*Article In Silico* **Exploration of Potential Natural Inhibitors against SARS-Cov-2 nsp10**

**Ibrahim H. Eissa 1,\* , Mohamed M. Khalifa <sup>1</sup> , Eslam B. Elkaeed <sup>2</sup> , Elsayed E. Hafez <sup>3</sup> , Aisha A. Alsfouk <sup>4</sup> and Ahmed M. Metwaly 5,\***


**Abstract:** In continuation of our previous effort, different *in silico* selection methods were applied to 310 naturally isolated metabolites that exhibited antiviral potentialities before. The applied selection methods aimed to pick the most relevant inhibitor of SARS-CoV-2 nsp10. At first, a structural similarity study against the co-crystallized ligand, S-Adenosyl Methionine (**SAM**), of SARS-CoV-2 nonstructural protein (nsp10) (PDB ID: 6W4H) was carried out. The similarity analysis culled 30 candidates. Secondly, a fingerprint study against **SAM** preferred compounds **44**, **48**, **85**, **102**, **105**, **182**, **220**, **221**, **282**, **284**, **285**, **301**, and **302**. The docking studies picked **48**, **182**, **220**, **221**, and **284**. While the ADMET analysis expected the likeness of the five candidates to be drugs, the toxicity study preferred compounds **48** and **182**. Finally, a density-functional theory (DFT) study suggested vidarabine (**182**) to be the most relevant SARS-Cov-2 nsp10 inhibitor.

**Keywords:** COVID-19; natural products; SARS-Cov-2 nsp10; structural similarity; fingerprint; molecular docking; ADMET; toxicity; DFT

### **1. Introduction**

More than 217 million humans around the world were confirmed to be infected with COVID-19 and another 4.5 million families lost one of their beloveds as stated by the WHO on 2 September 2021 [1]. In response, all scientists in the field of drug discovery should work unceasingly to discover a cure against the notorious virus.

Computer-assisted (based or aided) drug design is a well-established branch of drug design that covers various *in silico* computational and theoretical approaches. These approaches are essential contributors to the development of new bioactive agents [2–8]. Computer-assisted drug design has been applied in drug discovery [9–11], computational chemistry [12,13], toxicity prediction [14–16], ADMET assessment [17–19], molecular modeling [20], molecular design [21,22], and rational drug design [23–27]. All these techniques have great popularity and have been used in both academic fields in addition to the pharmaceutical industries [28]. This approach has been introduced successfully and recurrently as a powerful weapon in the global fight against COVID-19 [29–32].

The relationship between humans and nature dates back to the prehistoric ages. The latter supplied the former with food, tools of beauty, and treatment [33,34]. Plants [35,36] and lately microorganisms [37,38] have been extensively screened to explore their healing power. Scientists isolated the secondary metabolites produced by these natural sources

**Citation:** 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. https:// doi.org/10.3390/molecules26206151

Academic Editors: Giovanni N. Roviello and Giosuè Costa

Received: 9 September 2021 Accepted: 6 October 2021 Published: 12 October 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

and labeled them as the key element in bioactivity. These candidates belonged to various classes as isochromenes [39], α-pyrones [40], diterpenes [41,42], sesquiterpenes [43,44], steroids [45], flavonoids [46,47], alkaloids [48], and saponins [49,50]. classes as isochromenes [39], α-pyrones [40], diterpenes [41,42], sesquiterpenes [43,44], steroids [45], flavonoids [46,47], alkaloids [48], and saponins [49,50]. SARS-CoV-2 is an enveloped positive-sensed RNA virus. The replication of SARS-

power. Scientists isolated the secondary metabolites produced by these natural sources and labeled them as the key element in bioactivity. These candidates belonged to various

*Molecules* **2021**, *26*, x FOR PEER REVIEW 2 of 18

SARS-CoV-2 is an enveloped positive-sensed RNA virus. The replication of SARS-CoV-2 depends on a group of 16 non-structural proteins. These proteins have the codes of nsp1–nsp16. Between them, the two proteins nsp10 and nsp16 make an essential protein complex [51]. That complex is responsible for the vital methylation reaction at the ribose 2 0 -O position of the penultimate nucleotide of the viral RNA cap [52]. Accordingly, if a molecule could bind with that enzyme and inhibit this essential step, the replication process will be stopped. CoV-2 depends on a group of 16 non-structural proteins. These proteins have the codes of nsp1–nsp16. Between them, the two proteins nsp10 and nsp16 make an essential protein complex [51]. That complex is responsible for the vital methylation reaction at the ribose 2′-O position of the penultimate nucleotide of the viral RNA cap [52]. Accordingly, if a molecule could bind with that enzyme and inhibit this essential step, the replication process will be stopped. The targeting of SARS-CoV-2 nsp-16 with a library of 10 [53] and 265 [54] FDA-ap-

The targeting of SARS-CoV-2 nsp-16 with a library of 10 [53] and 265 [54] FDA-approved compounds was studied before. Likely, a group set of 22 natural compounds from some Indian plants was computationally screened against six non-structural-proteins of SARS-CoV-2 [55]. proved compounds was studied before. Likely, a group set of 22 natural compounds from some Indian plants was computationally screened against six non-structural-proteins of SARS-CoV-2 [55]. In this study, different computational (*in silico*) selection methods were applied to

In this study, different computational (*in silico*) selection methods were applied to 310 candidates. The examined candidates were chosen through a deep database search according to three parameters. The first parameter was to be naturally isolated. The second was having exhibited antiviral potentiality before. Lastly, we considered that the culled compounds belong to different chemical classes and accordingly have various chemical structures. The applied computational techniques were a structural similarity study against **SAM** followed by a fingerprint study against the same target. The selected candidates were docked against nsp10 (PDB ID: 6W4H) to prefer **44**, **48**, **85**, **102**, **105**, **182**, **220**, **221**, **282**, **284**, **285**, **301**, and **302**. Then ADMET and toxicity studies further picked two candidates. Finally, a DFT study suggested the most relevant inhibitor of SARS-Cov-2 nsp10 (Figure 1). 310 candidates. The examined candidates were chosen through a deep database search according to three parameters. The first parameter was to be naturally isolated. The second was having exhibited antiviral potentiality before. Lastly, we considered that the culled compounds belong to different chemical classes and accordingly have various chemical structures. The applied computational techniques were a structural similarity study against **SAM** followed by a fingerprint study against the same target. The selected candidates were docked against nsp10 (PDB ID: 6W4H) to prefer **44**, **48**, **85**, **102**, **105**, **182**, **220**, **221**, **282**, **284**, **285**, **301**, and **302**. Then ADMET and toxicity studies further picked two candidates. Finally, a DFT study suggested the most relevant inhibitor of SARS-Cov-2 nsp10 (Figure 1).

**Figure 1.** The applied *in silico* selection protocols. **Figure 1.** The applied *in silico* selection protocols.

#### **2. Results and Discussion 2. Results and Discussion**

#### *2.1. Molecular Similarity against* **SAM** *2.1. Molecular Similarity against* **SAM**

The basic principle of 2D Molecular similarity is that molecules with similar chemical structures are expected to have similar biological activities [56]. The basic principle of 2D Molecular similarity is that molecules with similar chemical structures are expected to have similar biological activities [56].

To measure the similarity of two objects, their general features have to be compared. On a molecular level, the molecular features or descriptors of any compound start from To measure the similarity of two objects, their general features have to be compared. On a molecular level, the molecular features or descriptors of any compound start from the general physicochemical properties and extend to more specific structural features such as partition coefficient (ALog p) [57], molecular weight (M. Wt) [58], hydrogen bond donors (HBA) [59], hydrogen bond acceptors (HBD) [60], number of rotatable bonds [61], number

of rings, and also aromatic rings [62], in addition to molecular fractional polar surface area (MFPSA) [63]. ilarity with **SAM**. In detail, most candidates have a sugar-like moiety as that of **SAM** as candidates **85**, **102**, **105**, **120**, **182**, **183**, **203**, **204**, **220**, **221**, **282**, **284**, **285**, **301**, and **302**. These

As shown in Figure 2, the similar candidates showed a high degree of structural sim-

the general physicochemical properties and extend to more specific structural features such as partition coefficient (ALog p) [57], molecular weight (M. Wt) [58], hydrogen bond donors (HBA) [59], hydrogen bond acceptors (HBD) [60], number of rotatable bonds [61], number of rings, and also aromatic rings [62], in addition to molecular fractional polar

All mentioned molecular properties were used in the applied similarity study between the natural candidate's set (Figure S1, Supplementary Materials) and the co-crystallized ligand (**SAM**) of SARS-CoV-2 nonstructural protein (nsp10) (PDB ID: 6W4H) using Discovery studio software. Thirty candidates (Figure 2) were chosen to be the most

All mentioned molecular properties were used in the applied similarity study between the natural candidate's set (Figure S1, Supplementary Materials) and the co-crystallized ligand (**SAM**) of SARS-CoV-2 nonstructural protein (nsp10) (PDB ID: 6W4H) using Discovery studio software. Thirty candidates (Figure 2) were chosen to be the most similar to **SAM**. moieties may serve as a good center for hydrogen bonding interaction with the target receptor. Furthermore, most candidates have hetero bicyclic structures as present in **SAM**. Besides, xanthine-like structures were defined in many similar candidates such as **182**, **284**, **285**, and **301**.

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surface area (MFPSA) [63].

similar to **SAM**.

**Figure 2. Figure 2.**The most similar candidates with ( The most similar candidates with ( **SAM SAM** ). ).

As shown in Figure 2, the similar candidates showed a high degree of structural similarity with **SAM**. In detail, most candidates have a sugar-like moiety as that of **SAM** as candidates **85**, **102**, **105**, **120**, **182**, **183**, **203**, **204**, **220**, **221**, **282**, **284**, **285**, **301**, and **302**. These moieties may serve as a good center for hydrogen bonding interaction with the target receptor. Furthermore, most candidates have hetero bicyclic structures as present in **SAM**. Besides, xanthine-like structures were defined in many similar candidates such as **182**, **284**, **285**, and **301**.

As shown in Figure 3, the candidate's set was divided into six smaller sets. From the first set to the fifth comprised 50 candidates while the sixth set was 60.

**Figure 3.** The results of similarity analysis of the test sets and **SAM**. Green = **SAM**, red = similar candidate, blue = not **Figure 3.** The results of similarity analysis of the test sets and **SAM**. Green = **SAM**, red = similar candidate, blue = not similar candidate. (**A**) Candidates 1–50, (**B**) 51–100, (**C**) 101–150, (**D**) 151–200, (**E**) 201–250, (**F**) 251–310.

similar candidate. (**A**) Candidates 1–50, (**B**) 51–100, (**C**) 101–150, (**D**) 151–200, (**E**) 201–250, (**F**) 251–310.

Table 1 demonstrates the molecular properties of the similar candidates as well as **SAM**.

As shown in Figure 3, the candidate's set was divided into six smaller sets. From the

first set to the fifth comprised 50 candidates while the sixth set was 60.


**Table 1.** Structural properties of the similar candidates with **SAM**.

<sup>1</sup> Partition coefficient, <sup>2</sup> Molecular weight, <sup>3</sup> Hydrogen bond acceptors, <sup>4</sup> Hydrogen bond donors, <sup>5</sup> Molecular fractional polar surface area.

#### *2.2. Filter Using Fingerprints*

The fingerprint is another similarity technique that depends on the 2D molecular structures of two different ligands in a binary format. This technique computes the presence and/or absence of several sub-structural fragments to calculate the degree of intermolecular structural similarity. This technique is utilized as a tool to detect the degree of similarity between a hit candidate and a lead one [64] The fingerprint approach examines the following parameters: charges [65], hybridization [66], H-bond acceptors, and donors [67], positive and negative ionizable moieties [68], halogens, and aromatic rings beside the ALogP category of candidates. The experiment was carried out using Discovery Studio.

The fingerprint's output depends on Tanimoto coefficient (SA/(SA + SB + SC)). SA is a symbol that represents the number of bits present in the reference molecule (**SAM**) and the examined candidate. On the other hand, SB and SC represent the number of bits in the examined candidate but not **SAM** and the number of bits in **SAM** but not the examined candidate, respectively. The Tanimoto coefficient gives values with a range of zero (no shared bits) to one (all bits the same).

The results revealed the significant fingerprint similarity of **44**, **48**, **85**, **102**, **105**, **182**, **220**, **221**, **282**, **284**, **285**, **301**, and **302** with **SAM** (Table 2).

The reported antiviral potentialities of the preferred metabolites were summarized in the Supplementary Materials.

#### *2.3. Docking Studies*

Molecular docking studies were achieved to study the binding modes, orientations, and affinities of the candidates **44**, **48**, **85**, **102**, **105**, **182**, **220**, **221**, **282**, **284**, **285**, **301**, and **302** inside the SARS-CoV-2 nonstructural protein (nsp10) (PDB ID: 6W4H, resolution: 1.80 Å) active site using MOE 14.0 software.

The docking process was validated through a redocking step of **SAM** against active pockets of SARS-CoV-2 nonstructural protein (nsp10). The suitability of the performed protocol was demonstrated by the small RMSD (0.60 Å) that was found between the docked pose and **SAM** (Figure 4).


**Table 2.** Fingerprint similarity between the tested candidates and **SAM**. **301** 0.642 145 −11 92

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**Table 2.** Fingerprint similarity between the tested candidates and **SAM**.

**Comp. Similarity SA SB SC SAM** 1 237 0 0 0.503 159 79 78 0.423 110 23 127 0.423 200 236 37 0.497 149 63 88 0.529 165 75 72 0.717 160 −14 77 0.475 135 47 102 0.458 125 36 112 0.443 250 327 −13 0.685 150 −18 87 0.671 159 0 78

docked pose and **SAM** (Figure 4).

**Figure 4.** Superimposition of the co-crystallized ligand pose (**green**) and the docking pose (**wheat**). **Figure 4.** Superimposition of the co-crystallized ligand pose (**green**) and the docking pose (**wheat**).

The mode of binding of **SAM** inside COVID-19 nsp10 was illustrated in Figure 5. It was noticed that **SAM** interacted with the active site via the formation of six hydrogen bonds with Lys6844, Leu6898, Asn6899, Asp6912, Cys6913, and Tyr6930.

Among all studied metabolites, members **220**, **48**, **182**, **221**, and **284** exhibited the greatest binding free energies of docking (Table 3).

The methylpyrimidine-2,4-dione derivative (**220**) possessed a good potential affinity of −21.17 into the COVID-19 nsp10 active site. This high affinity is attributed to the formation of five hydrogen bond interactions. The pyrimidine moiety of candidate **220** was involved in two hydrogen-bonding interactions with Asp6912 and Cys6913. While the furan part interacted with the active site by three hydrogen bonds with Leu6898 and Tyr6930 (Figure 6).

**Figure 5.** The proposed binding pattern of **SAM**. **Figure 5.** The proposed binding pattern of **SAM**.

Among all studied metabolites, members **220**, **48**, **182**, **221**, and **284** exhibited the greatest binding free energies of docking (Table 3). **Table 3.** The calculated binding free energies of the examined candidates and **SAM** inside COVID-19 nsp10.

The mode of binding of **SAM** inside COVID-19 nsp10 was illustrated in Figure 5. It was noticed that **SAM** interacted with the active site via the formation of six hydrogen

bonds with Lys6844, Leu6898, Asn6899, Asp6912, Cys6913, and Tyr6930.


**102** −18.98 **285** −19.02

Candidate (**48**) exhibited a binding mode like that of **SAM** with the formation of four

Investigation of the top docking poses of the 6-aminopurine member (**182**) showed that it interacted with the COVID-19 nsp10 active site through the formation of three hydrogen bond interactions. Its amino group was involved in a hydrogen bond with Asp6912 while one purine nitrogen atom formed a hydrogen bond with Cys6913. In ad-

dition, the furan oxygen interacted by a hydrogen bond with Tyr6930 (Figure 8).

**Figure 6.** The proposed binding pattern of candidate **220**. **Figure 6.** The proposed binding pattern of candidate **220**.

**Figure 7.** The proposed binding pattern of candidate **48**.

hydrogen bonds with Cys6913, Tyr6930, and Leu6898 (Figure 7).

**Figure 6.** The proposed binding pattern of candidate **220**.

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Candidate (**48**) exhibited a binding mode like that of **SAM** with the formation of four hydrogen bonds with Cys6913, Tyr6930, and Leu6898 (Figure 7). Candidate (**48**) exhibited a binding mode like that of **SAM** with the formation of four hydrogen bonds with Cys6913, Tyr6930, and Leu6898 (Figure 7).

**Figure 7.** The proposed binding pattern of candidate **48**. **Figure 7.** The proposed binding pattern of candidate **48**.

Investigation of the top docking poses of the 6-aminopurine member (**182**) showed that it interacted with the COVID-19 nsp10 active site through the formation of three hydrogen bond interactions. Its amino group was involved in a hydrogen bond with Asp6912 while one purine nitrogen atom formed a hydrogen bond with Cys6913. In ad-Investigation of the top docking poses of the 6-aminopurine member (**182**) showed that it interacted with the COVID-19 nsp10 active site through the formation of three hydrogen bond interactions. Its amino group was involved in a hydrogen bond with Asp6912 while one purine nitrogen atom formed a hydrogen bond with Cys6913. In addition, the furan oxygen interacted by a hydrogen bond with Tyr6930 (Figure 8).

dition, the furan oxygen interacted by a hydrogen bond with Tyr6930 (Figure 8). The proposed binding pattern of the pyrimidinedione derivative (**221**) was illustrated in Figure 9. It interacted with the active site via the formation of five hydrogen bonds with Asn6899, Asp6897, Cys6913, and Tyr6930. *Molecules* **2021**, *26*, x FOR PEER REVIEW 9 of 18

> The proposed binding pattern of the pyrimidinedione derivative (**221**) was illustrated in Figure 9. It interacted with the active site via the formation of five hydrogen bonds with

> Figure 10 The proposed binding mode of candidate **284**. The purine moiety of **284** formed a hydrogen bond with Asp6912 while the attached amino group interacted with another hydrogen bond with Cys6913. The tetrahydrofuran-3-ol part formed two hydrogen bonds with Tyr6930 and Asn6899. Furthermore, the hydroxymethyl side chain was

**Figure 8.** The proposed binding pattern of candidate **182**. **Figure 8.** The proposed binding pattern of candidate **182**.

**Figure 9.** The proposed binding pattern of candidate **221**.

involved by a hydrogen bond with Gly6871.

Asn6899, Asp6897, Cys6913, and Tyr6930.

**Figure 8.** The proposed binding pattern of candidate **182**.

Asn6899, Asp6897, Cys6913, and Tyr6930.

**Figure 9.** The proposed binding pattern of candidate **221**. **Figure 9.** The proposed binding pattern of candidate **221**.

Figure 10 The proposed binding mode of candidate **284**. The purine moiety of **284** formed a hydrogen bond with Asp6912 while the attached amino group interacted with another hydrogen bond with Cys6913. The tetrahydrofuran-3-ol part formed two hydrogen bonds with Tyr6930 and Asn6899. Furthermore, the hydroxymethyl side chain was involved by a hydrogen bond with Gly6871. Figure 10 The proposed binding mode of candidate **284**. The purine moiety of **284** formed a hydrogen bond with Asp6912 while the attached amino group interacted with another hydrogen bond with Cys6913. The tetrahydrofuran-3-ol part formed two hydrogen bonds with Tyr6930 and Asn6899. Furthermore, the hydroxymethyl side chain was involved by a hydrogen bond with Gly6871. *Molecules* **2021**, *26*, x FOR PEER REVIEW 10 of 18

The proposed binding pattern of the pyrimidinedione derivative (**221**) was illustrated in Figure 9. It interacted with the active site via the formation of five hydrogen bonds with

**Figure 10.** The proposed binding pattern of candidate **284**. **Figure 10.** The proposed binding pattern of candidate **284**.

#### *2.4. In Silico ADMET Analysis*  Five parameters were examined for candidates **48**, **182**, **220**, **221**, and **284** using Dis-*2.4. In Silico ADMET Analysis*

ther investigations.

**Figure 11.** The expected ADMET study.

covery studio software. Acyclovir, the potent anti-viral drug, was used as a reference candidate. The results are illustrated in Figure 11. All the tested candidates have a very low chance to penetrate BBB. This indicates the Five parameters were examined for candidates **48**, **182**, **220**, **221**, and **284** using Discovery studio software. Acyclovir, the potent anti-viral drug, was used as a reference candidate. The results are illustrated in Figure 11.

high safety margin of such derivatives against the CNS. Additionally, all candidates exhibited an aqueous solubility character. For intestinal absorption, candidates **48**, **182**, **220**, and **221** were predicted to have poor to very poor levels, while candidate **284** was expected to have a moderate level. Furthermore, all candidates were predicted to be CYP2D6 noninhibitors and can bind plasma protein by less than 90%. These results indicated that all

**Figure 10.** The proposed binding pattern of candidate **284**.

didate. The results are illustrated in Figure 11.

Five parameters were examined for candidates **48**, **182**, **220**, **221**, and **284** using Discovery studio software. Acyclovir, the potent anti-viral drug, was used as a reference can-

All the tested candidates have a very low chance to penetrate BBB. This indicates the high safety margin of such derivatives against the CNS. Additionally, all candidates exhibited an aqueous solubility character. For intestinal absorption, candidates **48**, **182**, **220**, and **221** were predicted to have poor to very poor levels, while candidate **284** was expected to have a moderate level. Furthermore, all candidates were predicted to be CYP2D6 noninhibitors and can bind plasma protein by less than 90%. These results indicated that all the tested candidates have good pharmacokinetic properties and can be utilized for fur-

*2.4. In Silico ADMET Analysis* 

ther investigations.

**Figure 11. Figure 11.**The expected ADMET study. The expected ADMET study.

All the tested candidates have a very low chance to penetrate BBB. This indicates the high safety margin of such derivatives against the CNS. Additionally, all candidates exhibited an aqueous solubility character. For intestinal absorption, candidates **48**, **182**, **220**, and **221** were predicted to have poor to very poor levels, while candidate **284** was expected to have a moderate level. Furthermore, all candidates were predicted to be CYP2D6 noninhibitors and can bind plasma protein by less than 90%. These results indicated that all the tested candidates have good pharmacokinetic properties and can be utilized for further investigations.

#### *2.5. In Silico Toxicity Studies*

Candidates **48**, **182**, **220**, **221**, and **284** were tested *in silico* for their proposed toxicity using Discovery studio software. In this test, seven toxicity models were utilized using ribavirin as a reference. The results are summarized in Table 4.



<sup>a</sup> Unit = g/kg body weight.

FDA rodent carcinogenicity in female mice indicated that candidates **48** and **182** were non-carcinogenic, while candidates **220**, **221**, and **284** had some sort of carcinogenicity. Besides, candidates **48**, **182**, and **284** showed TD<sup>50</sup> values of 9.295, 4.245, and 6.402 mg/kg body weight/day, respectively. Candidates **220** and **221** showed high carcinogenic potency TD<sup>50</sup> values of 67.851 and 55.437 mg/kg body weight/day, respectively. Furthermore, candidates **48** and **182** showed high rat maximum tolerated dose values of 0.191 and 0.175 g/kg body weight, respectively. On the other hand, candidates **220** and **221** showed low rate maximum tolerated dose values of 0.095 and 0.094 g/kg body weight, respectively. Candidate **284** showed a comparable rat maximum tolerated dose value (0.155 g/kg body

weight) with ribavirin (0.154 g/kg body weight). The tested candidates showed rat oral LD<sup>50</sup> values ranging from 0.778 to 6.173 g/kg body weight, which were higher than the reference drug LD<sup>50</sup> = 0.750 g/kg body weight. For the rat chronic LOAEL model, candidates **48** and **182** showed high values of 0.018 and 0.010 g/kg body weight, while candidates **220**, **221**, and **284** showed low values of 0.009, 0.006, and 0.004 g/kg body weight, respectively. All candidates were predicted to have mild to moderate irritant effects against ocular irritancy and skin irritancy models. Accordingly, candidates **48** and **182** had low toxicity profiles and were preferred for further studies.

#### *2.6. DFT Studies*

DFT parameters (Table 5) were studied for candidates 48 and 182 [69,70] against **SAM** as a reference using Discovery studio software (Table 5, Figures 12 and 13).


**Table 5.** Spatial distribution of molecular orbitals for candidates **48** and **182**.

\* Unit = kcal/mol for all descriptors except Dipole Mag.

#### 2.6.1. Molecular Orbital Analysis

Candidates **48**, **182**, and **SAM** exhibited total energy values of −664.379, −955.658, and −1675.931 kcal/mol, respectively. The higher total energy of candidate **182** indicates a higher reactivity against the biological target. The two tested candidates, **48** and **182**, showed almost equal dipole moment values of 1.391 and 1.396, respectively. The Molecular Orbital (MO) analysis of EHOMO represents the energy of the highest occupied molecular orbital. On the other side, ELUMO represents the lowest unoccupied molecular orbital energies. The MO analysis is one of the essential parameters that is linked to the chemical reactivity and stability of a molecule. The HOMO spatial distributions of **SAM** are mainly presented on the 2-aminobutanoic acid moiety (the electron transfer zones), while its LUMO spatial distributions are located on the tetrahydrofuran-3,4-diol moiety (the electron acceptor zones). For candidate **48**, the HOMO spatial distributions are mainly located on the (2*R*,3*R*,4*R*)-2-(hydroxymethyl)pyrrolidine-3,4-diol moiety, while its LUMO spatial distributions are found on the (*S*)-pyrrolidin-3-ol moiety. For candidate **182**, the HOMO spatial distributions are mainly presented on the 9*H*-purin-6-amine moiety, while its LUMO spatial distributions are located on the (2*R*,3*S*,4*R*)-2-(hydroxymethyl)tetrahydrofuran-3,4 diol moiety. Furthermore, the gap energy of candidate **182** (0.128 kcal/mol) was less than that of candidate **48** (0.210 kcal/mol), confirming the high reactivity of candidate **182**. Consequently, candidate **182** may serve as a promising candidate for further studies.

#### 2.6.2. Molecular Electrostatic Potential Maps (MEP)

MEP was used to specify the electrostatic potential of **48**, **182**, and **SAM** in a 3D form via the calculation of the partial charges, electronegativity, and chemical reactivity [71]. The electrostatic potential affects the binding of a drug with a specific protein and gives a deeper insight into drug–receptor interaction [72]. In MEP, the red color denotes the electronegative atoms, which can go through hydrogen bonding interactions as an acceptor. Additionally, the blue color denotes the electron-poor atoms that can form a donor in hydrogen bonding. The green to yellow color denotes the neutral atoms, which can form hydrophobic interactions [73].

studies.

**Name Total** 

**Energy** 

**Figure 12.** Spatial distribution of molecular orbitals for (**A**) **SAM**, (**B**) candidate **48**, and (**C**) candidate **182**.

**Figure 12.** Spatial distribution of molecular orbitals for (**A**) **SAM**, (**B**) candidate **48**, and (**C**) candidate **182**. 2.6.2. Molecular Electrostatic Potential Maps (MEP) The MEPs of **SAM**, **48**, and **182**, were illustrated in Figure 13A, B, and C, respectively. Investigating these figures indicated that **SAM** has eight red patches that are suitable for hydrogen bonding acceptors and are considered favorable sites for the electrophilic attack. Also, it comprises six blue patches that are suitable for hydrogen bond donors (the most favorable sites for the nucleophilic attack). Candidate **182** has six red patches and five blue patches. In addition, there is a yellow patch on the 9*H*-purine nucleus indicating a high possibility for hydrophobic interaction. These findings are highly like that of **SAM**. The MEP of candidate **48** is slightly different from **SAM**. In detail, it has four red patches and four blue patches. These results indicated that candidate **182** has a greater similarity with **SAM** than candidate **48**. Because of that, candidate **182** was singled out.

its LUMO spatial distributions are located on the (2*R*,3*S*,4*R*)-2-(hydroxymethyl)tetrahydrofuran-3,4-diol moiety. Furthermore, the gap energy of candidate **182** (0.128 kcal/mol) was less than that of candidate **48** (0.210 kcal/mol), confirming the high reactivity of candidate **182**. Consequently, candidate **182** may serve as a promising candidate for further

> **HOMO Energy**

**48** −664.379 −4.841 −0.366 −0.156 1.391 0.210 **182** −955.658 −6.102 −0.195 −0.068 1.396 0.128 **SAM** −1675.931 −8.815 −0.270 −0.174 3.631 0.097

**LUMO Energy**  **Dipole Mag** 

**Band Gap Energy** 

**Table 5.** Spatial distribution of molecular orbitals for candidates **48** and **182**.

**Binding Energy** 

\* Unit = kcal/mol for all descriptors except Dipole Mag.

The antiviral activities of the preferred candidate, vidarabine (**182**), were reported against several viruses in different reports. It was active against herpes simplex encephalitis and neonatal herpes simplex infection [74,75], HBV [76], varicella-zoster virus [77], human polyomavirus [78], adenovirus [79], and Epstein–Barr virus infection [80].

**Figure 13.** Molecular electrostatic potential map of (**A**) **SAM**, (**B**) candidate **48**, and (**C**) candidate **182**. **Figure 13.** Molecular electrostatic potential map of (**A**) **SAM**, (**B**) candidate **48**, and (**C**) candidate **182**.

### **3. Method**

The antiviral activities of the preferred candidate, vidarabine (**182**), were reported *3.1. Molecular Similarity Detection*

hydrophobic interactions [73].

against several viruses in different reports. It was active against herpes simplex encephalitis and neonatal herpes simplex infection [74,75], HBV [76], varicella-zoster virus [77], Achieved by Discovery studio software (see method part in Supplementary Materials).

MEP was used to specify the electrostatic potential of **48**, **182**, and **SAM** in a 3D form via the calculation of the partial charges, electronegativity, and chemical reactivity [71]. The electrostatic potential affects the binding of a drug with a specific protein and gives a deeper insight into drug–receptor interaction [72]. In MEP, the red color denotes the electronegative atoms, which can go through hydrogen bonding interactions as an acceptor. Additionally, the blue color denotes the electron-poor atoms that can form a donor in hydrogen bonding. The green to yellow color denotes the neutral atoms, which can form

The MEPs of **SAM**, **48**, and **182**, were illustrated in Figure 13A, B, and C, respectively. Investigating these figures indicated that **SAM** has eight red patches that are suitable for hydrogen bonding acceptors and are considered favorable sites for the electrophilic attack. Also, it comprises six blue patches that are suitable for hydrogen bond donors (the most favorable sites for the nucleophilic attack). Candidate **182** has six red patches and five blue patches. In addition, there is a yellow patch on the 9*H*-purine nucleus indicating a high possibility for hydrophobic interaction. These findings are highly like that of **SAM**. The MEP of candidate **48** is slightly different from **SAM**. In detail, it has four red patches and four blue patches. These results indicated that candidate **182** has a greater similarity

with **SAM** than candidate **48**. Because of that, candidate **182** was singled out.

human polyomavirus [78], adenovirus [79], and Epstein–Barr virus infection [80]. *3.2. Pharmacophoric Study*

Achieved by Discovery studio software (see method part in Supplementary Materials).

**3. Method**  *3.3. Docking Studies*

*3.1. Molecular Similarity Detection*  Docking studies were achieved by MOE.14 software (see method part in Supplementary Materials).

*3.4. ADMET Analysis*

Achieved by Discovery studio 4.0 (see method part in Supplementary Materials).

*3.5. Toxicity Studies*

Achieved by Discovery studio software [81–83] (see method part in Supplementary Materials).

#### *3.6. DFT Studies*

Achieved by Discovery studio software [84] (see method part in Supplementary Materials).

#### **4. Conclusions**

Vidarabine (**182**) was suggested to be the most relevant SARS-Cov-2 nsp10 inhibitor among 310 naturally isolated metabolites that exhibited antiviral potentialities before. This suggestion was based on different computational (*in silico*) selection methods that included molecular similarity assessment, molecular fingerprint, docking studies, toxicity, ADMET, and DFT. The selected candidate showed various antiviral activities before. Further in vitro and in vivo biological studies have to be conducted to confirm the effect of **182** against SARS-Cov-2 nsp10 and its potential as an anti-COVID-19 drug.

**Supplementary Materials:** The following are available online, Figure S1: Chemical structures of the examined natural antiviral compounds, Table S1: Detailed toxicity report, in addition to the method (Molecular Similarity, Pharmacophore, Docking studies, ADMET studies, Toxicity studies and DFT studies).

**Author Contributions:** Conceptualization, A.M.M. and I.H.E.; methodology I.H.E., E.E.H.; software, M.M.K., E.B.E. and I.H.E.; writing—review and editing, A.M.M., E.B.E., E.E.H., A.A.A. and I.H.E. supervision, A.M.M. and I.H.E.; project administration, A.M.M. and I.H.E.; funding acquisition, E.B.E. All authors have read and agreed to the published version of the manuscript.

**Funding:** The authors extend their appreciation to the Research center at Almaarefa University for funding this work.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.

**Sample Availability:** Samples of the compounds are not available from the authors.

#### **References**

