Antiviral Potential of Antillogorgia americana and elisabethae Natural Products against nsp16–nsp10 Complex, nsp13, and nsp14 Proteins of SARS-CoV-2: An In Silico Investigation

Pokharkar, Omkar; Lakshmanan, Hariharan; Zyryanov, Grigory V.; Tsurkan, Mikhail V.

doi:10.3390/microbiolres14030068

Open AccessArticle

Antiviral Potential of Antillogorgia americana and elisabethae Natural Products against nsp16–nsp10 Complex, nsp13, and nsp14 Proteins of SARS-CoV-2: An In Silico Investigation

¹

Department of Organic & Bio-Molecular Chemistry, Chemical Engineering Institute, Ural Federal University, Mira St. 19, Yekaterinburg 620002, Russia

²

La Trobe Institute of Molecular Science, Plenty Rd & Kingsbury Dr., Bundoora, Melbourne, VIC 3086, Australia

³

Postovsky Institute of Organic Synthesis of RAS (Ural Division), 22/20, S. Kovalevskoy, Akademicheskaya St., Yekaterinburg 620990, Russia

⁴

Leibniz Institute of Polymer Research Dresden, 01069 Dresden, Germany

^*

Author to whom correspondence should be addressed.

Microbiol. Res. 2023, 14(3), 993-1019; https://doi.org/10.3390/microbiolres14030068

Submission received: 3 July 2023 / Revised: 22 July 2023 / Accepted: 24 July 2023 / Published: 28 July 2023

Download

Browse Figures

Versions Notes

Abstract

:

Biomolecules of marine origin have many applications in the field of biotechnology and medicine, but still hold great potential as bioactive substances against different diseases. The purification or total synthesis of marine metabolites is expensive, and requires a reliable selection method to reveal their pharmaceutical potential prior to clinical validation. This study aimed to explore the hidden potential of natural products from the gorgonian genus Antillogorgia as anti-SARS-CoV-2 agents, via binding affinity assessments and molecular dynamics (MDs) simulations. The three-dimensional protein structures of the nsp16–nsp10 complex, nsp13, and nsp14 were acquired from the RCSB PDB database. All 165 natural products (NPs) were discovered using the PubChem, ChemSpider, and CMNPD databases. The freeware Autodock Vina was used to conduct the molecular docking procedure, once the proteins and ligands were prepared using BIOVIA discovery studio and Avogadro software v1.95. Before running MDs simulations using the CABS-flex 2.0 website, the binding affinity assessments and amino acid interactions were carefully examined. Just twelve NPs were selected, and five of those NPs interacted optimally with the catalytic amino acids of proteins. To conclude, pseudopterosin A (−8.0 kcal/mol), seco-pseudopterosin A (−7.2 kcal/mol), sandresolide B (−6.2 kcal/mol), elisabatin A (−7.0 kcal/mol), and elisapterosin A (−10.7 kcal/mol) appeared to be the most promising candidates against the nsp16–nsp10, nsp13, and nsp14 proteins.

Keywords:

SARS-CoV-2; COVID-19; nsp16–nsp10; nsp13; nsp14; antiviral; gorgonian; Antillogorgia; americana; elisabethae; soft coral

1. Introduction

By July 2023, the COVID-19 pandemic, which continues to have a catastrophic effect on the economy and on humankind as a whole, is expected to have caused over 768 million COVID-19 cases, and almost 7.0 million fatalities globally. Effective pharmaceutical treatments and vaccinations are essential to containing the outbreak. Coronavirus research has received an unusually high level of attention throughout this global crisis, which has sped up the development of vaccines [1,2]. The decrease in mortality and morbidity was made possible by the early adoption of drugs such as dexamethasone and remdesivir [3,4,5,6,7,8]. Remdesivir, however, appears to have a minimal effect on patients’ chances of survival, according to available research [9]. To address SARS-CoV-2 infections, research on novel therapies is still important.

The nsp10 is known to be a distinctive protein and, as a complex of nsp16–nsp10, it performs C2′-O methylation at the 5′ end of the viral RNA, promoting efficient virus replication. The majority of the residues involved in catalysis and Cap/SAM binding are conserved, and the nsp16–nsp10 complex in SARS-CoV-2 shares a high degree of similarity with other coronaviruses [10,11,12,13,14]. Furthermore, the SARS-CoV-2 nsp13 is also a crucial protein for the replication of the virus, because it enables the initial phase of RNA capping, which is required to stabilize the virus, block the innate immune response, and ensure the translation process. The nsp13, a member of the 1B superfamily, consists of 601-amino-acid-long conserved protein sequences. This helicase is an enzyme that facilitates viral replication, as it enables the initial phase of the formation of the RNA cap. This step contributes to the overall stability of the virus, protects the virus from the host’s innate immune response, and promotes its translation [15,16,17,18,19]. Furthermore, the nonstructural protein 14 (nsp14) is a dual-tasking protein that performs both the 3′-to-5′ exoribonuclease and N7-MTase operations, which are in control of premature RNA proofreading and mRNA capping throughout the replication process [20,21,22]. Thus, these proteins are desirable candidates for anti-viral therapy, due to their significant importance in viral RNA replication, and their efficient evasion of detection by the host’s immune system [23,24,25].

Finding therapeutic interventions in a pandemic can be done rationally and quickly by repositioning currently available medications, investigational drug candidates, and endorsed bioactive substances of which the toxicological, pharmacokinetic, and pharmacodynamic properties are well-understood [26]. High-throughput in vitro experiments, in vivo animal investigations, and computer-aided drug design (CADD) approaches are all usually employed in the identification of repurpose-worthy drug candidates. Nevertheless, these methods can be expensive and time-consuming. MD simulations, if performed properly, is an inexpensive approach to gaining valuable information about drug candidates. Significant numbers of ligands can be rapidly screened computationally, and binding affinities, along with amino acid interactions, allow for the quick identification of the candidates for targeted in vitro and in vivo investigation, followed by clinical trials [27,28].

NPs, including secondary metabolites, have various biological activities, including various anti-viral activities, and are suspected by us to contain strong anti-SARS-CoV-2 agents. In silico assessments could be a very fast method to evaluate NPs. For example, some of the natural coumarins, well-known compounds with anti-viral activity [29], were found through molecular docking to be active against COVID-19 [30]. Our research focuses on the in silico search for NPs active against COVID-19 or COVID-19-associated pathologies [31], via binding-affinity assessments and MDs simulations. We utilized a general methodology (see Figure 1), which included target information and ligand information collection from the literature, followed by competence visualization, molecular docking, and MDs simulations.

This in silico investigation aimed to assess the effectiveness of metabolites from the Antillogorgia genus against three significant known non-structural enzymes of SARS-CoV-2, with a focus on the two species americana and elisabethae [32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73]. The anti-SARS-CoV-2 capabilities of all 165 NPs with low molecular weights from the two aforementioned species of soft corals were tested. This study serves as an illustration of effective data collecting packaged as a species-specific chemical library. Additionally, this study’s docking experiments demonstrated how to use such a library. For the first time, this work discovered several unique NPs that interacted with the concerned catalytic residues of the nsp16–10 complex, nsp13, and nsp14 proteins. The possible toxicity of the NPs was also evaluated during this research, and a detailed analysis of their pharmacokinetic and bioactivity predictions was conducted. The schematic workflow of this work is shown in Figure 1.

2. Materials and Methods

2.1. Obtaining Target Proteins

Through a literature review, details regarding the nonstructural COVID-19 proteins were discovered. From the RCSB PDB database, the X-ray-diffraction-determined crystal structures of the SARS-CoV-2 nsp16–nsp10 complex, nsp13 (helicase), and nsp14 were downloaded [74] (see Figure S1). For this work, the 3D structures of nsp16–nsp10 (PDB ID: 6W4H), nsp13 (PDB ID: 6ZSL), and nsp14 (PDB ID: 7R2V) attached with the inhibitor SAH, each of which was resolved at 1.80 Å, 1.94 Å, and 2.53 Å, were utilized in this study [75,76,77].

2.2. Obtaining Ligands

Through an extensive literature search, information on the natural products from Antillogorgia americana and Antillogorgia elisabethae was examined. These two types of soft corals include approximately 165 naturally occurring products (see Table S1). To find all of the ligands’ 2D or 3D conformers, a thorough database search was conducted. Firstly, the SDF chemically formatted 3D conformers of ligands were obtained from the PubChem database [78]. 2D conformers were downloaded in the event that 3D structures were not available. For the ligands that were missing from the PubChem database, a second source, the ChemSpider database, was utilized. The ChemSpider database’s ligands were downloaded in Mol chemical format [79]. If the ligands were not found in either the PubChem or Chemspider database, then a third database, CMNPD, was employed as a backup source [80]. In order to advance this work, all 165 ligands were eventually effectively obtained.

2.3. Visualization

We used BIOVIA Discovery Studio Visualizer 4.5 [81] to display the protein structures of nsp16–nsp10, nsp13, and nsp14. By doing so, we were able to learn how many ligands, HETATM groups, and amino acid chains were associated with each protein. On the other hand, prior to being used for the ligand preparations, Avogadro software version 1.95 was used to display the ligands [82,83].

2.4. Pre-Docking Preparations

Prior to the docking process, it was essential to prepare the proteins and ligands. Using the information compiled throughout the visualization process, BIOVIA Discovery Studio Visualizer was employed to prepare both the target proteins. The amino acid chains A and B were present in all three nonstructural proteins. Upon visualization, all target proteins revealed the presence of ligand groups, HETATM, and water molecules. Thus, unnecessary molecules and amino acid chains were cleaned and removed from all the 3D structures. In this investigation, all the proteins had chain A retained. In addition, the program Swiss-pdb viewer [84] was employed to attempt to repair the damaged amino acid chains, hydrogen bonds were inserted, and energy minimization was accomplished. In order to protect the active site, the ligand groups were only removed following the energy minimization procedure. Additionally, the Swiss-pdb viewer was used to export these structures in the PDB format, which was then converted to the appropriate PDBQT format using Autodock tools, a component of MGL tools [85].

Using the Avogadro program v. 1.95, the 2D and 3D conformers of ligands received in SDF and Mol chemical forms were prepared. The ligands were first converted to 3D by creating 3D coordinates, by opening the 2D structures in Avogadro. All the 2D structures were transformed into 3D conformers in this manner, and were archived. Once all 165 ligands were in 3D format, they were each manually subjected to a process of geometry optimization and energy minimization. The PDB format was used to record all the processed ligands. In order to add Gasteiger charges, all ligands were converted from PDB to PDBQT format, using the auto dock tools.

2.5. Molecular Docking

The molecular docking process was carried out using Autodock Vina 1.2.0, the most recent version [86,87]. The Grid box was built and positioned in the best possible manner, using the information on the location of the active site, consisting of vital catalytic amino acids for nsp16–nsp10, nsp13, and nsp14. The PDBQT-formatted nsp16–nsp10, nsp13, and nsp14 protein files were firstly opened individually in the program, and 165 ligands were manually docked. For nsp16–nsp10, nsp13, and nsp14, the cavity volume was 461 Å, 1126 Å, and 1960 Å, respectively. For nsp16–nsp10, the grid box coordinates were set to X = −80 Å, Y = 29 Å, and Z = 39 Å. The grid box for nsp13 was located at X = 15 Å, Y = 17 Å, and Z = −72 Å, but for nsp14, the grid box was maintained at X = 13 Å, Y = −10 Å, and Z = −18 Å. In this study, all ligands were flexible, whereas proteins were rigid. To assure the correctness of the results, the docking operation was carried out three times (refer to Table 1, Table 2 and Table 3). To further understand the amino acid interactions, all the protein–ligand complexes in the PDB format were imported by creating 2D and 3D maps illustrating all binding and non-binding residues (see Figure 2, Figure 3, Figure 4, Figure 5, Figure 6, Figure 7 and Figure 8); BIOVIA Discovery Studio Visualizer 4.5 facilitated this task.

2.6. Docking Validation

The crystal structure of nsp14 was attached to its inhibitor S-adenosyl-L-homocysteine (SAH) [77]. Thus, the validation of protocol for docking was performed for the protein nsp14 by redocking said inhibitor in the same pocket. The binding affinity score and RMSD value were noted. Additionally, the amino acid interactions were observed and compared, to ensure the correctness of the docking procedure.

2.7. Molecular Dynamics Simulation

Using the CABS-flex 2.0 website, MDs simulations were performed for the protein–ligand complexes, demonstrating the appropriate amino acid associations [88]. Rapid simulations are used on this platform to adequately assess protein flexibility. The server was loaded with the PDB files for the chosen complexes. The simulation’s specifications, including the number of cycles (50), the distance among the trajectory frames (50), the simulation temperature set at default, as well as the random number generator seed, were set to their default values by the web server. The simulation produced 3D models, contact maps, and graphs of the root-mean-square fluctuation. We conducted comparative analyses using these RMSF graphs (in Figure 9).

2.8. Toxicity Assessment

ProTox-II and StopTox web servers were employed to assess the possible toxicity of the top leads [89]. In a virtual environment, ProTox-II offers quick forecasts regarding the potential toxicity of small molecules, circumventing the requirement for animal experiments. The toxicity class, toxicity endpoints (hepatotoxicity, carcinogenicity, immunotoxicity, mutagenicity and, lastly, cytotoxicity), and LD50 values for the ligands were all provided by this server (see Table 4). The likelihood of ligands causing acute toxicity was quickly and accurately estimated using the user-friendly application StopTox [90]. Information on acute oral, dermal, and inhalation toxicity, as well as on the likelihood of skin and eye irritation and corrosion and skin sensitization, and other critical information, was all provided by this website (refer to Table 5).

2.9. Pharmacokinetics Studies

Using the canonical SMILES of best leads (see Table S12) on the Swiss-ADME server, the drug-like features were examined [91,92]. The first step was to observe and note the five different Lipinski variables (Ro5). The molecular weight (≤500), consensus log p-value (≤5), and hydrogen bond donors and acceptors (≤5) were the criteria. Two additional indicators were included, the topological polar surface area (≤140) and the number of rotatable bonds (≤10), in order to more precisely ascertain the likeliness of the ligands being orally efficacious (in Table 6). The likelihood of GI absorption and water solubility were also noted. A simple mathematical approach was employed to calculate the absorption percentage (AB%) further for an accurate assessment of the GI absorption [93,94] (in Table 7).

A B % = 109 - (0.345 \times T P S A)

Additionally, the Swiss-ADME boiled egg plot was used to assess the cell and blood–brain-barrier penetrability [95] of the lead metabolites (see Figure 10). Ultimately, the PASSonline web server [96] was used to investigate probable bioactivities (refer to Table 8).

3. Results and Discussion

3.1. Binding Affinity Analyses

In order for the results to be easily understood, the docking results for all three targets were categorized into three groups. The detailed binding affinities for all the ligands and targets can be found in the supplementary information (Tables S2–S11).

3.1.1. nsp16–nsp10 Docking

For the nsp16–nsp10 complex, the docking scores obtained were classified into three categories: the lower class, moderate class, and higher class. The moderate class was further divided into a lower-moderate and an upper-moderate class.

The lowest docking scores, which ranged from −4.2 to −5.9 kcal/mol, were observed for 22 metabolites (see Table 1). Elisabethadienol, elisabethin D, furanotriene, and ichthyothereol acetate exhibited the highest docking scores of -5.9 kcal/mol in category 1. Nevertheless, cumbiasin C and cumbiasin B showed the lowest binding scores, measuring −4.2 and −4.7 kcal/mol, respectively (refer to Table S2). A total of 66 NPs in category 2 demonstrated lower-moderate affinities for the nsp16–nsp10 complex. Cumbiasin A, CMNPD12139, CMNPD16185, CMNPD17188, and seco-pseudopterosin J showed the best affinities in this category, at −6.9 kcal/mol. Eight NPs, including calamenene, curcuhydroquinone, elisapterosin E, furanogermacrene, germacrene D, isofuranotriene, methoxyamericanolide I, and pseudopterolide achieved the lowest value of −6.0 kcal/mol. In addition, 68 NPs demonstrated considerably better binding than the NPs listed above (see Table S3). Elisabatin B, and pseudopterosin B, D, and M all showed a docking value of −7.9 kcal/mol, closely followed by pseudopterosin C and E at −7.8 kcal/mol. On the other hand, 19 NPs demonstrated the lowest docking scores (refer to Table S4) in the upper-moderate class. Lastly, category 3 is stated, which consists of nine NPs with the best binding affinities (in Table 1). Among all of the NPs in this group, the docking values varied from −8.0 to −8.2 kcal/mol (see Table S5).

Table 1. Binding affinities of all the NPs for the nsp16–nsp10 protein complex.

Categorization of Ligands nsp16–nsp10
Low	Moderate		High
−4.2 to −5.9 kcal/mol	−6.0 to 6.9 kcal/mol	−7.0 to 7.9 kcal/mol	−8.0 kcal/mol and above
5-Hydroperoxyicosa-2,4,6,8-tetraenoic acid	1-Monoacetate	1β,3β-dihydroxy-5α,6α-epoxy-9-oxo-9,11-secogorgostan-11-ol	Pseudopterosin A
(+)-Aristolone	3-β-Hydroxy-5α,6α-epoxy-9-oxo-9,11-secogorgostan-11-ol	3-O-Methylquercetin	Pseudopterosin H
(+)-α-Terpineol	3-epi-Elisabanolide	15-α-(Angeloyloxy)kaura-16-ene-18-oicacid	Pseudopterosin I
(−)-β-Chamigrene	4-Acetylamphilectolide	Ameristerol A	Pseudopterosin R
β-Gorgonene	7-Hydroxyerogorgiaene	Ameristerenol A	Pseudopterosin T
β-Selinene	7-Hydroxyerogorgiaenone	Amphilectosin A	Pseudopterosin V
Calarene	7,14-Erogorgiaenediol	Amphilectosin B	Pseudopterosin X
Cumbiasin B	8-epi-Americanolide C	Americanolide D	Pseudopterosin Y
Cumbiasin C	8-epi-Methoxyamericanolide A	Americanolide E	Sandresolide B
Cyperene	10-epi-Americanolide C	Americanolide F
Elisabethadienol	10-epi-Methoxyamericanolide A	Amphiphenalone
Elisabethatrienol	12-Acetoxypseudopterolide	Bis-7-Hydroxyerogorgiaene
Elisabethin B	Aberrarone	Caribenol A
Elisabethin C	Ameristerenol B	CMNPD12140
Elisabethin D	Americanolide A	CMNPD12142
Elisabethin G	Americanolide B	CMNPD16184
Elisabetholide	Americanolide C	CMNPD16186
Elisapterosin A	Amphilectolide	Elisabethol
Furanotriene	β-Eudesmol	Elisabatin A
γ-Maaliene	Bicyclogermacrene	Elisabatin B
Ichthyothereol acetate	Biformene	Elisabatin C
Preclavulone A	Caribenol B	Elisabethin A acetate
	Calamenene	Elisabethin D acetate
	Caridiene	Elisapterosin F
	Curcuhydroquinone	Grandifloric acid
	(−)-Curcuquinone	Homopseudopteroxazole
	Colombiasin A	Hyperoside
	Cumbiasin A	Isoquercitrin
	CMNPD5089	Ileabethin
	CMNPD11416	Ileabethoxazole
	CMNPD12137	iso-Pseudopterosin E
	CMNPD12138	Kaempferol
	CMNPD12139	Methoxyamericanolide B
	CMNPD16185	O-Methylelisabethadione
	CMNPD16187	O-Methyl-nor-elisabethadione
	CMNPD17188	Pseudopterosin B
	ent-Kaur-16-en-19-ol	Pseudopterosin C
	Elisabanolide	Pseudopterosin D
	Elisabethamine	Pseudopterosin E
	Elisabethin A	Pseudopterosin F
	Elisabethin E	Pseudopterosin G
	Elisabethin F	Pseudopterosin J
	Elisabethin H	Pseudopterosin K
	Elisapterosin B	Pseudopterosin L
	Elisapterosin C	Pseudopterosin M
	Elisapterosin D	Pseudopterosin N
	Elisapterosin E	Pseudopterosin O
	Erogorgiaene	Pseudopterosin P
	Furanogermacrene	Pseudopterosin Q
	Furanoguaian-4-ene	Pseudopterosin U
	Germacrene D	Pseudopterosin W
	Isofuranotriene	Pseudopterosin Z
	Kaurane-16,18-diol18-acetate	Pseudopterosin G-J aglycone
	Kaurenoicacid	Pseudopteroxazole
	Methoxyamericanolide A	Quercetin
	Methoxyamericanolide E	Taxifolin
	Methoxyamericanolide G	seco-Pseudopterosin A
	Methoxyamericanolide H	seco-Pseudopterosin C
	Methoxyamericanolide I	seco-Pseudopterosin D
	Pseudopterolide	seco-Pseudopterosin E
	Pseudopterosin S	seco-Pseudopterosin F
	Pseudopterosin-MA	seco-Pseudopterosin G
	Quercetin-3-O-β-d-arabinofuranoside	seco-Pseudopterosin H
	seco-Pseudopterosin B	seco-Pseudopterosin I
	seco-Pseudopterosin J	seco-Pseudopterosin K
	seco-Gorgosterol	Sandresolide A
		Sandresolide C
		seco-pseudopteroxazole

3.1.2. nsp13 Docking

By separating the output into the three categories shown below, the docking data for nsp13 were made more comprehensible.

The 36 metabolites in category 1 are those with modest docking scores for the helicase protein (in Table 2). The molecules with the lowest binding values were 12-Acetoxypseudopterolide, β-gorgonene, pseudopterosin methylated aglycone, and calarene. The strongest affinity values in this group, however, were found to be for β-eudesmol, furanotriene, and pseudopterosin G at −5.9 kcal/mol (see Table S6). Furthermore, the 85 metabolites in category 2 had moderate binding affinities, with values between −6.0 and −6.9 kcal/mol. Eight metabolites, including ameristerenol A, caribenol B, CMNPD12140, pseudopterosin J, R, and S, and seco-pseudopterosin B and J demonstrated the best affinity values in this group, whereas 12 metabolites showed the lowest affinity, −6.0 kcal/mol (refer to Table S7). Last but not least, the values for the 44 metabolites with high affinities stated in category 3 ranged from −7.0 kcal/mol to a maximum of −8.2 kcal/mol. The most effective metabolites in this group were CMNPD16187, ileabethoxazole, pseudopterosin T, and seco-pseudopterosin K. Nevertheless, among this set of metabolites, fifteen of them showed the lowest value, which was −7.0 kcal/mol (see Table S8).

Table 2. Binding affinities of all the NPs for the nsp13 protein.

Binding Affinity Values—nsp13
Low	Moderate	High
−5.0 to −5.9 kcal/mol	−6.0 to −6.9 kcal/mol	−7.0 to −8.2 kcal/mol
5-Hydroperoxyicosa-2,4,6,8-tetraenoicacid	1-Monoacetate	3-epi-Elisabanolide
7,14-Erogorgiaenediol	1β,3β-dihydroxy-5α,6α-epoxy-9-oxo-9,11-secogorgostan-11-ol	4-Acetylamphilectolide
12-Acetoxypseudopterolide	3β-Hydroxy-5α,6α-epoxy-9-oxo-9,11-secogorgostan-11-ol	10-epi-Americanolide C
(+)-α-Terpineol	3-O-Methylquercetin	Aberrarone
β-Eudesmol	7-Hydroxyerogorgiaene	Ameristerenol B
β-Selinene	7-Hydroxyerogorgiaenone	Americanolide A
Bicyclogermacrene	8-epi-Americanolide C	Americanolide B
β-Gorgonene	8-epi-Methoxyamericanolide A	Amphilectosin A
Cyperene	10-epi-Methoxyamericanolide A	Amphilectosin B
Calamenene	15-α-(Angeloyloxy)kaura-16-ene-18-oicacid	Amphilectolide
Calarene	(+)-Aristolone	Amphiphenalone
Caridiene	Amersiterol A	Colombiasin A
CMNPD12139	Ameristerenol A	Cumbiasin A
CMNPD16184	Americanolide C	Cumbiasin B
CMNPD16185	Americanolide D	CMNPD12142
Elisabethol	Americanolide E	CMNPD16186
Elisabethin A acetate	Americanolide F	CMNPD16187
Elisabethin B	Biformene	Elisabatin A
Elisabethin C	Bis-7-hydroxyerogorgiaene	Elisapterosin A
Elisabethin D acetate	(−)-β-Chamigrene	Elisapterosin B
Elisabethin E	(−)-Curcuquinone	Elisapterosin D
Elisabethin G	Curcuhydroquinone	Elisapterosin E
Elisabetholide	Cumbiasin C	Elisapterosin F
Furanogermacrene	Caribenol A	Ileabethin
Furanotriene	Caribenol B	Ileabethoxazole
γ-Maaliene	CMNPD5089	Isoquercitrin
Germacrene D	CMNPD11416	Kaempferol
Grandifloric acid	CMNPD12137	Kaurenoicacid
Ichthyothereol acetate	CMNPD12138	O-Methyl-nor-elisabethadione
Isofuranotriene	CMNPD12140	Pseudopterosin K
Kaurane-16,18-diol18-acetate	CMNPD17188	Pseudopterosin T
Pseudopterolide	Elisabanolide	Pseudopterosin U
Pseudopterosin D	Elisabatin B	Pseudopterosin V
Pseudopterosin G	Elisabatin C	Pseudopteroxazole
Pseudopterosin-MA	Elisabethadienol	Quercetin
Sandresolide A	Elisabethamine	Quercetin3-O-beta-d-arabinofuranoside
	Elisabethatrienol	seco-Pseudopterosin E
	Elisabethin A	seco-Pseudopterosin F
	Elisabethin D	seco-Pseudopterosin G
	Elisabethin F	seco-Pseudopterosin H
	Elisabethin H	seco-Pseudopterosin I
	Elisapterosin C	seco-Pseudopterosin K
	ent-Kaur-16-en-19-ol	Sandresolide C
	Erogorgiaene	Taxifolin
	Furanoguaian-4-ene
	Homopseudopteroxazole
	Hyperoside
	iso-Pseudopterosin E
	Methoxyamericanolide A
	Methoxyamericanolide B
	Methoxyamericanolide E
	Methoxyamericanolide G
	Methoxyamericanolide H
	Methoxyamericanolide I
	O-Methylelisabethadione
	Preclavulone A
	Pseudopterosin A
	Pseudopterosin B
	Pseudopterosin C
	Pseudopterosin E
	Pseudopterosin F
	Pseudopterosin H
	Pseudopterosin I
	Pseudopterosin J
	Pseudopterosin L
	Pseudopterosin M
	Pseudopterosin N
	Pseudopterosin O
	Pseudopterosin P
	Pseudopterosin Q
	Pseudopterosin R
	Pseudopterosin S
	Pseudopterosin W
	Pseudopterosin X
	Pseudopterosin Y
	Pseudopterosin Z
	Pseudopterosin G-J aglycone
	seco-Gorgosterol
	seco-Pseudopterosin A
	seco-Pseudopterosin B
	seco-Pseudopterosin C
	seco-Pseudopterosin D
	seco-Pseudopterosin J
	seco-Pseudopteroxazole
	Sandresolide B

3.1.3. nsp14 Docking

Studies on binding affinities have shown that Antillogorgia genus metabolites might prove more effective against the SARS-CoV-2 nsp14 protein, compared to other nonstructural proteins. As with the other protein targets described above, the docking results are shown in the table below. The numbers we obtained for nsp14, however, were not considered to be low. The output was therefore split into three categories: moderate, high, and extremely high.

A total of 32 metabolites with moderate docking scores, ranging from −6.5 kcal/mol to −7.9 kcal/mol, are displayed in category 1 (in Table 3). Elisabethin F and aurene-16, 18-diol-18-acetate had the maximum value of −7.9 kcal/mol, whereas (−)-β-chamigrene, bicyclogermacrene, and germacrene D displayed the lowest value, of −7.0 kcal/mol, in this group (see Table S9). Category 2, listed in the table above, contains 95 metabolites with high docking scores ranging from −8.0 kcal/mol to −9.9 kcal/mol. Seco-pseudopterosin B came in second place, with a docking score of −9.8 kcal/mol, closely behind ileabethin and pseudopterosin V. Eleven NPs showed the lowest affinities in the group, with a −8.0 kcal/mol value (refer to Table S10). Finally, category 3 refers to the 38 NPs that showed the strongest binding scores, which were −10.0 kcal/mol and higher. Pseudopterosin O, and elisabatin B and C demonstrated the highest score of −11.2 kcal/mol, whereas 14 NPs exhibited the lowest docking score in this group, at −10 kcal/mol (see Table S11).

Table 3. Binding affinities of all the metabolites for the nsp14 protein.

Categorization of Ligands—nsp14
Moderate	High	Very High
−6.5 to −7.9 kcal/mol	−8.0 to −9.9 kcal/mol	−10.0 kcal/mol and above
1-Monoacetate	1β,3β-Dihydroxy-5α,6α-epoxy-9-oxo-9,11-secogorgostan-11-ol	Amphilectosin A
5-Hydroperoxyicosa-2,4,6,8-tetraenoicacid	3β-Hydroxy-5α,6α-epoxy-9-oxo-9,11-secogorgostan-11-ol	Amphilectosin B
8-epi-Methoxyamericanolide A	3-O-Methylquercetin	Amersiterol A
10-epi-Methoxyamericanolide A	3-epi-Elisabanolide	Cumbiasin C
Americanolide E	4-Acetylamphilectolide	CMNPD16186
Americanolide F	7-Hydroxyerogorgiaene	Elisabatin A
(+)-α-Terpineol	7-Hydroxyerogorgiaenone	Elisabatin B
(+)-Aristolone	7,14-erogorgiaenediol	Elisabatin C
(−)-β-Chamigrene	8-epi-Americanolide C	Elisapterosin A
β-Eudesmol	10-epi-Americanolide C	Homopseudopteroxazole
β-Selinene	12-Acetoxypseudopterolide	Ileabethoxazole
β-Gorgonene	15alpha-(Angeloyloxy)kaura-16-ene-18-oicacid	Iso-pseudopterosin E
Bicyclogermacrene	Aberrarone	Pseudopterosin A
Calarene	Ameristerenol A	Pseudopterosin D
Cyperene	Ameristerenol B	Pseudopterosin E
Curcuhydroquinone	Americanolide A	Pseudopterosin G
(−)-Curcuquinone	Americanolide B	Pseudopterosin I
ent-Kaur-16-en-19-ol	Americanolide C	Pseudopterosin J
Elisabethin C	Americanolide D	Pseudopterosin M
Elisabethin D	Amphilectolide	Pseudopterosin N
Elisabethin F	Amphiphenalone	Pseudopterosin O
Elisabethin G	Bis-7-hydroxyerogorgiaene	Pseudopterosin P
Elisabethin H	Biformene	Pseudopterosin Q
Furanogermacrene	Calamenene	Pseudopterosin R
Furanotriene	Caridiene	Pseudopterosin S
γ-Maaliene	Colombiasin A	Pseudopterosin T
Germacrene D	Cumbiasin A	Pseudopterosin U
Ichthyothereol acetate	Cumbiasin B	Pseudopterosin W
Isofuranotriene	Caribenol A	Pseudopterosin X
Kaurane-16,18-diol-18-acetate	Caribenol B	Pseudopterosin Y
Methoxyamericanolide A	CMNPD5089	Pseudopterosin Z
Preclavulone A	CMNPD11416	Pseudopteroxazole
	CMNPD12137	Pseudopterolide
	CMNPD12138	seco-Pseudopterosin E
	CMNPD12139	seco-Pseudopterosin F
	CMNPD12140	seco-Pseudopterosin G
	CMNPD12142	seco-Pseudopterosin H
	CMNPD16184	Sandresolide C
	CMNPD16185
	CMNPD16187
	CMNPD17188
	Elisabethol
	Elisabanolide
	Elisabethadienol
	Elisabethamine
	Elisabethatrienol
	Elisabethin A
	Elisabethin A acetate
	Elisabethin B
	Elisabethin D acetate
	Elisabethin E
	Elisabetholide
	Elisapterosin B
	Elisapterosin C
	Elisapterosin D
	Elisapterosin E
	Elisapterosin F
	Erogorgiaene
	Furanoguaian-4-ene
	Grandifloric acid
	Hyperoside
	Isoquercitrin
	Ileabethin
	Kaempferol
	Kaurenoicacid
	Methoxyamericanolide B
	Methoxyamericanolide E
	Methoxyamericanolide G
	Methoxyamericanolide H
	Methoxyamericanolide I
	O-Methylelisabethadione
	O-Methyl-nor-elisabethadione
	Pseudopterosin B
	Pseudopterosin C
	Pseudopterosin F
	Pseudopterosin H
	Pseudopterosin K
	Pseudopterosin L
	Pseudopterosin V
	Pseudopterosin G-J aglycone
	Pseudopterosin-MA
	Quercetin
	Quercetin-3-O-beta-d-arabinofuranoside
	seco-Pseudopterosin A
	seco-Pseudopterosin B
	seco-Pseudopterosin C
	seco-Pseudopterosin D
	seco-Pseudopterosin I
	seco-Pseudopterosin J
	seco-Pseudopterosin K
	Sandresolide A
	Sandresolide B
	seco-Gorgosterol
	seco-pseudopteroxazole
	Taxifolin

3.1.4. Docking Protocol Validation—nsp14

After being re-docked, the original inhibitor gave a docking score of −7.5 kcal/mol, and the RMSD value for the superimposed crystal structures was calculated as 0.89 Å. The computed RMSD value was below the threshold of 2.0 Å, indicating that the docking operation was carried out with good precision.

3.2. Amino Acid Interactions

To find complexes exhibiting optimum amino acid interactions in accordance with the criteria listed below, 495 docked complexes for all three SARS-CoV-2 nonstructural proteins were rigorously examined.

3.2.1. Selection Criteria to Filter Candidate Complexes

The complex must interact with key catalytic residues
It must interact by forming hydrogen and/or electrostatic bonds
The bond distance for conventional hydrogen must not exceed (Å ≤ 3.00)
The bond distance for electrostatic bonds must not exceed (Å ≤ 5.00)

Twelve docked complexes that satisfied the established criteria and, in our opinion, might serve as effective inhibitors were found after a thorough screening approach for the nsp16–nsp10 complex, nsp13, and nsp14 proteins. NPs that solely exhibited hydrophobic interactions (VdW force) and/or no interactions with the concerned residues were eliminated as prospective candidates.

3.2.2. Selected Docked Complexes

Just three docked complexes out of 165 satisfied the selection requirements for the target nsp16–nsp10 protein complex. The important amino acids for the nsp16–nsp10 complex are LYS: 46, ASP: 130, LYS: 170, and GLU: 203. The three NPs that demonstrated desirable residue interactions involving the aforementioned amino acids are shown in Figure 3 and Figure 4 below.

Figure 3. Display of the selected potential inhibitors of the nsp16–nsp10 protein complex docked in the active site.

Figure 4. Display of the amino acid interactions with the nsp16–nsp10 complex protein and the bond distances formed: (A) nsp16–nsp10-pseudopterosin A, (B) nsp16–nsp10-secopseudopterosin A, and (C) nsp16–nsp10-amersiterenol A.

In Figure 4, pseudopterosin A connects to residue GLU: 203 by two conventional hydrogen bonds (2.34 Å and 2.48 Å). Hydrophobic interactions with LYS: 46 and LYS: 170 were observed. Moreover, ASP: 130 and seco-pseudopterosin A interacted by creating a single hydrogen bond (2.61 Å). Using LYS: 170 (4.97 Å), an alkyl bond was created. A weak Van der Waals bond was observed with LYS: 46 and GLU: 203. Ultimately, a hydrogen bond (2.43 Å) was created between ameristerenol A and GLU: 203. Nevertheless, LYS: 46 and LYS: 170 showed evidence of hydrophobic interactions.

Five docked complexes from the 165 complexes passed the selection criteria for the target nsp13 protein. The critical residues for this enzyme were PHE: 367, ASN: 388, and PRO: 429. The Figure 5 and Figure 6 below present the five NPs that showed desirable amino acid interactions involving the three stated residues.

Figure 5. The five candidate inhibitors docked in the active site of the nsp13 protein.

Figure 6. The amino acid interactions with the nsp13 protein, and the bond distances formed: (A) nsp13–sandresolide B, (B) nsp13–amphilectosin A, (C) nsp13–amphilectosin B, (D) nsp13–pseudopteroxazole, and (E) nsp13–elisabatin A.

Figure 6 shows a single hydrogen bond between sandresolide B and ASP: 374. (2.43 Å). With the residue LYS: 288, one carbon–hydrogen bond (2.86 Å) and one alkyl bond (5.00 Å) were noted. Hydrophobic connections were formed with SER: 289; GLU: 375; GLN: 404; and ARG: 567. GLN: 404 and amphilectosin A formed a hydrogen bond (2.98 Å). For GLU: 375, a carbon–hydrogen bond was seen (3.74 Å). The LYS: 288, SER: 289, ASP: 374, and ARG: 567 were seen to interact with the VdW force. A carbon–hydrogen bond was created by amphilectosin B with GLU: 375 (3.69 Å), and a hydrogen bond with GLN: 404 (2.99 Å). Hydrophobic connections were observed with LYS: 288, SER: 289, ASP: 374, and ARG: 567. Moreover, no hydrogen bonds were formed between the relevant residues and pseudopteroxazole. However, it generated three electrostatic bonds: one pi–cation link with ARG: 567 (4.32 Å), and two more pi–cation linkages with LYS: 288 (3.25 Å and 3.91 Å). SER: 289, ASP: 374, GLU: 375, and GLN: 404 interacted with Van der Waals forces. Finally, elisabatin A and GLU: 375 were connected via a hydrogen bond (2.60 Å). With LYS: 288, there were two pi–cation connections (4.00 Å and 4.12 Å). Hydrophobic interactions with ASP: 374, SER: 289, GLN: 404, and ARG: 567 were also observed.

Just four docked complexes, out of 165, met the inhibitory requirements for the target nsp14 protein around six residues: LYS: 288, SER: 289, ASP: 374, GLU: 375, GLN: 404, and ARG: 567. The four NPs that demonstrated advantageous interactions with the residues of importance are shown in Figure 7 and Figure 8 below.

Figure 7. Four potential inhibitors of the nsp14 protein docked in the active site.

Figure 8. The amino acid interactions with the nsp14 protein, and the bond distances formed: (A) nsp14–elisapterosin A, (B) nsp14–ameristerol A, (C) nsp14–pseudopterolide, and (D) nsp14–pseudopterosin R.

In Figure 8, we can see that elisapterosin A interacted with all three of the critical residues of nsp14. A conventional hydrogen bond (2.96 Å) was formed with ASN: 388. One alkyl bond (4.86 Å) was made with PHE: 367, and one pi–alkyl bond (4.35 Å) with PRO: 429, respectively. Amersiterol A produced a pi–alkyl bond (4.07 Å) with PRO: 429, and two alkyl bonds with PHE: 367 (4.49 Å and 4.94 Å). Furthermore, pseudopterolide made a carbon–hydrogen connection (3.64 Å) with PRO: 429, and two electrostatic linkages with PHE: 367 were evident (4.49 Å and 4.76 Å). Lastly, pseudopterosin R interacted strongly by forming six electrostatic bonds. Three bonds were made with PHE: 367 (4.20 Å, 4.24 Å, and 4.57 Å), and the other three bonds were created with PRO: 429 (4.07 Å, 4.65 Å, and 4.71 Å). Elisapterosin A being an exception, the other three NPs displayed only hydrophobic interactions with the amino acid ASN: 388.

3.2.3. MDs Simulations

Throughout the simulation (in Figure 9), the three docked nsp16–nsp10 complexes revealed minimal residue variations for the four essential catalytic amino acids. The residue LYS: 46 (0.180 Å) has the lowest RMSF score for pseudopterosin A, followed by LYS: 170 (0.225 Å), GLU: 203 (0.364 Å), and ASP: 130. (0.441 Å). The least fluctuating amino acid in the case of seco-pseudopterosin A was LYS: 170 (0.108 Å), followed by ASP: 130 (0.174 Å), GLU: 203 (0.279 Å), and LYS: 46. (0.324 Å). Last but not least, ameristerenol A bound with the nsp16–nsp10 complex, which also highlighted the residue LYS: 170 as being the least fluctuating (0.108 Å) among all the other amino acids, along with ASP: 130 (0.153 Å), LYS: 46 (0.325 Å), and GLU; 203. (0.673 Å).

Figure 9. RMSF Plots for MDs simulations: (A) nsp16–nsp10-pseudopterosin A, (B) nsp16–nsp10-secopseudopterosin A, (C) nsp16–nsp10-amersiterenol A, (D) nsp13−Sandresolide B, (E) nsp13–amphilectosin A, (F) nsp13–amphilectosin B, (G) nsp13–pseudopteroxazole, (H) nsp13–elisabatin A, (I) nsp14–elisapterosin A, (J) nsp14–ameristerol A, (K) nsp14–pseudopterolide, and (L) nsp14–pseudopterosin R. The red lines mark the locations of the residues.

The helicase protein of SARS-CoV-2, when docked with sandresolide B, obtained the complex with the least fluctuating residue ASP: 374 (0.283 Å), whereas ARG: 567 showed the highest RMSF value, 1.638 Å. The amphilectosin A and B complexes showed the highest fluctuations for the amino acid GLN: 404, whereas the lowest RMSF score was seen for ASP: 374 (0.083 Å and 0.145 Å, respectively). Furthermore, in the case of pseudopteroxazole, LYS: 288 showed the maximum fluctuation value, at 1.098 Å. On the other hand, the minimal fluctuating residue was ASP: 374, with an RMSF value of 0.265 Å. Lastly, for the elisabatin A–nsp13 complex, the lowest RMSF score was evident for the amino acid ARG: 567 (0.295 Å), whereas LYS: 288 fluctuated the most (0.760 Å), compared to the other residues.

The elisapterosin A–nsp14 protein docked complex demonstrated that ASN: 388 fluctuated the least (0.919 Å), compared to PHE: 367 (1.176 Å), and PRO: 429 (1.459 Å). The nsp14 docked with ameristerol A showed PHE: 367 to be the least fluctuating (0.698 Å), whereas ASN: 388 had the slightly higher RMSF value of 0.850 Å, and PRO: 429 demonstrated the highest fluctuation score, 1.099 Å. The results for pseudopterolide displayed the highest fluctuation for the residue ASN: 388 (1.016 Å), followed by PRO: 429 (0.926 Å). On the other hand, the PHE: 367 residue fluctuated the least, at 0.713 Å. Finally, pseudopterosin R also showed PHE: 367 as the least fluctuating amino acid, with an RMSF value of 0.940 Å. Comparatively, ASN: 388 and PRO: 429 had the slightly higher RMSF values of 1.015 Å and 1.020 Å, respectively. All the complexes were found to be far below the 3.0 Å criterion. Thus, all twelve NPs formed a stable conformation with the proteins of interest.

3.3. Toxicity Evaluation

The toxic properties of the selected natural products were investigated, using the ProTox-II (see Table 4) and StopTox (see Table 5) web servers.

Table 4. The ProTox-II server results for the selected NPs.

ProTox-II Toxicity Report
Top Ligands	Toxicity Values		Probability
Top Ligands	LD50 mg/kg	Toxicity Class	Hepatotoxicity	Carcinogenicity	Immunotoxicity	Mutagenicity
Amphilectosin A	3000	5	Inactive	Inactive	Active	Inactive
Amphilectosin B	3000	5	Inactive	Inactive	Active	Inactive
Ameristerenol A	2842	5	Inactive	Inactive	Inactive	Inactive
Ameristerol A	750	4	Inactive	Inactive	Active	Inactive
Elisapterosin A	50	2	Inactive	Inactive	Active	Inactive
Elisabatin A	220	3	Inactive	Inactive	Active	Inactive
Pseudopterosin A	3000	5	Inactive	Inactive	Active	Inactive
Pseudopterosin R	3000	5	Inactive	Inactive	Active	Inactive
Pseudopterolide	274	3	Inactive	Inactive	Inactive	Active
Pseudopteroxazole	1600	4	Inactive	Inactive	Inactive	Inactive
Sandresolide B	34	2	Inactive	Active	Inactive	Inactive
seco-Pseudopterosin A	3000	5	Inactive	Inactive	Active	Inactive

Table 5. The StopTox server results for the chosen NPs.

StopTox Acute Toxicity Report
Top Ligands	Endpoints
Top Ligands	Inhalation	Oral	Dermal	Irritation and Corrosion	Skin Sensitization
Amphilectosin A	Non-Toxic	Non-Toxic	Non-Toxic	Eyes (−) Skin (−)	Non-Sensitizer
Amphilectosin B	Non-Toxic	Non-Toxic	Non-Toxic	Eyes (−) Skin (−)	Non-Sensitizer
Ameristerenol A	Non-Toxic	Non-Toxic	Non-Toxic	Eyes (−) Skin (−)	Non-Sensitizer
Ameristerol A	Non-Toxic	Non-Toxic	Non-Toxic	Eyes (−) Skin (−)	Non-Sensitizer
Elisapterosin A	Non-Toxic	Non-Toxic	Non-Toxic	Eyes (−) Skin (−)	Non-Sensitizer
Elisabatin A	Toxic	Non-Toxic	Toxic	Eyes (−) Skin (−)	Sensitizer
Pseudopterosin A	Non-Toxic	Non-Toxic	Non-Toxic	Eyes (−) Skin (−)	Non-Sensitizer
Pseudopterosin R	Non-Toxic	Non-Toxic	Non-Toxic	Eyes (−) Skin (−)	Non-sensitizer
Pseudopterolide	Toxic	Toxic	Toxic	Eyes (+) Skin (−)	Non-Sensitizer
Pseudopteroxazole	Non-Toxic	Non-Toxic	Non-Toxic	Eyes (−) Skin (−)	Sensitizer
Sandresolide B	Non-Toxic	Non-Toxic	Non-Toxic	Eyes (−) Skin (−)	Non-Sensitizer
seco-Pseudopterosin A	Non-Toxic	Non-Toxic	Non-Toxic	Eyes (−) Skin (−)	Non-Sensitizer

3.3.1. ProTox-II Results

The ProTox-II program categorizes ligands into six toxicity classes (a higher number means lower toxicity) on the basis of LD50 scores, which is an estimate of the median fatal dosage at which 50% of the test subjects die upon oral exposure. Moreover, the risk of liver damage, a propensity to induce cancer, adverse immune system effects, and the potential to produce genetic mutations are also evaluated.

Among the twelve NPs, only six NPs were found to be class 5 members (2000 < LD50 ≤ 5000), suggesting their probable non-toxic nature, and were found to be generally harmless when exposed orally. The NPs ameristerol A and pseudopteroxazole were the class 4 (300 < LD50 ≤ 2000) candidates, suggesting a low probability of being toxic when ingested orally. Elisabatin A and pseudopterolide were found to be Class 3 (50 < LD50 ≤ 300), indicating that they could be harmful when consumed. Only two NPs (5 < LD50 ≤ 50) belonged to class 2. They had a significant likelihood of becoming lethal when consumed. Sandresolide B and pseudopterolide, from class 2 and 3, might be a carcinogen and a mutagen, respectively. None of the proposed NPs caused hepatotoxicity. A slight immunotoxicity is desirable, as immunosuppression and immunostimulation to treat SARS-CoV-2 is of paramount importance. To summarize, around half of our recommended NPs may be considered safe to develop into oral drugs.

3.3.2. StopTox Server Output

The estimates of the probability of acute toxicity caused by the NPs were evaluated by the StopTox server.

According to our acute toxicity analysis, the majority of the selected NPs are safe. Just two NPs, elisabatin A and pseudopterolide, showed a probability of toxicity following inhalation and cutaneous exposure. Moreover, pseudopterolide was also found capable of causing oral toxicity. Moreover, both NPs have the potential to produce skin sensitivity. The likelihood of the NPs causing skin and ocular irritation and corrosion is minimal, except for pseudopterolide, which could cause eye irritation.

3.4. Drug-Likeness Evaluation

Checking the potential drug-like characteristics of the candidate compounds through in silico research is practically valuable in order to developing them into new therapeutics. Using a set of guidelines known as Lipinski or Pfizer’s rule of five, the drug-like characteristics of all the nominated NPs in this study were investigated (see Table 6).

Table 6. The drug-likeness assessment of the shortlisted NPs.

Drug-Likeness Assessment
Top Ligands	Mol. Weight (MW ≤ 500)	Rotatable Bonds RB ≤ 10	H Bond Acceptors HBA ≤ 10	H Bond Donors HBD ≤ 5	C Log p Log p ≤ 5	TPSA (Å²) ≤ 140
Amphilectosin A	432.55	5	6	4	3.56	99.38
Amphilectosin B	432.55	5	6	4	3.56	99.38
Ameristerenol A	440.7	4	2	1	6.57	29.46
Ameristerol A	456.7	7	3	2	5.81	57.53
Elisapterosin A	348.43	0	5	2	2.08	83.83
Elisabatin A	310.39	1	3	1	3.71	54.37
Pseudopterosin A	432.55	3	6	4	3.34	99.38
Pseudopterosin R	488.61	5	7	3	4.06	105.45
Pseudopterolide	370.40	4	6	0	3.11	78.27
Pseudopteroxazole	309.45	1	2	0	5.31	26.03
Sandresolide B	320.42	1	4	2	2.91	66.76
seco-Pseudopterosin A	434.57	6	6	4	3.57	99.38

3.4.1. Lipinski Rule

Drug development commonly employs Lipinski’s Ro5. This criterion makes it possible to determine the likelihood of biologically active substances possessing the desired chemical and physical characteristics necessary for oral bioavailability and absorption.

All of the NPs that were shortlisted for this study followed the majority of the Lipinski principles. All molecules had molecular weights that were below the threshold of 500 Dalton. Moreover, the restriction of no more than ten rotatable bonds was not breached. The acceptors for hydrogen bonds were likewise discovered to be below ten. None of the candidate NPs went beyond the permitted limit of five hydrogen bond donors. Nonetheless, the consensus log p-value was slightly exceeded by ameristerenol A and pseudopteroxazole. The non-Lipinski parameter for the topological surface area cannot be more than 140 Å, and all of the NPs complied with this requirement as well. At least four of the five requirements must be met in order to pass Pfizer’s or Lipinski’s rule. As a result, all the NPs seem reasonable in terms of pharmaceutical drug development against viral diseases such as SARS-CoV-2.

3.4.2. Swiss-ADME

All of the selected NPs were put through the scanner on the Swiss-ADME website (see Table 7), which offers vital information regarding the absorption, distribution, metabolism, and excretion of the questioned molecules.

Table 7. The ADME results for the filtered NPs.

Swiss-ADME Output
Top Ligands	Water Solubility	Bioavailability	GI Absorption	Absorption (%)	BBB Permeant
Amphilectosin A	Soluble	0.55	High	74.71	No
Amphilectosin B	Soluble	0.55	High	74.71	No
Ameristerenol A	Poor	0.55	Low	98.83	No
Ameristerol A	Moderate	0.55	High	89.15	No
Elisapterosin A	Soluble	0.55	High	80.07	No
Elisabatin A	Moderate	0.55	High	90.24	Yes
Pseudopterosin A	Soluble	0.55	High	74.71	No
Pseudopterosin R	Moderate	0.55	High	72.62	No
Pseudopterolide	Soluble	0.55	High	81.99	Yes
Pseudopteroxazole	Moderate	0.55	High	100.00	No
Sandresolide B	Soluble	0.55	High	85.96	Yes
seco-Pseudopterosin A	Soluble	0.55	High	74.71	No

Of the twelve NPs, it was determined that seven were easily soluble in water. The moderate solubility of the other four NPs, namely ameristerol A, elisabatin A, pseudopterosin R, and pseudopteroxazole, is probable. Contrarily, only ameristerenol A is weakly soluble, and may not absorb well through the GI tract. All the NPs were shown to have high GI absorption values and good bioavailability scores, however. The highest AB% score was calculated for pseudopteroxazole. Just three of the twelve NPs screened positive for blood–brain barrier penetration (in Figure 10).

Figure 10. The Swiss-ADME boiled egg for the twelve shortlisted NPs.

The only natural product that the GI tract cannot passively absorb is ameristerenol A. There is a higher probability of Elisabatin A and Sandresolide B being the penetrators of the blood–brain barrier. Pseudopterolide, on the other hand, seems to be virtually partly permeable. The remaining eight NPs, including amphilectosin A and B, ameristerol A, elisapterosin A, pseudopterosin A and R, pseudopteroxazole, and seco-pseudopterosin A may be absorbed passively through the GI tract. This analysis, taken as a whole, shows positive results for some of the NPs we proposed.

3.4.3. Bioactivity Prediction

The filtered ten NPs were screened for their potential bioactivities, to estimate the usefulness of these products as potential anti-SARS-CoV-2 agents.

Except for the three NPs named pseudopterolide, pseudopteroxazole, and sandresolide B, nine of the twelve NPs were anticipated to have anti-viral properties. Pseudopterolide, however, was shown to be a viral entrance inhibitor, and might also be effective in the treatment of severe acute respiratory syndromes. All the NPs had a significant likelihood of affecting the host’s immune system (in Table 8). The immune system needs to be strengthened to combat the illness in the early phases of infection. In contrast, the immune system has to be restrained in the later stages, to prevent inflammation, morbidity and, ultimately, the death of the patient [97,98]. Eleven NPs were predicted to be anti-inflammatory, except for seco-pseudopterosin A. Thus, the NPs mentioned above would be capable of either increasing or reducing the immune system activity. Depending on the stage of infection, the NPs could be chosen for treatment. Apart from seco-pseudopterosin A, eleven NPs were projected to have anti-inflammatory properties. Hence, the NPs indicated above showed the ability to alter the activity of the immune system. The NPs used for therapy might vary according to the stage of infection.

Table 8. The predicted biological activities of the candidate NPs.

PASSonline Program
Top Ligands	Predicted Relevant Bioactivity
Amphilectosin A	Antifungal, Antibiotic, Anti-diabetic, Anti-infective, Anti-viral, Antibacterial, Anti-parasitic, Anticancer, Antioxidant, Beta glucuronidase inhibitor, Histidine kinase inhibitor, 1,3-β-Glucan synthase inhibitor, Anti-inflammatory, Immunosuppressant, Immunomodulator, Interferon antagonist, Interferon gamma antagonist, Transcription factor NF kappa B stimulant, Wound healing agent, Free radical scavenger, Expectorant, and a Cytokine release inhibitor.
Amphilectosin B	Antifungal, Antibiotic, Anti-diabetic, Anti-infective, Anti-viral, Antibacterial, Anti-parasitic, Anticancer, Antioxidant, Beta glucuronidase inhibitor, Histidine kinase inhibitor, 1,3-β-Glucan synthase inhibitor, Anti-inflammatory, Immunosuppressant, Immunomodulator, Interferon antagonist, Interferon gamma antagonist, Transcription factor NF kappa B stimulant, Wound healing agent, Free radical scavenger, Expectorant, and a Cytokine release inhibitor.
Ameristerenol A	Antifungal, Anti-viral, Anti-inflammatory, Antineoplastic, 1,3-Beta-glucan synthase inhibitor, Transcription factor NF kappa B stimulant, Immunosuppressant, Respiratory analeptic, Antioxidant, Interferon antagonist, Interferon gamma antagonist, and an Interleukin 2 agonist.
Ameristerol A	Anti-viral, Antioxidant, Antifungal, Antibacterial, 1,3-Beta-glucan synthase inhibitor, Mucolytic, Respiratory analeptic, Transcription factor NF kappa B inhibitor, Expectorant, Anti-inflammatory steroid, Interferon gamma antagonist, Interleukin 2 agonist and an Immunosuppressant.
Elisapterosin A	Antiviral, Antifungal, Antioxidant, Histidine kinase inhibitor, Anti-parasitic, Antibacterial, JAK2 expression inhibitor, Interferon gamma antagonist, Immunosuppressant, Transcription factor NF kappa B stimulant, Tumour necrosis factor alpha release inhibitor, Cytokine release inhibitor, TNF expression inhibitor, Interleukin 2 agonist, Interleukin 1a and 10 antagonist, T cell inhibitor, RdRp Inhibitor, Respiratory analeptic, and an Anti-inflammatory steroid.
Elisabatin A	Antineoplastic, Anti-viral, Antifungal, histidine kinase inhibitor, β-Glucuronidase inhibitor, Anti-inflammatroy, Antibacterial, Anti-parasitic, Antimutagenic, hepatoprotectant, Immunosuppressant, JAK2 expression inhibitor, MMP9 expression inhibitor, TNF expression inhibitor, Cytokine release inhibitor, T cell inhibitor, Interleukin 10 antagonist, Interferon antagonist.
Pseudopterosin A	Anti-viral, Anti-infective, Antibiotic, Antineoplastic, Antioxidant, Anticancer, Free radical scavenger, Antibacterial, Antifungal, 1,3-β-Glucan synthase inhibitor, Beta glucuronidase inhibitor, Wound healing agent, Immunosuppressant, Anti-inflammatory, Transcription factor NF kappa B stimulant, Interleukin 2 agonist, Cytokine release inhibitor, Interferon antagonist, Interferon gamma antagonist, T cell inhibitor, Expectorant
Pseudopterosin R	Anti-bacterial, Anti-parasitic, Anti-infective, Anti-viral, Antifungal, Antifungal enhancer, β –Glucuronidase inhibitor, 1,3-β-Glucan synthase inhibitor, Antineoplastic, Anti-inflammatory, Immunosuppressant, Interleukin 6, 10 antagonist, Interferon antagonist, Interferon gamma antagonist, Cytokine release inhibitor, T cell inhibitor, GRP78 expression inhibitor, Expectorant
Pseudopterolide	Anti-inflammatory, Antioxidant, Antineoplastic, Antifungal, Antifungal enhancer, β-Glucuronidase inhibitor, Transcription factor NF kappa B stimulant, Antibacterial, Anti-parasitic, Histidine kinase inhibitor, Immunosuppressant, Antibiotic, Expectorant, Interleukin 10 antagonist, Severe acute respiratory syndrome treatment, Antineoplastic alkaloid, Interferon gamma antagonist, Cytokine release inhibitor, and a Viral entry inhibitor.
Pseudopteroxazole	Antibacterial, Antibiotic, Anti-parasitic, Antineoplastic alkaloid, Anti-inflammatory, Antioxidant, transcription factor NF kappa B stimulant, and an Immunosuppressant.
Sandresolide B	Antifungal, Antifungal enhancer, Mucolytic, Antibacterial, Antibiotic, Antineoplastic alkaloid, Anti-parasitic, Anti-infective, Immunosuppressant, Transcription factor NF kappa B stimulant, Interferon antagonist, Cytokine release inhibitor, Interferon gamma antagonist, T cell inhibitor, JAK2 expression inhibitor, TNF expression inhibitor, Macrophage colony stimulating factor agonist, Anti-inflammatory, Respiratory analeptic, Histidine kinase inhibitor, Beta glucuronidase inhibitor, Leukotriene agonist, and an Interleukin 2, 6, 10 antagonist.
Seco-Pseudopterosin A	Anti-viral, Anti-infective, Anti-tuberculosic, Anticancer, Antioxidant, Free radical scavenger, Antibiotic, Anti-diabetic, Antineoplastic, Antibacterial, Anti-parasitic, Antifungal, Mucolytic, Histidine kinase inhibitor, Immunosuppressant, Anti-inflammatory, Wound healing agent, Transcription factor NF kappa B stimulant, β –Glucuronidase inhibitor, 1,3-β-Glucan synthase inhibitor, Expectorant, Respiratory analeptic, Interleukin 2 and 12 agonist, and a Cytokine release inhibitor.

4. Conclusions

In conclusion, the COVID-19 pandemic has already claimed millions of lives [99], and continues to pose a threat to human health. The non-structural proteins of SARS-CoV-2 undoubtedly play important roles in infection [100,101,102,103]. Thus, these protein targets are important in drug development to tackle the virus. Our study demonstrated the potential of natural compounds from two species of soft coral, A. americana, and A. elisabethae, to combat SARS-CoV-2. In light of our findings, only 12 out of 165 NPs may function as inhibitors of the nonstructural proteins; namely, nsp16–nsp10, nsp13, and nsp14. With the catalytic residues of the target proteins, five NPs showed advantageous residue interactions. These five NPs were pseudopterosin A, seco-pseudopterosin A, sandresolide B, elisabatin A, and elisapterosin A. Fast MDs simulations also showed that all the docked complexes were stable. To validate their anti-viral propensities, in vivo and in vitro research must be conducted. Overall, the majority of the NPs shortlisted passed toxicity and pharmacokinetic assessments. Therefore, these NPs, with minimal laboratory interventions, can be transformed into novel therapeutics to treat SARS-CoV-2 infection.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/microbiolres14030068/s1, Figure S1: Protein crystal structures and their respective active sites (A) nsp16–nsp10 complex (B) nsp13 (C) nsp14; Table S1: List of NPs from Antillogorgia americana and Antillogorgia elisabethae; Table S2: Low affinity NPs for target nsp16–nsp10; Table S3: Lower-moderate affinity NPs for target nsp16–nsp10; Table S4: Upper-moderate affinity NPs for the nsp16–nsp10 protein complex; Table S5: High affinity NPs for the nsp16–nsp10 enzyme complex; Table S6: Low affinity NPs for target nsp13; Table S7: Moderate affinity NPs for target nsp13; Table S8: High affinity NPs for target nsp13; Table S9: Medium affinity NPs for target nsp14; Table S10: High affinity NPs for target nsp14; Table S11: Very high affinity NPs for target nsp14; Table S12: SMILES line notations for filtered NPs.

Author Contributions

Conceptualization, O.P. and G.V.Z.; methodology, O.P.; software, O.P.; formal analysis, O.P.; investigation, O.P.; data curation, O.P. and H.L.; writing—original draft preparation, O.P.; writing—review and editing, O.P., H.L., G.V.Z. and M.V.T.; supervision, G.V.Z. and M.V.T. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Ministry of Science and Higher Education of the Russian Federation, Agreement #075-15-2022-1118, dated 29 June 2022.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

References

Lu, S. Timely development of vaccines against SARS-CoV-2. Emerg. Microbes Infect. 2020, 9, 542–544. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Ball, P. The lightning-fast quest for COVID vaccines—And what it means for other diseases. Nature 2021, 589, 16–18. [Google Scholar] [CrossRef] [PubMed]
Cárdenas, G.; Chávez-Canales, M.; Espinosa, A.M.; Jordán-Ríos, A.; Malagon, D.A.; Murillo, M.F.M.; Araujo, L.V.T.; Campos, R.L.B.; Wong-Chew, R.M.; González, L.E.R.; et al. Intranasal dexamethasone: A new clinical trial for the control of inflammation and neuroinflammation in COVID-19 patients. Trials 2022, 23, 148. [Google Scholar] [CrossRef] [PubMed]
Horby, P.; Lim, W.S.; Emberson, J.R.; Mafham, M.; Bell, J.L.; Linsell, L.; Staplin, N.; Brightling, C.; Ustianowski, A.; Elmahi, E.; et al. Dexamethasone in Hospitalized Patients with COVID-19. N. Engl. J. Med. 2021, 384, 693–704. [Google Scholar] [CrossRef] [PubMed]
Bouadma, L.; Mekontso-Dessap, A.; Burdet, C.; Merdji, H.; Poissy, J.; Dupuis, C.; Guitton, C.; Schwebel, C.; Cohen, Y.; Bruel, C.; et al. High-Dose Dexamethasone and Oxygen Support Strategies in Intensive Care Unit Patients With Severe COVID-19 Acute Hypoxemic Respiratory Failure: The COVIDICUS Randomized Clinical Trial. JAMA Intern. Med. 2022, 182, 906–916. [Google Scholar] [CrossRef] [PubMed]
Maláska, J.; Stašek, J.; Duška, F.; Balík, M.; Máca, J.; Hruda, J.; Vymazal, T.; Klementová, O.; Zatloukal, J.; Gabrhelík, T.; et al. Effect of dexamethasone in patients with ARDS and COVID-19—Prospective, multi-centre, open-label, parallel-group, randomised controlled trial (REMED trial): A structured summary of a study protocol for a randomised controlled trial. Trials 2021, 22, 172. [Google Scholar] [CrossRef]
Beigel, J.H.; Tomashek, K.M.; Dodd, L.E.; Mehta, A.K.; Zingman, B.S.; Kalil, A.C.; Hohmann, E.; Chu, H.Y.; Luetkemeyer, A.; Kline, S.; et al. Remdesivir for the Treatment of COVID-19—Final Report. N. Engl. J. Med. 2020, 383, 1813–1826. [Google Scholar] [CrossRef]
Gottlieb, R.L.; Vaca, C.E.; Paredes, R.; Mera, J.; Webb, B.J.; Perez, G.; Oguchi, G.; Ryan, P.; Nielsen, B.U.; Brown, M.; et al. Early Remdesivir to Prevent Progression to Severe COVID-19 in Outpatients. N. Engl. J. Med. 2022, 386, 305–315. [Google Scholar] [CrossRef] [PubMed]
Dyer, O. COVID-19: Remdesivir has little or no impact on survival, WHO trial shows. BMJ 2020, 371, m4057. [Google Scholar] [CrossRef]
Rogstam, A.; Nyblom, M.; Christensen, S.; Sele, C.; Talibov, V.O.; Lindvall, T.; Rasmussen, A.A.; André, I.; Fisher, Z.; Knecht, W.; et al. Crystal Structure of Nonstructural Protein 10 from Severe Acute Respiratory Syndrome Coronavirus-2. Int. J. Mol. Sci. 2020, 21, 7375. [Google Scholar] [CrossRef]
Donaldson, E.F.; Sims, A.C.; Graham, R.L.; Denison, M.R.; Baric, R.S. Murine hepatitis virus replicase protein nsp10 is a critical regulator of viral RNA synthesis. J. Virol. 2007, 81, 6356–6368. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Decroly, E.; Ferron, F.; Lescar, J.; Canard, B. Conventional and unconventional mechanisms for capping viral mRNA. Nat. Rev. Microbiol. 2011, 10, 51–65. [Google Scholar] [CrossRef]
Viswanathan, T.; Arya, S.; Chan, S.H.; Qi, S.; Dai, N.; Misra, A.; Park, J.G.; Oladunni, F.; Kovalskyy, D.; Hromas, R.A.; et al. Structural basis of RNA cap modification by SARS-CoV-2. Nat. Commun. 2020, 11, 3718. [Google Scholar] [CrossRef]
Chang, L.J.; Chen, T.H. NSP16 2′-O-MTase in Coronavirus Pathogenesis: Possible Prevention and Treatments Strategies. Viruses 2021, 13, 538. [Google Scholar] [CrossRef]
Chen, J.; Malone, B.; Llewellyn, E.; Grasso, M.; Shelton, P.M.M.; Olinares, P.D.B.; Maruthi, K.; Eng, E.T.; Vatandaslar, H.; Chait, B.T.; et al. Structural Basis for Helicase-Polymerase Coupling in the SARS-CoV-2 Replication-Transcription Complex. Cell 2020, 182, 1560–1573.e13. [Google Scholar] [CrossRef] [PubMed]
Prajapat, M.; Sarma, P.; Shekhar, N.; Avti, P.; Sinha, S.; Kaur, H.; Kumar, S.; Bhattacharyya, A.; Kumar, H.; Bansal, S.; et al. Drug targets for corona virus: A systematic review. Indian J. Pharmacol. 2020, 52, 56–65. [Google Scholar] [CrossRef] [PubMed]
Rohaim, M.A.; El Naggar, R.F.; Clayton, E.; Munir, M. Structural and functional insights into nonstructural proteins of coronaviruses. Microb. Pathog. 2021, 150, 104641. [Google Scholar] [CrossRef]
Spratt, A.N.; Gallazzi, F.; Quinn, T.P.; Lorson, C.L.; Sönnerborg, A.; Singh, K. Coronavirus helicases: Attractive and unique targets of anti-viral drug-development and therapeutic patents. Expert Opin. Ther. Pat. 2021, 31, 339–350. [Google Scholar] [CrossRef]
Tanner, J.A.; Watt, R.M.; Chai, Y.B.; Lu, L.Y.; Lin, M.C.; Peiris, J.S.; Poon, L.L.; Kung, H.F.; Huang, J.D. The severe acute respiratory syndrome (SARS) coronavirus NTPase/helicase belongs to a distinct class of 5′ to 3′ viral helicases. J. Biol. Chem. 2003, 278, 39578–39582. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Chen, Y.; Cai, H.; Pan, J.; Xiang, N.; Tien, P.; Ahola, T.; Guo, D. Functional screen reveals SARS coronavirus nonstructural protein nsp14 as a novel cap N7 methyltransferase. Proc. Natl. Acad. Sci. USA 2009, 106, 3484–3489. [Google Scholar] [CrossRef] [PubMed]
Shannon, A.; Selisko, B.; Le, N.T.; Huchting, J.; Touret, F.; Piorkowski, G.; Fattorini, V.; Ferron, F.; Decroly, E.; Meier, C.; et al. Rapid incorporation of Favipiravir by the fast and permissive viral RNA polymerase complex results in SARS-CoV-2 lethal mutagenesis. Nat. Commun. 2020, 11, 4682. [Google Scholar] [CrossRef] [PubMed]
Ma, Y.; Wu, L.; Shaw, N.; Gao, Y.; Wang, J.; Sun, Y.; Lou, Z.; Yan, L.; Zhang, R.; Rao, Z. Structural basis and functional analysis of the SARS coronavirus nsp14-nsp10 complex. Proc. Natl. Acad. Sci. USA 2015, 112, 9436–9441. [Google Scholar] [CrossRef] [PubMed]
Becares, M.; Pascual-Iglesias, A.; Nogales, A.; Sola, I.; Enjuanes, L.; Zuñiga, S. Mutagenesis of Coronavirus nsp14 Reveals Its Potential Role in Modulation of the Innate Immune Response. J. Virol. 2016, 90, 5399–5414. [Google Scholar] [CrossRef] [Green Version]
Hastie, K.M.; Kimberlin, C.R.; Zandonatti, M.A.; MacRae, I.J.; Saphire, E.O. Structure of the Lassa virus nucleoprotein reveals a dsRNA-specific 3′ to 5′ exonuclease activity essential for immune suppression. Proc. Natl. Acad. Sci. USA 2011, 108, 2396–2401. [Google Scholar] [CrossRef]
Hsu, J.C.; Laurent-Rolle, M.; Pawlak, J.B.; Wilen, C.B.; Cresswell, P. Translational shutdown and evasion of the innate immune response by SARS-CoV-2 NSP14 protein. Proc. Natl. Acad. Sci. USA 2021, 118, e2101161118. [Google Scholar] [CrossRef]
Santos, J.; Brierley, S.; Gandhi, M.J.; Cohen, M.A.; Moschella, P.C.; Declan, A.B.L. Repurposing Therapeutics for Potential Treatment of SARS-CoV-2: A Review. Viruses 2020, 12, 705. [Google Scholar] [CrossRef] [PubMed]
Dotolo, S.; Marabotti, A.; Facchiano, A.; Tagliaferri, R. A review on drug repurposing applicable to COVID-19. Brief. Bioinform. 2021, 22, 726–741. [Google Scholar] [CrossRef]
Mohamed, K.; Yazdanpanah, N.; Saghazadeh, A.; Rezaei, N. Computational drug discovery and repurposing for the treatment of COVID-19: A systematic review. Bioorg. Chem. 2021, 106, 104490. [Google Scholar] [CrossRef] [PubMed]
Sharapov, A.D.; Fatykhov, R.F.; Khalymbadzha, I.A.; Zyryanov, G.V.; Chupakhin, O.N.; Tsurkan, M.V. Plant Coumarins with Anti-HIV Activity: Isolation and Mechanisms of Action. Int. J. Mol. Sci. 2023, 24, 2839. [Google Scholar] [CrossRef] [PubMed]
Abdelmohsen, U.R.; Albohy, A.; Abdulrazik, B.S.; Bayoumi, S.A.L.; Malak, L.G.; Khallaf, I.S.A.; Bringmann, G.; Farag, S.F. Natural coumarins as potential anti-SARS-CoV-2 agents supported by docking analysis. RSC Adv. 2021, 11, 16970–16979. [Google Scholar] [CrossRef]
Pokharkar, O.; Lakshmanan, H.; Zyryanov, G.; Tsurkan, M. In Silico Evaluation of Antifungal Compounds from Marine Sponges against COVID-19-Associated Mucormycosis. Mar. Drugs 2022, 20, 215. [Google Scholar] [CrossRef]
Faulkner, D.J. Marine natural products: Metabolites of marine invertebrates. Nat. Prod. Rep. 1984, 1, 551–598. [Google Scholar] [CrossRef]
Faulkner, D.J. Marine natural products. Nat. Prod. Rep. 1992, 9, 323–364. [Google Scholar] [CrossRef]
Faulkner, D.J. Marine natural products. Nat. Prod. Rep. 1993, 10, 497–539. [Google Scholar] [CrossRef]
Faulkner, D.J. Marine natural products. Nat. Prod. Rep. 2001, 18, 1R–49R. [Google Scholar] [CrossRef]
Blunt, J.W.; Copp, B.R.; Munro, M.H.; Northcote, P.T.; Prinsep, M.R. Marine natural products. Nat. Prod. Rep. 2003, 20, 1–48. [Google Scholar] [CrossRef]
Blunt, J.W.; Copp, B.R.; Munro, M.H.; Northcote, P.T.; Prinsep, M.R. Marine natural products. Nat. Prod. Rep. 2004, 21, 1–49. [Google Scholar] [CrossRef]
Blunt, J.W.; Copp, B.R.; Munro, M.H.; Northcote, P.T.; Prinsep, M.R. Marine natural products. Nat. Prod. Rep. 2005, 22, 15–61. [Google Scholar] [CrossRef]
Blunt, J.W.; Copp, B.R.; Hu, W.P.; Munro, M.H.; Northcote, P.T.; Prinsep, M.R. Marine natural products. Nat. Prod. Rep. 2007, 24, 31–86. [Google Scholar] [CrossRef]
Blunt, J.W.; Copp, B.R.; Hu, W.P.; Munro, M.H.; Northcote, P.T.; Prinsep, M.R. Marine natural products. Nat. Prod. Rep. 2008, 25, 35–94. [Google Scholar] [CrossRef]
Blunt, J.W.; Copp, B.R.; Hu, W.P.; Munro, M.H.; Northcote, P.T.; Prinsep, M.R. Marine natural products. Nat. Prod. Rep. 2009, 26, 170–244. [Google Scholar] [CrossRef]
Blunt, J.W.; Copp, B.R.; Munro, M.H.; Northcote, P.T.; Prinsep, M.R. Marine natural products. Nat. Prod. Rep. 2011, 28, 196–268. [Google Scholar] [CrossRef] [PubMed]
Rivero, R.B.; Pérez, A.R.; Castro, H.V.; Argilagos, C.S.; Henriquez, R.D. Caridiene: A new sesquiterpene from Pseudoterogorgia Americana. Z. Naturforschung B 1990, 45, 1571–1572. [Google Scholar] [CrossRef]
Rodríguez, A.D.; Rivera, J.; Boulanger, A. New polyhydroxydinostane sterols from the Caribbean gorgonian octocoral Pseudopterogorgia Americana. Tetrahedron Lett. 1998, 39, 7645–7648. [Google Scholar] [CrossRef]
Naz, S.; Kerr, R.G.; Narayanan, R. New antiproliferative epoxysecosterols from Pseudopterogorgia americana. Tetrahedron Lett. 2000, 41, 6035–6040. [Google Scholar] [CrossRef]
Rodríguez, A.D.; Boulanger, A. New guaiane metabolites from the Caribbean gorgonian coral, Pseudopterogorgia americana. J. Nat. Prod. 1997, 60, 207–211. [Google Scholar] [CrossRef]
Chan, W.R.; Tinto, W.F.; Moore, R. New germacrane derivatives from gorgonian octocorals of the genus Pseudopterogorgia. Tetrahedron 1990, 46, 1499–1502. [Google Scholar] [CrossRef]
Rodríguez, A.D.; Boulanger, A.; Martínez, J.R.; Huang, S.D. Sesquiterpene lactones from the Caribbean Sea plume Pseudopterogorgia Americana. J. Nat. Prod. 1998, 61, 451–455. [Google Scholar] [CrossRef]
Rodríguez, A.D.; Boulanger, A. Americanolides A–C, new guaianolide sesquiterpenes from the Caribbean Sea plume Pseudopterogorgia americana. J. Nat. Prod. 1996, 59, 653–657. [Google Scholar] [CrossRef]
Rodríguez, A.D.; Ramírez, C.; Rodríguez, I.I. Elisabatins A and B: New amphilectane-type diterpenes from the West Indian sea whip Pseudopterogorgia elisabethae. J. Nat. Prod. 1999, 62, 997–999. [Google Scholar] [CrossRef]
Rodríguez, A.D.; Ramirez, C.; Medina, V.; Shi, Y.P. Novel lactones from Pseudopterogorgia elisabethae (Bayer). Tetrahedron Lett. 2000, 41, 5177–5180. [Google Scholar] [CrossRef]
Rodriguez, A.D.; Ramirez, C.; Rodriguez, I.I.; Barnes, C.L. Novel terpenoids from the west Indian Sea Whip Pseudopterogorgia elisabethae (Bayer). Elisapterosins A and B: Rearranged diterpenes possessing an unprecedented cage like framework. J. Org. Chem. 2000, 65, 1390–1398. [Google Scholar] [CrossRef] [PubMed]
Rodríguez, A.D.; González, E.; Huang, S.D. Unusual Terpenes with Novel Carbon Skeletons from the West Indian Sea Whip Pseudopterogorgia elisabethae (Octocorallia). J. Org. Chem. 1998, 63, 7083–7091. [Google Scholar] [CrossRef] [PubMed]
Rodríguez, A.D.; Ramírez, C. Serrulatane Diterpenes with Antimycobacterial Activity Isolated from the West Indian Sea Whip Pseudopterogorgia elisabethae. J. Nat. Prod. 2001, 64, 100–102. [Google Scholar] [CrossRef] [PubMed]
Rodríguez, I.I.; Rodríguez, A.D. Homopseudopteroxazole, a New Antimycobacterial Diterpene Alkaloid from Pseudopterogorgia elisabethae. J. Nat. Prod. 2003, 66, 855–857. [Google Scholar] [CrossRef]
Rodríguez, A.D.; Ramírez, C.; Rodríguez, I.I.; González, E. Novel Antimycobacterial Benzoxazole Alkaloids, from the West Indian Sea Whip Pseudopterogorgia elisabethae. Org. Lett. 1999, 1, 527–530. [Google Scholar] [CrossRef]
Coleman, A.C.; Kerr, R.G. Radioactivity-guided isolation and characterization of the bicyclic pseudopterosin diterpene cyclase product from Pseudopterogorgia elisabethae. Tetrahedron 2000, 56, 9569–9574. [Google Scholar] [CrossRef]
Ata, A.; Kerr, R.G. Elisabethamine: A new diterpene alkaloid from Pseudopterogorgia elisabethae. Tetrahedron Lett. 2000, 41, 5821–5825. [Google Scholar] [CrossRef]
Ata, A.; Kerr, R.G. 12-acetoxypseudopterolide: A new diterpene from Pseudopterogorgia elisabethae. Heterocycles 2000, 53, 717. [Google Scholar] [CrossRef]
Shi, Y.P.; Rodríguez, I.I.; Rodríguez, A.D. Elisapterosins D and E: Complex polycyclic diterpenes of the rare elisapterane class of natural products from the Caribbean Sea whip Pseudopterogorgia elisabethae (Bayer). Tetrahedron Lett. 2003, 44, 3249–3253. [Google Scholar] [CrossRef]
Ata, A.; Win, H.Y.; Holt, D.; Holloway, P.; Segstro, E.P.; Jayatilake, G.S. New antibacterial diterpenes from Pseudopterogorgia elisabethae. Helv. Chim. Acta 2004, 87, 1090–1098. [Google Scholar] [CrossRef]
Rodríguez, I.I.; Shi, Y.P.; García, O.J.; Rodríguez, A.D.; Mayer, A.M.; Sánchez, J.A.; Ortega-Barria, E.; González, J. New pseudopterosin and seco-pseudopterosin diterpene glycosides from two Colombian isolates of Pseudopterogorgia elisabethae and their diverse biological activities. J. Nat. Prod. 2004, 67, 1672–1680. [Google Scholar] [CrossRef] [PubMed]
Correa, H.; Aristizabal, F.; Duque, C.; Kerr, R. Cytotoxic and Antimicrobial Activity of Pseudopterosins and seco-Pseudopterosins Isolated from the Octocoral Pseudopterogorgia elisabethae of San Andrés and Providencia Islands (Southwest Caribbean Sea). Mar. Drugs 2011, 9, 334–344. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Duque, C.; Puyana, M.; Narváez, G.; Osorno, O.; Hara, N.; Fujimoto, Y. Pseudopterosins P–V, new compounds from the gorgonian octocoral Pseudopterogorgia elisabethae from Providencia island, Colombian Caribbean. Tetrahedron 2004, 60, 10627–10635. [Google Scholar] [CrossRef]
Look, S.A.; Fenical, W.; Matsumoto, G.K.; Clardy, J. The pseudopterosins: A new class of anti-inflammatory and analgesic diterpene pentosides from the marine sea whip Pseudopterogorgia elisabethae (Octocorallia). J. Org. Chem. 1986, 51, 5140–5145. [Google Scholar] [CrossRef]
Roussis, V.; Wu, Z.; Fenical, W.; Strobel, S.A.; Van Duyne, G.D.; Clardy, J. New anti-inflammatory pseudopterosins from the marine octocoral Pseudopterogorgia elisabethae. J. Org. Chem. 1990, 55, 4916–4922. [Google Scholar] [CrossRef]
Rodríguez, A.D.; Ramírez, C.; Rodríguez, I.I. Sandresolides A and B: Novel nor-diterpenes from the sea whip Pseudopterogorgia elisabethae (Bayer). Tetrahedron Lett. 1999, 40, 7627–7631. [Google Scholar] [CrossRef]
Rodríguez, A.D.; Shi, Y.P. Structurally diverse terpenoids from the sea whip Pseudopterogorgia elisabethae (Bayer). Tetrahedron 2000, 56, 9015–9023. [Google Scholar] [CrossRef]
Duque, C.; Puyana, M.; Castellanos, L.; Arias, A.; Correa, H.; Osorno, O.; Asai, T.; Hara, N.; Fujimoto, Y. Further studies on the constituents of the gorgonian octocoral Pseudopterogorgia elisabethae collected in San Andrés and Providencia islands, Colombian Caribbean: Isolation of a putative biosynthetic intermediate leading to erogorgiaene. Tetrahedron 2006, 62, 4205–4213. [Google Scholar] [CrossRef]
Shi, Y.P.; Wei, X.; Rodríguez, I.I.; Rodríguez, A.D.; Mayer, A.M. New terpenoid constituents of the southwestern Caribbean sea whip Pseudopterogorgia elisabethae (Bayer), including a unique pentanorditerpene. Eur. J. Org. Chem. 2009, 2009, 493–502. [Google Scholar] [CrossRef]
Ata, A.; Kerr, R.G.; Moya, C.E.; Jacobs, R.S. Identification of anti-inflammatory diterpenes from the marine gorgonian Pseudopterogorgia elisabethae. Tetrahedron 2003, 59, 4215–4222. [Google Scholar] [CrossRef]
Rodríguez, A.D.; Ramírez, C.; Shi, Y.P. The Cumbiasins, Structurally Novel Diterpenes Possessing Intricate Carbocyclic Skeletons from the West Indian Sea Whip Pseudopterogorgia elisabethae (Bayer). J. Org. Chem. 2000, 65, 6682–6687. [Google Scholar] [CrossRef]
Rodríguez, A.D.; Ramírez, C. A Marine Diterpene with a Novel Tetracyclic Framework from the West Indian Gorgonian Octocoral Pseudopterogorgia elisabethae. Org. Lett. 2000, 2, 507–510. [Google Scholar] [CrossRef] [PubMed]
Home—Protein—NCBI. Available online: https://www.ncbi.nlm.nih.gov/protein/ (accessed on 5 December 2022).
Rosas-Lemus, M.; Minasov, G.; Shuvalova, L.; Inniss, N.L.; Kiryukhina, O.; Brunzelle, J.; Satchell, K.J.F. High-resolution structures of the SARS-CoV-2 2′-O-methyltransferase reveal strategies for structure-based inhibitor design. Sci. Signal. 2020, 13, eabe1202. [Google Scholar] [CrossRef] [PubMed]
Newman, J.A.; Douangamath, A.; Yadzani, S.; Yosaatmadja, Y.; Aimon, A.; Brandão-Neto, J.; Dunnett, L.; Gorrie-Stone, T.; Skyner, R.; Fearon, D.; et al. Structure, mechanism and crystallographic fragment screening of the SARS-CoV-2 NSP13 helicase. Nat. Commun. 2021, 12, 4848. [Google Scholar] [CrossRef] [PubMed]
Czarna, A.; Plewka, J.; Kresik, L.; Matsuda, A.; Karim, A.; Robinson, C.; O’Byrne, S.; Cunningham, F.; Georgiou, I.; Wilk, P.; et al. Refolding of lid subdomain of SARS-CoV-2 nsp14 upon nsp10 interaction releases exonuclease activity. Structure 2022, 30, 1050–1054.e2. [Google Scholar] [CrossRef]
Kim, S.; Thiessen, P.A.; Bolton, E.E.; Chen, J.; Fu, G.; Gindulyte, A.; Han, L.; He, J.; He, S.; Shoemaker, B.A.; et al. PubChem Substance and Compound databases. Nucleic Acids Res. 2016, 44, D1202. [Google Scholar] [CrossRef]
Pence, H.E.; Williams, A. ChemSpider: An Online Chemical Information Resource. J. Chem. Educ. 2010, 87, 1123–1124. [Google Scholar] [CrossRef]
Lyu, C.; Chen, T.; Qiang, B.; Liu, N.; Wang, H.; Zhang, L.; Liu, Z. CMNPD: A comprehensive marine natural products database towards facilitating drug discovery from the ocean. Nucleic Acids Res. 2021, 49, D509–D515. [Google Scholar] [CrossRef]
BIOVIA DS, Discovery, Studio. Available online: https://discover.3ds.com/discovery-studio-visualizer-download (accessed on 15 December 2022).
Avogadro: An Open-Source Molecular Builder and Visualization Tool. Version 1.95. Available online: http://avogadro.cc/ (accessed on 18 October 2022).
Hanwell, M.D.; Curtis, D.E.; Lonie, D.C.; Vandermeersch, T.; Zurek, E.; Hutchison, G.R. Avogadro: An advanced semantic chemical editor, visualization, and analysis platform. J. Cheminform. 2012, 4, 17. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Guex, N.; Peitsch, M.C. SWISS-MODEL and the Swiss-PdbViewer: An environment for comparative protein modeling. Electrophoresis 1997, 18, 2714–2723. [Google Scholar] [CrossRef] [PubMed]
Morris, G.M.; Huey, R.; Lindstrom, W.; Sanner, M.F.; Belew, R.K.; Goodsell, D.S.; Olson, A.J. AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J. Comput. Chem. 2009, 30, 2785–2791. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Eberhardt, J.; Santos-Martins, D.; Tillack, A.F.; Forli, S. AutoDock Vina 1.2.0: New Docking Methods, Expanded Force Field, and Python Bindings. J. Chem. Inf. Model. 2021, 61, 3891–3898. [Google Scholar] [CrossRef]
Trott, O.; Olson, A.J. AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem. 2010, 31, 455–461. [Google Scholar] [CrossRef] [Green Version]
Kuriata, A.; Gierut, A.M.; Oleniecki, T.; Ciemny, M.P.; Kolinski, A.; Kurcinski, M.; Kmiecik, S. CABS-flex 2.0: A web server for fast simulations of flexibility of protein structures. Nucleic Acids Res. 2018, 46, W338–W343. [Google Scholar] [CrossRef] [Green Version]
Banerjee, P.; Eckert, A.O.; Schrey, A.K.; Preissner, R. ProTox-II: A webserver for the prediction of toxicity of chemicals. Nucleic Acids Res. 2018, 46, W257. [Google Scholar] [CrossRef] [Green Version]
Borba, J.; Alves, V.; Overdahl, K.; Silva, A.; Hall, S.; Overdahl, E.; Braga, R.; Kleinstreuer, N.; Strickland, J.; Allen, D.; et al. STopTox: An In-Silico Alternative to Animal Testing for Acute Systemic and TOPical TOXicity. Environ. Health Perspect. 2022, 130, 27012. [Google Scholar] [CrossRef]
Daina, A.; Michielin, O.; Zoete, V. SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci. Rep. 2017, 71, 42717. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Daina, A.; Michielin, O.; Zoete, V. iLOGP: A simple, robust, and efficient description of n-octanol/water partition coefficient for drug design using the GB/SA approach. J. Chem. Inf. Model. 2014, 54, 3284–3301. [Google Scholar] [CrossRef]
Zhao, Y.H.; Abraham, M.H.; Le, J.; Hersey, A.; Luscombe, C.N.; Beck, G.; Sherborne, B.; Cooper, I. Rate-limited steps of human oral absorption and QSAR studies. Pharm. Res. 2002, 19, 1446–1457. [Google Scholar] [CrossRef] [PubMed]
Husain, A.; Ahmad, A.; Khan, S.A.; Asif, M.; Bhutani, R.; Al-Abbasi, F.A. Synthesis, molecular properties, toxicity and biological evaluation of some new substituted imidazolidine derivatives in search of potent anti-inflammatory agents. Saudi Pharm. J. 2016, 24, 104–114. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Daina, A.; Zoete, V. A BOILED-Egg To Predict Gastrointestinal Absorption and Brain Penetration of Small Molecules. ChemMedChem 2016, 11, 1117–1121. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Lagunin, A.; Stepanchikova, A.; Filimonov, D.; Poroikov, V. PASS: Prediction of activity spectra for biologically active substances. Bioinformatics 2000, 16, 747–748. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Tang, Y.; Liu, J.; Zhang, D.; Xu, Z.; Ji, J.; Wen, C. Cytokine Storm in COVID-19: The Current Evidence and Treatment Strategies. Front. Immunol. 2020, 11, 1708. [Google Scholar] [CrossRef]
Cron, R.Q.; Caricchio, R.; Chatham, W.W. Calming the cytokine storm in COVID-19. Nat. Med. 2021, 27, 1674–1675. [Google Scholar] [CrossRef]
Worldometer Coronavirus Statistics. Available online: https://www.worldometers.info/coronavirus/ (accessed on 25 March 2023).
Yan, W.; Zheng, Y.; Zeng, X.; He, B.; Cheng, W. Structural biology of SARS-CoV-2: Open the door for novel therapies. Signal Transduct. Target. Ther. 2022, 7, 26. [Google Scholar] [CrossRef]
Bhardwaj, A.; Sharma, S.; Singh, S.K. Molecular Docking Studies to Identify Promising Natural Inhibitors Targeting SARS-CoV-2 Nsp10-Nsp16 Protein Complex. Turk. J. Pharm. Sci. 2022, 19, 93–100. [Google Scholar] [CrossRef]
Piplani, S.; Singh, P.; Winkler, D.A.; Petrovsky, N. Potential COVID-19 Therapies from Computational Repurposing of Drugs and Natural Products against the SARS-CoV-2 Helicase. Int. J. Mol. Sci. 2022, 23, 7704. [Google Scholar] [CrossRef] [PubMed]
Ahmed-Belkacem, R.; Hausdorff, M.; Delpal, A.; Sutto-Ortiz, P.; Colmant, A.M.G.; Touret, F.; Ogando, N.S.; Snijder, E.J.; Canard, B.; Coutard, B.; et al. Potent Inhibition of SARS-CoV-2 nsp14 N7-Methyltransferase by Sulfonamide-Based Bisubstrate Analogues. J. Med. Chem. 2022, 65, 6231–6249. [Google Scholar] [CrossRef]

Figure 1. Illustration of the schematic workflow of the research.

Figure 2. The docking validation. (A) The default inhibitor SAH re-docked in the active site; (B) the superimposed crystal structures of the re-docked and 7R2V proteins; and (C) the original 7R2V protein chain A from the RCSB PDB database.

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

Share and Cite

MDPI and ACS Style

Pokharkar, O.; Lakshmanan, H.; Zyryanov, G.V.; Tsurkan, M.V. Antiviral Potential of Antillogorgia americana and elisabethae Natural Products against nsp16–nsp10 Complex, nsp13, and nsp14 Proteins of SARS-CoV-2: An In Silico Investigation. Microbiol. Res. 2023, 14, 993-1019. https://doi.org/10.3390/microbiolres14030068

AMA Style

Pokharkar O, Lakshmanan H, Zyryanov GV, Tsurkan MV. Antiviral Potential of Antillogorgia americana and elisabethae Natural Products against nsp16–nsp10 Complex, nsp13, and nsp14 Proteins of SARS-CoV-2: An In Silico Investigation. Microbiology Research. 2023; 14(3):993-1019. https://doi.org/10.3390/microbiolres14030068

Chicago/Turabian Style

Pokharkar, Omkar, Hariharan Lakshmanan, Grigory V. Zyryanov, and Mikhail V. Tsurkan. 2023. "Antiviral Potential of Antillogorgia americana and elisabethae Natural Products against nsp16–nsp10 Complex, nsp13, and nsp14 Proteins of SARS-CoV-2: An In Silico Investigation" Microbiology Research 14, no. 3: 993-1019. https://doi.org/10.3390/microbiolres14030068

Article Menu

Antiviral Potential of Antillogorgia americana and elisabethae Natural Products against nsp16–nsp10 Complex, nsp13, and nsp14 Proteins of SARS-CoV-2: An In Silico Investigation

Abstract

1. Introduction

2. Materials and Methods

2.1. Obtaining Target Proteins

2.2. Obtaining Ligands

2.3. Visualization

2.4. Pre-Docking Preparations

2.5. Molecular Docking

2.6. Docking Validation

2.7. Molecular Dynamics Simulation

2.8. Toxicity Assessment

2.9. Pharmacokinetics Studies

3. Results and Discussion

3.1. Binding Affinity Analyses

3.1.1. nsp16–nsp10 Docking

3.1.2. nsp13 Docking

3.1.3. nsp14 Docking

3.1.4. Docking Protocol Validation—nsp14

3.2. Amino Acid Interactions

3.2.1. Selection Criteria to Filter Candidate Complexes

3.2.2. Selected Docked Complexes

3.2.3. MDs Simulations

3.3. Toxicity Evaluation

3.3.1. ProTox-II Results

3.3.2. StopTox Server Output

3.4. Drug-Likeness Evaluation

3.4.1. Lipinski Rule

3.4.2. Swiss-ADME

3.4.3. Bioactivity Prediction

4. Conclusions

Supplementary Materials

Author Contributions

Funding

Institutional Review Board Statement

Informed Consent Statement

Data Availability Statement

Conflicts of Interest

References

Share and Cite

Article Metrics

Article Access Statistics

Further Information

Guidelines

MDPI Initiatives

Follow MDPI