Scientists have anticipated that Coronavirus is going to be endemic, albeit with diminished virulence and fatality rate, and will continue to produce variants. Omicron, Alpha, and Delta are some of the prominent variants. Few of these variants also tend to produce some likely sub-variants into nature. When a virus is transmitted from one individual to another, it sometimes faces some changes in the structure which in medical layman terms is also known as mutations. When two or more mutations occur, a variant is born. There were over 5000+ mutations discovered in 200,000 genome isolates present in the spike proteins of SARS-CoV-2.

Covid variants have the potential to emerge from animals, humans, and from infected animals and humans. We have seen its effect in humans but not much research has been in animals. Some variants may not be strong enough to be treated and hence can be cured easily over the time but some variants may be produced in the future which might have potential to bring pandemic once again. Some reports also claim that some of the variants and sub-variants are getting drug resistant.

According to WHO reports, as of February 2023, the confirmed cases of COVID-19 are a total of 756,411,740 and confirmed deaths are 6,842,462. Because of the restrictions implemented due to COVID-19 virus, it is challenging for humans to exist under them. So, there is a need for compounds which can be developed as drugs. It is the need of an hour to derive compounds from various different sources such as plants, chemicals, gasses, etc.

In this research, we have selected Streptomyces to derive potential compounds for further drug development because Streptomyces bacteria produce a significant portion of antibiotics used today [1]. Actinobactriae’s largest genus is Streptomyces [4]. A gram- positive bacteria’s genus i.e. Streptomyces grows in a variety of environments and resembles filamentous fungi in appearance [5]. A necessary component of Streptomyces’ morphological differentiation is the production layer of the hyphae that can differentiate into a chain of spores. The potential of Streptomyces to produce bioactive secondary metabolites, such as not only antibiotics but also antifungals, antivirals, antitumorales, antihypertensive-ness, and immunosuppressants, is particularly noteworthy. As the production of the majority of the antibiotics is species-specific, these secondary metabolites are Streptomyces species in order to compete with other bacteria that come into contact, even ones belonging to the same genre.

Around two-thirds of clinically relevant natural antibiotics, such as neomycin, cypemycin, grisemycin, bottromycin, and chloramphenicol, are produced by streptomyces [6,7]. From the genus Streptomyces comes the antibiotic streptomycin [8].

Streptomyces has 679 recognized species and an additional 121 tentative species. Only one species is used in development of COVID-19 drugs i.e. Streptomyces lividans. To find out more compounds that have potential for drug development, we have selected Streptomyces for our research due to its properties.

In-silico analysis is used for determining potential compounds because it is a computational method that uses computer simulation, modeling, and data analysis to investigate many areas of biology, chemistry, and other specific domains. It includes evaluating and interpreting data via the use of software programs and mathematical algorithms, as opposed to doing laboratory tests. In the context of drug development, in- silico analysis is utilized to anticipate the behavior of molecules and their interactions with target receptors or enzymes, in addition to optimizing drug design and identifying valuable therapeutic candidates. This makes this technique potential enough to start the research. This technique also has a number of benefits over conventional experimental approaches, including cost effectiveness, speed, and a thorough knowledge of complex biological systems.

Selection and preparation of ligands

Compounds produced in regards to Streptomyces species were identified and were about 190 in number. With the help of the official PUBCHEM website (https://pubchem/ncbi/nlm.nih.gov), the canonical smiles of the produced ligands were recovered. Further with the help of CORINA online server (https://demos.mn- am.com/corina.html), after embracing the SMILES string in the appropriate places the 3-D structure of each and every ligand was obtained for further steps. In the subsequent steps, by detecting the torsion root and correcting the angles of torsion, optimizing and assigning charges using the UFF i.e Universal Force Field, in order to make it easier to create the molecules’ 3D atomic coordinates, the ligands were created and transformed into PDBQT (Protein Data Bank, Partial Charge (Q), & the Atom type (T)) format.

Screening of the ligands based on druglikeness

Using the SwissADME online server (http://swissadme.ch/index.php), compounds’ drug-likeness were evaluated. The drug-likeness is an essential criterion for validating compounds as prospective ligands against potential therapeutic targets. Using Lipinski's Rule of Five, the 190 identified Streptomyces phytochemical compounds were screened and the positive drug likeness showing compounds were further used for docking studies.

Preparation of target proteins and identification of binding sites

The RBD of SARS-CoV-2, which is shown in Figure 1, served as the study's primary focus. Using the Protein data bank reserves (http://www.rcsb.org/), the 3D crystallographic structure of the proteins was retrieved. Using the PyMol software server (http://www.pymol.org) the proteins were visualized and the ligands, water molecules and co-crystal ligand structures which were bound to the COX-2 were further removed while processing. By using an open-source software Auto Dock Tools, the protein was prepared by adding charges and energy minimization in the Swiss PDB viewer by converting it subsequently into PDBQT format. Further with the help of CASTp online server, the binding sites for every protein were identified. The ligands having the largest pocket size of the protein were to be used as binding sites.

Molecular docking and protein-ligand interactions analysis

The docking engine used for the molecular docking analysis was the PyRx tool from Auto Dock wizard. The protein was anticipated to be rigid during docking, but the ligands were thought to be flexible. Based on the binding amino acid analysis from CASTp server, they served as the foundation for the configuration file for the grid settings. The ligand with the greatest binding affinity i.e., the one with the most negative value was identified as having the highest binding energy. Using Biovia Drug discovery studio 2020, visual inspection of the docking site and Protein-Ligand interactions was carried out.

Drug likeness analysis

From the drug likeness analysis of 190 compounds, 61 compounds showed drug likeness properties. Therefore 61 compounds were further subjected to docking and interaction analysis. Table 1 contains all the names of these compounds.

CastP Binding site analysis

Docking analysis

Table 3 depicts the binding energies of all the 61 compounds.

Protein-ligand interaction analysis

The compounds showing higher binding energies (<-8 Kcal/mol) were selected and subjected to interaction analysis. The interaction of compounds on the binding sites of the target protein and nature of bonding were analyzed as shown in Table 4.

Figures 4 to 17 show the results of the finalized compounds below:

According to research papers published and studies conducted worldwide, few of the compounds that were used in drug development of COVID are enlisted below with their test results -

Escherichia coli - This bacterium is typically utilized to manufacture recombinant protein and is used in production of antibodies and vaccines such as Pfizer-BioNTech and Moderna. Vaccine candidates frequently contain the COVID-19 spike protein RBD. Due to the small molecular mass along with their structural rearrangement of full-length SP and detachment, the RBD's immunogenicity is rather limited [9].

Saccharomyces cerevisiae - In immunocompromised and critically ill patients, the saccharomyces organisms are reported as agents of invasive infection. The antitoxin of MazE i.e. MazF along with the toxin itself from Escherichia coli were combined with a protease cleavage site in the linker peptide. The viral protease cleaves the MazEF fusion, liberating MazF toxin from its antitoxin and inhibiting growth. A fluorescent marker protein on the modified yeast strain lets track its development with or without inhibitors [10].

CHO cells - Biopharmaceuticals are often made using CHO cells. A recombinant vaccine was made using a prefusion-stabilized SARS-CoV-2 spike trimer and aluminum hydroxide. CHO cells were used to isolate and stabilize spike protein. In mice and non- human primates, candidate vaccinations strongly induced CD4+ T cell and neutralizing antibody responses [11].

HEK293 cells - Genetically engineered human embryonic kidney cells produce protein efficiently. They manufacture COVID-19 vaccines and antibodies for possible therapies. SARS-CoV infection induced CPE, and quantitative real-time polymerase chain reaction measured viral replication. Indirect immunofluorescence confirmed. 10 SARS-CoV- susceptible cell lines were found. SARS-CoV replication and CPE occurred in HEK-293 cells [12].

African green monkey kidney (vero) cells - Sequencing revealed that influenza B viruses formed from Vero cells differ from egg-grown viruses at amino acids 196–198 but share a hemagglutinin with MDCK-grown viruses. Fluorescence-activated cell sorting shows that human influenza A and B viruses can infect Vero cells with alpha2,3 galactose-linked sialic acid. Electron microscopy and immunofluorescence showed that infected Vero cells become polarized epithelia. Vero cells can generate pandemic-vaccinating influenza A and B viruses [13].

Bacillus subtilis - Inactivated B. subtilis spores increase neutralizing antibody levels for weeks following booster treatment. The inactivated oral vaccination booster candidate's immediate effects are predicted to elicit gut mucosal immune responses simply by the spore surface, followed by systemic effects. When neutralizing antibodies decline and stabilize after 6 months, Bacillus subtilis is the key component for booster doses [14].

Lactococcus lactis - L. lactis NZ3900 expressed the HCR spike protein gene from pNZ8149. ELISA evaluated anti-SARS-CoV2 spike antibody (IgG and IgA) levels before and after therapy. Plasma and lymph and intestinal CD4+ and CD8+ lymphocytes were examined. L. lactis bearing the HCR gene raised IgG/IgA-SARS-CoV-2 levels in experimental mice after nasal and oral treatment [15].

Pichia pastoris - Heterologous protein expression in vaccine development is best in Pichia pastoris (P. pastoris). It reduces production costs by combining the speed and convenience of highly efficient prokaryotic platforms with certain mammalian system characteristics. Low- and middle-income countries produced vaccines cheaply [16].

Tobacco and Tomato Plants - Vaccines, pharmaceuticals, immunomodulatory proteins, and monoclonal antibodies are bioreactive or factory products produced from plants with the help of molecular farming/transient expression systems. These biological products are stable, safe, efficacious, inexpensive, and readily available. Plant molecular farming might speed up industrial biologics manufacturing in emergencies like the COVID-19 pandemic [17].

Aspergillus niger and Aspergillus oryzae - Coronavirus-associated pulmonary aspergillosis (CAPA) may cause morbidity and death in COVID-19 patients, while many elements of the illness are yet unknown. Endogenous immunomodulation may restore immunological homeostasis in COVID-19 patients and minimize aspergillosis risk. COVID-19's opposite side is CAPA [18].

Chlorella vulgaris and Chlamydomonas reinhardtii - To test an oral algae-based vaccination, mice were given a Chlamydomonas-made SARS-CoV-2 RBD. In freeze-dried algal biomass, the test immunogens were quite stable in nature and systemic and mucosal humoral responses against the spike protein by mouth at similar levels as injected antigen + alum adjuvant were produced. IgG subclass analysis showed a Th2-bias response lasting over 4 months following the previous vaccination. Induced antibodies reacted similarly to Delta and Omicron versions [19].

Haloferax volcanii - Halophilic enzymes may be useful in bio-catalysis, biological remediation, bio-plastics, nano-biotechnology, and biofuels. A wide genetic toolbox with possibilities for controlled protein overexpression has allowed H. volcanii to purify biotechnologically significant enzymes from diverse halophiles. New biotechnology applications could be made possible by the use of expressed active proteins in immobilized cells in a porous biocompatible matrix for H. volcanii [20].

Call free systems - Cell-free protein synthesis (CFPS) systems, which utilize removed transcriptional and translational machinery from cells, may rapidly create proteins without the restrictions of living cells and be used for on-demand biomanufacturing. CFPS systems have been used to produce subunit, conjugate, virus-like particle (VLP), and membrane-augmented vaccinations and design vaccines [21].

Baculoviruses - Baculovirus expression vectors express glycoproteins, enzymes, vaccines, and recombinant viruses. Non-Structural Proteins (NSPs), another recombinant SARS-CoV-2 protein produced by BEV, are utilized to find new COVID-19 treatments or repurpose current ones. BEV produces therapeutic proteins, recombinant antibodies, MMLVRT, and ACE2 for SARS-CoV-2 diagnostic and therapy. Modified recombinant proteins increased production or stability [22].

Pseudomonas fluorescens - Gram-negative bacteria's ubiquitous transcription regulator Ferric uptake regulator (Fur) controls several biological activities. The pathogenic Pseudomonas fluorescens strain TSS, which was acquired from locally farmed Japanese flounder, was used to clone the fur gene. Escherichia coli fur mutants can partially benefit from TSS Fur [23].

Streptomyces lividans - Streptomyces coelicolor and Mycobacterium TB' genomes imply a shared Actinomycete ancestry. Streptomyces vaccination can be used as a live vector and Mycobacterial infection protecter. The theoretical proteomes of S. coelicolor A3(2) and M. tuberculosis H37Rv and Mycobacterium bovis AF2122/97 exhibited substantial gene sequence similarity and shared membrane proteins. This bacterium produces a COVID-19- diagnostic protein [24].

Lactic acid bacteria - Gram-positive, nonpathogenic recombinant lactic acid bacteria (LAB) strains induce particular systemic and mucosal immune responses against chosen antigens. Modern vaccinology prioritizes vaccine design, mucosally administered vaccines, in vivo antigen production monitoring, optimal vaccine dosage, strain selection, and characterization [25].

Mycobacterium smegmatis - SARS-spike CoV-2 and nucleocapsid are carried by M. smegmatis. S or N antigens were fused with Mycobacterium TB cell wall trafficking component PE PGRS33 protein to mimic viral particle surface localization (M.tb). After receiving the COVID-19 vaccine candidate based on M. smegmatis, mice developed CD4+ and CD8+ T cells with central memory as well as S or N antigen-specific T cell immune responses. In T cell-based vaccines for SARS-CoV-2, M. smegmatis may be utilized [26].

It was observed that only one compound Streptomyces lividans was effectively utilized for drug development even though Streptomyces has been a proven antibiotic of natural origin.

Lee et al. used genome sequences for mining novel secondary metabolite biosynthetic gene clusters [27]. Senges, et al. [28] used the technique metabolomics. In Ouchene et al. study [29], integrated metabolomics, molecular networking and genome mining analysis was used. In Iglesias et al. performed identification, characterization and in-silico analysis in their studies [30]. Edison et al. [31] used in-silico analysis in structural elucidation. Some researchers used comparative genomics in their studies [32-37]. Kumar et al. [38] used in vitro and in-silico analysis using machine learning in their studies. It was observed that genome mining, metabolomics, structural modeling, comparative genomics, and machine learning were the most used in-silico techniques with Streptomyces.

In this research, in-silico technique is used and the following steps were used to perform the complete process. The first step being the Selection and Preparation of Ligands followed by Screening of these Ligands based on the Druglikeness. The last few steps to complete the entire process are Preparation of Target proteins with identifying the binding sites and the final step being the Molecular docking and Protein Ligand interaction analysis to obtain the end results of the study. All these steps play a crucial role for the entire process. The first step allows one to identify and select the potential ligands which would be useful for the study from multiple databases. Screening on the basis of druglikeness helps in identifying whether the potential ligands selected would be a good candidate for the development of drugs on the basis of their physicochemical properties. Preparation of target proteins and identifying the binding sites aid in the successfulness of molecular docking simulations. These also improve the accuracy and lower the chances of getting false positive results. The final step is used to predict the binding affinity and the mode of interaction between ligands with their target proteins. Before a compound is synthesized and evaluated in vitro or in vivo, this can provide invaluable information about its potential efficacy and safety.

Streptomyces is a type genus of the Streptomycetaceae family and presently encompasses close to 576 species, with the number growing annually. There was a significant scope for research; and hence this research work lays a groundwork focused on discovering the other potential compounds that can further be used for the drug development for the novel virus.

From a total of 190, compounds were screened based on drug likeness analysis. 61 compounds showed drug likeness properties and hence were further subjected to docking studies. The compounds showing higher binding energies (<-8 Kcal/mol) were utilized for interaction analysis. All the 14 compounds except Kendomycin showed interaction on binding sites of target proteins. Therefore, the compounds identified from the study i.e. From Streptomyces tanashiensis, Thaxtomine A, Bafilomycin A1 from Streptomyces griseus; Bafilomycin A1, Streptomyces griseus; Borrelidin, 6,7,12,13-Tetrahydro-5H-indolo[2,3- a]pyrrolo[3,4-c]carbazol-5-one, Antibiotic DC 107, Thaxtomin A, Lactoquinomycin A, Bafilomycin A1, Leinamycin, Schembl20768056, (3Z,5Z)-3,5-Bis(3,4- dichlorobenzylidene)-1-(3-morpholinopropanoyl)-4-piperidone, hydrochloride can be used for drug development for treatment of COVID-19 infections. Further studies will confirm the proper usage and implementation of new compounds for procuring the COVID-19 antivirals.

The authors are thankful to the Darwin Conference for giving them an opportunity for presenting the following research and grateful to them as well as Scientific Archives for publishing this work into their journals.

No funding received for this research work.

There are no conflicts of interest pertaining to this paper by the authors.