Recognition of Plausible Therapeutic Agents to Combat COVID-19: An Omics Data Based Combined Approach

doi:10.21203/rs.3.rs-25807/v1

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Recognition of Plausible Therapeutic Agents to Combat COVID-19: An Omics Data Based Combined Approach

https://doi.org/10.21203/rs.3.rs-25807/v1

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published 01 Mar, 2021

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Coronavirus disease-2019 (COVID-19) has become an immense threat to global public health. The causative agent of this disease is a novel zoonotic pathogen called Severe Acute Respiratory Syndrome related Coronavirus-2 (SARS-CoV-2). Since this is a newly evolved pathogen, very limited information is available to develop effective therapeutics against this deadly virus. Although bioinformatics based analysis could be handy to unveil drugs or vaccines against bacteria and fungus, such approaches are hardly seen for acellular viruses. However, in this study we rationally merged several powerful in silico techniques and proposed prospective therapeutics based on available omics data for COVID-19. Through meticulous analysis of conserved regions of 67 SARS-CoV-2 strains, spike and membrane glycoproteins were chosen to develop and propose a chimeric vaccine against this virus. siRNAs were also designed against these glycoprotein genes to silence them. Moreover, six drug compound candidates were suggested to inhibit the conserved RNA-directed RNA polymerase protein. Finally, due to the close relationship of SARS-CoV-2 and SARS-CoV, publicly available gene expression datasets of SARS-CoV were analyzed to identify 13 immunoregulatory genes that might develop interferon based therapy. Our study will quicken the researches among pharmaceuticals, researchers and clinicians to develop rapid therapeutics for controlling this notorious pandemic disease.

Authors Mohammad Uzzal Hossain, Arittra Bhattacharjee, Md. Tabassum Hossain Emon, and Zeshan Mahmud Chowdhury contributed equally to this work.

Pharmacodynamics

COVID-19

SARS-CoV-2

chimeric vaccine

small molecule drugs

siRNAs

Interferons

Coronavirus Disease–2019 (COVID–19) has drastically spread throughout the globe and become a massive threat to humanity¹. Most of the healthcare system and governing bodies from developed countries failed to stop the spreading and infection of this pandemic disease². Regrettably, the quick prevention of COVID–19 is challenging because the causative agent of this disease is a novel Severe Acute Respiratory Syndrome (SARS) related Coronavirus (CoV) or SARS-CoV–2. This virus was totally unknown before the outbreak that took place in Wuhan, China; therefore, traditional development of therapeutics will not yield a rapid solution to control this pestilence ^2,3.

CoVs, members of Coronaviridae family and Coronavirinae subfamily, were considered as insignificant pathogens for humans before the outbreak of SARS occurred in 2002 and 2003 from Guangdong province, China⁴. Coronavirinae has been divided into four categories such as Alpha, Beta, Gamm and Deltacoronavirus⁴. These pathogens were described as the “virology backwater” since very little was known about their virulence⁵. In recent decades, they have become scandalous several times due to their zoonotic characteristics. Especially, the Betacoronavirus group consists of the most perilous Human CoVs (HCoVs). For example, HCoV-OC43, HCoV-HKU1, SARS-CoV and Middle East respiratory syndrome coronavirus (MERS-CoV) are members of Betacoronavirus⁴.

Coronavirinae has vast genetic diversity among them but they mostly reside in Bat⁷. Therefore, this mammalian bird always possesses a serious threat for CoV outbreaks, particularly in China⁸. The possible reason behind this occurrence is that the members of Coronavirinae frequently go under RNA recombination inside bat and often generate novel CoVs^6,7. SARS-CoV–2, a member of Betacoronavirus, also originated in bats and transmitted to humans via unidentified intermediary animals in Wuhan, China. This transmission started the pandemic COVID–19 which was instigated on December 2019⁹.

SARS-CoV–2, a positive-stranded (+) RNA virus, spread via respiratory droplets of an infected person to a healthy individual through sneezing and coughing³. Moreover, the presence of SARS-CoV–2 is also observed in the stool; thereby contaminated water supply might promote transmission¹⁰. The virions enter through the upper-respiratory tract and enter inside host by engaging Angiotensin Converting Enzyme 2 (ACE2) and Transmembrane Serine Protease 2 (TMPRSS2) of the lung cells¹¹. This entry triggers innate immunity as a result inflammatory cytokines e.g., Interleukin (IL) - 2, IL–10, IL–7, Granulocyte-colony stimulating factor (GCSF), monocyte chemoattractant protein 1 (MCP1), Macrophage Inflammatory Proteins (MIP) 1A, Interferon gamma-induced protein- 10 (IP–10) and tumor necrosis factor alpha (TNFα) get elevated⁹. Consequently, the patients develop indistinguishable flu like symptoms such as cough, sore throat, headache, fatigue, myalgia, breathlessness, conjunctivitis and fever. In worst cases, patients develop pneumonia leading to respiratory failure and death¹². To handle this condition, physicians are non-specifically applying anti-malarial Chloroquine, antiviral drug e.g. Oseltamivir, Ganciclovir, Lopinavirritonavir etc. and plasma of the patients that recovered from COVID–19^9,13,14. However, this situation demands precise therapeutics for the quick betterment of the COVID–19 patients.

A recent in vitro study revealed potential clinically proven protease inhibitor that can block TMPRSS2 and consequently the entry of SARS-CoV–2¹¹. Similar to this study, revealing more plausible therapeutics and druggable targets will elevate the probability to achieve successful treatments against COVID–19. In silico approaches are the most suitable way to execute this type of research in a short time with nearly zero cost. Powerful computational methods such as subtractive proteomics, comparative genomics or virtual screening can disclose therapeutic targets and drugs against bacteria, fungi or non-communicable diseases^15,16,17. Unfortunately, for viral infections, this type of rational approach is hardly seen. As a result, unveiling the potential targets, novel drugs, interferons and immunodominant vaccines are extremely difficult against COVID–19.

In this study, we merged several in silico techniques for developing treatments against COVID–19. Here, we analyzed the genome of SARS-CoV–2 and selected several conserved regions to establish small molecules, chimeric vaccine and Small interfering RNAs (siRNAs). Moreover, we carried out a comparative genome analysis of SARS-CoV–2 with SARS- CoV and MERS-CoV to look for the genetic similarity. SARS-CoV showed close genetic relation and virulence pattern with SARS-CoV–2. Thus, a transcriptomic analysis of this related virus was performed to retrieve the interferon inducing genes for implementing interferon based therapy.

Retrieval of Whole Genomes and Conserved Region:

Public database Global Initiative on Sharing All Influenza Data (GISAID) was explored to retrieve the whole-genomes of novel SARS CoV–2¹⁸. Sixty-seven (67) whole-genomes of SARS CoV–2 were selected for this present study (January, 2020). In quest for developing universal therapeutics, multiple sequence alignment and construction of phylogenetic cladogram were performed using neighbor joining method in CLC Drug Discovery Workbench (Version 3.0). For further study, consensus sequences were collected to develop a vaccine, small molecules and SiRNAs.

Chimeric Vaccine against SARS-CoV–2

Antigenicity analysis of the candidate proteins

The antigenicity of the selected Membrane Glycoprotein (NCBI accession:YP_009724393.1) and Spike Glycoprotein (NCBI accession: QHR63250.2) were determined by Vaxijen 2.0¹⁸and AntigenPro¹⁹. These servers predicted antigenicity through alignment independent methods.

Identification of B cell and T cell Epitopes

The outside amino acid residues of the vaccine candidates were identified with THMM¹⁹. The identified sequences were uploaded in BepiPred–2.0 server to select the most potential B cell epitopes²¹. During this selection, surface exposed amino acids were prioritized mostly. The Cytotoxic T cell (CTL), Helper T cell (HTL) epitopes were disclosed by NetCTL 1.2 and NetMHC 4.0 respectively^22,23. NetCTL 1.2 was implemented to discover the CTL epitopes for 12 classes of Major Histocompatibility Complex 1 (MHC I) supertypes. The best CTL epitopes were selected based on combined score. NetMHCII 2.2 was used to detect 15-mer HTL epitopes for human HLA-DP, HLA-DQ, and HLA-DR alleles. Most potential epitopes were chosen by evaluating affinity, percentage ranking and binding level.

Construction of Chimeric Vaccine

To construct a chimeric or multi-epitope vaccine, all the epitopes were joined with EAAK, GPGPG and AAY linkers²⁴. Human Beta Defensin–2 (HBD–2) (PDB ID: 1FD3) and a recombinant viral protein were added in the N terminal and the C terminal of the vaccine respectively^25,26. HBD–2 was conjugated because the protein can activate Toll Like Receptor 4 (TLR 4) and have chemotactic activity^25,27. The recombinant viral protein was added to stimulate the antiviral responses²⁶.

Evaluation of Immune Response and Interferon Gamma (IFNγ)

The fasta sequence of the vaccine was uploaded to an agent-based immune system simulator C-ImmSim (http://150.146.2.1/C-IMMSIM/index.php) for measuring the immune responses²⁸. The parameters were kept default during this simulation. C-ImmSim showed an adequate secretion of IFNγ which was further evaluated by IFNepitope²⁹.

Allergenicity and Toxicity Exploration

Recombinant vaccine can initiate allergic response or lead to various types of toxicity. Therefore, evaluation of toxicity and allergenicity is an essential step for vaccine design. Allergenicity of the vaccine was calculated by AlgPred and AllerTop v.2^30,31. Toxicity was measured by ToxinPred³².

Analysis of Physicochemical Properties and Tertiary Structure

The physicochemical analysis of the protein was executed via ProtParam³². The tertiary structure was generated through Contact-guided Iterative Threading ASSEmbly Refinement (C-I-TASSER) (17). The structure was refined by GalaxyRefine³⁵ and validated with Procheck³⁶ and ProSAWeb³⁷.

Molecular Docking Analysis

The crustal structure of TLR 4 was collected from Research Collaboratory for Structural Bioinformatics (RCSB) Protein Data Bank (PDB) (www.rcsb.org). The PDB ID of the structure was 3FXI. For molecular docking analysis, only Chain A of TLR 4 was preserved and prepared by deleting the heterogeneous atoms. This preparation was executed by BIOVIA Discovery Studio (https://www.3dsbiovia.com/). To determine the active site pocket of Chain A, the PDB file was uploaded in Computer Atlas of Surface Topology of Proteins (CASTp)³⁸. Finally, the vaccine and Chain A of TLR–4 was uploaded in ClusPro 2.0 for protein-protein docking³⁹.

Codon Optimization and Visualization of Cloning

The vaccine sequence was reversely translated and the codons were optimized for Escherichia coli (strain K12) through JCat⁴⁰. The optimized DNA sequence was kept free from rho-independent transcription terminators and prokaryotic ribosome binding sites. This sequence was flanked by Nde I and Xho I restriction sites. Stop codons were added at the end of 3’OH or C terminal end. After these preparations, the DNA sequence was inserted in pET28a (+) Plasmid Vector via SnapGene tools (www.snapgene.com).

Small Molecule Therapeutics against COVID–19

Homology Modelling and Binding site analysis

The conserved RNA directed RNA polymerase (RdRp) sequence was retrieve to develop small molecules. C-I-TASSER was employed to build the 3D model of RdRP³⁴. Thereafter, CASTp was applied to identify the drug binding sites that allowed to recognize the critical amino acids for drug-protein interactions³⁸.

Screening the druggable compounds and molecular docking simulation

DrugBank Database (www.drugbank.ca)was utilized to search for the potent drugs against RdRP. Molecular docking of receptorand the selected compounds were performed in AutodockVina to observe the binding affinity into the binding site of RdRp⁴². Here, AutoDock tools 1.5.6 was used to prepare the input pdbqt file for the receptor. A grid box parameter were set in size with X = 70, Y = 70, Z = 36 points (center grid box:x = 23.448, y = 53.587, z = –2.012, spacing = 0.5˚A). The molecular visualization of protein-ligands were analyzed by BIOVIA Discovery Studio and PyMol⁴³.

Pharmacoinformatics illustration

Osiris property explorer⁴⁴, Molinspiration⁴⁵, ACToR (Aggregated Computational Toxicology Resource)⁴⁶, admetSAR (absorption, distribution, metabolism, excretion, and toxicity Structure-Activity Relationship database)⁴⁷ and ACD/I-lab⁴⁸ were exploited for the calculation of Absorption, distribution, metabolism, excretion (ADME) properties and toxicity profile. ADMET properties are necessary to establish an effective drug.

Nucleic Acid Based Therapeutics Development

Designing of potential siRNA molecules

In order to design the effective siRNA molecule, I-Score Designer was employed⁴⁹. I-Score Designer computes nine different siRNA designing scores such as Ui-Tei⁵⁰, Amarzguioui⁵¹, Hsieh⁵²‚ Takasaki⁵³‚ s-Biopredsi⁵⁴‚ i-Score⁵⁵‚ Reynolds⁵⁶‚ Katoh⁵⁷‚ and Design of SIRna (DSIR) ⁵⁸ along with other essential parameters. The server also ranks the best siRNA molecules. From there, the best siRNA sequence was taken for further analysis. The secondary structure of the siRNA was predicted via RNA structure webserver⁵⁹. RNAfold web server was applied to calculate the free energy of the thermodynamic ensemble for the secondary structures⁶⁰. Transcription and Translation Tool (http://biomodel.uah.es/en/lab/cybertory/analysis/trans.htm) was implemented to transcribe the viral DNA sequences. Finally, the designated Antisense siRNAs were hybridized against viral RNA sequences using the HNADOCK server⁶¹. HNADOCK executed RNA-RNA docking and performed molecular dynamics simulations to refine the best 10 predicted siRNA-mRNA complexes. The model with best docking score for Membrane Glycoprotein mRNA and Spike Glycoprotein mRNA were visualized with PyMol.

Inteferon stimulating genes (ISGs) based Interferon Therapy

Comparative Genomics

The genome of SARS Cov–2 was compared with SARS CoV and MERS CoV. We Blasted SARS CoV–2 against SARS CoV and MERS CoV, using Megablast and Discontiguous Megablast algorithm. The graphical representation of side by side genome comparison is demonstrated in Artemis Comparison Tool⁶².

Exploring SARS-CoV expression profile

The Gene Expression profile (GSE5972) of SARS-CoV was collected from National center for Biotechnology Information (NCBI). Afterwards, the normalization study was performed in between 10 Healthy samples and 54 SARS patient samples, whereas recovered cases were excluded from this study. We used limma R package to identify the differentially expressed genes⁶³. IDEP tools was utilized to create Hierarchical Clustering Heatmap and Boxplot to the visualization and distribution of the data for both up and downregulated genes⁶⁴.

Gene Enrichment Analysis

Kyoto Encyclopedia of Genes and Genomes (KEGG) and Enrichr4 were employed to identify the genes which were involved in pathways and Gene Ontology (GO) processes such as biological process (BP), molecular function (MF) and cellular component (CC)^65–67. The genes with GO accession ID &KEGG pathways were enlisted for further study. ShinyGO was utilized to construct a clustered tree of the top 30 GO terms for both up and downregulated genes of BP, MF and CC⁶⁸.

Identification of Viral Genes

We securitized the down and upregulated genes in BP, MF and CC from gene ontology dataset to find out the viral connected genes especially enriched viral production regulation and cytokine regulation.

Exploration of Interferon Stimulating Genes (ISGs)

INTERFEROME⁶⁹ database was analyzed to recognize the Interferon Stimulating Genes (ISGs) and potential Interferons. Further, we tried to explore the pathway of ISGs which modulate the immune system and interferon regulation by Reactome⁷⁰.

Sixty seven (67) SARS-CoV-2 whole genome strains were collected from different countries (Supplementary Data 1). Multiple Sequence Alignment (MSA) and Phylogenetic Tree depicted the similarity and distant relationship of SARS-CoV-2 among the different geographic area and populations (Supplementary Data 2 and Supplementary Fig. 1). From these analysis, 3 consensus sequences from the whole genomes were collected (Supplementary Data 3). The conserved sequences were translated and their functions were analyzed manually to develop vaccine, siRNAs and drugs.

Chimeric Vaccine against SARS-CoV-2

The selected membrane glycoprotein (MG) (Vaxijen score 0.55 and AntigenPro score 0.232547) and spike glycoprotein (SG) (Vaxijen score 0.4695 and AntigenPro score 0.717053) were predicted as “antigenic” by the assigned programs. The MG and SG have a total of 24 and 1214 outside amino acids respectively. Among those residues, one potential B cell epitope NGTITVEELKKLLEQ was identified in MG (Table 1). Four possible B-cell epitopes (HAPATVCGPKKSTN, NNSYECDIP, FYEPQIITTD, VEGFNCYFPLQ) from the SG and only one from the MG (NGTITVEELKKLLEQ) were found (Table 1). The selected four Cytotoxic T-cell (CTL) epitopes from MG and SG cover HLA-A12, A24, A26, B58, B8 alleles. Moreover, Helper T-cell (HTL) epitopes can interact with HLA DRB1-0101, DPA10103-DPB10301, DQA10101-DQB10501 and DRB1-1501 (Supplementary Table 1). The chimeric vaccine exposed the strong binding affinity with Toll Like Receptor-4 (TLR4) (Fig. 2A). A single injection of the vaccine has showed an adequate release of IFNγ in C-ImmSim simulator (Fig. 2B). The IFNepitope also showed 127 IFNγ inducing epitopes by different methods. Additionally, Interleukin (IL)-2 was also released significantly for lymphocyte differentiation. This differentiations are mentioned as danger signal D (Sympshon Index) (Fig. 2B). After release of inflammatory cytokines, the immune system released anti-inflammatory IL-10 at 2.5^th day and consequently generated Immunoglobulin M (IgM) and later IgG (Fig. 2C). Generation of memory B cells was also observed (Fig. 2D). The vaccine specifically stimulated the differentiation of T helper Cell- 1 (Th 1) with certain level of T regulatory cell (Fig. 2E). Memory Helper T Cell also released in between 2.5 days (Figs. 2F and 2G). The vaccine does not contain any major allergenic properties according to AlgPred and AllerTop. ToxinPred identified 343 non-toxic peptides in different combinations and only 11 peptides with toxic properties. The vaccine is stable according to the instability index of ProtParam (Supplementary Table 2). The estimated half-life of the protein is 30 hours in mammalian reticulocytes (in vitro), >20 hours in yeast (in vivo) and >10 hours in Escherichia coli (in vivo) with -0.010 Grand Average of Hydropathicity (GRAVY) and 81.38 Aliphatic index (Supplementary Table 2). The refined three dimensional (3D) structure of the vaccine has 1.905 MolProbity score (provided by GalaxyWEB), -2.62 Z-Score and 87.9% residues in the most favored regions of Ramachandran Plot (Supplementary Figs. 2 and 3). When this protein was docked against TLR 4 (Chain A) via ClusPro 2.0, it interacted toward the receptor via active site pocket and HBD-2 with lowest energy score -1401.1.

Table 1: The potential B cell, Cytotoxic T cell and Helper T cell epitopes.

Type of Epitope	Serial No.	Epitope Sequence	Protein Name
B Cell epitopes	1	NGTITVEELKKLLEQ	Membrane Glycoprotein
	2	HAPATVCGPKKSTN	Spike Glycoprotein
	3	NNSYECDIP	Spike Glycoprotein
	4	FYEPQIITTD	Spike Glycoprotein
	5	VEGFNCYFPLQ	Spike Glycoprotein
CTL Epitopes	6	SSDNIALLV	Membrane Glycoprotein
	7	YFIASFRLF	Membrane Glycoprotein
	8	YIIKLIFLW	Membrane Glycoprotein
	9	LAAVYRINW	Membrane Glycoprotein
	10	LTDEMIAQY	Spike Glycoprotein
	11	FVFKNIDGY	Spike Glycoprotein
	12	YLQPRTFLL	Spike Glycoprotein
	13	RSFIEDLLF	Spike Glycoprotein
HTL Epitopes	14	TLSYYKLGASQRVAG	Membrane Glycoprotein
	15	ASFRLFARTRSMWSF	Membrane Glycoprotein
	16	SNLLLQYGSFCTQLN	Spike Glycoprotein
	17	LIRAAEIRASANLAA	Spike Glycoprotein
	18	WFVTQRNFYEPQIIT	Spike Glycoprotein

Codon Optimization and Cloning for chimeric vaccine

The DNA sequence of the vaccine has 1089 nucleotides that demonstrated 0.96 Codon Adaptation Index (CAI) with 54.26% GC content. Here, the GC content of the host organism E. coli strain K12 is 50.73 (Fig. 3). This sequence was taken under modifications for cloning

Small Molecule Drugs against COVID-19

Six experimental compounds were collected from the DrugBank. The chemical properties of the compounds showed the possibility to be suitable candidates against the RdRp (Supplementary Table 3). Further, the target protein and six compounds were selected for Molecular docking analysis to ensure the binding affinity. Strong binding affinity was observed for benzyl (2-oxopropyl)carbamate (-4.2 Kcal/mol) (Fig. 4A), 2-[(2,4-dichloro-5-methylphenyl)sulfonyl]-1,3-dinitro-5-(trifluoromethyl)benzene (-7.6 Kcal/mol) (Fig. 4B),; s-[5-(trifluoromethyl)-4h-1,2,4-triazol-3-yl]5-(phenylethynyl)furan-2-carbothioate (-5.9 Kcal/mol) (Fig. 4C), 5-amino-2-methyl-N-[(1R)-1-naphthalen-1-ylethyl]benzamide (-6.3 Kcal/mol) (Fig. 4D), Nalpha-[(benzyloxy)carbonyl]-n-[(1r)-4-hydroxy-1-methyl-2-oxobutyl]-l-phenylalaninamide (-7.4 Kcal/mol) (Fig. 4E), 4-(Dimethylamino)benzoic acid (-4.0 Kcal/mol) (Fig. 4F). Fifteen common amino acid residues including Val473, Glu474, Asp477, Lys478, Leu636, Val637, Arg735, Asp736, Val737, Asp304, Arg305, Arg640, His642, Arg651 and Leu648 of RdRp interacted with the druggable molecules. Later, ADMET analysis of the experimental compounds were enlisted (Supplementary Table 4).

Silencing the glycoproteins of SARS-CoV-2

The Sense and Antisense RNA molecules of both MG and SG were 19mer and 21mer respectively (Supplementary Table 5 and 6). The siRNA generated hairpin like secondary structure at 310.15 K temperature. The free energy of the thermodynamic ensembles were -18.30 kcal/mol and -17.90 kcal/mol respectively for MG and SG siRNA secondary structures. Results of HNADOCK server demonstrate that the antisense siRNAs can interact with the three dimensional (3D) viral mRNA molecules with around -260 docking score (Fig. 5).

Interferon Based Therapeutic for SARS-CoV-2

The whole genome of SARS-CoV and MERS-CoV were compared with SARS-CoV-2. SARS-CoV and SARS-CoV-2 share 88% query sequence coverage with 82.30% sequence identity. Besides, MERS-CoV share only 29% query coverage with 67.06% sequence identity (Supplementary Fig. 4). Genome alignment showed higher homology between SARS-CoV and SARS CoV-2 with a very few synteny break region. Whereas, MERS-CoV shares low scoring homology with SARS-CoV-2 with a larger synteny breakage area. The entry of SARS-CoV-2 and SARS-CoV are similar so differential gene expression investigation was run on SARS-CoV (Fig. 6).

64 different samples were normalized and median was centralized (Fig. 7A). 19,200 differentially expressed genes were found (Fig. 7B and Supplementary Fig. 5). Genes with official human gene symbol (HGNC) and have a significant p-value (<0.05) were selected (Supplementary Data 4 and 5). 1265 genes were considered for further analysis, among them 616 genes were upregulated (Supplementary Data 6) and 649 genes were downregulated (Supplementary Data 7). In the hierarchical clustering heatmap genes based on LogFC, t-stat and adjusted p-value were clustered together (Figs. 7C, D, E and F). The genes that we have considered as upregulated seem to have major frequency around LogFC≈ 2.1 and t value≈ 2.8, which dictates significant upregulation (Supplementary Data 8). On the other side, the downregulated genes seem to have major frequency around LogFC≈ -1.7 and t value≈ -2.4 (Supplementary Data 9). 582 upregulated and 579 downregulated genes were mapped through Gene Ontology and KEGG Pathway (Supplementary Data 8 and 9). For overexpression, 231 BP (Supplementary Data 10), 61 MF (Supplementary Data 11) and 44 CC (Supplementary Data 12) genes with their GO accession and 77 KEGG human pathways (Supplementary Data 13) were enlisted. For underexpression, 97 BP (Supplementary Data 14), 60 MF (Supplementary Data 15) and 54 CC (Supplementary Data 16) genes with GO accession and 101 KEGG pathways (Supplementary Data 17) were enlisted as well.

The biological process of Gene Ontology (GO) reported Upregulated 91 genes and Downregulated 76 genes to be connected with viral regulation process (Supplementary Data 18, 19, 20 and 21). The upregulated (KPNA4, IRF7, OAS3, CAMK2G, IFITM1, IFIT1, EGR1, MX1 and IFNAR2) and the downregulated (TRIM8, VCAM1, IFNGR2 and JAK1) genes were then identified to be effective to stimulate the Interferons via the pathway regulation (Fig. 8, Supplementary Fig. 6, and Supplementary Data 21, 22, 23, 24 and 25).

COVID-19 has become a pandemic disease and causing severe trouble in social, political and economic stability^71,72. The causative agent of this pandemic can go under frequent mutations, adaptations and transmission that possess continuous risk of outbreaks^73,74. To handle the current situation and future risks, sustainable development of therapeutics against this deadly virus is a crying need. To address this global issue, we have merged several computational approaches and proposed therapeutics that could be effective against all the strains of SARS CoV-2 (Fig 1). To achieve this goal, all available whole genomes of SARS-CoV-2 from different countries were exploited (Supplementary Data 1). Among all the strains, several consensus region was obtained with minimal evolutionary distance (Supplementary Data 2, 3 and Supplementary Fig 1). The predicted chimeric vaccine, drug compounds and siRNA based therapeutics were proposed on the basis of those conserved sequences (Supplementary Data 3).

Two proteins namely Membrane and Spike Glycoprotein were identified through blastx of the conserved nucleotide sequence. The vaccine candidates contain several well-conserved B and T cell epitopes (Table 1, Supplementary Table 1). The epitopes were exploited to design a potential chimeric vaccine which is expected to elicit TLR4 mediated Th₁antiviral response. Simulation study also showing the ability of this vaccine is producing memory B and T cell along with the protective immune-globulins (Figs. 2 and 3). The vaccine is expected to induce inflammatory and anti-inflammatory cytokines in sufficient amounts with non-allergen activities. Finally, since the vaccine contains very few toxic peptides; therefore, in vitro and in vivo validation are required prior to the actual application. Again, the constructed expression vector should successfully produce adequate vaccine molecules in E. coli strain K12 since the half-life of the protein is >10 hours inside the bacterial host with an acceptable stability index (Fig. 3).

A universal drug is necessary with an effective vaccine to reduce the post infection consequences of COVID-19. For coronaviruses, RNA‐directed RNA polymerase (RdRp) is an attractive therapeutic target which catalyzes the replication of RNA from RNA template. Thus, we targeted the conserved (region 256nt-13458nt) RNA directed RNA polymerase (RdRP) protein from SARS-CoV-2 (Supplementary Data 3). The virtual screening directed by the RdRP protein identified six experimental compounds from the DrugBank server (Supplementary Table 3). Later, the molecular docking analysis revealed a higher binding affinity between the compounds and the RdRp receptor. It is important that all the interacting residues between drug compounds and RdRp receptor are located in the predicted drug binding site (Fig. 4). Among the ADMET properties, all the experimental compounds exhibited the sufficient human intestinal absorption rate, a higher metabolic rate, no blood-brain barrier permeability, and balanced amounts of drug supply to the body (Supplementay Table 4). Moreover, the toxicity levels of these compounds appeared to be safe for the human body (Supplementay Table 4). These characteristics are significant for drug development because poor ADMET properties could direct a drug to be failing in phase III clinical trials⁷⁵.

RNA interference (RNAi) pathway is very efficient and specific in silencing the viral genes just after post-transcription. Here, siRNAs (small interfering RNAs) can play a therapeutic role^76,77. The mRNA sequences have potential sense siRNA strands CAGUAUAAUUAAUAACUAA and GGUUUUUGUAUAUAAUUAA from SG and MG respectively (Supplementary Table 5 and 6). Therefore, 19mer and 21mer nucleotide of sense and antisense strand were designed on the basis of siRNA universal rules. The standard thermodynamic value and strong interaction between antisense and viral mRNA strongly supports its prospective application (Figs. 5A and 5B). These siRNAs might be conjugated with aptamer or delivered through various delivery systems to inhibit the intracellular growth of SARS-CoV-2.

Interferons (IFNs) instigate an array of antiviral effectors. Every virus contains a distinctive property, however, partly overlapping antiviral Interferon Stimulating Genes or "ISG profile" could be potential target. IFN effectors target various phases of the virus life cycle to interfere with viral infection and proliferation. To identify the ISG and IFN of COVID-19, the gene expression profile of COVID-19 patients was needed. Due to a lack of the expression profile of COVID-19 till January 2020, three genomes of Betacoronavirus were taken including SARS-CoV, MERS-CoV and SARS-CoV-2, which have common virulence patterns, sign and symptoms. A comparison of their genome was performed. The whole genome comparison between SARS-CoV and SARS-CoV-2 showed 88% similarity with each other (Fig. 6 and Supplementary Fig 4). Therefore, the SARS CoV expression profile was explored for the identification of ISG and IFNs for the IFNs based therapeutics development. On the basis of this connection, we analyzed the genome-wide expression profile of SARS-CoV to identify the ISG and IFNs which might be suitable candidates for the IFNs based therapeutic development (Supplementary Data 4, 5, 6, 7, 8 and 9). Among the up and down regulated genes, a total of 167 immunoregulatory genes (Upregulated 91 genes and Downregulated 76 genes) were found from the biological process of Gene Ontology (GO) (Fig. 7, Supplementary Fig. 5, Supplementary Data 9, 10, 11, 12, 13, 14, 15, 16 and 17). We then identified the Interferon stimulating genes KPNA4, IRF7, OAS3, CAMK2G, IFITM1, IFIT1, EGR1, MX1 and IFNAR2 from the upregulated immunoregulatory genes and TRIM8, VCAM1, IFNGR2, JAK1 from the downregulated immunoregulatory genes (Fig.8). These immunoregulatory genes can prompt the interferon IFN alpha, IFN beta and IFN gamma which then directly gets involved to induce the immunological response by regulating the pathways (Fig. 8, Supplementary Fig. 6, Supplementary Data 17, 18, 19, 20, 21, 22, 23, 24 and 25). This analysis gives a deep insight for the IFN based therapy during COVID-19.

The pandemic coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is of great global public health concern due to its spreading rate in every part of this world with increasing death rate that ultimately has led to global socioeconomic disruption. The global urgency for the antiviral therapy against the SARS-CoV-2 has been the superior priority of this world to survive the existence of human as no medication is available so far. The present study proposed all possible novel therapeutics such as chimeric vaccine, potent drug compounds, effective siRNA and interferons to the purpose of shortening the time and reduce the cost for effective medication discovery against the global pandemic COVID-19. With the aid of computational biology and Bioinformatics analyses, these therapeutics confirmed its effectiveness against the COVID-19 which further offer for the wet lab validation. Lastly, we systematically proposed the possible therapeutics in the hope of providing a reference to minimize the longer time required antiviral therapy in wet lab and help for the prevention as well as control of the COVID-19 pandemic.

The present study has proposed four types of therapeutics including chimeric vaccine, potent drugs, siRNAs and ISG targets against COVID–19. We have analyzed all the genomic, transcriptomic and proteomic data to anticipate the possible therapeutics against the recent pandemic COVID–19. Therefore, these findings might be of interest to the researchers and pharmaceuticals company to discover the novel therapeutics against the SARS-CoV–2. We hope this in silico pipeline will also help many researchers to combat against other viral outbreaks in future.

Acknowledgements

The Authors are grateful to Ministry of Science and Technology for the extensive support during this work.

Author Contributions

MS: Conceived, designed, and guided the study, analyzed the data, drafting the manuscript and performed critical revision. CAK and KCD: Guided the study, acquisition and analyzed the data, helped in drafting the manuscript. MM and MGM: Helped in Bioinformatics analysis and helped in drafting the manuscript. MUH, AB, MTHE, ZMC: Helped to design the study, performed bioinformatics analysis, drafted, and developed the manuscript and performed critical revision. All authors have approved the manuscript.

Competing Interest

The authors declare that they have competing interest on proposed therapeutics.

Funding

There is no funding source for this work

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Recognition of Plausible Therapeutic Agents to Combat COVID-19: An Omics Data Based Combined Approach

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Journal Publication

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Abstract

Figures

Introduction

Materials and Methods

Retrieval of Whole Genomes and Conserved Region:

Chimeric Vaccine against SARS-CoV–2

Antigenicity analysis of the candidate proteins

Identification of B cell and T cell Epitopes

Construction of Chimeric Vaccine

Evaluation of Immune Response and Interferon Gamma (IFNγ)

Allergenicity and Toxicity Exploration

Analysis of Physicochemical Properties and Tertiary Structure

Molecular Docking Analysis

Codon Optimization and Visualization of Cloning

Small Molecule Therapeutics against COVID–19

Homology Modelling and Binding site analysis

Screening the druggable compounds and molecular docking simulation

Pharmacoinformatics illustration

Nucleic Acid Based Therapeutics Development

Designing of potential siRNA molecules

Inteferon stimulating genes (ISGs) based Interferon Therapy

Comparative Genomics

Exploring SARS-CoV expression profile

Gene Enrichment Analysis

Identification of Viral Genes

Exploration of Interferon Stimulating Genes (ISGs)

Results

Chimeric Vaccine against SARS-CoV-2

Codon Optimization and Cloning for chimeric vaccine

Small Molecule Drugs against COVID-19

Silencing the glycoproteins of SARS-CoV-2

Interferon Based Therapeutic for SARS-CoV-2

Discussion

Conclusion

Declarations

References

Supplementary Files

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