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Article

Multi-Phase In Silico Discovery of Potential SARS-CoV-2 RNA-Dependent RNA Polymerase Inhibitors among 3009 Clinical and FDA-Approved Related Drugs

by
Eslam B. Elkaeed
1,
Hazem Elkady
2,
Amany Belal
3,
Bshra A. Alsfouk
4,
Tuqa H. Ibrahim
5,6,
Mohamed Abdelmoaty
5,
Reem K. Arafa
5,6,
Ahmed M. Metwaly
7,8,* and
Ibrahim H. Eissa
2,*
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 Chemistry, College of Pharmacy, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
4
Department of Pharmaceutical Sciences, College of Pharmacy, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
5
Drug Design and Discovery Laboratory, Zewail City of Science and Technology, Cairo 12578, Egypt
6
Biomedical Sciences Program, University of Science and Technology, Zewail City of Science and Technology, Cairo 12578, Egypt
7
Pharmacognosy and Medicinal Plants Department, Faculty of Pharmacy (Boys), Al-Azhar University, Cairo 11884, Egypt
8
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.
Processes 2022, 10(3), 530; https://doi.org/10.3390/pr10030530
Submission received: 9 February 2022 / Revised: 24 February 2022 / Accepted: 1 March 2022 / Published: 7 March 2022
(This article belongs to the Special Issue Natural Products for Drug Discovery and Development)
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Abstract

:
Proceeding our prior studies of SARS-CoV-2, the inhibitory potential against SARS-CoV-2 RNA-dependent RNA polymerase (RdRp) has been investigated for a collection of 3009 clinical and FDA-approved drugs. A multi-phase in silico approach has been employed in this study. Initially, a molecular fingerprint experiment of Remdesivir (RTP), the co-crystallized ligand of the examined protein, revealed the most similar 150 compounds. Among them, 30 compounds were selected after a structure similarity experiment. Subsequently, the most similar 30 compounds were docked against SARS-CoV-2 RNA-dependent RNA polymerase (PDB ID: 7BV2). Aloin 359, Baicalin 456, Cefadroxil 1273, Sophoricoside 1459, Hyperoside 2109, and Vitexin 2286 exhibited the most precise binding modes, as well as the best binding energies. To confirm the obtained results, MD simulations experiments have been conducted for Hyperoside 2109, the natural flavonoid glycoside that exhibited the best docking scores, against RdRp (PDB ID: 7BV2) for 100 ns. The achieved results authenticated the correct binding of 2109, showing low energy and optimum dynamics. Our team presents these outcomes for scientists all over the world to advance in vitro and in vivo examinations against COVID-19 for the promising compounds.

1. Introduction

The WHO reported on 4 February 2022 that the confirmed global infected cases of SARS-CoV-2 were 386,548,962. Unfortunately, 5,705,754 of that total passed away [1]. To respond to these alarming numbers, extensive work is required from scientists globally to discover a cure.
The FDA approval of any drug means that the drug’s effect and side effects have been judged by the Center for Drug Evaluation and Research (CDER) in the FDA [2]. Accordingly, FDA approval indicates the efficacy and the general safety of that drug [3]. Hence, FDA-approved drugs could be an invaluable source in drug discovery, as they can be repurposed to be utilized for alternate functions [4]. Whereas the traditional drug discovery process costs an average of 2.6 billion USD over twelve years [5], computational or in silico screening can be utilized efficiently and effectively to discover new drugs [6,7].
Ligand-based virtual screening is an in silico approach in which the software utilizes the chemical structure of an active molecule as a reference. This reference is utilized and based on the principles of the Structure–Activity Relationship (SAR), which anticipates the activity of other molecules with unidentified or different known activity [8]. Ligand-based in silico methods have been employed across different fields of drug design and discovery, such as in molecular design [9,10], rational drug design [11,12,13,14,15,16], computational chemistry [17,18], docking [19,20], DFT [21,22,23] evaluation, toxicity [24,25,26], and ADME-Tox [27,28,29]. In addition, molecular dynamic simulation is considered one of the most efficient computational techniques that confirms the affinity of a compound to a particular receptor [30,31].
Our team utilized ligand-based in silico methods to discover potential inhibitors for COVID-19 essential enzymes. We described the potential inhibitions of a big group of isoflavonoids [32] in addition to the natural metabolites that were isolated from Monanchora sp. [33] and Artemisia sublessingiana [34]. Likewise, we reported a multi-stage in silico method (ligand- and structure-based) to identify the best potential SARS-CoV-2 nsp10 inhibitor of 310 antiviral natural compounds [35]. The same method decided the most potential semisynthetic compound of 69 ligands against SARS-CoV-2 PLpro [36].
In this manuscript, 3009 clinical and FDA-approved drugs have been utilized as an exam group to explore the most potent SARS-CoV-2 RdRp inhibitors, depending on a multi-stage in silico method (ligand- and structure-based). All the tested drugs were obtained from approved institutions such as the FDA (U.S. Food and Drug Administration, Sliver Spring, MD, USA), EMA (European Medicines Agency, Amsterdam, The Netherlands, European), HMA (Heads of Medicines Agency, Amsterdam, The Netherlands, European), CFDA (China Food and Drug Administration, Beijing, China), PMDA (Pharmaceuticals and Medical Devices Agency, Tokyo, Japan), pharmacopeias such as USP, BP, EP, JP, and Ph, or from Selleckchem.com (https://www.selleckchem.com/screening/fda-approved-drug-library.html, accessed on 1 October 2021) The employed method started with the similarity detection of the test group with the co-crystallized ligand (RTP) of SARS-CoV-2 RdRp, utilizing molecular fingerprint and structure similarity studies. Then, the binding modes of the selected compounds were examined by molecular docking and confirmed by molecular dynamic (MD) simulation experiments.

2. Results and Discussion

2.1. Molecular Fingerprint Study

Molecular fingerprinting is a type of ligand-based in silico study that links the biological activities of the tested molecules to their chemical structures [37]. It is based on the scientific basics of the Structure–Activity Relationship (SAR). Agreeing with the principle of SAR, the likeness in the chemical structure of two molecules is predicted to be linked to a likeness in bioactivity [38]. We herein considered the co-crystallized ligand, RTP, as a reference due to its high binding affinity with SARS-CoV-2 RdRp (PDB ID: 7BV2). Consequently, molecules that have similar chemical structures to RTP are predicted to exhibit a high binding affinity that inhibits the target protein.
In the fingerprint study, the software extracts chemical and physical descriptors of the examined and reference molecules, and the presence and/or the absence of these descriptors is calculated for all atoms. The calculation of the tested descriptors is performed by converting it to bit strings (mathematical symbols). The obtained strings are used to compare and expect the likeness [39,40]. Discovery Studio software has been employed to reveal the similarity of the fingerprints of RTP with 3009 FDA-approved drugs. The experiment was adapted to select the highest 5% (150) of compounds in similarity (Table 1). The following descriptors were investigated in the atoms as well as fragments of the examined molecules and RTP: H-bond acceptors [41], H-bond donors [42], charges [43], hybridization [44], positive ionizable atoms [45], negative ionizable atoms [46], halogens [47], and aromatic groups [48] aligned with the ALogP [49].

2.2. Molecular Similarity

Molecular similarity is also another kind of ligand-based in silico study. The molecular similarity study examines the whole chemical structure of both the reference molecule and the experiment set. The study computes different descriptors, which may be topological, electronic, steric, and/or physical [50]. In contrast, the fingerprint study computes the descriptors in atoms or substructures [51]. The selected 150 FDA-approved drugs, after the molecular fingerprint experiment, were subjected to the molecular similarity study of RTP, using Discovery studio software. The tested descriptors (Figure 1 and Table 2) were partition coefficient (ALog p) [52], molecular weight (M. W) [53], H-bond donors (HBA) [54], H-bond acceptors (HBD) [55], rotatable bonds number (RB) [56], number of rings (R) and aromatic rings (AR) [57], minimum distance (MD) [58], and the molecular fractional polar surface area (MFPSA) [59]. The study revealed the most similar 1% compounds (30) (Figure 2).

2.3. Docking Studies

The thirty most similar FDA-approved drugs to Remdesivir, the co-crystallized ligand of SARS-CoV-2 RdRp, were docked against the target protein. The carried-out study aims to examine the ability of the selected compounds to bind to and inhibit SARS-CoV-2 RdRp (PDB ID: 7BV2). The study also investigated the binding free energies as well as the binding modes of the examined FDA-approved drugs. Table 3 illustrates the calculated ΔG (binding free energies) of the tested compounds and the reference drug (Remdesivir) against SARS-CoV-2 RdRp.
The docking approach was validated by re-docking Remdesivir against the RdRp- active site. The validation step established the protocol’s applicability, as demonstrated by the small RMSD (1.29 Å) between the re-docked pose and the co-crystallized one (Figure 3).
At first, to understand the docking modes, we have to understand the structure of the SARS-CoV-2 RdRp enzyme. RdRp consists of three main parts. Firstly, an ATP-binding site that is represented by a network of different amino acids, including the key amino acid residue (Arg555). Secondly, an RNA primer that is represented by many nucleotides including uridine 20 (U20), uridine 10 (U10), and adenine 11 (A11). Finally, a pyrophosphate group (POP1003).
The mode of binding of RTP inside the SARS-CoV-2 RdRp is illustrated in Figure 4. It was noticed that RTP interacted with the active site via the formation of four hydrogen bonds (H-bonds), four hydrophobic interactions, and five electrostatic interactions. In detail, the pyrrolo[2,1-f][1,2,4]triazin-4-amine moiety interacted with RNA primer, forming four hydrophobic interactions with A11 and U20 and one H-bond with U10. Moreover, the 5-cyano-3,4-dihydroxytetrahydrofuran-2-yl)methyl moiety formed one H- bond with U20. Finally, the dihydrogen phosphate moiety occupied the ATP-binding site, forming one H-bond and one electrostatic interaction with Arg555. Additionally, it formed one H-bond with U20 and two electrostatic interactions with the pyrophosphate group.
The proposed binding mode of 359 revealed an affinity value of −23.11 kcal/mol. The 3,4,5-trihydroxy-6-(hydroxymethyl)tetrahydro-2H-pyran moiety was buried in the ATP binding site to form one H-bonds with the key amino acid Arg555. It also interacted with the RNA primer via the formation of two H-bonds with U10. On the other hand, 1,8-dihydroxyanthracen-9(10H)-one interacted with the pyrophosphate group via a couple of H-bonds. Moreover, it formed one electrostatic interaction with Arg555 and one pi–pi interaction with U20 (Figure 5).
Compound 456 exerted a binding affinity of -20.52 kcal/mol. It was noticed that 3,4,5-trihydroxytetrahydro-2H-pyran-2-carboxylic acid moiety formed two H-bonds with the key amino acid residues in the ATP binding site (Arg555 and Asp760). Additionally, it formed one H-bond with U20 in the RNA primer and another H-bond with pyrophosphate group. Moreover, the 5,6-dihydroxy-4-oxo-2-phenyl-4H-chromen moiety was incorporated in the RNA primer, forming two H-bonds and two hydrophobic interactions with U10 and A11, respectively. Additionally, it formed two electrostatic interactions with Arg555 in the ATP-binding site (Figure 6).
The docking simulation of compound 2109 revealed that it fit well into the enzyme active site, with a docking score of -24.46 kcal/mol. The ATP-binding site was occupied by the 3-(3,4,5-trihydroxy-6-(hydroxymethyl)tetrahydro-2H-pyran-2-yl)oxy) moiety through the formation of four H-bonds with Arg555, Asp760, Asp691, and Ser759. Moreover, the RNA primer was occupied with 2-(3,4-dihydroxyphenyl)4H-chromen-4-one moiety via formation of one H-bond, one electrostatic, and one hydrophobic interaction with U10 and U20, respectively. Likewise, the later moiety formed one H-bond with the pyrophosphate group via its 7-hydroxy group. Such a binding pattern encourages us to study the MD simulation of this member over the rest of the candidates (Figure 7).
Compound 2286 displayed the highest binding energy score among the series with ΔG = −25.00 kcal/mol. This high binding affinity is presumably attributed to the formation of many hydrophobic, electrostatic, and H-bonding interactions. The chromen-4-one moiety interacted with the RNA primer via the formation of four pi–pi interactions with the key nucleotide U20 and A11. Moreover, it reacted with Arg555 and Ser682, forming two electrostatic interactions and one H-bond, respectively. The sugar moiety (2H-pyran) formed two extra H-bonds with Asn691 and the pyrophosphate group (Figure 8).
The docking poses accomplished by compound 1273 (ΔG = −21.24 kcal/mol) produced key interactions in the RdRp active sites via the formation of seven H-bonds with U20, U10, Arg555, Asn691, and the Pyrophosphate group. Additionally, it formed one electrostatic with Arg555 and one pi–pi interaction with U20 (Figure 9).
As illustrated in Figure 10, compound 1459 (ΔG = −21.43 kcal/mol) possessed a significant potential binding affinity to the RdRp. It was buried in the ATP-binding site to form three H-bonds with Arg555, Thr680, and Cys622 and one pi–pi interaction with Cys622. Moreover, compound 1459 interacted with the RNA primer to form three H-bonds with U10 and U20. Finally, it formed one electrostatic interaction with the pyrophosphate group.

2.4. Molecular Dynamic Simulations

Compound 2109, Hyperoside, is a natural flavonoid of galactoside (Quercetin 3-galactoside) (Figure 11). Interestingly, the inhibitory effect of hyperoside against COVID-19 has recently been reported as a key molecule in the Chinese Qing-Fei-Pai-Du herbal formula [60]. Additionally, hyperoside inhibited HBV in vivo and in vitro through the inhibition of inhibitors of HBsAg and HBeAg, and decreased DHBV-DNA levels [61]. Additionally, hyperoside showed anti-inflammatory activities via the inhibition of the NF-κB signaling pathway [62].
The trajectory obtained from the 100 ns MD simulation was analyzed using GROMACS and VMD to check the integrity of the system and examine the stability and strength of hyperoside-SARS-CoV-2 RdRp binding throughout the simulation. Firstly, the radius of gyration of SARS-CoV-2 RdRp was estimated to range from 2.85 to 2.92 nm (Figure 12). The obtained values indicate that SARS-CoV-2 RdRp remained compact and stably folded throughout the simulation.
The RMSD profile of SARS-CoV-2 RdRp was found to be nearly invariable (Figure 13), implying that its structure is relatively stable during the simulation. The RMSD profile of hyperoside (Figure 14) implies only minor conformational and positional changes relative to the protein backbone. These results were confirmed by visualizing the trajectory using VMD.
Additionally, the SARS-CoV-2 RdRp-hyperoside interaction was analyzed to measure its strength as an indication of the ligand’s affinity towards the protein.
The Coulomb interaction (Coulomb force or electrostatic force) is a physical parameter that describes the magnitude of the electrostatic interaction force between two charged points. The Coulomb interaction is directly proportional to the electrical charge magnitudes and inversely proportional to the distance between them [63]. The energetics analysis showed that the average Coulombic interaction energy between hyperoside and SARS-CoV-2 RdRp was −131.994 kJ/mol (Figure 15).
Furthermore, Lennard-Jones energy was proposed by Sir John Edward Lennard-Jones and describes the potential interaction energy between two non-bonding molecules. Lennard-Jones energy computes the difference between several attractive forces, such as dipole–dipole and London interactions, as well as repulsive forces [64]. The average Lennard-Jones energy between hyperoside and SARS-CoV-2 RdRp was computed to be -67.0503 kJ/mol (Figure 16), indicating that hyperoside has a high affinity towards the RNA-dependent RNA polymerase.
For a closer look at the electrostatic interactions between hyperoside and SARS-CoV-2 RNA-dependent RNA polymerase, the VMD program was used to calculate the number of H-bonds formed over the course of the simulation. The analysis showed that during most of the simulation time, the number of stable H-bonds varies between 2 and 3, and reaches 4 during the last 35 ns of the simulation (Figure 17), indicating the strength of the SARS-CoV-2 RdRp-ligand binding.

3. Methods

3.1. Molecular Similarity Detection

Discovery studio 4.0 software was used (see method part in Supplementary data).

3.2. Fingerprint Studies

Discovery studio 4.0 software [65,66,67] was used (see method part in Supplementary data).

3.3. Docking Studies

Docking studies were performed with target enzymes using Discovery studio software [68,69] (see method part in Supplementary data).

3.4. Molecular Dynamics Simulation

The system was prepared using the web-based CHARMM-GUI [70,71,72,73] interface utilizing CHARMM36 force field and NAMD 2.13 packages [74]. The TIP3P explicit solvation model was used (See Supplementary data).

4. Conclusions

Among 3009 clinical and FDA-approved drugs, 5 (Aloin 359, Baicalin 456, Cefadroxil 1273, Sophoricoside 1459, Hyperoside 2109, and Vitexin 2286) were determined as the most potent inhibitors of SARS-CoV-2 RdRp(PDB ID: 7BV2). The study depended on a multi-phase in silico approach that included molecular fingerprint studies of RTP (the co-crystallized ligand of the examined protein), structure similarity experiments of RTP, molecular docking experiments of SARS-CoV-2 RdRp, and MD-simulation experiments for Hyperoside 2109 against SARS-CoV-2 RdRp for 100 ns. These results open a window of hope to find treatment through further in vitro and in vivo examinations for the determined compounds against COVID-19.

Supplementary Materials

The detailed experimental data can be downloaded at: https://www.mdpi.com/article/10.3390/pr10030530/s1.

Author Contributions

Conceptualization, A.M.M. and I.H.E.; Data curation, A.M.M. and I.H.E. Project administration, A.M.M. and I.H.E.; Supervision, A.M.M. and I.H.E.; Funding acquisition, A.B. and B.A.A.; Software, E.B.E., H.E., T.H.I., M.A. and R.K.A.; Writing—review & editing, E.B.E., A.B., B.A.A., A.M.M. 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. Amany Belal would like to thank Taif University Researchers Supporting Project number (TURSP-2020/35), Taif University, Taif, Saudi Arabia.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

All data is contained in the published article.

Acknowledgments

Amany Belal would like to thank Taif University Researchers Supporting Project number (TURSP-2020/35), Taif University, Taif, Saudi Arabia.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Structural similarity of the FDA-approved compounds and RTP.
Figure 1. Structural similarity of the FDA-approved compounds and RTP.
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Figure 2. The most similar thirty compounds to RTP.
Figure 2. The most similar thirty compounds to RTP.
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Figure 3. Superimposition of the co-crystallized pose (orange) and the re-docking pose (turquoise) of RTP in the active site of the RdRp.
Figure 3. Superimposition of the co-crystallized pose (orange) and the re-docking pose (turquoise) of RTP in the active site of the RdRp.
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Figure 4. (A) 3D binding mode of Remdesivir in the active site of RNA-dependent RNA polymerase. (B) 2D binding mode of Remdesivir in the active site of RNA-dependent RNA polymerase.
Figure 4. (A) 3D binding mode of Remdesivir in the active site of RNA-dependent RNA polymerase. (B) 2D binding mode of Remdesivir in the active site of RNA-dependent RNA polymerase.
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Figure 5. (A) 3D binding mode of compound 359 into 7BV2 active site. (B) 2D binding mode of compound 359 in the 7BV2 active site.
Figure 5. (A) 3D binding mode of compound 359 into 7BV2 active site. (B) 2D binding mode of compound 359 in the 7BV2 active site.
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Figure 6. (A) 3D binding mode of compound 456 into 7BV2 active site. (B) 2D binding mode of compound 456 in the 7BV2 active site.
Figure 6. (A) 3D binding mode of compound 456 into 7BV2 active site. (B) 2D binding mode of compound 456 in the 7BV2 active site.
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Figure 7. (A) 3D binding mode of compound 2109 into 7BV2 active site. (B) 2D binding mode of compound 2109 in the 7BV2 active site.
Figure 7. (A) 3D binding mode of compound 2109 into 7BV2 active site. (B) 2D binding mode of compound 2109 in the 7BV2 active site.
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Figure 8. (A) 3D binding mode of compound 2286 into 7BV2 active site. (B) 2D binding mode of compound 2286 in the 7BV2 active site.
Figure 8. (A) 3D binding mode of compound 2286 into 7BV2 active site. (B) 2D binding mode of compound 2286 in the 7BV2 active site.
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Figure 9. (A) 3D binding mode of compound 1273 into 7BV2 active site. (B) 2D binding mode of compound 1273 in the 7BV2 active site.
Figure 9. (A) 3D binding mode of compound 1273 into 7BV2 active site. (B) 2D binding mode of compound 1273 in the 7BV2 active site.
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Figure 10. (A) 3D binding mode of compound 1459 into 7BV2 active site. (B) 2D binding mode of compound 1459 in the 7BV2 active site.
Figure 10. (A) 3D binding mode of compound 1459 into 7BV2 active site. (B) 2D binding mode of compound 1459 in the 7BV2 active site.
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Figure 11. Chemical structure of hyperoside.
Figure 11. Chemical structure of hyperoside.
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Figure 12. Radius of gyration of SARS-CoV-2 RdRp when complexed with hyperoside, calculated over the course of a 100 ns MD simulation.
Figure 12. Radius of gyration of SARS-CoV-2 RdRp when complexed with hyperoside, calculated over the course of a 100 ns MD simulation.
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Figure 13. The RMSD of SARS-CoV-2 RdRp with reference to its backbone, calculated over the course of the 100 ns simulation.
Figure 13. The RMSD of SARS-CoV-2 RdRp with reference to its backbone, calculated over the course of the 100 ns simulation.
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Figure 14. The RMSD of hyperoside with reference to SARS-CoV-2 RdRp backbone, calculated over the course of the 100 ns simulation.
Figure 14. The RMSD of hyperoside with reference to SARS-CoV-2 RdRp backbone, calculated over the course of the 100 ns simulation.
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Figure 15. Coulombic electrostatic interaction energy between hyperoside and SARS-CoV-2 RdRp during MD simulation, showing an average value of −131.994 kJ/mol.
Figure 15. Coulombic electrostatic interaction energy between hyperoside and SARS-CoV-2 RdRp during MD simulation, showing an average value of −131.994 kJ/mol.
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Figure 16. Lennard-Jones interaction energy between hyperoside and SARS-CoV-2 RdRp during the MD simulation, showing an average value of −67.0503 kJ/mol.
Figure 16. Lennard-Jones interaction energy between hyperoside and SARS-CoV-2 RdRp during the MD simulation, showing an average value of −67.0503 kJ/mol.
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Figure 17. Changes in the number of H-bonds formed between hyperoside and SARS-CoV-2 RdRp.
Figure 17. Changes in the number of H-bonds formed between hyperoside and SARS-CoV-2 RdRp.
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Table
Table 1. Fingerprint similarity between the FDA-approved compounds and RTP.
Table 1. Fingerprint similarity between the FDA-approved compounds and RTP.
Comp.SimilaritySASBSCComp.SimilaritySASBSC
RTP12060021400.513672263306−57
40.5323941891491718800.4939762052091
420.52252311616907360.49216315711349
500.589136257022320.508159218223−12
560.682161304513450.51358208199−2
1520.55121148524830.49837115310153
1590.5161291601044624740.48751173489
1860.571128187812680.50393719217514
1990.5153062021864
2410.58613016765370.5076921981848
3100.58613016765490.5075191356071
3650.6061544852510.5132741162090
3740.529617152815423990.4935915410652
4100.601155525121860.513253213209−7
4350.52143696324960.48997117114335
4460.585162714410750.5064619618110
4470.54013249255−433800.4965751458661
4500.577138336818020.504785211212−5
4580.557252146566018070.519618610
4610.54109615886488070.4967111519855
5020.606154485217470.51327417413332
5390.55980911738914110.50145817213734
5730.5206611891571713320.4904762062140
6210.518182114149225730.48818918617520
6590.56296315264544700.50145817213734
7110.568126168022860.50252011945
7230.59135237120090.5081971243882
7770.519313121278514050.4888891326474
7880.521531218212−129370.49061718316723
8560.5223119917579510.4915251458961
8740.535377227218−2121110.495516221240−15
9280.620413423590.511002209203−3
10170.543554156815017890.497696216228−10
11630.5235461891551729880.49244716312543
12320.533762166105406250.49494919619010
12730.5411391711103512260.49723818015626
13690.60314941579190.5132741162090
13910.566214172−819110.5016721509356
14450.54216913543717340.497653212220−6
14580.5261441611004517020.49354815310453
14590.516291206193029990.487524254315−48
14780.5355031811322527510.49575117514731
14960.5321111612903710.49363919418712
15690.519722224225−1820230.4889711336673
15950.57712028629210.50404318716519
16310.568147535928860.50282517814828
16510.566291308−856180.5018321376769
17280.51660514065664560.4878642012065
17320.554264143526310680.498812210215−4
17780.5555561801182617230.498812210215−4
18120.51839515593511890.4888891101996
18580.5179217213−1114900.48818918617520
19170.598171803518390.50953718716119
19180.577138336816620.5040981233883
20170.67818364239970.51358208199−2
20310.54545513236746380.4980841305576
20420.6150445616690.510461223384
20560.55319113029767860.49862318115725
21760.601176873020240.51212116912437
22330.675185682116100.51344119116615
22680.55157123178315840.49856717414332
23760.575138346816420.50397919017116
24630.5207761881551821090.4904762062140
24880.556251781142827640.499006251297−45
25010.519713145736128500.488889264334−58
25230.52447615080568830.4928232062120
25850.548673124208224200.4980991315775
26120.56615975477810.50147517013336
26180.52577315385536790.4929081397667
27320.53246812325834040.49418617013836
27860.556522128247818730.515710849
28310.588141346511850.508143312408−106
28440.542986120158629800.497674107999
28760.631210127−421040.5133081355771
28790.57713833686630.50529119117215
29910.581312331−1064980.5061432062010
SA: The number of shared bits in both RTP and the examined molecule. SB: The number of present bits in the examined molecule but not RTP. SC: The number of present bits in RTP but not the examined molecule.
Table
Table 2. Similarity descriptors of the FDA-approved compounds and RTP.
Table 2. Similarity descriptors of the FDA-approved compounds and RTP.
Comp.ALog pM. WHBAHBDRBRARMFPSAM D
RTP−1.5371.241154320.6120
50−1.38297.27943320.5080.516
56−1.38365.211154320.6020.04
152−0.77287.21835220.5020.769
186−1.31285.23842320.520.638
241−1.88267.24842320.5390.656
310−1.88267.24842320.5390.656
359−0.4418.39973420.4380.775
446−0.51340.28953310.4760.702
4560.61446.361164420.4630.695
458−0.85328.27952310.4890.719
461−0.34354.31965210.4870.775
4980.21432.381064420.4240.724
659−1.61295.29852310.5210.765
723−2.38283.24852310.5870.76
9970.45416.38954420.3840.811
1017−0.43442.221165320.4590.565
1273−2.7381.4854310.5010.718
1332−0.3464.381284420.4990.83
14590.21432.381064420.4240.724
1917−3.25398.441047320.4810.657
2017−2.16365.241264320.6550.284
2042−2.09285.26952320.5890.491
2109−0.3464.381284420.4990.83
2176−1.93390.351054430.4910.675
2233−2.24427.21466320.6780.582
22860.02432.381073420.4550.75
2376−1.32269.26842320.540.649
2612−1.98460.771048220.5720.735
2732−0.82299.22835320.5040.69
2831−0.98305.23945220.550.545
Table
Table 3. ∆G values of the FDA-approved drugs and RTP.
Table 3. ∆G values of the FDA-approved drugs and RTP.
Comp.ΔG (kcal/mol)Comp.ΔG (kcal/mol)
Remdesivir−18.65Brimonidine Tartrate (1017)−15.95
Nelarabine (50)−18.36Cefadroxil (1273)−21.24
Fludarabine Phosphate (56)−17.73Isoquercitrin (1332)−23.40
Ramelteon (152)−17.74Sophoricoside (1459)−21.43
Fludarabine (186)−15.99Ademetionine (1917)−22.70
Adenosine (241)−16.36Adenosine 5’−monophosphate monohydrate (2017)−17.73
vidarabine (310)−16.63Vidarabine monohydrate (2042)−16.63
Aloin (359)−23.11Hyperoside (2109)−24.46
Esculin (446)−19.26Regadenoson (2176)−22.85
Baicalin (456)−20.62ADP (2233)−17.42
Bergenin (458)−19.18Vitexin (2286)−25.00
Chlorogenic Acid (461)−19.382’−Deoxyadenosine monohydrate (2376)−16.33
Puerarin (498)−22.35Thiamine−pyrophosphate−hydrochloride (2612)−17.78
Entecavir hydrate (659)−18.30Besifovir (2732)−17.50
Guanosine (723)−16.14Tenofovir hydrate (2831)−17.26
Daidzin (997)−21.34
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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. https://doi.org/10.3390/pr10030530

AMA Style

Elkaeed EB, Elkady H, Belal A, Alsfouk BA, Ibrahim TH, Abdelmoaty M, Arafa RK, Metwaly AM, Eissa IH. 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(3):530. https://doi.org/10.3390/pr10030530

Chicago/Turabian Style

Elkaeed, Eslam B., Hazem Elkady, Amany Belal, Bshra A. Alsfouk, Tuqa H. Ibrahim, Mohamed Abdelmoaty, Reem K. Arafa, Ahmed M. Metwaly, and Ibrahim H. Eissa. 2022. "Multi-Phase In Silico Discovery of Potential SARS-CoV-2 RNA-Dependent RNA Polymerase Inhibitors among 3009 Clinical and FDA-Approved Related Drugs" Processes 10, no. 3: 530. https://doi.org/10.3390/pr10030530

APA Style

Elkaeed, E. B., Elkady, H., Belal, A., Alsfouk, B. A., Ibrahim, T. H., Abdelmoaty, M., Arafa, R. K., Metwaly, A. M., & Eissa, I. H. (2022). Multi-Phase In Silico Discovery of Potential SARS-CoV-2 RNA-Dependent RNA Polymerase Inhibitors among 3009 Clinical and FDA-Approved Related Drugs. Processes, 10(3), 530. https://doi.org/10.3390/pr10030530

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