain descriptors regarding the whole chemical structure of a molecule. The computed descriptors are topological, steric, electronic, and/or physical properties [50]. On the other hand, the fingerprints study compares the absence or presence of certain 2D atom paths, fragments, or substructures in the chemical structures of reference and test molecules [51].
Employing Discovery studio software, the molecular similarities of the selected 100 natural metabolites were investigated correlating TTT. The employed properties in this study (Figure 1 and Table 2) were partition coefficient (ALog p) [52], molecular weight (M. Wt) [53], H- bond donors (HBA) [54], H- bond acceptors (HBD) [55], rotatable bonds number [56], number of rings along with aromatic rings [57], and minimum distance [58] as well as the molecular fractional polar surface area (MFPSA) [59]. The experiment was adjusted to extract the most similar 40 compounds (Figure 2).

3.3. Docking Studies

The forty most structurally similar compounds were subjected to a molecular docking study in the hope of getting an insight into the way they interact with their biomolecular target. The papain-like protease (PLpro) crystal structure PDB ID: 3E9S in complex with the co-crystallized ligand, TTT, was adopted for the present study. A docking study was performed using MOE 14.0 software, Montreal, Canada. The calculated ΔG of the tested compounds is cited in Table 3.
The docking protocol was first validated via the redocking of the co-crystallized ligand (TTT) against the active pocket of SARS papain-like protease (PLpro) active pocket. However, the validation step proved the suitability of the performed protocol for the intended docking study, as demonstrated by the small RMSD (0.51 Å) between the docked pose and the co-crystallized ligand (Figure 3).
TTT, the well-known PLpro inhibitor, was used as a reference in the current study. The binding affinity value of TTT was −9.30 kcal/mol. TTT interacted with the active pocket through the formation of two H-bonds. The amidic NH group of TTT formed an H-bond with the carboxylic acid side chain of Asp165, while the amidic carbonyl bound to the nitrogen backbone of Gln270. Additionally, the naphthyl moiety was involved in a hydrophobic interaction with the Pro249 side chain (Figure 4).
Results of the docking study showed that most of the tested compounds have a similar position and orientation inside the SARS PLpro active site. Among them, members 2195, 1952, 2982, and 1330 revealed the greatest binding free energies of docking, which were almost close to the redocked ligand.
The docking simulation of compound 2195 revealed that it has the highest fitting into the enzyme active site with a docking score of −24.88 kcal/mol. It was stabilized in the active site through the formation of six H-bond interactions and many hydrophobic interactions. The chromenone moiety, via its carbonyl and hydroxyl groups, formed five H-bonds with Tyr269, Gln270, Leu263, and Lys253. On the other side, the p-hydroxyphenyl moiety was involved in an H-bond with Arg167 Figure 5. The chromenone moiety and the phenyl ring also formed hydrophobic interactions with Tyr269 and Lys158, respectively.
Compound 1952 exhibited a binding mode similar to that of the co-crystallized ligand with the formation of two H-bonds. One H-bond was formed between a hydroxyl group side chain with Asp165. The other H-bond was formed between the oxygen bridge and Gln270 (Figure 6). Additionally, a hydrophobic interaction was formed between one phenyl moiety and Tyr265 of the active site.
A study of the top docking poses of member 2982 (Figure 7) showed that it interacted with the PLpro active site through the formation of three H-bond interactions. The hydroxyphenyl moiety was involved in an H-bonding with Leu163, while the aliphatic hydroxyl group formed another H-bond with Asp165. In addition, the carbonyl group formed an H-bond with Gln270.
Figure 8 illustrates the proposed binding mode of compound 1330. The two phenolic hydroxyl groups interacted with the active site by two H-bonds with Ala247 and Gln270. Furthermore, the carbonyl group formed an H-bond with Asp165.

3.4. ADMET Studies

The ability of a molecule to be a drug is decided not only by activity but also by acceptable pharmacokinetic properties. ADMET profile describes absorption, distribution, metabolism, excretion, and toxicity. Although the determination of the ADMET profile is available via several medium- and high-throughput in vitro methods, the ability to predict it depending on in silico is available with the advantage of saving time, money, effort, and animal lives [60]. ADMET prediction is an essential step in drug discovery [61].
The computed ADMET descriptors for 17 compounds that displayed correct binding mode and energy, as well as remdesivir as a reference drug, are listed in (Table 4 and Figure 9). Compounds 181, 182, 204, 212, 213, 215, 1952, 2981, 3040, and 3396 were expected to have a high ability to pass BBB and, accordingly, were eliminated. Fortunately, the absorption levels of all compounds were computed as good. Similarly, all of them showed low to good aqueous solubility levels. All compounds were expected to bind to plasma protein with a ratio of more than 90%. Finally, according to these results, compounds Hippacine (164), Naamine D (2197), (±)-Enterofuran (3412), Daphnelone (2982), 4,2′-dihydroxy-4′-methoxychalcone (1330), 2′,5′-dihydroxy-4-methoxychalcone (1331), and wighteone (2195) were favored and subjected to the next toxicity examination.

3.5. Toxicity Studies

The prediction of toxicity of a molecule depending on computer software (in silico) has been employed effectively to select drug leads in the field of drug design, as in vitro and in vivo methods are usually limited by lack of time, budget, and ethical restrictions [62,63].
The toxicity of 7 compounds that displayed good ADMET profiles was predicted in silico using Discovery Studio software concerning 7 different models. The employed models are FDA rat carcinogenicity [64,65], carcinogenic potency median toxic dose, (TD50) [66], rat maximum tolerated dose (MTD) [67,68], rat oral LD50 [69], rat chronic lowest-observed-adverse-effect level (LOAEL) [70,71], and ocular and skin irritancy [72]. As shown in Table 5, compounds 2982 and 3412 were proposed as carcinogenic. In consequence, both were refused. Also, all compounds, excluding 2197, are expected to have TD50 and TD50 values more than the reference. Thus, 2197 was excluded too. All compounds were computed to have LOAEL values more than the reference and to be non-irritant in the skin model. On the other hand, all compounds except 1330 showed different degrees of ocular irritancy.
The acquired results privilege compounds Hippacine (164), 4,2′-dihydroxy-4′-methoxychalcone (1330), 2′,5′-dihydroxy-4-methoxychalcone (1331), and wighteone (2195) as the most convenient inhibitors against the target enzyme. Among the selected compounds, wighteone displayed the most favorable docking score and energy. Wighteone is an isoflavonoid that has been isolated from the bark of a South African Erythrina species showing promising antibacterial effects [73]. It was also isolated from several Maclura species before [74,75]. The antiviral activity of wighteone against HIV has been reported in vitro [76], and its in silico potentiality against HIV-1 protease enzyme with a binding affinity of −8.7 Kcal/mol [77].

3.6. Molecular Dynamics (MD) Simulations

Although molecular docking can predict the correct binding poses of a molecule inside the active site of a certain protein, it has a major drawback in that it considers the proteins rigid, and thus doesn’t allow the protein to adjust its conformation during the docking process [78]. On the other hand, MD simulations can efficiently predict how every single atom in a specific protein will move over a specific time, depending on a physical model of the interatomic interactions [79]. Correspondingly, MD simulations have been successfully utilized to examine the conformation changes in protein-ligand interactions and protein dynamics and folding [80]. MD simulation is an effective and accurate in silico technique that can describe the binding mode, stability, and flexibility of a certain receptor and a specific ligand for a determined time [81].
Molecular dynamics (MD) simulations were carried out to mimic the dynamic nature of PLpro-wighteone interaction under physiological conditions and to investigate the stability of binding complex simulation for 100 ns.

RMSD and RMSF Analysis

The binding of a certain ligand in a specific protein causes notable changes in the structure [82]. Consequently, the root mean square deviation (RMSD) parameter was investigated to explore whether the structure of the PLpro-wighteone complex is stable and near the experimental structure. Figure 10 shows that the PLpro-wighteone complex exhibited a good RMSD value along with 100 ns MD; the PLpro showed an RMSD value of 2.5 Å too, while the complex exhibited an average RMSD value of 3.5 Å, below the acceptable range of 4 Å. After 60 ns, no dramatic increment in the RMSD values was noticed and the complex system reached equilibrium.
Root mean square fluctuation (RMSF) was utilized to describe the flexibility differences among wighteone, PLpro, and their complex during the MD simulation for 100 ns. Increasing RMSF value denotes a higher degree of flexibility, while the low value is related to limited movement during the MD simulation. To investigate the average fluctuation of PLpro during the MD study, the RMSF of PLpro upon the binding of wighteone was plotted as a function of residue number (Figure 11). RMSF plot indicated that the residual fluctuation of PLpro was minimized upon binding of wighteone. This result indicates that PLpro residues were more rigid in the presence of wighteone because of binding to wighteone.
The radius of gyration (Rg) is an essential parameter that gives a clear insight into the protein stability in terms of volume change. Rg is defined as the RMSD of the mass-weighted of a group of atoms from their common mass center [83,84]. Accordingly, the analysis of Rg of PLpro during the MD simulation will describe its overall dimensions. The average Rg values were found to suggest the tight packing of PLpro in its native state and when bound to wighteone. PLpro-wighteone complex reached a stable conformation with the radius of gyration fluctuating around 24.4 Å (Figure 12).
The solvent-accessible surface area (SASA) is the surface area of the protein which can be accessible to a solvent [85]. The evaluation of SASA provides information about the conformational changes that happen in a protein because of ligand binding. The average SASA values for PLpro were monitored during 100 ns MD simulations. As shown in Figure 13, there were no major changes in the values of SASA of PLpro due to wighteone binding.

4. Discussion

The recent advancement in software enabled computational chemistry to perfectly describe the physical and chemical properties of a compound in addition to its potential to interact with a particular protein.
Accordingly, several researchers utilized computational chemistry to identify potential inhibitors against SARS-CoV-2 using different approaches. Exploring the potentialities of FDA-approved antivirus drugs against SARS-CoV-2 was one of the first computational approaches. For instance, the computational potentialities of remdesivir, the FDA-approved anti-ebola, and respiratory syncytial viruses against SARS-CoV-2 main protease were investigated [86]. The same approach was applied to lopinavir/ritonavir [87] and ribavirin [88] targeting SARS-CoV-2 3-chymotrypsin-like protease. One of the employed approaches was the computational-based drug repurposing of non-antiviral FDA-approved drugs such as lurasidone (anti-schizophrenia) against SARS-CoV-2 3CL hydrolase and protease enzymes [89], aclarubicin [90], and selinexor [91], FDA-approved anti-cancers that exhibited computational activities against SARS-CoV-2 main protease.
Our team employed computational chemistry to develop a multiphase in silico technique to discover the most appropriate natural inhibitor via large sets of molecules against a specific enzyme of COVID-19. Within 310 natural antiviral metabolites, the most effective inhibitor against SARS-CoV-2 nsp10 [92], main protease [93,94], and papain-like protease [95] were predicted. Also, within 3009 FDA approved drugs, the most potent inhibitors against SARS-CoV-2 nsp16-nsp10 2′-o-Methyltransferase Complex [96] and SARS-CoV-2 RNA-Dependent RNA Polymerase [97] were anticipated. The SARS-CoV-2 Helicase potential natural inhibitors were expected among 5956 compounds of traditional Chinese medicine also [98]. Further, the most active semisynthetic COVID-19 papain-like protease inhibitor was discovered amidst 69 molecules [99].
Unfortunately, at the current time, we don’t have access to investigate the experimental inhibitory effects of the pointed 4 metabolites (Hippacine, 4,2′-dihydroxy-4′-methoxychalcone, 2′,5′-dihydroxy-4-methoxychalcone, and wighteone) among 4924 African natural metabolites against SARS-CoV-2. However, we presented those 4 metabolites for all scientists worldwide to conduct further in vitro and in vivo studies. The binding potentialities of those metabolites against the SARS-CoV-2 papain-like protease were confirmed through 4 stages of in silico experiments:
Stage I: Selection of the most similar metabolites to the co-crystallized ligand (TTT) of SARS-CoV-2 papain-like protease (PDB ID: 3E9S) (fingerprints and molecular similarity studies). This stage selected the most similar 40 metabolites to the co-crystallized ligand;
Stage II: Evaluation and filtration according to the binding against papain-like protease by molecular docking to select 17 metabolites that showed correct binding;
Stage III: Evaluation and the filtration according to drug-likeness by ADMET and toxicity studies to point out the safest and most drug-like 4 metabolites;
Stage IV: Confirmation of the binding against papain-like protease by MD simulations to confirm the binding, conformational and energetic changes that combine the binding process. SARS-CoV-2 papain-like protease (PLpro, PDB ID: 3E9S). A multi-phased in silico approach was employed to select the most similar metabolites to the co-crystallized ligand (TTT).

5. Conclusions

Four metabolites, Hippacine (164), 4,2′-dihydroxy-4′-methoxychalcone (1330), 2′,5′-dihydroxy-4-methoxychalcone (1331), and Wighteone (2195), were selected through 4924 African natural products as the most potent inhibitor against Sars-Cov-2 papain-like protease. The selection is based on multiphase (six experiments) in silico studies. The structural fingerprint study against the co-crystallized ligand (TTT) of SARS-CoV-2 papain-like protease (PDB ID: 3E9S), chemical structural similarity study, molecular docking studies against SARS-CoV-2 papain-like protease (PDB ID: 3E9S), ADMET, and toxicity profiles. Wighteone (2195), the metabolite with the best docking score, was subjected to the molecular dynamics simulation (MD) at 100 ns confirming the binding of wighteone against the target enzyme. We present these interesting results for all scientists worldwide to conduct further in vitro and in vivo studies concerning these promising natural metabolites.

Supplementary Materials

The detailed methodology of this manuscript can be downloaded at: https://www.mdpi.com/article/10.3390/metabo12111122/s1, (Similarity, fingerprints, docking, and MD simulations).

Author Contributions

Project administration, A.M.M. and I.H.E.; Supervision, A.M.M. and I.H.E.; Funding acquisition, E.B.E., A.A.A. and B.A.A.; Methodology, M.M.K. and A.-A.M.M.E.-A.; Validation, I.H.E.; Writing—original draft, A.M.M.; Writing—review and editing, E.B.E., A.A.A., A.M.M., B.A.A. and I.H.E. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2022R142), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available in the main article andthe supplementary materials.

Acknowledgments

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

Conflicts of Interest

No conflict of interest regarding this paper to be declared.

References

  1. WHO. WHO Coronavirus (COVID-19) Dashboard. Available online: https://covid19.who.int/ (accessed on 12 November 2022).
  2. Engel, T. Basic overview of chemoinformatics. J. Chem. Inf. Model. 2006, 46, 2267–2277. [Google Scholar] [CrossRef] [PubMed]
  3. Xu, J.; Hagler, A. Chemoinformatics and drug discovery. Molecules 2002, 7, 566–600. [Google Scholar] [CrossRef]
  4. Marrone, T.J.; Briggs, A.; James, M.; McCammon, J.A. Structure-based drug design: Computational advances. Annu. Rev. Pharmacol. Toxicol. 1997, 37, 71–90. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  5. Li, N.; Wang, Y.; Li, W.; Li, H.; Yang, L.; Wang, J.; Mahdy, H.A.; Mehany, A.; Jaiash, D.A.; Santali, E.Y. Screening of Some Sulfonamide and Sulfonylurea Derivatives as Anti-Alzheimer’s Agents Targeting BACE1 and PPARγ. J. Chem. 2020, 2020, 1631243. [Google Scholar] [CrossRef]
  6. Abdel-Aziz, H.A.; Eldehna, W.M.; Fares, M.; Al-Rashood, S.T.; Al-Rashood, K.A.; Abdel-Aziz, M.M.; Soliman, D.H. Synthesis, biological evaluation and 2D-QSAR study of halophenyl bis-hydrazones as antimicrobial and antitubercular agents. Int. J. Mol. Sci. 2015, 16, 8719–8743. [Google Scholar] [CrossRef] [Green Version]
  7. March-Vila, E.; Pinzi, L.; Sturm, N.; Tinivella, A.; Engkvist, O.; Chen, H.; Rastelli, G. On the integration of in silico drug design methods for drug repurposing. Front. Pharmacol. 2017, 8, 298. [Google Scholar] [CrossRef] [Green Version]
  8. Zhang, W.; Pei, J.; Lai, L. Computational multitarget drug design. J. Chem. Inf. Model. 2017, 57, 403–412. [Google Scholar] [CrossRef]
  9. Youssef, M.I.; Zhou, Y.; Eissa, I.H.; Wang, Y.; Zhang, J.; Jiang, L.; Hu, W.; Qi, J.; Chen, Z. Tetradecyl 2,3-dihydroxybenzoate alleviates oligodendrocyte damage following chronic cerebral hypoperfusion through IGF-1 receptor. Neurochem. Int. 2020, 138, 104749. [Google Scholar] [CrossRef]
  10. Alsaif, N.A.; Taghour, M.S.; Alanazi, M.M.; Obaidullah, A.J.; Al-Mehizia, A.A.; Alanazi, M.M.; Aldawas, S.; Elwan, A.; Elkady, H. Discovery of new VEGFR-2 inhibitors based on bis ([1, 2, 4] triazolo)[4,3-a:3′,4′-c] quinoxaline derivatives as anticancer agents and apoptosis inducers. J. Enzym. Inhib. Med. Chem. 2021, 36, 1093–1114. [Google Scholar] [CrossRef]
  11. Alsaif, N.A.; Dahab, M.A.; Alanazi, M.M.; Obaidullah, A.J.; Al-Mehizia, A.A.; Alanazi, M.M.; Aldawas, S.; Mahdy, H.A.; Elkady, H. New quinoxaline derivatives as VEGFR-2 inhibitors with anticancer and apoptotic activity: Design, molecular modeling, and synthesis. Bioorg. Chem. 2021, 110, 104807. [Google Scholar] [CrossRef]
  12. El-Adl, K.; Ibrahim, M.-K.; Alesawy, M.S.; Eissa, I.H. [1, 2, 4] Triazolo [4, 3-c] quinazoline and bis ([1, 2, 4] triazolo)[4,3-a:4′,3′-c] quinazoline derived DNA intercalators: Design, synthesis, in silico ADMET profile, molecular docking and anti-proliferative evaluation studies. Bioorg. Med. Chem. 2021, 30, 115958. [Google Scholar] [CrossRef] [PubMed]
  13. El-Metwally, S.A.; Abou-El-Regal, M.M.; Eissa, I.H.; Mehany, A.B.; Mahdy, H.A.; Elkady, H.; Elwan, A.; Elkaeed, E.B. Discovery of thieno [2, 3-d] pyrimidine-based derivatives as potent VEGFR-2 kinase inhibitors and anti-cancer agents. Bioorg. Chem. 2021, 112, 104947. [Google Scholar] [CrossRef] [PubMed]
  14. Alanazi, M.M.; Eissa, I.H.; Alsaif, N.A.; Obaidullah, A.J.; Alanazi, W.A.; Alasmari, A.F.; Albassam, H.; Elkady, H.; Elwan, A. Design, synthesis, docking, ADMET studies, and anticancer evaluation of new 3-methylquinoxaline derivatives as VEGFR-2 inhibitors and apoptosis inducers. J. Enzym. Inhib. Med. Chem. 2021, 36, 1760–1782. [Google Scholar] [CrossRef] [PubMed]
  15. Alanazi, M.M.; Alaa, E.; Alsaif, N.A.; Obaidullah, A.J.; Alkahtani, H.M.; Al-Mehizia, A.A.; Alsubaie, S.M.; Taghour, M.S.; Eissa, I.H. Discovery of new 3-methylquinoxalines as potential anti-cancer agents and apoptosis inducers targeting VEGFR-2: Design, synthesis, and in silico studies. J. Enzym. Inhib. Med. Chem. 2021, 36, 1732–1750. [Google Scholar] [CrossRef]
  16. Elkaeed, E.B.; Yousef, R.G.; Elkady, H.; Alsfouk, A.A.; Husein, D.Z.; Ibrahim, I.M.; Alswah, M.; Elzahabi, H.S.A.; Metwaly, A.M.; Eissa, I.H. A New Theobromine-Based EGFRWT and EGFRT790M Inhibitor and Apoptosis Inducer: Design, Semi-Synthesis, Docking, DFT, MD Simulations, and In Vitro Studies. Processes 2022, 10, 2290. [Google Scholar] [CrossRef]
  17. Metwaly, A.M.; Ghoneim, M.M.; Eissa, I.H.; Elsehemy, I.A.; Mostafa, A.E.; Hegazy, M.M.; Afifi, W.M.; Dou, D. Traditional ancient Egyptian medicine: A review. Saudi J. Biol. Sci. 2021, 28, 5823–5832. [Google Scholar] [CrossRef]
  18. Han, X.; Yang, Y.; Metwaly, A.M.; Xue, Y.; Shi, Y.; Dou, D. The Chinese herbal formulae (Yitangkang) exerts an antidiabetic effect through the regulation of substance metabolism and energy metabolism in type 2 diabetic rats. J. Ethnopharmacol. 2019, 239, 111942. [Google Scholar] [CrossRef]
  19. Shin, D.; Mukherjee, R.; Grewe, D.; Bojkova, D.; Baek, K.; Bhattacharya, A.; Schulz, L.; Widera, M.; Mehdipour, A.R.; Tascher, G.; et al. Papain-like protease regulates SARS-CoV-2 viral spread and innate immunity. Nature 2020, 587, 657–662. [Google Scholar] [CrossRef]
  20. Báez-Santos, Y.M.; John, S.E.S.; Mesecar, A.D. The SARS-coronavirus papain-like protease: Structure, function and inhibition by designed antiviral compounds. Antivir. Res. 2015, 115, 21–38. [Google Scholar] [CrossRef]
  21. Alesawy, M.S.; Abdallah, A.E.; Taghour, M.S.; Elkaeed, E.B.; Eissa, I.H.; Metwaly, A.M. In Silico Studies of Some Isoflavonoids as Potential Candidates against COVID-19 Targeting Human ACE2 (hACE2) and Viral Main Protease (Mpro). Molecules 2021, 26, 2806. [Google Scholar] [CrossRef]
  22. El-Demerdash, A.; Metwaly, A.M.; Hassan, A.; El-Aziz, A.; Mohamed, T.; Elkaeed, E.B.; Eissa, I.H.; Arafa, R.K.; Stockand, J.D. Comprehensive virtual screening of the antiviral potentialities of marine polycyclic guanidine alkaloids against SARS-CoV-2 (COVID-19). Biomolecules 2021, 11, 460. [Google Scholar] [CrossRef] [PubMed]
  23. ANPDB African Natural Products Database. Available online: http://african-compounds.org/anpdb/compounds_list/ (accessed on 11 November 2021).
  24. Fu, Z.; Huang, B.; Tang, J.; Liu, S.; Liu, M.; Ye, Y.; Liu, Z.; Xiong, Y.; Zhu, W.; Cao, D. The complex structure of GRL0617 and SARS-CoV-2 PLpro reveals a hot spot for antiviral drug discovery. Nat. Commun. 2021, 12, 488. [Google Scholar] [CrossRef]
  25. R.P.D. Bank. X-ray Structural and Biological Evaluation of a Series of Potent and Highly Selective Inhibitors of Human Coronavirus Papain-Like Proteases. Available online: https://www.rcsb.org/structure/4OW0 (accessed on 14 August 2022).
  26. Parmar, D.R.; Soni, J.Y.; Guduru, R.; Rayani, R.H.; Kusurkar, R.V.; Vala, A.G.; Talukdar, S.N.; Eissa, I.H.; Metwaly, A.M.; Khalil, A. Discovery of new anticancer thiourea-azetidine hybrids: Design, synthesis, in vitro antiproliferative, SAR, in silico molecular docking against VEGFR-2, ADMET, toxicity, and DFT studies. Bioorg. Chem. 2021, 115, 105206. [Google Scholar] [CrossRef] [PubMed]
  27. Yousef, R.; Sakr, H.; Eissa, I.; Mehany, A.; Metwaly, A.; Elhendawy, M.A.; Radwan, M.; ElSohly, M.A.; Abulkhair, H.S.; El-Adl, K. New quinoxaline-2 (1H)-ones as potential VEGFR-2 inhibitors: Design, synthesis, molecular docking, ADMET profile and anti-proliferative evaluations. New J. Chem. 2021, 45, 16949–16964. [Google Scholar] [CrossRef]
  28. Amer, H.H.; Alotaibi, S.H.; Trawneh, A.H.; Metwaly, A.M.; Eissa, I.H. Anticancer activity, spectroscopic and molecular docking of some new synthesized sugar hydrazones, Arylidene and α-Aminophosphonate derivatives. Arab. J. Chem. 2021, 14, 103348. [Google Scholar] [CrossRef]
  29. Alesawy, M.S.; Al-Karmalawy, A.A.; Elkaeed, E.B.; Alswah, M.; Belal, A.; Taghour, M.S.; Eissa, I.H. Design and discovery of new 1,2,4-triazolo[4,3-c] quinazolines as potential DNA intercalators and topoisomerase II inhibitors. Arch. Der Pharm. 2021, 354, 2000237. [Google Scholar] [CrossRef]
  30. Jo, S.; Kim, T.; Iyer, V.G.; Im, W. CHARMM-GUI: A web-based graphical user interface for CHARMM. J. Comput. Chem. 2008, 29, 1859–1865. [Google Scholar] [CrossRef] [PubMed]
  31. Brooks, B.R.; Brooks, C.L., III; Mackerell, A.D., Jr.; Nilsson, L.; Petrella, R.J.; Roux, B.; Won, Y.; Archontis, G.; Bartels, C.; Boresch, S.; et al. CHARMM: The biomolecular simulation program. J. Comput. Chem. 2009, 30, 1545–1614. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  32. Lee, J.; Cheng, X.; Swails, J.M.; Yeom, M.S.; Eastman, P.K.; Lemkul, J.A.; Wei, S.; Buckner, J.; Jeong, J.C.; Qi, Y.; et al. CHARMM-GUI Input Generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM Simulations Using the CHARMM36 Additive Force Field. J. Chem. Theory Comput. 2016, 12, 405–413. [Google Scholar] [CrossRef] [PubMed]
  33. Best, R.B.; Zhu, X.; Shim, J.; Lopes, P.E.; Mittal, J.; Feig, M.; Mackerell, A.D., Jr. Optimization of the additive CHARMM all-atom protein force field targeting improved sampling of the backbone phi, psi and side-chain chi(1) and chi(2) dihedral angles. J. Chem. Theory Comput. 2012, 8, 3257–3273. [Google Scholar] [CrossRef]
  34. Phillips, J.C.; Braun, R.; Wang, W.; Gumbart, J.; Tajkhorshid, E.; Villa, E.; Chipot, C.; Skeel, R.D.; Kale, L.; Schulten, K. Scalable molecular dynamics with NAMD. J. Comput. Chem. 2005, 26, 1781–1802. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  35. Wawer, M.; Peltason, L.; Weskamp, N.; Teckentrup, A.; Bajorath, J. Structure–activity relationship anatomy by network-like similarity graphs and local structure–activity relationship indices. J. Med. Chem. 2008, 51, 6075–6084. [Google Scholar] [CrossRef] [PubMed]
  36. Farhadi, F.; Khameneh, B.; Iranshahi, M.; Iranshahy, M. Antibacterial activity of flavonoids and their structure–activity relationship: An update review. Phytother. Res. 2019, 33, 13–40. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  37. Burke, B.J. Developments in Molecular Shape Analysis to Establish Spatial Similarity among Flexible Molecules; University of Illinois at Chicago, Health Sciences Center: Chicago, IL, USA, 1993. [Google Scholar]
  38. Willett, P. Similarity-based virtual screening using 2D fingerprints. Drug Discov. Today 2006, 11, 1046–1053. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  39. Briem, H.; Kuntz, I.D. Molecular similarity based on DOCK-generated fingerprints. J. Med. Chem. 1996, 39, 3401–3408. [Google Scholar] [CrossRef] [PubMed]
  40. Willett, P. Similarity searching using 2D structural fingerprints. Chemoinform. Comput. Chem. Biol. 2010, 672, 133–158. [Google Scholar]
  41. Spackman, M.A.; McKinnon, J.J. Fingerprinting intermolecular interactions in molecular crystals. CrystEngComm 2002, 4, 378–392. [Google Scholar] [CrossRef]
  42. Chu, H.; He, Q.-X.; Wang, J.; Hu, Y.; Wang, Y.-Q.; Lin, Z.-H. In silico design of novel benzohydroxamate-based compounds as inhibitors of histone deacetylase 6 based on 3D-QSAR, molecular docking, and molecular dynamics simulations. New J. Chem. 2020, 44, 21201–21210. [Google Scholar] [CrossRef]
  43. Ieritano, C.; Campbell, J.L.; Hopkins, W.S. Predicting differential ion mobility behaviour in silico using machine learning. Analyst 2021, 146, 4737–4743. [Google Scholar] [CrossRef]
  44. Taha, M.; Ismail, N.H.; Ali, M.; Rashid, U.; Imran, S.; Uddin, N.; Khan, K.M. Molecular hybridization conceded exceptionally potent quinolinyl-oxadiazole hybrids through phenyl linked thiosemicarbazide antileishmanial scaffolds: In silico validation and SAR studies. Bioorg. Chem. 2017, 71, 192–200. [Google Scholar] [CrossRef]
  45. Heikamp, K.; Bajorath, J. How do 2D fingerprints detect structurally diverse active compounds? Revealing compound subset-specific fingerprint features through systematic selection. J. Chem. Inf. Model. 2011, 51, 2254–2265. [Google Scholar] [CrossRef] [PubMed]
  46. Opo, F.A.D.M.; Rahman, M.M.; Ahammad, F.; Ahmed, I.; Bhuiyan, M.A.; Asiri, A.M. Structure based pharmacophore modeling, virtual screening, molecular docking and ADMET approaches for identification of natural anti-cancer agents targeting XIAP protein. Sci. Rep. 2021, 11, 4049. [Google Scholar] [CrossRef] [PubMed]
  47. Duan, J.; Dixon, S.L.; Lowrie, J.F.; Sherman, W. Analysis and comparison of 2D fingerprints: Insights into database screening performance using eight fingerprint methods. J. Mol. Graph. Model. 2010, 29, 157–170. [Google Scholar] [CrossRef] [PubMed]
  48. Sastry, M.; Lowrie, J.F.; Dixon, S.L.; Sherman, W. Large-scale systematic analysis of 2D fingerprint methods and parameters to improve virtual screening enrichments. J. Chem. Inf. Model. 2010, 50, 771–784. [Google Scholar] [CrossRef]
  49. Kogej, T.; Engkvist, O.; Blomberg, N.; Muresan, S. Multifingerprint based similarity searches for targeted class compound selection. J. Chem. Inf. Model. 2006, 46, 1201–1213. [Google Scholar] [CrossRef]
  50. Maggiora, G.; Vogt, M.; Stumpfe, D.; Bajorath, J. Molecular similarity in medicinal chemistry: Miniperspective. J. Med. Chem. 2014, 57, 3186–3204. [Google Scholar] [CrossRef]
  51. Muegge, I.; Mukherjee, P. An overview of molecular fingerprint similarity search in virtual screening. Expert Opin. Drug Discov. 2016, 11, 137–148. [Google Scholar] [CrossRef]
  52. Turchi, M.; Cai, Q.; Lian, G. An evaluation of in-silico methods for predicting solute partition in multiphase complex fluids—A case study of octanol/water partition coefficient. Chem. Eng. Sci. 2019, 197, 150–158. [Google Scholar] [CrossRef]
  53. Sullivan, K.M.; Enoch, S.J.; Ezendam, J.; Sewald, K.; Roggen, E.L.; Cochrane, S. An adverse outcome pathway for sensitization of the respiratory tract by low-molecular-weight chemicals: Building evidence to support the utility of in vitro and in silico methods in a regulatory context. Appl. Vitr. Toxicol. 2017, 3, 213–226. [Google Scholar] [CrossRef] [Green Version]
  54. Altamash, T.; Amhamed, A.; Aparicio, S.; Atilhan, M. Effect of hydrogen bond donors and acceptors on CO2 absorption by deep eutectic solvents. Processes 2020, 8, 1533. [Google Scholar] [CrossRef]
  55. Wan, Y.; Tian, Y.; Wang, W.; Gu, S.; Ju, X.; Liu, G. In silico studies of diarylpyridine derivatives as novel HIV-1 NNRTIs using docking-based 3D-QSAR, molecular dynamics, and pharmacophore modeling approaches. RSC Adv. 2018, 8, 40529–40543. [Google Scholar] [CrossRef] [PubMed]
  56. Escamilla-Gutiérrez, A.; Ribas-Aparicio, R.M.; Córdova-Espinoza, M.G.; Castelán-Vega, J.A. In silico strategies for modeling RNA aptamers and predicting binding sites of their molecular targets. Nucleosides Nucleotides Nucleic Acids 2021, 40, 798–807. [Google Scholar] [CrossRef] [PubMed]
  57. Kaushik, A.C.; Kumar, A.; Bharadwaj, S.; Chaudhary, R.; Sahi, S. Ligand-Based Approach for In-silico Drug Designing. In Bioinformatics Techniques for Drug Discovery; Springer: Berlin/Heidelberg, Germany, 2018; pp. 11–19. [Google Scholar]
  58. Jain, A.N. Morphological similarity: A 3D molecular similarity method correlated with protein-ligand recognition. J. Comput.-Aided Mol. Des. 2000, 14, 199–213. [Google Scholar] [CrossRef] [PubMed]
  59. Zhang, H.; Ren, J.-X.; Ma, J.-X.; Ding, L. Development of an in silico prediction model for chemical-induced urinary tract toxicity by using naïve Bayes classifier. Mol. Divers. 2019, 23, 381–392. [Google Scholar] [CrossRef]
  60. Norinder, U.; Bergström, C.A. Prediction of ADMET properties. ChemMedChem Chem. Enabling Drug Discov. 2006, 1, 920–937. [Google Scholar]
  61. Ferreira, L.L.; Andricopulo, A.D. ADMET modeling approaches in drug discovery. Drug Discov. Today 2019, 24, 1157–1165. [Google Scholar] [CrossRef]
  62. Idakwo, G.; Luttrell, J.; Chen, M.; Hong, H.; Zhou, Z.; Gong, P.; Zhang, C. A review on machine learning methods for in silico toxicity prediction. J. Environ. Sci. Health Part C 2018, 36, 169–191. [Google Scholar] [CrossRef]
  63. Raies, A.B.; Bajic, V.B. In silico toxicology: Computational methods for the prediction of chemical toxicity. Wiley Interdiscip. Rev. Comput. Mol. Sci. 2016, 6, 147–172. [Google Scholar] [CrossRef] [Green Version]
  64. Xia, X.; Maliski, E.G.; Gallant, P.; Rogers, D. Classification of kinase inhibitors using a Bayesian model. J. Med. Chem. 2004, 47, 4463–4470. [Google Scholar] [CrossRef] [Green Version]
  65. BIOVIA QSAR, ADMET and Predictive Toxicology. Available online: https://www.3dsbiovia.com/products/collaborative-science/biovia-discovery-studio/qsar-admet-and-predictive-toxicology.html (accessed on 18 August 2022).
  66. Venkatapathy, R.; Wang, N.C.Y.; Martin, T.M.; Harten, P.F.; Young, D. Structure–Activity Relationships for Carcinogenic Potential. Gen. Appl. Syst. Toxicol. 2009. [Google Scholar] [CrossRef]
  67. Goodrnan, G.; Wilson, R. Comparison of the dependence of the TD50 on maximum tolerated dose for mutagens and nonmutagens. Risk Anal. 1992, 12, 525–533. [Google Scholar] [CrossRef] [PubMed]
  68. Council, N.R. Correlation Between Carcinogenic Potency and the Maximum Tolerated Dose: Implications for Risk Assessment. In Issues in Risk Assessment; National Academies Press (US): Washington, DC, USA, 1993. [Google Scholar]
  69. Gonella Diaza, R.; Manganelli, S.; Esposito, A.; Roncaglioni, A.; Manganaro, A.; Benfenati, E. Comparison of in silico tools for evaluating rat oral acute toxicity. SAR QSAR Environ. Res. 2015, 26, 1–27. [Google Scholar] [CrossRef] [PubMed]
  70. Pizzo, F.; Benfenati, E. In silico models for repeated-dose toxicity (RDT): Prediction of the no observed adverse effect level (NOAEL) and lowest observed adverse effect level (LOAEL) for drugs. In In Silico Methods for Predicting Drug Toxicity; Springer: Berlin/Heidelberg, Germany, 2016; pp. 163–176. [Google Scholar]
  71. Venkatapathy, R.; Moudgal, C.J.; Bruce, R.M. Assessment of the oral rat chronic lowest observed adverse effect level model in TOPKAT, a QSAR software package for toxicity prediction. J. Chem. Inf. Comput. Sci. 2004, 44, 1623–1629. [Google Scholar] [CrossRef] [PubMed]
  72. Wilhelmus, K.R. The Draize eye test. Surv. Ophthalmol. 2001, 45, 493–515. [Google Scholar] [CrossRef]
  73. Pillay, C.C.; Jäger, A.K.; Mulholland, D.A.; Van Staden, J. Cyclooxygenase inhibiting and anti-bacterial activities of South African Erythrina species. J. Ethnopharmacol. 2001, 74, 231–237. [Google Scholar] [CrossRef]
  74. Oyama, S.D.O.; Souza, L.A.D.; Baldoqui, D.C.; Sarragiotto, M.H.; Silva, A.A. Prenylated flavonoids from Maclura tinctoria fruits. Química Nova 2013, 36, 800–802. [Google Scholar] [CrossRef] [Green Version]
  75. Delle Monache, G.; De Rosa, M.C.; Scurria, R.; Vitali, A.; Cuteri, A.; Monacelli, B.; Pasqua, G.; Botta, B. Comparison between metabolite productions in cell culture and in whole plant of Maclura pomifera. Phytochemistry 1995, 39, 575–580. [Google Scholar] [CrossRef]
  76. Atta-ur-Rahman. Studies in Natural Products Chemistry: Indices Part A; Elsevier Science: Amsterdam, The Netherlands, 1997. [Google Scholar]
  77. Akhtar, A.; Hussain, W.; Rasool, N. Probing the pharmacological binding properties, and reactivity of selective phytochemicals as potential HIV-1 protease inhibitors. Univ. Sci. 2019, 24, 441–464. [Google Scholar] [CrossRef]
  78. Sousa, S.F.; Fernandes, P.A.; Ramos, M.J. Protein–ligand docking: Current status and future challenges. Proteins Struct. Funct. Bioinform. 2006, 65, 15–26. [Google Scholar] [CrossRef]
  79. Hollingsworth, S.A.; Dror, R.O. Molecular dynamics simulation for all. Neuron 2018, 99, 1129–1143. [Google Scholar] [CrossRef] [Green Version]
  80. Liu, X.; Shi, D.; Zhou, S.; Liu, H.; Liu, H.; Yao, X. Molecular dynamics simulations and novel drug discovery. Expert Opin. Drug Discov. 2018, 13, 23–37. [Google Scholar] [CrossRef] [PubMed]
  81. Hansson, T.; Oostenbrink, C.; van Gunsteren, W. Molecular dynamics simulations. Curr. Opin. Struct. Biol. 2002, 12, 190–196. [Google Scholar] [CrossRef]
  82. Kuzmanic, A.; Zagrovic, B. Determination of ensemble-average pairwise root mean-square deviation from experimental B-factors. Biophys. J. 2010, 98, 861–871. [Google Scholar] [CrossRef] [Green Version]
  83. Liu, P.; Lu, J.; Yu, H.; Ren, N.; Lockwood, F.E.; Wang, Q.J. Lubricant shear thinning behavior correlated with variation of radius of gyration via molecular dynamics simulations. J. Chem. Phys. 2017, 147, 084904. [Google Scholar] [CrossRef]
  84. Kumar, K.; Anbarasu, A.; Ramaiah, S. Molecular docking and molecular dynamics studies on β-lactamases and penicillin binding proteins. Mol. Biosyst. 2014, 10, 891–900. [Google Scholar] [CrossRef] [PubMed]
  85. Mitternacht, S. FreeSASA: An open source C library for solvent accessible surface area calculations. F1000Research 2016, 5, 189. [Google Scholar] [CrossRef] [PubMed]
  86. Naik, V.R.; Munikumar, M.; Ramakrishna, U.; Srujana, M.; Goudar, G.; Naresh, P.; Kumar, B.N.; Hemalatha, R.J. Dynamics, Remdesivir (GS-5734) as a therapeutic option of 2019-nCOV main protease–in silico approach. J. Biomol. Struct. Dyn. 2021, 39, 4701–4714. [Google Scholar] [CrossRef]
  87. Magro, P.; Zanella, I.; Pescarolo, M.; Castelli, F.; Quiros-Roldan, E.J.B.J. Lopinavir/ritonavir: Repurposing an old drug for HIV infection in COVID-19 treatment. Biomed. J. 2021, 44, 43–53. [Google Scholar] [CrossRef]
  88. Alexpandi, R.; De Mesquita, J.F.; Pandian, S.K.; Ravi, A.V. Quinolines-based SARS-CoV-2 3CLpro and RdRp inhibitors and Spike-RBD-ACE2 inhibitor for drug-repurposing against COVID-19: An in silico analysis. Front. Microbiol. 2020, 11, 1796. [Google Scholar] [CrossRef]
  89. Elmezayen, A.D.; Al-Obaidi, A.; Şahin, A.T.; Yelekçi, K. Dynamics, Drug repurposing for coronavirus (COVID-19): In silico screening of known drugs against coronavirus 3CL hydrolase and protease enzymes. J. Biomol. Struct. Dyn. 2021, 39, 2980–2992. [Google Scholar] [CrossRef] [Green Version]
  90. Keretsu, S.; Bhujbal, S.P.; Cho, S.J. Rational approach toward COVID-19 main protease inhibitors via molecular docking, molecular dynamics simulation and free energy calculation. Sci. Rep. 2020, 10, 17716. [Google Scholar] [CrossRef] [PubMed]
  91. Koulgi, S.; Jani, V.; Uppuladinne, M.; Sonavane, U.; Nath, A.K.; Darbari, H.; Joshi, R. Dynamics, Drug repurposing studies targeting SARS-CoV-2: An ensemble docking approach on drug target 3C-like protease (3CLpro). J. Biomol. Struct. Dyn. 2021, 39, 5735–5755. [Google Scholar] [CrossRef] [PubMed]
  92. Eissa, I.H.; Khalifa, M.M.; Elkaeed, E.B.; Hafez, E.E.; Alsfouk, A.A.; Metwaly, A.M. In Silico Exploration of Potential Natural Inhibitors against SARS-Cov-2 nsp10. Molecules 2021, 26, 6151. [Google Scholar] [CrossRef]
  93. Elkaeed, E.B.; Youssef, F.S.; Eissa, I.H.; Elkady, H.; Alsfouk, A.A.; Ashour, M.L.; El Hassab, M.A.; Abou-Seri, S.M.; Metwaly, A.M. Multi-Step In Silico Discovery of Natural Drugs against COVID-19 Targeting Main Protease. Int. J. Mol. Sci. 2022, 23, 6912. [Google Scholar] [CrossRef]
  94. Elkaeed, E.B.; Eissa, I.H.; Elkady, H.; Abdelalim, A.; Alqaisi, A.M.; Alsfouk, A.A.; Elwan, A.; Metwaly, A.M. A Multistage In Silico Study of Natural Potential Inhibitors Targeting SARS-CoV-2 Main Protease. Int. J. Mol. Sci. 2022, 23, 8407. [Google Scholar] [CrossRef] [PubMed]
  95. 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. [Google Scholar] [CrossRef]
  96. Eissa, I.H.; Alesawy, M.S.; Saleh, A.M.; Elkaeed, E.B.; Alsfouk, B.A.; El-Attar, A.-A.M.M.; Metwaly, A.M. Ligand and Structure-Based In Silico Determination of the Most Promising SARS-CoV-2 nsp16-nsp10 2′-o-Methyltransferase Complex Inhibitors among 3009 FDA Approved Drugs. Molecules 2022, 27, 2287. [Google Scholar]
  97. Elkaeed, E.B.; Elkady, H.; Belal, A.; Alsfouk, B.A.; Ibrahim, T.H.; Abdelmoaty, M.; Arafa, R.K.; Metwaly, A.M.; Eissa, I.H. Multi-Phase In Silico Discovery of Potential SARS-CoV-2 RNA-Dependent RNA Polymerase Inhibitors among 3009 Clinical and FDA-Approved Related Drugs. Processes 2022, 10, 530. [Google Scholar] [CrossRef]
  98. Metwaly, A.M.; Elwan, A.; El-Attar, A.-A.M.M.; Al-Rashood, S.T.; Eissa, I.H. Structure-Based Virtual Screening, Docking, ADMET, Molecular Dynamics, and MM-PBSA Calculations for the Discovery of Potential Natural SARS-CoV-2 Helicase Inhibitors from the Traditional Chinese Medicine. J. Chem. 2022, 2022, 7270094. [Google Scholar] [CrossRef]
  99. Alesawy, M.S.; Elkaeed, E.B.; Alsfouk, A.A.; Metwaly, A.M.; Eissa, I. In Silico Screening of Semi-Synthesized Compounds as Potential Inhibitors for SARS-CoV-2 Papain-Like Protease: Pharmacophoric Features, Molecular Docking, ADMET, Toxicity and DFT Studies. Molecules 2021, 26, 6593. [Google Scholar] [CrossRef]
Metabolites 12 01122 g001 550
Figure 1. Molecular similarity analysis of the African metabolites and TTT.
Figure 1. Molecular similarity analysis of the African metabolites and TTT.
Metabolites 12 01122 g001
Metabolites 12 01122 g002 550
Figure 2. The most similar African metabolites to TTT.
Figure 2. The most similar African metabolites to TTT.
Metabolites 12 01122 g002
Metabolites 12 01122 g003 550
Figure 3. Superimposition of the co-crystallized ligand pose (pink) and the docking pose (wheat).
Figure 3. Superimposition of the co-crystallized ligand pose (pink) and the docking pose (wheat).
Metabolites 12 01122 g003
Metabolites 12 01122 g004 550
Figure 4. The proposed binding pattern of TTT against the PLpro active site.
Figure 4. The proposed binding pattern of TTT against the PLpro active site.
Metabolites 12 01122 g004
Metabolites 12 01122 g005 550
Figure 5. The proposed binding pattern of compound 2195.
Figure 5. The proposed binding pattern of compound 2195.
Metabolites 12 01122 g005
Metabolites 12 01122 g006 550
Figure 6. The proposed binding pattern of compound 1952.
Figure 6. The proposed binding pattern of compound 1952.
Metabolites 12 01122 g006
Metabolites 12 01122 g007 550
Figure 7. The proposed binding pattern of compound 2982.
Figure 7. The proposed binding pattern of compound 2982.
Metabolites 12 01122 g007
Metabolites 12 01122 g008 550
Figure 8. The proposed binding pattern of compound 1330.
Figure 8. The proposed binding pattern of compound 1330.
Metabolites 12 01122 g008
Metabolites 12 01122 g009 550
Figure 9. ADMET profile of the African metabolites and the reference.
Figure 9. ADMET profile of the African metabolites and the reference.
Metabolites 12 01122 g009
Metabolites 12 01122 g010 550
Figure 10. RMSD value during MD runs. (Red: (wighteone), blue: (PLpro), black: (PLpro- wighteone complex).
Figure 10. RMSD value during MD runs. (Red: (wighteone), blue: (PLpro), black: (PLpro- wighteone complex).
Metabolites 12 01122 g010
Metabolites 12 01122 g011 550
Figure 11. RMSF of PLpro in the MD run.
Figure 11. RMSF of PLpro in the MD run.
Metabolites 12 01122 g011
Metabolites 12 01122 g012 550
Figure 12. The radius of gyration of PLpro in the MD run.
Figure 12. The radius of gyration of PLpro in the MD run.
Metabolites 12 01122 g012
Metabolites 12 01122 g013 550
Figure 13. SASA of PLpro in the MD run.
Figure 13. SASA of PLpro in the MD run.
Metabolites 12 01122 g013
Table
Table 1. Fingerprint similarity between the tested African metabolites and TTT.
Table 1. Fingerprint similarity between the tested African metabolites and TTT.
CompoundSimilaritySASBSCCompoundSimilaritySASBSC
TTT1.0004540034480.632653279−13175
25380.758322−2913236470.63247929614158
35180.747324−201302920.6322494472537
33230.743324−1813017950.632054280−11174
29820.743329−1112534140.631699554423−100
29810.738327−1112722590.631188255−50199
1820.732314−2514030400.6303532460130
26770.720317−1413711570.63008131038144
25580.7124421671211410.629797279−11175
25540.710316−913821800.62963255−49199
18750.710320−31342030.628889283−4171
1970.7083341812034130.628831554427−100
11680.703298−3015621080.628062282−5172
25560.703298−3015613320.6280092873167
10010.702297−3115730390.62790732462130
1650.701303−2215134200.627273276−14178
22210.7013281412630850.626506260−39194
25790.700301−2415331150.62622332057134
45790.699588387−13411540.6255412898165
11950.698296−3015811530.6255412898165
9000.698296−3015811690.625282277−11177
21970.69735555991610.625282277−11177
2120.697306−1514811400.625282277−11177
2110.697306−1514825880.625282277−11177
25780.694300−2215445980.622562877167
2050.69131861368480.622517282−1172
25550.688296−2415811550.622032889166
30790.6873981255634120.620843280−3174
45730.6874171533721370.620536278−6176
25570.686308−514624070.62045135812396
45720.6864101444411470.62279−4175
3421131101436370.61904832571129
22021282−4217222010.61889338016074
2131311114321990.61848736814186
2067144920651890.617849270−17184
1261294−2116011560.6175331048144
20701470239−1622030.617021261−31193
13301293−2116126850.616725354120100
1681301−915319920.616279265−24189
457514171633734690.615246−54208
113213871196748790.61423232880126
34190.673289305−114920.614191277−3177
11330.6730433871216739240.613333276−4178
1900.67177230731477130.6126912803174
13310.670507291−2016322060.6101322770177
1810.67033305114919520.609977269−13185
10000.67030630741479900.609865272−8182
1630.668161298−815633920.609865272−8182
29580.667814388127661660.609589267−16187
39230.667431291−1816334110.607692553456−99
9261304315021890.606762341108113
34731304315034450.605905472325−18
219813751107945800.60414467319−13
20651455231−118610.60414467319−13
2041290−1716418590.60414467319−13
339513152113934100.603712553462−99
2151291−1316345770.603359467320−13
457114051604916420.60279430247152
1690.658314289−1516516430.60279430247152
31140.6575053111914316440.60279430247152
33940.6556023162813821070.60268731467140
1980.655012281−251736710.60263732077134
12120.655012281−2517345780.602581467321−13
31240.6532773091914545810.602581467321−13
30980.6532773091914519770.602076348124106
22270.652268302915234420.60177272−2182
24710.65299615521940.60164329333161
34440.649886284−1717024670.600887542448−88
36290.649874258−5719626360.600751480345−26
1990.648402284−1617026350.600751480345−26
113714191933547500.600742486355−32
113614191933524960.600671537440−83
113814191933548700.60037232384131
11391419193356920.59958929233162
14640.647208255−6019921950.59958929233162
45740.6469654051724945490.599567554470−100
17681357989745510.599341546457−92
37541486298−3211430.5978022721182
22221285−1316941280.5978022721182
339613133114113520.5978022721182
1550.6451613001115446010.5978022721182
1670.644295288−716621180.5978022721182
28881284−1317026670.59689930862146
42660.643991284−1317044860.5964912722182
47590.643392258−5319631180.596429334106120
11180.642132253−6020130730.595133269−2185
31130.642127314351402270.5947712735181
39250.641553281−161732280.5947712735181
22200.6402443153813930710.5947142700184
48991282−1317245700.5947142700184
2791282−1317248970.5947142700184
14471282−1317226840.594454343123111
15591282−1317244850.59442127712177
2810.639456282−131722780.594298542458−88
1640.639269280−1617448960.5934072701184
44680.6390983407811411170.5934072701184
11340.6380664091874511520.59325429950155
11350.6380664091874514480.59314827713177
220013761387841290.59314827713177
19490.634033272−2518213290.59314827713177
45920.63304729512159
SA: The number of bits in both TTT and the target. SB: The number of bits in the target but not TTT. SC: The number of bits in TTT but not the target.
Table
Table 2. Molecular structural properties of the investigated compounds and TTT.
Table 2. Molecular structural properties of the investigated compounds and TTT.
CompoundALog pM. WtHBAHBDRotatable BondsRingsAromatic RingsMFPSAMinimum Distance
1262.73272.30422320.220.731
1652.84270.28412320.2110.731
25792.88268.26412320.2150.730
1822.71256.30312320.1510.718
1892.41267.28311430.2160.696
22033.24252.27220430.2440.661
2044.18270.32312320.1390.660
1643.46272.30422320.220.659
33953.55298.33424320.2290.656
1813.18300.35413320.1530.652
22023.48236.27110430.1670.649
21374.14266.33222320.1450.619
33963.64284.35324320.1730.587
2123.21265.26311430.2070.556
2113.21265.26311430.2070.556
2133.43279.29302430.1480.545
21973.31323.39426330.210.545
18754.69264.32221330.1470.502
34213.01281.31312430.1570.493
34123.09228.24332220.2640.843
29823.29270.32326220.2040.822
1662.63242.23412320.2450.816
33233.14222.24201320.1230.815
1973.29262.31102440.1160.811
2152.69223.23300430.1540.809
29814.37252.31215220.1390.807
1982.32257.28423320.1960.788
34692.46182.22110330.1540.785
34443.04268.26411320.2410.781
25783.20270.28424220.2390.774
13303.20270.28424220.2390.774
13313.20270.28424220.2390.774
21954.00338.35533320.2570.772
30404.83350.41424320.1850.768
45982.86298.29513320.220.761
35182.99226.27211320.1330.757
1682.98251.24320430.2780.754
26773.30265.31302320.1070.743
1992.73271.31414320.140.741
19523.88230.26322220.2050.739
TTT3.65304.39223330.171
Table
Table 3. The calculated ΔG values of the African metabolites and TTT.
Table 3. The calculated ΔG values of the African metabolites and TTT.
CompoundΔGCompoundΔG
126−10.702982−11.85
165−9.81166−7.96
2579−9.173323−10.11
182−12.74197−9.72
189−8.65215−13.18
2203−8.142981−13.72
204−12.21198−8.01
164−13.223469−5.40
3395−7.623444−8.88
181−11.552578−9.51
2202−9.221330−12.20
2137−9.981331−14.09
3396−14.142195−16.52
212−12.393040−14.25
211−10.354598−10.13
213−12.633518−7.54
2197−13.10168−16.41
1875−8.652677−9.54
3421−7.76199−10.79
3412−14.001952−12.93
TTT−9.30
Table
Table 4. Predicted ADMET descriptors for the African metabolites and the reference.
Table 4. Predicted ADMET descriptors for the African metabolites and the reference.
CompoundBBB Level aHIA bAq cCYP2D6 dPPB e
164203ft
181102tt
182103tt
204102tt
212102ft
213102ft
215102tt
1330203ft
1331203ft
1952103ft
2195202tt
2197202tt
2981102ft
2982203tt
3040102ft
3396103ft
3412203ft
Remdesivir433ff
a BBB, Ability to pass the blood-brain barrier, 1 is high, 2 is medium, 3 is low, 4 is very low. b HIA, human intestinal absorption level, 0 is good, 1 is moderate, 2 is poor, and 3 is very poor. c Aq, Aqueous solubility level, 0 is extremely low, 1 is very low, 2 is low, 3 is good, and 4 is optimal. d CYP2D6, inhibition of CYP2D6 enzyme, t is an inhibitor, f is a non-inhibitor. e PPB, f means less than 90%, t means more than 90%.
Table
Table 5. Toxicity properties of filtered African metabolites and the reference.
Table 5. Toxicity properties of filtered African metabolites and the reference.
CompoundFDA Rat
Carcinogenicity		TD50
(Rat) a		MTD bRat Oral LD50 bLOAEL bOcular
Irritancy		Skin
Irritancy
Hippacine (164)Not carcinogenic63.0190.2850.4410.052MildNone
Naamine D (2197)Not carcinogenic4.0220.0862.4400.015ModerateNone
(±)-Enterofuran (3412)Carcinogenic87.4840.6902.4830.089SevereNone
Daphnelone (2982)Carcinogenic184.7230.8290.6460.173SevereNone
4,2′-dihydroxy-4′-methoxychalcone (1330)Not carcinogenic259.5320.3201.0100.060NoneNone
2′,5′-dihydroxy-4-methoxychalcone (1331)Not carcinogenic259.5320.3201.0100.060MildNone
Wighteone (2195)Not carcinogenic42.5730.5250.9620.053SevereNone
RemdesivirNot carcinogenic9.2460.2350.3090.004MildMild
a Unit: mg kg−1 day−1. b Unit: g. kg−1.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Elkaeed, E.B.; Khalifa, M.M.; Alsfouk, B.A.; Alsfouk, A.A.; El-Attar, A.-A.M.M.; Eissa, I.H.; Metwaly, A.M. The Discovery of Potential SARS-CoV-2 Natural Inhibitors among 4924 African Metabolites Targeting the Papain-like Protease: A Multi-Phase In Silico Approach. Metabolites 2022, 12, 1122. https://doi.org/10.3390/metabo12111122

AMA Style

Elkaeed EB, Khalifa MM, Alsfouk BA, Alsfouk AA, El-Attar A-AMM, Eissa IH, Metwaly AM. The Discovery of Potential SARS-CoV-2 Natural Inhibitors among 4924 African Metabolites Targeting the Papain-like Protease: A Multi-Phase In Silico Approach. Metabolites. 2022; 12(11):1122. https://doi.org/10.3390/metabo12111122

Chicago/Turabian Style

Elkaeed, Eslam B., Mohamed M. Khalifa, Bshra A. Alsfouk, Aisha A. Alsfouk, Abdul-Aziz M. M. El-Attar, Ibrahim H. Eissa, and Ahmed M. Metwaly. 2022. "The Discovery of Potential SARS-CoV-2 Natural Inhibitors among 4924 African Metabolites Targeting the Papain-like Protease: A Multi-Phase In Silico Approach" Metabolites 12, no. 11: 1122. https://doi.org/10.3390/metabo12111122

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop