Setomimycin as a potential molecule for COVID‑19 target: in silico approach and in vitro validation

Abstract COVID-19 pandemic caused by the SARS-CoV-2 virus has led to a worldwide crisis. In view of emerging variants time to time, there is a pressing need of effective COVID-19 therapeutics. Setomimycin, a rare tetrahydroanthracene antibiotic, remained unexplored for its therapeutic uses. Herein, we report our investigations on the potential of setomimycin as COVID-19 therapeutic. Pure setomimycin was isolated from Streptomyces sp. strain RA-WS2 from NW Himalayan region followed by establishing in silico as well as in vitro anti-SARS-CoV-2 property of the compound against SARS-CoV-2 main protease (Mpro). It was found that the compound targets Mpro enzyme with an IC50 value of 12.02 ± 0.046 μM. The molecular docking study revealed that the compound targets Glu166 residue of Mpro enzyme, hence preventing dimerization of SARS-CoV-2 Mpro monomer. Additionally, the compound also exhibited anti-inflammatory and anti-oxidant property, suggesting that setomimycin may be a viable option for application against COVID-19 infections. Graphical abstract


Introduction
Coronaviruses (CoVs) are a group of diverse enveloped positive-sense single-stranded RNA viruses that infect both animals other aves [1]. Human coronaviruses (HCoVs) are categorized into two types based on the frequency of spreading and severity, viz. those which are widespread and less severe (OC43, 229E, HKU1, and NL63) and those that are very infectious and severe (Severe Acute Respiratory Syndrome Coronavirus: SARS-CoV, Middle East Respiratory Syndrome Coronavirus: MERS-CoV, and Severe Acute Respiratory Syndrome Coronavirus-2: SARS-CoV-2) [2]. Over the last two decades, the three extremely infectious viruses SARS-CoV, MERS-CoV, and SARS-CoV-2 have caused a considerable number of fatalities in 2003, 2013, and 2019, respectively [3][4][5]. The recent SARS-CoV-2 pandemic has produced an unsettling scenario on a worldwide scale. During late 2019, the first case of SARS-CoV-2 was reported in the wet market of Wuhan city in China [6]. In March 2020, soon after global spread of the virus, the disease got pandemic status by World Health Organization (WHO) [7]. As of now, it has claimed almost 55 million lives worldwide.
SARS-CoV-2 is a pleomorphic beta virus containing ~ 30kb single-stranded RNA genome [8,9]. This virus genome encodes two large overlapping polyprotein precursors, pp1a (4405 amino acids) and pp1ab (7096 amino acids), responsible for viral replication and transcription [10]. These polypeptides synthesize five structural proteins spike (S), envelope (E), membrane, nucleocapsid (M), and hemagglutinin-esterase dimer glycoproteins (HE), sixteen non-structural proteins nsp (PL pro ; nsp3, 3CLpro; nsp5 and RdRP; nsp12, etc.), and several auxiliary proteins [11]. Two viral cysteine proteases cleave these polyproteins into nonstructural proteins: chymotrypsin-like main protease (M pro ) and papain-like protease (PL pro ) [12]. M pro is a 33.8 kDa enzyme that digests the polyprotein at 11 conserved sites. M pro is unique in that it cleaves polypeptides solely after a glutamine (Gln) residue, a cleavage specificity shared by no other human protease [13]. PL pro is a 35.6 kDa enzyme that catalyzes the cleavage of the polypeptide at three distinct sites. Along with proteolytic action, it also performs the deubiquitinating and deISG15ylating (interferon-induced gene 15) activities [14]. These viral proteases are therefore important antiviral targets due to their critical involvement in viral replication and maturation [14,15]. RNA-dependent RNA polymerase (RdRP/ nsp12) is an important part of the virus replication and transcription complex. Along with the cofactors, nsp7 and nsp8 aid the viral replication and transcription. RdRP is highly specific among RNA viruses, hence representing a good starting point for antiviral drug discovery [16]. Furthermore, structural viral proteins are also important for viral survival, infectivity, and transmissibility [17]. Spike protein is crucial for the entry of the virus into host cells. The cellular transmembrane serine protease (TMPRSS2) cleaves the spike protein into unique polypeptides; S1 and S2.S1 and S2 polypeptides are essential for anchoring virus to the cellular receptor ACE2 and for membrane fusion. The TMPRSS2 is highly expressed in the cell surface of nasal, bronchial, and gastrointestinal epithelium. The antibodies developed in COVID-19 patients were neutralized by the membrane protein (M). The transmembrane envelope protein (E) functions as an ion channel and is required for viral genetic material to be released into the host cell. Although the protein is not required for viral replication, it facilitates pathogenicity by allowing the virus to be assembled and released. The nucleocapsid protein (N) aids in the viral genome encapsulation into virus particles by attaching the viral genome network of proteins to the replicase-transcriptase complex (RTC) machinery. Acetylesterase activity found in the hemagglutinin-esterase dimer protein (HE) helps it in attachment and detachment from the host. The drugs that are investigated for COVID-19 work by inhibiting one of these critical proteins, viz. remdesivir, sofosbuvir, etc. (SARS-CoV-2 RdRp inhibitor), protease inhibitors, i.e., lopinavir, ritonavir, etc. (SARS-CoV-2 M pro ), camostat mesilate (TMPRSS-2 inhibitor), etc. Thus, targeting proteins essential for viral lifecycle is an important strategy for curing COVID-19 disease [18][19][20][21]. However, despite numerous efforts worldwide, options for COVID-19 therapy are still limited.
Following viral entry, the first inflammatory reaction drives the recruitment of virus-specific T lymphocytes. The recruited virus-specific T lymphocytes kill infected cells before the virus spreads, resulting in recovery of the majority of patients. However, the virus generates an atypical host immune reaction in individuals with severe SARS-CoV-2 infections. These responses result in pathological condition known as acute respiratory distress syndrome (ARDS) characterized by inflammation, alveolar damage, hyaline membrane formation, interstitial mononuclear inflammatory infiltrates [22]. In contrast with SARS and MERS, COVID-19 patients have mucus plugs in the respiratory tract with fibrinous exudate because of an excess of pro-inflammatory cytokines accumulating in the lungs and finally causing lung tissue destruction. If the immune response continues to deteriorate and cytokines accumulate in other organs, severe tissue damage or a cytokine release syndrome (cytokine storm) may result in capillary leakage, thrombus development, and organ failure [23]. At this stage, immunosuppressive drugs such as tocilizumab and anti-inflammatory corticosteroids reported to be effective therapy to prevent lung injury and organ damage [24].
Therapeutics for COVD-19 disease can be discovered/ repurposed by two main approaches, i.e., experimental (high throughput screenings) and computational methodologies (virtual screening) [25][26][27]. Experimental approaches to find COVID-19 therapeutics are costly and time-consuming process. Computational approaches provide a number of benefits over experimental methods, including the ability to drastically reduce the cost and time associated with the drug development process [28]. Additionally, the availability of a huge amount of data linked to COVID-19 disease in the public domain facilitated the deployment of several in silico drug development approaches [29].
The aim of present research was to examine setomimycin, a rare tetrahydroanthracene antibiotic for its therapeutic potential against COVID-19. Setomimycin was first isolated in 1978 from Streptomyces pseudovenezuela with in vitro antibacterial activity and in vivo antitumor activity [30]. Further, few compounds of the same class were isolated from different actinobacteria; however, this antibiotic remains unexplored for commercial exploitation. Therefore, efforts were made by our group to isolate and purify setomimycin from a new source. Subsequently, the binding potential of setomimycin was investigated with COVID-19 targets including M pro , S protein-ACE2 complex, RdRp, and TMPRSS2 using molecular docking studies.
Following the in silico study, in vitro evaluation of the compound was carried out against the target (M pro ) suggested by molecular docking studies. Additionally, the antiinflammatory and antioxidant activity of the antibiotic was also evaluated to support the evident use of setomimycin as a potential anti-COVID-19 drug candidate.

Source of setomimycin and fermentation conditions
The actinobacterium used in the present study was isolated from a soil sample of the Shivalik region of NW-Himalayas. Freshly grown culture of Streptomyces sp. isolate RA-WS2 was inoculated into 250-ml Erlenmeyer flask containing 50 ml of pre-seed medium (constituents in g/L: soluble starch 25.0, soyabean meal 15.0, calcium carbonate 4.0, and yeast extract 2.0; pH 6.8) and was grown at 28 °C and 200 rpm on a rotary shaker. Two days old pre-seed culture was transferred to 1-L Erlenmeyer flasks containing 250 ml of the same medium to prepare seed inoculum. 10% of twoday-old seed inoculum was transferred to a 7-L fermenter containing a 4-L production medium having constituents same as that of pre-seed and seed medium. The fermentation was carried out at 28 °C and 100 RPM with 0.5 vvm air for 6 days.

Preparation of solvent extract
After fermentation for 6 days in production medium, broth was homogenized with 10% methanol for about two hours. The solvent (ethyl acetate) was added in a ratio of 1:1(v:v) and vigorously shaken for 20 min. The ethyl acetate phase that contains antibiotic was separated from the aqueous phase using separating funnel. The process was repeated three times to obtain all the extractable constituents. Solvent fraction was concentrated by evaporation under vacuum at 50 °C to obtain dry extract [31].

Activity guided isolation of setomimycin
The crude solvent extract obtained above was subjected to thin layer chromatography and observed under UV. Antimicrobial activity on TLC was evaluated by the method described by Rahalison et al. [32]. Various fractions were evaluated for activity against Staphylococcus aureus, and active fractions were further purified by preparative TLC to obtain setomimycin. The structure of the isolated compound was confirmed by PMR and CMR spectroscopy and submitted to institutional compound repository (IN00664).

Molecular Docking Studies of setomimycin with selected targets
Four therapeutic targets, i.e., SARS-CoV-2 M pro (PDB ID: 6LU7), SARS-CoV-2 S-protein-ACE2 complex (PDB ID: 6M17), SARS-CoV-2 RdRp/NSP12 (PDB ID: 6NUR), and transmembrane protease, serine 2 (TMPRSS2), were selected for molecular docking studies [33][34][35]. The threedimensional structure coordinates of all the targets were retrieved from Protein Data Bank (PDB) except TMPRSS2. The homology model of TMPRSS2 was downloaded from the SARS-CoV-2 repository of SWISS-MODEL [36]. The structure was built using serine protease hepsin as a template and showed Q Mean score is -1.62 for the modeled structure. Before docking studies, all the proteins structures were prepared with Protein preparation wizard of Schrödinger 2017-3 suit [37]. This stage included the addition of missing hydrogen bonds, the assignment of bond ordering, and the energy minimization of the structure using the OPLS3e force field.

Ligand dataset preparation
The three-dimensional structures of setomimycin and other selected standard compounds were downloaded from the PubChem database [38][39][40][41][42][43]. The prodrugs favipiravir, remdesivir, and ribavirin were converted to active form for the study. Prior to molecular docking, all the compounds were prepared with LigPrep module [37,44].

Molecular docking studies
The active site/receptor grids were generated with the receptor grid generation module. For SARS-CoV-2 M pro , the active site was predicted with the sitemap module [45]. After sitemap prediction, the site showing best site score was selected for grid generation. For SARS-CoV-2 Sprotein-ACE2 complex, interface of SARS-CoV-2 S-protein and ACE2 interface were used for grid generation. The residues of SARS-CoV-2 S-protein found in the 5 Å proximity of ACE2 are Arg403, Gly446, Tyr449, Tyr453, Leu455, Phe456, Tyr473, Ala475, Gly476, Ser477, Phe486, Asn487, Tyr489, Gln493, Tyr495, Gly496, Gln498, Thr500, Asn501, Gly502, Tyr505. Molecular grid in SARS-CoV-2 RdRP was generated using the binding site of the drug sofosbuvir. Although the drug sofosbuvir was found to inhibit RdRP enzyme, the crystal structure information was not available [44]. Since the catalytic site of SARS-CoV-2 and Hepatitis C virus (HCV) RdRp is structurally conserved, it was assumed that the drug will bind to the similar residues on the COVID-19 virus [46]. Therefore, the HCV RdRp in complex pp-sofosbuvir (PDB ID 4WTG) was superimposed on SARS-CoV-2 RdRp. The grid was generated around the superimposed active site of SARS-CoV-2 and HCV RdRp [47,48]. The TMPRSS2 active site was also predicted using the sitemap module. Because the active site of serine proteases consists of amino acid residues His 296, Asp345, and Ser441, the site containing the catalytic triad was selected to generate the molecular grid.
All molecular docking experiments were carried out using Schrödinger glide (Grid-Based Ligand Docking with Energetics) program in extra precision (XP) mode [49]. After molecular docking, the docking scores and interaction of setomimycin and standard compounds were compared.

Molecular dynamic (MD) simulation
Molecular dynamics (MD) simulation studies of the setomimycin-SARS-CoV2-M pro were carried out using Desmond 2018-3 [50]. The MD simulation was initiated with the best docking pose of setomimycin with M pro . The system was solvated with three point (TIP3P) water model and neutralized by adding Cl − or Na + counter ions in an orthorhombic box (10 Å × 10 Å × 10 Å). Before the MD simulation, system was minimized via Steepest descent energy minimization method till 2000 iterations. The 50 ns MD simulation was performed with NPT condition. Finally, the generated trajectories were analyzed to obtain root-mean-square deviation (RMSD), root-mean square fluctuation (RMSF), protein-ligand interaction, protein-ligand contacts, and radius of gyration using simulation interaction diagram utility.

Principal component analysis
Principal component analysis (PCA) was performed to understand the correlated motions of the residues. The PCA was performed using ProDy implemented using normal mode wizard (NMW) of VMD software [51,52]. For the calculation of the principal components describing the conformational dynamics, the covariance matrix of the C-α atoms of M pro protein was used. The PCA scatter plots of the major components were generated using the plotly Python library (https:// plotly. com/).

MMGBSA energy calculations
After MD simulation, the average binding free energy (dG_ bind) was estimated with the molecular mechanics-generalized born surface area (MM-GBSA) analysis [53]. The MMGBSA calculation was performed on 200 complexes by extracting each tenth frame of the last 10 ns of stable trajectory with thermal_mmgbsa.py script provided by Schrodinger [54]. The formula used for binding free energy is as follows:

In vitro protease activity assay
In order to validate in silico observations, in vitro activity of setomimycin against main protease (M pro ) was performed. For M protease proteolytic assay, the fluorogenic substrate DABCYL-KTSAVLQSGFRKME-EDANS was custom synthesized (Thermo Fisher scientific). The protease assay was performed in fluorescence spectrophotometer (Cary Eclipse, Agilent Technology) 1-ml cuvette with final reaction volume of 400 μl at room temperature; 750 nM M pro recombinant protein and various concentrations of setomimycin (1.

In vitro anti-inflammatory activity
Cell culture RAW 264.7 macrophage cells were grown in Dulbecco's Modified Eagle Medium (DMEM; GIBCO) containing 10% fetal bovine serum (FBS), 100U/mL penicillin, and 100 ng/ mL streptomycin. The process was followed by incubation at 37 °C under 5% CO 2 .

Cytokines estimation
To quantify cytokines (TNF-α, IL-1β or IL-6), an ELISA assay (using Invitrogen ELISA kit) was carried out according to the manufacturer's instructions. To conduct the assay, 96-well plates were seeded with RAW 264.7 cells (1lac cells /well). Following overnight incubation, the cells were treated with setomimycin (1.0, 0.3, 0.1, 0.03, and 0.01 µM) and standard dexamethasone (10 µM) for 1 h. The treated cells were incubated with 1 µg/ml LPS for 24 h. After 24 h, the supernatants were collected and kept at -80 °C until analysis. The concentration of the cytokines TNF-α, IL-1β, or IL-6 in the samples was quantified using a standard curve. The data have been shown as the mean (of quadruplicate determinations) ± standard error of the mean. The formula used to calculate the percent inhibition is given below: wherein C represents cytokine concentration of LPS alone treated cells supernatant; D represents cytokine concentration of cells supernatant pretreated with test sample followed by stimulation with LPS.

Measurement of nitric oxide (NO assay)
For NO assay, RAW 264.7 macrophage cells were treated with setomimycin (1, 0.3, 0.1, 0.03, and 0.01 µM), dexamethasone (10 µM) and positive control L-NAME (100, 10 & 1 µM) and incubated for 1 h. After the incubation, 1 µg/ml LPS was added and the sample was again incubated for 24 h. After overnight incubation, the cell culture supernatants were collected and used for the nitrite concentration was determined using Greiss reagent (1% sulfanilamide/0.1% naphtylethyenediamine dihydrochloride in 2.5% H 3 PO 4 ) as per manufacturer's instructions. Briefly, equal volumes of cell culture supernatant and griess reagent were mixed. After 10 min at room temperature, the Inhibition (%) = ((C − D)∕C) * 100 absorbance was measured at 540 nm using Spectramax ABS Plus microplate reader (Molecular Devices). A sodium nitrite standard curve was used to estimate the concentration of nitrite in the samples. The data provide mean (of quadruplicate determinations) ± standard error of the mean. The nitric oxide (NO) inhibition (%) was calculated as follows wherein E represents the concentration of NO in the supernatant collected from LPS only treated cells and F represents NO concentration of cells supernatant pretreated with test sample followed by LPS.

Antioxidant activity of setomimycin
The antioxidant potential of the compound was measured using 2,2-diphenyl-1-picryl-hydrazyl-hydrate (DPPH) free radical method. For the experimentation, the method described by Xie et al. was used with slight adjustments [55][56][57]. Briefly, for the assay the DPPH solution was prepared by adding180 µL of methanol to the 10 µL of 1 mg/ml solution of DPPH. To this DPPH solution, 10 µL of different concentrations of test sample/standard was added. The absorbance of the solutions was measured at 60 min time point at 515 nm with Spectramax ABS Plus microplate reader (Molecular Devices). Ascorbic acid and quercetin were used as standard at a concentration range of 150 to 0.11 µM, and setomimycin was used at a concentration range of 500 to 0.36 µM. The following equation was used to measure free radical scavenging activity (DPPH inhibition) wherein A is the DPPH solution's absorbance, B the DPPH solution added with sample's absorbance.

Cytotoxicity evaluation using MTT assay
The cytotoxicity of the compound was measured with 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. For cytotoxicity determination, RAW 264.7 macrophage cells were treated for 48 h with either test samples or camptothecin (10 µM-used as standard) or vehicle. After the treatment, the cells were incubated for 4 h with MTT solution (2.5 mg/ml). The incubation is followed by removal of supernatant, addition of DMSO to solubilize the formazan crystals, and determination of absorbance at 570 nm using Spectramax ABS Plus microplate reader (Molecular Devices). The data represent the mean (of quadruplicate) ± standard error of the mean. The % cell viability was calculated as follows

Fermentation and production of setomimycin
The setomimycin-producing Streptomyces strain used in the study presented potent antimicrobial activity against Bacillus cereus and Staphylococcus aureus during preliminary experiments. Production of the setomimycin was carried out by fermentation of Streptomyces sp. RA-WS2 strain under specified conditions (as stated in the materials & methods section) for 6 days. After termination of fermentation batch, the fermented broth was homogenized with 10% MeOH followed by extraction with 1:1(v:v) ethyl acetate. In order to extract all the bioactives, the broth was extracted at least thrice with ethyl acetate. A mobile phase of ethyl acetate in hexane (1:1) was used to run the TLC for the separation of crude extract constituents and observed under UV. The TLC was further subjected to antimicrobial activity against S. aureus to observe the zone of inhibition and the major spot showing zone of inhibition was scratched to get pure compound (Fig. 1).

Characterization of pure compound
The pure compound from preparative TLC was obtained as a red solid with the molecular mass of 580.0 as determined by LCMS fragmentation. The PMR spectra suggest that the compound contains 28 protons: 12 protons from 4 methyl groups, 2 protons from a methylene group, 2 protons from two C-H, 7 protons from olefinic and/or aromatic moieties, and 5 protons from 5 hydroxyl groups, and in CMR, 34 carbons were found, 4 in carbonyl region, 5 in aromatic with substitution, 17 in aromatic, 3 in the oxygenated region, 1 in methylene, and 4 in methyl region. IR spectra revealed that the compound contains C=C and hydroxyl groups. By calculating the 21 degrees of unsaturation and UV absorptions of the compound, it indicates that it contains naphthocyclinone or anthracyclinone skeleton as a chromophore and it contains no sugar moiety. The analysis of NMR spectra characterized the pure compound as 1,1'-diacetyl-2,5,5',10,10'-pentahydroxy-2,2'-dimethyl-2,3-dihydro- [9,9'-bianthracene]-4,4'(1H,1'H)-dione a 9,9' bianthryl antibiotic known as setomimycin.

Molecular docking studies of setomimycin with selected targets
The activity of setomimycin against selected targets was assessed using a molecular docking study. Setomimycin exhibited the best docking score with SARS-CoV-2 main protease (− 7.462 kcal/mol) among all the selected targets ( Table 1). The docking score of setomimycin was comparable to the docking score of standard protease inhibitor lopinavir (-7.953 kcal/mol) and nelfinavir (− 7.884) ( Table 1). From the 2D interaction diagrams, it was observed that all the compounds were interacting with Glu166 via hydrogen bonding (Fig. 2). Because Glu166 is required for dimerization of SARS-CoV-2 M pro monomers, compounds targeting this residue will inhibit catalytic activity of the enzyme [58]. Setomimycin formed three hydrogen bonds with Glu166 and one each with Asn142 and Gly143 (Fig. 2 A-G). The findings show that setomimycin may efficiently inhibit M pro to prevent SARS-CoV-2 infections. Therefore, SARS-CoV-2 main protease was selected as potential target for in vitro study.

Molecular dynamic (MD) simulation
To understand the behavior of the identified hit setomimycin in the dynamic system, 50 ns molecular dynamics simulation of setomimycin-SARS-CoV2-M pro was performed. From the RMSD diagram, it can be inferred that the protein and inhibitor remain stable throughout the simulation time (Fig. 3A). The RMSF analysis confers that the M pro remains stable in the presence of setomimycin (RMSF 0.4-4 Å) (Fig. 3B). The protein-ligand interaction diagram (Fig. 3C) showed the hydroxyl groups of setomimycin interacted  with the Ser144 and Glu166 via direct and water mediated hydrogen bonding interactions, respectively. Additionally, Thr26 and Glu166 interacted with the carbonyl oxygen via water-mediated and direct hydrogen bonds, respectively. Moreover, setomimycin interacted with Glu166 throughout the simulation (Fig. 3D). All, these interactions, might be  (Fig. 3E).

Principal component analysis
The porcupine plots of PC mode 1 and mode 2 generated with normal mode wizard for capturing the dynamics revealed the local changes in the dynamics (Fig. 4A, B). The PCA scatter plot of the final trajectory obtained after 50 ns simulation is shown in Fig. 4C.

MMGBSA energy calculations
MMGBSA calculations demonstrate that setomimycin has a binding free energy (dG_Bind) of − 71.864 kcal/mol. It was also found that the van der Waals binding energy (dG_Bind_vdW) of -42.381 kcal/mol was the major contributor to the binding. The main energy contributors are shown in Table 2.

In vitro protease activity assay
For the determining setomimycin potential to inhibit SARS-CoV-2 M pro enzyme, fluorescence resonance energy transfer (FRET) enzymatic assay was performed. In this study, we found that setomimycin inhibited M pro enzyme with half-maximum inhibitory concentration (IC 50 ) of 12.02 ± 0.046 μM (IC 50 ) (Fig. 5).

Cytokines estimation
Pretreatment of RAW 264.7 cells with setomimycin in the concentration range of 0.01 to 1 µM resulted in the inhibition of the release of cytokines IL-1β, IL-6 and TNF-α from LPS stimulated RAW 264.7 cells in a dosedependent manner comparable to standard drug dexamethasone ( Fig. 6A-C).

Measurement of nitric oxide (NO assay)
Our results reveal that setomimycin pretreatment resulted in the reduction in LPS stimulated nitric oxide release. Setomimycin (0.01 to 1 µM) as well as positive control L-NAME (1-100 µM) inhibited NO release in a dosedependent manner (Fig. 7).

Antioxidant activity of setomimycin
Setomimycin has shown good anti-oxidant properties (as measured by the potential to scavenge DPPH free radicals) with maximum activity of around 71% achieved at 500 µM following 60 min incubation. The DPPH inhibition shown was concentration dependent. Ascorbic acid and quercetin showed concentration-dependent increase in free radical scavenging in the range of 0.011 to 150 µM. Ascorbic acid and quercetin showed around 90% inhibition of DPPH absorbance at 150 µM (Fig. 8).

Effect of setomimycin on viability of RAW 264.7 cells using MTT assay
RAW 264.7 cells treated with setomimycin in the concentration range of 0.15 to 1.25 µM for 48 h did not show appreciable cell death confirming that at these concentrations setomimycin was not toxic to cells (Fig. 9). This in turn confirms that the cytokine inhibition and nitric oxide inhibition of setomimycin were not due to cytotoxicity.

Conclusions
Despite enormous tireless efforts of the scientific community, COVID-19 infection continues to result in significant morbidity and mortality. Although several prospective   therapies have been proposed, World Health Organization has approved remdesivir, for the treatment of COVID-19, and several are under trial. Due to emergency health conditions, treatments for COVID-19 illness are gravely needed to alleviate the socioeconomic implications of the disease. In the present study, we combined in silico (molecular docking as well as molecular dynamics) with in vitro studies to assess anti SARS-Cov-2 potential of setomimycin. According to the molecular docking study, setomimycin exhibited the best docking score with the M pro enzyme among all the selected targets. Therefore, the in vitro studies were evaluated to determine the inhibitory potential of setomimycin against M pro . It was found that setomimycin inhibits M pro enzyme with an IC 50 value of 12.02 ± 0.046 μM. Additionally, anti-inflammatory and antioxidant properties of setomimycin were confirmed by ELISA assay, inhibition of nitric oxide, and DPPH assay, respectively. Overall, the findings suggest setomimycin as a potential COVID-19 therapeutics for COVID-19 disease and its related complications.
Author contributions RSM carried out most of the wet lab experiments and wrote the first draft of the manuscript. In silico work was carried out by HT. MN performed anti-inflammatory experiments, AA helped in purification and characterization of pure compound, SM supervised anti-inflammatory work and analyzed the results, JV and RB conducted in vitro assays and data analysis, RR provided intellectual inputs in assay & design and helped in analysing the data, DM supervised chemistry work and spectra analysis, AN supervised in silico work and analyzed the results, AC conceptualized, planned the experiments, supervised, coordinated entire work, and finalized the manuscript.
Funding The authors did not receive financial support for the submitted work. Authors thank ICMR and CSIR for providing fellowships to the students.